system

Abstract

The system according to this embodiment aims to efficiently organize and manage vast amounts of digital data. [Solution] The system according to the embodiment comprises an analysis unit, a classification unit, a detection unit, and a suggestion unit. The analysis unit analyzes the contents of a file. The classification unit classifies and tags the file based on the contents analyzed by the analysis unit. The detection unit scans the files classified by the classification unit and detects duplicate files. The suggestion unit makes suggestions for deleting the duplicate and unnecessary files detected by the detection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it takes a lot of time and effort to organize and manage a huge amount of digital data, and it is difficult to do so efficiently.

[0005] The system according to the embodiment aims to efficiently organize and manage a huge amount of digital data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a classification unit, a detection unit, and a suggestion unit. The analysis unit analyzes the contents of files. The classification unit classifies and tags files based on the contents analyzed by the analysis unit. The detection unit scans the files classified by the classification unit and detects duplicate files. The suggestion unit makes suggestions for deleting the duplicate and unnecessary files detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently organize and manage vast amounts of digital data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The digital data organization system according to an embodiment of the present invention is an application that utilizes generative AI to efficiently organize and manage the vast amount of digital data accumulated by individuals and companies. This digital data organization system uses generative AI to analyze file contents, automatically classify and tag them, and support appropriate storage management. It also detects duplicate files and suggests deleting unnecessary files. For example, the digital data organization system uses generative AI to analyze file contents. It analyzes various forms of digital data, such as document files, image files, and video files, and understands their content. Next, the generative AI automatically classifies files based on the analysis results and assigns appropriate tags. This allows users to easily search for files and quickly find the necessary data. Furthermore, the digital data organization system has a duplicate file detection function. The generative AI scans files in storage and identifies duplicate files. This allows for suggestions to delete unnecessary duplicate files, improving storage capacity efficiency. It also suggests deleting unnecessary files. The generative AI analyzes the frequency of use and importance of files and suggests deleting files deemed unnecessary. This reduces wasted storage and enables efficient data management. The digital data organization system provides individuals and companies with an environment that allows for easy and efficient organization and management of digital data. This multi-functional application solves the challenges of digital data management by providing highly accurate file analysis and classification using generation AI, detecting duplicate files, and suggesting the deletion of unnecessary files. As a result, the digital data organization system can efficiently organize and manage the vast amounts of digital data accumulated by individuals and businesses.

[0029] The digital data organization system according to this embodiment comprises an analysis unit, a classification unit, a detection unit, and a proposal unit. The analysis unit analyzes the contents of files using a generation AI. The analysis unit analyzes digital data such as document files, image files, and video files. The analysis unit uses a generation AI to understand the contents of files and outputs the analysis results. The classification unit uses a generation AI to classify and tag files based on the contents analyzed by the analysis unit. The classification unit automatically classifies files based on the analysis results and assigns appropriate tags. The classification unit uses a generation AI to understand the contents of files and assign appropriate tags. The detection unit uses a generation AI to scan files classified by the classification unit and detect duplicate files. The detection unit scans files in storage and identifies duplicate files. The detection unit uses a generation AI to understand the contents of files and detect duplicate files. The proposal unit uses a generation AI to propose the deletion of duplicate and unnecessary files detected by the detection unit. The proposal unit analyzes the frequency of use and importance of files and proposes the deletion of files deemed unnecessary. The proposal function uses a generation AI to understand the file contents and propose the deletion of unnecessary files. As a result, the digital data organization system according to this embodiment can efficiently analyze, classify, detect duplicates, and propose deletions for file contents.

[0030] The analysis unit analyzes the contents of files using generative AI. Specifically, it targets digital data such as document files, image files, and video files, and the generative AI understands the contents of these files and outputs analysis results. In the case of document files, the generative AI uses natural language processing technology to analyze the text content and extract the subject and keywords of the document. For example, it can generate important points and summaries from document files such as business reports and research papers. In the case of image files, the generative AI uses image recognition technology to analyze the content of the image and identify objects and scenes within the image. For example, it can extract images containing specific people or landscapes from a photo album. In the case of video files, the generative AI uses video analysis technology to analyze the content of the video and identify important scenes and events within the video. For example, it can detect abnormal movements from surveillance camera footage or extract highlight scenes from sports matches. Based on these analysis results, the analysis unit understands the contents of the files in detail and provides the information necessary for the next processing step. Furthermore, the analysis unit stores the analysis results in a database so that subsequent processing departments can easily access them. This allows the analysis unit to efficiently and accurately analyze the contents of digital data and improve the overall performance of the system.

[0031] The classification unit uses generative AI to classify and tag files based on the analysis performed by the analysis unit. Specifically, the generative AI automatically determines the file category based on the analysis results and assigns appropriate tags. For example, document files are classified into categories such as business, education, and personal, and then tagged with terms like "report," "presentation," and "notes" depending on the content. Image files are classified into categories such as landscapes, people, and events, and tagged with terms like "travel," "family," and "party." Video files are classified into categories such as movies, documentaries, and sports, and tagged with terms like "action," "drama," and "game." The classification unit enables users to easily search and manage files by allowing the generative AI to deeply understand the file content and assign appropriate tags. Furthermore, the classification unit can continuously improve the accuracy of tagging based on user feedback. For example, it learns from manually corrected tag information from users and incorporates it into subsequent tagging. This allows the classification unit to efficiently and accurately classify and tag files, significantly simplifying the management of digital data.

[0032] The detection unit uses a generation AI to scan files classified by the classification unit and detect duplicate files. Specifically, the generation AI understands the content of the files and identifies files with high similarity. For example, it detects duplicate files when the same document is saved in different folders or when the same image is saved at different resolutions. The detection unit identifies duplicate files by considering not only the content of the files but also metadata (creation date and time, file size, file format, etc.). Furthermore, the detection unit can also detect duplicate files based on conditions specified by the user. For example, conditions such as scanning only within a specific folder or targeting only a specific file format can be set. After detecting duplicate files, the detection unit provides the user with a list of the duplicate files and suggests deletion or merging. This allows the detection unit to reduce wasted storage and efficiently organize digital data. In addition, the detection unit saves the detection results to a database so that subsequent suggestion units can easily access them. This allows the detection unit to quickly and accurately detect duplicate files and improve the overall efficiency of the system.

[0033] The suggestion unit uses a generation AI to propose the deletion of duplicate and unnecessary files detected by the detection unit. Specifically, the generation AI analyzes the frequency of use and importance of files and proposes the deletion of files deemed unnecessary. For example, it may propose the deletion of files that have not been accessed for a long time or files with duplicate content. The suggestion unit provides the user with details of the deletion proposal, allowing the user to choose whether or not to delete the files. Furthermore, the suggestion unit can also propose file backups. For example, it may propose backing up important files to cloud storage to ensure data security. The suggestion unit can continuously improve the accuracy of its suggestions based on user feedback. For example, it can learn from information about files that users have refused to delete and incorporate this into future suggestions. This allows the suggestion unit to provide users with appropriate deletion suggestions and efficiently organize their digital data. In addition, the suggestion unit stores a history of deletion suggestions in a database, allowing users to review past suggestions. This enables the suggestion unit to provide users with reliable deletion suggestions and significantly simplify the management of digital data.

[0034] The analysis unit can analyze digital data such as document files, image files, and video files. For example, the analysis unit can analyze a document file and understand its contents. The analysis unit can also analyze an image file and understand its contents. The analysis unit can also analyze a video file and understand its contents. This allows for the analysis of various formats of digital data. Digital data includes, but is not limited to, PDF, JPEG, and MP4. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input a document file into a generation AI, and the generation AI can analyze the contents of the document file.

[0035] The classification unit can automatically classify files and assign appropriate tags based on the analysis results. For example, the classification unit can classify document files and assign appropriate tags based on the analysis results. The classification unit can also classify image files and assign appropriate tags. The classification unit can also classify video files and assign appropriate tags. This automates file classification and tagging, making searching easier. Appropriate tags include, but are not limited to, content-based tags and metadata-based tags. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit can input the analysis results into a generative AI, which can then classify the files and assign appropriate tags.

[0036] The detection unit can scan files in storage and identify duplicate files. For example, the detection unit can scan document files in storage and identify duplicate files. The detection unit can also scan image files and identify duplicate files. The detection unit can also scan video files and identify duplicate files. This allows for efficient detection of duplicate files. Scanning includes, but is not limited to, a full file system scan or a scan of a specific folder. Some or all of the above-described processes in the detection unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the detection unit can input files in storage into a generation AI, and the generation AI can detect duplicate files.

[0037] The suggestion unit can analyze the frequency of use and importance of files and propose the deletion of files deemed unnecessary. For example, the suggestion unit can analyze the frequency of use of files and propose the deletion of files that are used infrequently. The suggestion unit can also analyze the importance of files and propose the deletion of files that are not important. The suggestion unit can analyze both the frequency of use and importance and propose the deletion of files deemed unnecessary. This allows for the deletion of unnecessary files and improves storage efficiency. Frequency of use includes, but is not limited to, the number of accesses and the last access date and time. Importance includes, but is not limited to, the content of the file and the associated project. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the frequency of use and importance of files into a generation AI, and the generation AI can propose the deletion of unnecessary files.

[0038] The proposed function can provide a user interface that allows users to easily search for files and quickly find the data they need. For example, the proposed function can provide a search bar that allows users to enter keywords to search for files. The proposed function can also provide a filter function that allows users to narrow down files based on specific criteria. The proposed function can also provide a file preview function that allows users to check the contents of files. This enables users to efficiently search for files and quickly find the data they need. The user interface may include, but is not limited to, a search bar, a filter function, and a preview function. Some or all of the processing described above in the proposed function may be performed using a generative AI or not. For example, the proposed function can input the user interface into a generative AI, which can then provide the user interface.

[0039] The analysis unit can improve the accuracy of its analysis by considering the file's creation date and last modified date when analyzing the file's contents. For example, the analysis unit may determine that older files are of low importance and lower their analysis priority. The analysis unit may also determine that recently updated files are of high importance and raise their analysis priority. The analysis unit can also group files with similar creation dates and analyze them all at once. This improves the accuracy of the analysis by considering the file's creation date and last modified date. The creation date is obtained, for example, from the file metadata. The last modified date is also obtained, for example, from the file metadata. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the file's creation date and last modified date into a generation AI, which can then improve the accuracy of the analysis.

[0040] The analysis unit can improve the efficiency of analysis by utilizing file metadata when analyzing the contents of a file. For example, the analysis unit can identify the file type based on metadata and optimize the analysis method. The analysis unit can also obtain file creator information from metadata and group related files. The analysis unit can also use metadata to pre-classify the contents of files and improve the efficiency of analysis. In this way, the efficiency of analysis is improved by utilizing file metadata. Metadata includes, but is not limited to, creator information and file size. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input file metadata into a generation AI, which can then improve the efficiency of the analysis.

[0041] The analysis unit can improve the accuracy of its analysis by referring to the user's past file operation history when analyzing the contents of a file. For example, the analysis unit may prioritize the analysis of files that the user has frequently accessed in the past. The analysis unit can also optimize the analysis method for specific file formats based on the user's past operation history. The analysis unit can also group related files together and analyze them based on the user's past operation history. This improves the accuracy of the analysis by referring to the user's past file operation history. Past file operation history can be obtained, for example, by analyzing log data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's past file operation history into a generation AI, which can then improve the accuracy of the analysis.

[0042] The analysis unit can determine the priority of analysis by considering the user's geographical location information when analyzing the contents of files. For example, the analysis unit may prioritize the analysis of files related to the user's current location. The analysis unit can also group related files based on the user's past location information and analyze them accordingly. The analysis unit can also prioritize the analysis of files related to a specific region by utilizing the user's geographical location information. This ensures that the analysis priority is appropriately determined by considering the user's geographical location information. Geographical location information is obtained, for example, by using GPS data. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the user's geographical location information into a generation AI, which can then determine the analysis priority.

[0043] The classification unit can improve the accuracy of file classification by considering the relationships between files. For example, the classification unit can group related files together and classify them all at once. The classification unit can also analyze the contents of files and classify highly related files into the same category. The classification unit can also use file metadata to identify and classify highly related files. This improves the accuracy of classification by considering the relationships between files. File relationships are evaluated, for example, by similarity of content or matching of metadata. Some or all of the above processes in the classification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the classification unit can input file relationships into a generative AI, which can then improve the accuracy of classification.

[0044] The classification unit can improve the efficiency of file classification by considering file size and format. For example, the classification unit can classify large files into a separate category to improve analysis efficiency. The classification unit can also improve classification accuracy by using different classification criteria for each file format. The classification unit can also classify files to an appropriate storage location based on their size. This improves classification efficiency by considering file size and format. File size is considered, for example, by size range and size-based classification criteria. File format is considered, for example, by format type and format-based classification criteria. Some or all of the above processing in the classification unit may be performed using or without a generative AI. For example, the classification unit can input file size and format into a generative AI, which can then improve classification efficiency.

[0045] The classification unit can improve the accuracy of file classification by referring to the user's past classification history. For example, the classification unit may prioritize classification criteria previously used by the user. The classification unit can also optimize the classification method for specific file formats based on the user's past classification history. The classification unit can also group and classify related files based on the user's past classification history. This improves the accuracy of classification by referring to the user's past classification history. Past classification history can be obtained, for example, by analyzing log data. Some or all of the above processes in the classification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the classification unit can input the user's past classification history into a generative AI, which can then improve the accuracy of classification.

[0046] The classification unit can analyze a user's social media activity and assign relevant tags when classifying files. For example, the classification unit can analyze the content of a user's social media posts and assign relevant tags. The classification unit can also assign tags of high importance based on the frequency of the user's activity on social media. The classification unit can also assign relevant tags by referring to the activity of the user's followers and friends on social media. In this way, relevant tags are assigned by analyzing the user's social media activity. Social media activity is obtained, for example, by analyzing the content of posts and using follower information. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit can input the user's social media activity into a generative AI, and the generative AI can assign relevant tags.

[0047] The detection unit can improve the accuracy of duplicate file detection by considering not only the file content but also the file metadata. For example, the detection unit can analyze the file metadata to detect duplicate files with the same author or creation date. The detection unit can also detect duplicate files with the same tags or categories based on the file metadata. The detection unit can also use the file metadata to detect duplicate files with the same file size or format. This improves the accuracy of duplicate file detection by considering the file metadata. Metadata includes, but is not limited to, author information and file size. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input the file metadata into a generation AI, which can then improve the accuracy of duplicate file detection.

[0048] The detection unit can determine the detection priority when detecting duplicate files, taking into account the file access frequency. For example, the detection unit may prioritize the detection of frequently accessed files. The detection unit may also postpone the detection of files with low access frequency. The detection unit may also prioritize the detection of files of high importance based on access frequency. In this way, the detection priority of duplicate files is appropriately determined by considering the file access frequency. Access frequency is measured, for example, by the number of accesses or the last access date and time. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input the file access frequency to a generation AI, and the generation AI can determine the detection priority of duplicate files.

[0049] The detection unit can improve the accuracy of duplicate file detection by referring to the user's past file operation history. For example, the detection unit may prioritize the detection of files that the user has frequently accessed in the past. The detection unit can also optimize the detection method for specific file formats based on the user's past operation history. The detection unit can also group related files based on the user's past operation history and detect them together. This improves the accuracy of duplicate file detection by referring to the user's past file operation history. Past file operation history is obtained, for example, by analyzing log data. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input the user's past file operation history into a generation AI, which can then improve the accuracy of duplicate file detection.

[0050] The detection unit can determine the detection priority when detecting duplicate files, taking into account the user's geographical location information. For example, the detection unit may prioritize the detection of files related to the user's current location. The detection unit can also group and detect related files based on the user's past location information. The detection unit can also prioritize the detection of files related to a specific region using the user's geographical location information. This ensures that the detection priority of duplicate files is appropriately determined by considering the user's geographical location information. Geographical location information is obtained, for example, by using GPS data. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input the user's geographical location information into a generation AI, which can then determine the detection priority of duplicate files.

[0051] The suggestion unit can improve the accuracy of its deletion suggestions by analyzing the frequency of file use and importance when making deletion suggestions. For example, the suggestion unit may prioritize suggesting the deletion of files that are used infrequently. The suggestion unit may also prioritize suggesting the deletion of files that are of low importance. The suggestion unit may also consider both frequency of use and importance to make the optimal deletion suggestion. This improves the accuracy of deletion suggestions by analyzing the frequency of use and importance of files. Frequency of use is measured by, for example, the number of accesses and the last access date and time. Importance is evaluated by, for example, the content of the file and the project it is associated with. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or not. For example, the suggestion unit can input the frequency of use and importance of files into a generation AI, which can then improve the accuracy of its deletion suggestions.

[0052] The proposal unit can determine the priority of deletion proposals by considering the relevance of the files when making a deletion proposal. For example, the proposal unit may prioritize the deletion of less relevant files. The proposal unit may also lower the priority of deletion proposals for highly relevant files. The proposal unit can also analyze the relevance of files and make the optimal deletion proposal. This ensures that the priority of deletion proposals is appropriately determined by considering the relevance of the files. File relevance is evaluated by, for example, content similarity or metadata matching. Some or all of the above processing in the proposal unit may be performed using a generation AI, or not using a generation AI. For example, the proposal unit can input the relevance of files into a generation AI, and the generation AI can determine the priority of deletion proposals.

[0053] The suggestion unit can improve the accuracy of its deletion suggestions by referring to the user's past file operation history. For example, the suggestion unit can make optimal deletion suggestions based on the user's past file deletion history. The suggestion unit can also optimize deletion suggestions for specific file formats based on the user's past operation history. The suggestion unit can also group related files based on the user's past operation history and make deletion suggestions accordingly. This improves the accuracy of deletion suggestions by referring to the user's past file operation history. Past file operation history can be obtained, for example, by analyzing log data. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's past file operation history into a generation AI, which can then improve the accuracy of deletion suggestions.

[0054] The suggestion unit can determine the priority of deletion suggestions by considering the user's geographical location information. For example, the suggestion unit may prioritize the deletion of files related to the user's current location. The suggestion unit can also group related files based on the user's past location information and propose their deletion accordingly. The suggestion unit can also use the user's geographical location information to prioritize the deletion of files related to a specific region. This ensures that the priority of deletion suggestions is appropriately determined by considering the user's geographical location information. Geographical location information is obtained, for example, by using GPS data. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's geographical location information into a generation AI, which can then determine the priority of deletion suggestions.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analysis unit can improve the accuracy of its analysis by considering the file's creation date and last modified date when analyzing the file's contents. For example, the analysis unit may determine that older files are of low importance and lower their analysis priority. The analysis unit may also determine that recently updated files are of high importance and raise their analysis priority. The analysis unit can also group files with similar creation dates and analyze them all at once. This improves the accuracy of the analysis by considering the file's creation date and last modified date. The creation date is obtained, for example, from the file metadata. The last modified date is also obtained, for example, from the file metadata. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the file's creation date and last modified date into a generation AI, which can then improve the accuracy of the analysis.

[0057] The analysis unit can improve the efficiency of analysis by utilizing file metadata when analyzing the contents of a file. For example, the analysis unit can identify the file type based on metadata and optimize the analysis method. The analysis unit can also obtain file creator information from metadata and group related files. The analysis unit can also use metadata to pre-classify the contents of files and improve the efficiency of analysis. In this way, the efficiency of analysis is improved by utilizing file metadata. Metadata includes, but is not limited to, creator information and file size. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input file metadata into a generation AI, which can then improve the efficiency of the analysis.

[0058] The analysis unit can improve the accuracy of its analysis by referring to the user's past file operation history when analyzing the contents of a file. For example, the analysis unit may prioritize the analysis of files that the user has frequently accessed in the past. The analysis unit can also optimize the analysis method for specific file formats based on the user's past operation history. The analysis unit can also group related files together and analyze them based on the user's past operation history. This improves the accuracy of the analysis by referring to the user's past file operation history. Past file operation history can be obtained, for example, by analyzing log data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's past file operation history into a generation AI, which can then improve the accuracy of the analysis.

[0059] The classification unit can improve the accuracy of file classification by considering the relationships between files. For example, the classification unit can group related files together and classify them all at once. The classification unit can also analyze the contents of files and classify highly related files into the same category. The classification unit can also use file metadata to identify and classify highly related files. This improves the accuracy of classification by considering the relationships between files. File relationships are evaluated, for example, by similarity of content or matching of metadata. Some or all of the above processes in the classification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the classification unit can input file relationships into a generative AI, which can then improve the accuracy of classification.

[0060] The classification unit can improve the efficiency of file classification by considering file size and format. For example, the classification unit can classify large files into a separate category to improve analysis efficiency. The classification unit can also improve classification accuracy by using different classification criteria for each file format. The classification unit can also classify files to an appropriate storage location based on their size. This improves classification efficiency by considering file size and format. File size is considered, for example, by size range and size-based classification criteria. File format is considered, for example, by format type and format-based classification criteria. Some or all of the above processing in the classification unit may be performed using or without a generative AI. For example, the classification unit can input file size and format into a generative AI, which can then improve classification efficiency.

[0061] The classification unit can improve the accuracy of file classification by referring to the user's past classification history. For example, the classification unit may prioritize classification criteria previously used by the user. The classification unit can also optimize the classification method for specific file formats based on the user's past classification history. The classification unit can also group and classify related files based on the user's past classification history. This improves the accuracy of classification by referring to the user's past classification history. Past classification history can be obtained, for example, by analyzing log data. Some or all of the above processes in the classification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the classification unit can input the user's past classification history into a generative AI, which can then improve the accuracy of classification.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The analysis unit analyzes the file contents using a generating AI. The analysis unit analyzes digital data such as document files, image files, and video files, and the generating AI understands the file contents and outputs the analysis results. Step 2: The classification unit uses generation AI to classify and tag files based on the content analyzed by the analysis unit. The classification unit automatically classifies files and assigns appropriate tags based on the analysis results. Step 3: The detection unit scans the files classified by the classification unit using the generation AI to detect duplicate files. The detection unit scans the files in the storage and identifies the duplicate files. Step 4: The proposal unit uses the generation AI to propose the deletion of duplicate and unnecessary files detected by the detection unit. The proposal unit analyzes the frequency of use and importance of the files and proposes the deletion of files that are deemed unnecessary.

[0064] (Example of form 2) The digital data organization system according to an embodiment of the present invention is an application that utilizes generative AI to efficiently organize and manage the vast amount of digital data accumulated by individuals and companies. This digital data organization system uses generative AI to analyze file contents, automatically classify and tag them, and support appropriate storage management. It also detects duplicate files and suggests deleting unnecessary files. For example, the digital data organization system uses generative AI to analyze file contents. It analyzes various forms of digital data, such as document files, image files, and video files, and understands their content. Next, the generative AI automatically classifies files based on the analysis results and assigns appropriate tags. This allows users to easily search for files and quickly find the necessary data. Furthermore, the digital data organization system has a duplicate file detection function. The generative AI scans files in storage and identifies duplicate files. This allows for suggestions to delete unnecessary duplicate files, improving storage capacity efficiency. It also suggests deleting unnecessary files. The generative AI analyzes the frequency of use and importance of files and suggests deleting files deemed unnecessary. This reduces wasted storage and enables efficient data management. The digital data organization system provides individuals and companies with an environment that allows for easy and efficient organization and management of digital data. This multi-functional application solves the challenges of digital data management by providing highly accurate file analysis and classification using generation AI, detecting duplicate files, and suggesting the deletion of unnecessary files. As a result, the digital data organization system can efficiently organize and manage the vast amounts of digital data accumulated by individuals and businesses.

[0065] The digital data organization system according to this embodiment comprises an analysis unit, a classification unit, a detection unit, and a proposal unit. The analysis unit analyzes the contents of files using a generation AI. The analysis unit analyzes digital data such as document files, image files, and video files. The analysis unit uses a generation AI to understand the contents of files and outputs the analysis results. The classification unit uses a generation AI to classify and tag files based on the contents analyzed by the analysis unit. The classification unit automatically classifies files based on the analysis results and assigns appropriate tags. The classification unit uses a generation AI to understand the contents of files and assign appropriate tags. The detection unit uses a generation AI to scan files classified by the classification unit and detect duplicate files. The detection unit scans files in storage and identifies duplicate files. The detection unit uses a generation AI to understand the contents of files and detect duplicate files. The proposal unit uses a generation AI to propose the deletion of duplicate and unnecessary files detected by the detection unit. The proposal unit analyzes the frequency of use and importance of files and proposes the deletion of files deemed unnecessary. The proposal function uses a generation AI to understand the file contents and propose the deletion of unnecessary files. As a result, the digital data organization system according to this embodiment can efficiently analyze, classify, detect duplicates, and propose deletions for file contents.

[0066] The analysis unit analyzes the contents of files using generative AI. Specifically, it targets digital data such as document files, image files, and video files, and the generative AI understands the contents of these files and outputs analysis results. In the case of document files, the generative AI uses natural language processing technology to analyze the text content and extract the subject and keywords of the document. For example, it can generate important points and summaries from document files such as business reports and research papers. In the case of image files, the generative AI uses image recognition technology to analyze the content of the image and identify objects and scenes within the image. For example, it can extract images containing specific people or landscapes from a photo album. In the case of video files, the generative AI uses video analysis technology to analyze the content of the video and identify important scenes and events within the video. For example, it can detect abnormal movements from surveillance camera footage or extract highlight scenes from sports matches. Based on these analysis results, the analysis unit understands the contents of the files in detail and provides the information necessary for the next processing step. Furthermore, the analysis unit stores the analysis results in a database so that subsequent processing departments can easily access them. This allows the analysis unit to efficiently and accurately analyze the contents of digital data and improve the overall performance of the system.

[0067] The classification unit uses generative AI to classify and tag files based on the analysis performed by the analysis unit. Specifically, the generative AI automatically determines the file category based on the analysis results and assigns appropriate tags. For example, document files are classified into categories such as business, education, and personal, and then tagged with terms like "report," "presentation," and "notes" depending on the content. Image files are classified into categories such as landscapes, people, and events, and tagged with terms like "travel," "family," and "party." Video files are classified into categories such as movies, documentaries, and sports, and tagged with terms like "action," "drama," and "game." The classification unit enables users to easily search and manage files by allowing the generative AI to deeply understand the file content and assign appropriate tags. Furthermore, the classification unit can continuously improve the accuracy of tagging based on user feedback. For example, it learns from manually corrected tag information from users and incorporates it into subsequent tagging. This allows the classification unit to efficiently and accurately classify and tag files, significantly simplifying the management of digital data.

[0068] The detection unit uses a generation AI to scan files classified by the classification unit and detect duplicate files. Specifically, the generation AI understands the content of the files and identifies files with high similarity. For example, it detects duplicate files when the same document is saved in different folders or when the same image is saved at different resolutions. The detection unit identifies duplicate files by considering not only the content of the files but also metadata (creation date and time, file size, file format, etc.). Furthermore, the detection unit can also detect duplicate files based on conditions specified by the user. For example, conditions such as scanning only within a specific folder or targeting only a specific file format can be set. After detecting duplicate files, the detection unit provides the user with a list of the duplicate files and suggests deletion or merging. This allows the detection unit to reduce wasted storage and efficiently organize digital data. In addition, the detection unit saves the detection results to a database so that subsequent suggestion units can easily access them. This allows the detection unit to quickly and accurately detect duplicate files and improve the overall efficiency of the system.

[0069] The suggestion unit uses a generation AI to propose the deletion of duplicate and unnecessary files detected by the detection unit. Specifically, the generation AI analyzes the frequency of use and importance of files and proposes the deletion of files deemed unnecessary. For example, it may propose the deletion of files that have not been accessed for a long time or files with duplicate content. The suggestion unit provides the user with details of the deletion proposal, allowing the user to choose whether or not to delete the files. Furthermore, the suggestion unit can also propose file backups. For example, it may propose backing up important files to cloud storage to ensure data security. The suggestion unit can continuously improve the accuracy of its suggestions based on user feedback. For example, it can learn from information about files that users have refused to delete and incorporate this into future suggestions. This allows the suggestion unit to provide users with appropriate deletion suggestions and efficiently organize their digital data. In addition, the suggestion unit stores a history of deletion suggestions in a database, allowing users to review past suggestions. This enables the suggestion unit to provide users with reliable deletion suggestions and significantly simplify the management of digital data.

[0070] The analysis unit can analyze digital data such as document files, image files, and video files. For example, the analysis unit can analyze a document file and understand its contents. The analysis unit can also analyze an image file and understand its contents. The analysis unit can also analyze a video file and understand its contents. This allows for the analysis of various formats of digital data. Digital data includes, but is not limited to, PDF, JPEG, and MP4. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input a document file into a generation AI, and the generation AI can analyze the contents of the document file.

[0071] The classification unit can automatically classify files and assign appropriate tags based on the analysis results. For example, the classification unit can classify document files and assign appropriate tags based on the analysis results. The classification unit can also classify image files and assign appropriate tags. The classification unit can also classify video files and assign appropriate tags. This automates file classification and tagging, making searching easier. Appropriate tags include, but are not limited to, content-based tags and metadata-based tags. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit can input the analysis results into a generative AI, which can then classify the files and assign appropriate tags.

[0072] The detection unit can scan files in storage and identify duplicate files. For example, the detection unit can scan document files in storage and identify duplicate files. The detection unit can also scan image files and identify duplicate files. The detection unit can also scan video files and identify duplicate files. This allows for efficient detection of duplicate files. Scanning includes, but is not limited to, a full file system scan or a scan of a specific folder. Some or all of the above-described processes in the detection unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the detection unit can input files in storage into a generation AI, and the generation AI can detect duplicate files.

[0073] The suggestion unit can analyze the frequency of use and importance of files and propose the deletion of files deemed unnecessary. For example, the suggestion unit can analyze the frequency of use of files and propose the deletion of files that are used infrequently. The suggestion unit can also analyze the importance of files and propose the deletion of files that are not important. The suggestion unit can analyze both the frequency of use and importance and propose the deletion of files deemed unnecessary. This allows for the deletion of unnecessary files and improves storage efficiency. Frequency of use includes, but is not limited to, the number of accesses and the last access date and time. Importance includes, but is not limited to, the content of the file and the associated project. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the frequency of use and importance of files into a generation AI, and the generation AI can propose the deletion of unnecessary files.

[0074] The proposed function can provide a user interface that allows users to easily search for files and quickly find the data they need. For example, the proposed function can provide a search bar that allows users to enter keywords to search for files. The proposed function can also provide a filter function that allows users to narrow down files based on specific criteria. The proposed function can also provide a file preview function that allows users to check the contents of files. This enables users to efficiently search for files and quickly find the data they need. The user interface may include, but is not limited to, a search bar, a filter function, and a preview function. Some or all of the processing described above in the proposed function may be performed using a generative AI or not. For example, the proposed function can input the user interface into a generative AI, which can then provide the user interface.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing high-priority files. If the user is relaxed, the analysis unit may also analyze all files equally. If the user is in a hurry, the analysis unit may also prioritize analyzing the most frequently accessed files. This allows for more appropriate analysis by adjusting the analysis priority according to the user's emotions. The user's emotions are estimated using, for example, facial recognition or voice analysis. The analysis priority is adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the analysis priority.

[0076] The analysis unit can improve the accuracy of its analysis by considering the file's creation date and last modified date when analyzing the file's contents. For example, the analysis unit may determine that older files are of low importance and lower their analysis priority. The analysis unit may also determine that recently updated files are of high importance and raise their analysis priority. The analysis unit can also group files with similar creation dates and analyze them all at once. This improves the accuracy of the analysis by considering the file's creation date and last modified date. The creation date is obtained, for example, from the file metadata. The last modified date is also obtained, for example, from the file metadata. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the file's creation date and last modified date into a generation AI, which can then improve the accuracy of the analysis.

[0077] The analysis unit can improve the efficiency of analysis by utilizing file metadata when analyzing the contents of a file. For example, the analysis unit can identify the file type based on metadata and optimize the analysis method. The analysis unit can also obtain file creator information from metadata and group related files. The analysis unit can also use metadata to pre-classify the contents of files and improve the efficiency of analysis. In this way, the efficiency of analysis is improved by utilizing file metadata. Metadata includes, but is not limited to, creator information and file size. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input file metadata into a generation AI, which can then improve the efficiency of the analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display is achieved. The display method of the analysis results is adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the display method of the analysis results.

[0079] The analysis unit can improve the accuracy of its analysis by referring to the user's past file operation history when analyzing the contents of a file. For example, the analysis unit may prioritize the analysis of files that the user has frequently accessed in the past. The analysis unit can also optimize the analysis method for specific file formats based on the user's past operation history. The analysis unit can also group related files together and analyze them based on the user's past operation history. This improves the accuracy of the analysis by referring to the user's past file operation history. Past file operation history can be obtained, for example, by analyzing log data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's past file operation history into a generation AI, which can then improve the accuracy of the analysis.

[0080] The analysis unit can determine the priority of analysis by considering the user's geographical location information when analyzing the contents of files. For example, the analysis unit may prioritize the analysis of files related to the user's current location. The analysis unit can also group related files based on the user's past location information and analyze them accordingly. The analysis unit can also prioritize the analysis of files related to a specific region by utilizing the user's geographical location information. This ensures that the analysis priority is appropriately determined by considering the user's geographical location information. Geographical location information is obtained, for example, by using GPS data. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the user's geographical location information into a generation AI, which can then determine the analysis priority.

[0081] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated emotions. For example, if the user is stressed, the classification unit may use simple classification criteria. If the user is relaxed, the classification unit may also use detailed classification criteria. If the user is in a hurry, the classification unit may prioritize classifying high-priority files. This allows for more appropriate classification by adjusting the classification criteria according to the user's emotions. The classification criteria may be adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using or without generative AI. For example, the classification unit can input user emotion data into a generative AI, which can then adjust the classification criteria.

[0082] The classification unit can improve the accuracy of file classification by considering the relationships between files. For example, the classification unit can group related files together and classify them all at once. The classification unit can also analyze the contents of files and classify highly related files into the same category. The classification unit can also use file metadata to identify and classify highly related files. This improves the accuracy of classification by considering the relationships between files. File relationships are evaluated, for example, by similarity of content or matching of metadata. Some or all of the above processes in the classification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the classification unit can input file relationships into a generative AI, which can then improve the accuracy of classification.

[0083] The classification unit can improve the efficiency of file classification by considering file size and format. For example, the classification unit can classify large files into a separate category to improve analysis efficiency. The classification unit can also improve classification accuracy by using different classification criteria for each file format. The classification unit can also classify files to an appropriate storage location based on their size. This improves classification efficiency by considering file size and format. File size is considered, for example, by size range and size-based classification criteria. File format is considered, for example, by format type and format-based classification criteria. Some or all of the above processing in the classification unit may be performed using or without a generative AI. For example, the classification unit can input file size and format into a generative AI, which can then improve classification efficiency.

[0084] The classification unit can estimate the user's emotions and adjust the tagging method based on the estimated emotions. For example, if the user is nervous, the classification unit may use simple and easy-to-understand tags. If the user is relaxed, the classification unit may also use detailed tags. If the user is in a hurry, the classification unit may prioritize high-priority tags. This allows for more appropriate tagging by adjusting the tagging method according to the user's emotions. The tagging method may be adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using or without a generative AI. For example, the classification unit can input user emotion data into a generative AI, which can then adjust the tagging method.

[0085] The classification unit can improve the accuracy of file classification by referring to the user's past classification history. For example, the classification unit may prioritize classification criteria previously used by the user. The classification unit can also optimize the classification method for specific file formats based on the user's past classification history. The classification unit can also group and classify related files based on the user's past classification history. This improves the accuracy of classification by referring to the user's past classification history. Past classification history can be obtained, for example, by analyzing log data. Some or all of the above processes in the classification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the classification unit can input the user's past classification history into a generative AI, which can then improve the accuracy of classification.

[0086] The classification unit can analyze a user's social media activity and assign relevant tags when classifying files. For example, the classification unit can analyze the content of a user's social media posts and assign relevant tags. The classification unit can also assign tags of high importance based on the frequency of the user's activity on social media. The classification unit can also assign relevant tags by referring to the activity of the user's followers and friends on social media. In this way, relevant tags are assigned by analyzing the user's social media activity. Social media activity is obtained, for example, by analyzing the content of posts and using follower information. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit can input the user's social media activity into a generative AI, and the generative AI can assign relevant tags.

[0087] The detection unit can estimate the user's emotions and adjust the duplicate file detection criteria based on the estimated emotions. For example, if the user is stressed, the detection unit may detect duplicate files using strict criteria. If the user is relaxed, the detection unit may also detect duplicate files using lenient criteria. If the user is in a hurry, the detection unit may prioritize detecting files of high importance. This allows for more appropriate detection by adjusting the duplicate file detection criteria according to the user's emotions. The duplicate file detection criteria are adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using the generative AI or not. For example, the detection unit can input user emotion data into the generative AI, which can then adjust the duplicate file detection criteria.

[0088] The detection unit can improve the accuracy of duplicate file detection by considering not only the file content but also the file metadata. For example, the detection unit can analyze the file metadata to detect duplicate files with the same author or creation date. The detection unit can also detect duplicate files with the same tags or categories based on the file metadata. The detection unit can also use the file metadata to detect duplicate files with the same file size or format. This improves the accuracy of duplicate file detection by considering the file metadata. Metadata includes, but is not limited to, author information and file size. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input the file metadata into a generation AI, which can then improve the accuracy of duplicate file detection.

[0089] The detection unit can determine the detection priority when detecting duplicate files, taking into account the file access frequency. For example, the detection unit may prioritize the detection of frequently accessed files. The detection unit may also postpone the detection of files with low access frequency. The detection unit may also prioritize the detection of files of high importance based on access frequency. In this way, the detection priority of duplicate files is appropriately determined by considering the file access frequency. Access frequency is measured, for example, by the number of accesses or the last access date and time. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input the file access frequency to a generation AI, and the generation AI can determine the detection priority of duplicate files.

[0090] The detection unit can estimate the user's emotions and adjust the display method of duplicate files based on the estimated user emotions. For example, if the user is tense, the detection unit can provide a simple and highly visible display method. If the user is relaxed, the detection unit can also provide a display method that includes detailed information. If the user is in a hurry, the detection unit can also provide a display method that gets straight to the point. By adjusting the display method of duplicate files according to the user's emotions, a more appropriate display is achieved. The display method of duplicate files is adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using the generative AI or not. For example, the detection unit can input user emotion data into the generative AI, and the generative AI can adjust the display method of duplicate files.

[0091] The detection unit can improve the accuracy of duplicate file detection by referring to the user's past file operation history. For example, the detection unit may prioritize the detection of files that the user has frequently accessed in the past. The detection unit can also optimize the detection method for specific file formats based on the user's past operation history. The detection unit can also group related files based on the user's past operation history and detect them together. This improves the accuracy of duplicate file detection by referring to the user's past file operation history. Past file operation history is obtained, for example, by analyzing log data. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input the user's past file operation history into a generation AI, which can then improve the accuracy of duplicate file detection.

[0092] The detection unit can determine the detection priority when detecting duplicate files, taking into account the user's geographical location information. For example, the detection unit may prioritize the detection of files related to the user's current location. The detection unit can also group and detect related files based on the user's past location information. The detection unit can also prioritize the detection of files related to a specific region using the user's geographical location information. This ensures that the detection priority of duplicate files is appropriately determined by considering the user's geographical location information. Geographical location information is obtained, for example, by using GPS data. Some or all of the above processing in the detection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the detection unit can input the user's geographical location information into a generation AI, which can then determine the detection priority of duplicate files.

[0093] The suggestion unit can estimate the user's emotions and adjust the criteria for deletion suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit may make deletion suggestions based on strict criteria. If the user is relaxed, the suggestion unit may make deletion suggestions based on lenient criteria. If the user is in a hurry, the suggestion unit may prioritize deleting high-priority files. This allows for more appropriate deletion suggestions by adjusting the criteria for deletion suggestions according to the user's emotions. The criteria for deletion suggestions are adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust the criteria for deletion suggestions.

[0094] The suggestion unit can improve the accuracy of its deletion suggestions by analyzing the frequency of file use and importance when making deletion suggestions. For example, the suggestion unit may prioritize suggesting the deletion of files that are used infrequently. The suggestion unit may also prioritize suggesting the deletion of files that are of low importance. The suggestion unit may also consider both frequency of use and importance to make the optimal deletion suggestion. This improves the accuracy of deletion suggestions by analyzing the frequency of use and importance of files. Frequency of use is measured by, for example, the number of accesses and the last access date and time. Importance is evaluated by, for example, the content of the file and the project it is associated with. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or not. For example, the suggestion unit can input the frequency of use and importance of files into a generation AI, which can then improve the accuracy of its deletion suggestions.

[0095] The proposal unit can determine the priority of deletion proposals by considering the relevance of the files when making a deletion proposal. For example, the proposal unit may prioritize the deletion of less relevant files. The proposal unit may also lower the priority of deletion proposals for highly relevant files. The proposal unit can also analyze the relevance of files and make the optimal deletion proposal. This ensures that the priority of deletion proposals is appropriately determined by considering the relevance of the files. File relevance is evaluated by, for example, content similarity or metadata matching. Some or all of the above processing in the proposal unit may be performed using a generation AI, or not using a generation AI. For example, the proposal unit can input the relevance of files into a generation AI, and the generation AI can determine the priority of deletion proposals.

[0096] The suggestion unit can estimate the user's emotions and adjust how the delete suggestion is displayed based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide a simple and highly visible display. If the user is relaxed, the suggestion unit can also provide a display that includes detailed information. If the user is in a hurry, the suggestion unit can also provide a concise display. By adjusting how the delete suggestion is displayed according to the user's emotions, a more appropriate display is achieved. The display method of the delete suggestion is adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using a generative AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then adjust how the delete suggestion is displayed.

[0097] The suggestion unit can improve the accuracy of its deletion suggestions by referring to the user's past file operation history. For example, the suggestion unit can make optimal deletion suggestions based on the user's past file deletion history. The suggestion unit can also optimize deletion suggestions for specific file formats based on the user's past operation history. The suggestion unit can also group related files based on the user's past operation history and make deletion suggestions accordingly. This improves the accuracy of deletion suggestions by referring to the user's past file operation history. Past file operation history can be obtained, for example, by analyzing log data. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's past file operation history into a generation AI, which can then improve the accuracy of deletion suggestions.

[0098] The suggestion unit can determine the priority of deletion suggestions by considering the user's geographical location information. For example, the suggestion unit may prioritize the deletion of files related to the user's current location. The suggestion unit can also group related files based on the user's past location information and propose their deletion accordingly. The suggestion unit can also use the user's geographical location information to prioritize the deletion of files related to a specific region. This ensures that the priority of deletion suggestions is appropriately determined by considering the user's geographical location information. Geographical location information is obtained, for example, by using GPS data. Some or all of the above processing in the suggestion unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the suggestion unit can input the user's geographical location information into a generation AI, which can then determine the priority of deletion suggestions.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing high-priority files. If the user is relaxed, the analysis unit may also analyze all files equally. If the user is in a hurry, the analysis unit may also prioritize analyzing the most frequently accessed files. This allows for more appropriate analysis by adjusting the analysis priority according to the user's emotions. The user's emotions are estimated using, for example, facial recognition or voice analysis. The analysis priority is adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the analysis priority.

[0101] The analysis unit can improve the accuracy of its analysis by considering the file's creation date and last modified date when analyzing the file's contents. For example, the analysis unit may determine that older files are of low importance and lower their analysis priority. The analysis unit may also determine that recently updated files are of high importance and raise their analysis priority. The analysis unit can also group files with similar creation dates and analyze them all at once. This improves the accuracy of the analysis by considering the file's creation date and last modified date. The creation date is obtained, for example, from the file metadata. The last modified date is also obtained, for example, from the file metadata. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the file's creation date and last modified date into a generation AI, which can then improve the accuracy of the analysis.

[0102] The analysis unit can improve the efficiency of analysis by utilizing file metadata when analyzing the contents of a file. For example, the analysis unit can identify the file type based on metadata and optimize the analysis method. The analysis unit can also obtain file creator information from metadata and group related files. The analysis unit can also use metadata to pre-classify the contents of files and improve the efficiency of analysis. In this way, the efficiency of analysis is improved by utilizing file metadata. Metadata includes, but is not limited to, creator information and file size. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input file metadata into a generation AI, which can then improve the efficiency of the analysis.

[0103] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display is achieved. The display method of the analysis results is adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the display method of the analysis results.

[0104] The analysis unit can improve the accuracy of its analysis by referring to the user's past file operation history when analyzing the contents of a file. For example, the analysis unit may prioritize the analysis of files that the user has frequently accessed in the past. The analysis unit can also optimize the analysis method for specific file formats based on the user's past operation history. The analysis unit can also group related files together and analyze them based on the user's past operation history. This improves the accuracy of the analysis by referring to the user's past file operation history. Past file operation history can be obtained, for example, by analyzing log data. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's past file operation history into a generation AI, which can then improve the accuracy of the analysis.

[0105] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated emotions. For example, if the user is stressed, the classification unit may use simple classification criteria. If the user is relaxed, the classification unit may also use detailed classification criteria. If the user is in a hurry, the classification unit may prioritize classifying high-priority files. This allows for more appropriate classification by adjusting the classification criteria according to the user's emotions. The classification criteria may be adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using or without generative AI. For example, the classification unit can input user emotion data into a generative AI, which can then adjust the classification criteria.

[0106] The classification unit can improve the accuracy of file classification by considering the relationships between files. For example, the classification unit can group related files together and classify them all at once. The classification unit can also analyze the contents of files and classify highly related files into the same category. The classification unit can also use file metadata to identify and classify highly related files. This improves the accuracy of classification by considering the relationships between files. File relationships are evaluated, for example, by similarity of content or matching of metadata. Some or all of the above processes in the classification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the classification unit can input file relationships into a generative AI, which can then improve the accuracy of classification.

[0107] The classification unit can improve the efficiency of file classification by considering file size and format. For example, the classification unit can classify large files into a separate category to improve analysis efficiency. The classification unit can also improve classification accuracy by using different classification criteria for each file format. The classification unit can also classify files to an appropriate storage location based on their size. This improves classification efficiency by considering file size and format. File size is considered, for example, by size range and size-based classification criteria. File format is considered, for example, by format type and format-based classification criteria. Some or all of the above processing in the classification unit may be performed using or without a generative AI. For example, the classification unit can input file size and format into a generative AI, which can then improve classification efficiency.

[0108] The classification unit can estimate the user's emotions and adjust the tagging method based on the estimated emotions. For example, if the user is nervous, the classification unit may use simple and easy-to-understand tags. If the user is relaxed, the classification unit may also use detailed tags. If the user is in a hurry, the classification unit may prioritize high-priority tags. This allows for more appropriate tagging by adjusting the tagging method according to the user's emotions. The tagging method may be adjusted, for example, based on an emotion score. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using or without a generative AI. For example, the classification unit can input user emotion data into a generative AI, which can then adjust the tagging method.

[0109] The classification unit can improve the accuracy of file classification by referring to the user's past classification history. For example, the classification unit may prioritize classification criteria previously used by the user. The classification unit can also optimize the classification method for specific file formats based on the user's past classification history. The classification unit can also group and classify related files based on the user's past classification history. This improves the accuracy of classification by referring to the user's past classification history. Past classification history can be obtained, for example, by analyzing log data. Some or all of the above processes in the classification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the classification unit can input the user's past classification history into a generative AI, which can then improve the accuracy of classification.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The analysis unit analyzes the file contents using a generating AI. The analysis unit analyzes digital data such as document files, image files, and video files, and the generating AI understands the file contents and outputs the analysis results. Step 2: The classification unit uses generation AI to classify and tag files based on the content analyzed by the analysis unit. The classification unit automatically classifies files and assigns appropriate tags based on the analysis results. Step 3: The detection unit scans the files classified by the classification unit using the generation AI to detect duplicate files. The detection unit scans the files in the storage and identifies the duplicate files. Step 4: The proposal unit uses the generation AI to propose the deletion of duplicate and unnecessary files detected by the detection unit. The proposal unit analyzes the frequency of use and importance of the files and proposes the deletion of files that are deemed unnecessary.

[0112] 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the analysis unit, classification unit, detection unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 or the processor 28 of the data processing unit 12, and analyzes the contents of files using generating AI. The classification unit is implemented by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing unit 12, and classifies and tags files based on the analysis results. The detection unit is implemented by the processor 46 of the smart device 14 or the processor 28 of the data processing unit 12, and detects duplicate files. The proposal unit is implemented by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing unit 12, and proposes the deletion of duplicate and unnecessary files. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] 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.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] 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.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the analysis unit, classification unit, detection unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 or the processor 28 of the data processing unit 12, and analyzes the contents of files using generating AI. The classification unit is implemented by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12, and classifies and tags files based on the analysis results. The detection unit is implemented by the processor 46 of the smart glasses 214 or the processor 28 of the data processing unit 12, and detects duplicate files. The proposal unit is implemented by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12, and proposes the deletion of duplicate and unnecessary files. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] 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.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] 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.

[0139] 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.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the analysis unit, classification unit, detection unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 or the processor 28 of the data processing unit 12, and analyzes the contents of files using generation AI. The classification unit is implemented by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12, and classifies and tags files based on the analysis results. The detection unit is implemented by the processor 46 of the headset terminal 314 or the processor 28 of the data processing unit 12, and detects duplicate files. The proposal unit is implemented by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12, and proposes the deletion of duplicate and unnecessary files. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] 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.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] 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.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] 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.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the analysis unit, classification unit, detection unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 or the processor 28 of the data processing unit 12, and analyzes the contents of files using generating AI. The classification unit is implemented by the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing unit 12, and classifies and tags files based on the analysis results. The detection unit is implemented by the processor 46 of the robot 414 or the processor 28 of the data processing unit 12, and detects duplicate files. The proposal unit is implemented by the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing unit 12, and proposes the deletion of duplicate and unnecessary files. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0165] 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.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] 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.

[0168] 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.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] 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."

[0171] 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.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] 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.

[0183] (Note 1) An analysis unit that analyzes the contents of the file, A classification unit that classifies and tags files based on the content analyzed by the aforementioned analysis unit, A detection unit scans the files classified by the classification unit and detects duplicate files, The system includes a proposal unit that proposes the deletion of duplicate and unnecessary files detected by the detection unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyzes digital data such as document files, image files, and video files. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned classification unit is The system automatically categorizes files and assigns appropriate tags based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is Scan files in storage and identify duplicate files. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, It analyzes file usage frequency and importance, and suggests deleting files deemed unnecessary. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, It provides a user interface that allows users to easily search for files and quickly find the data they need. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing file contents, consider the file's creation date and last modified date to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing the contents of a file, utilize the file's metadata to improve the efficiency of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing file contents, the system improves analysis accuracy by referencing the user's past file operation history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing file contents, the analysis priority is determined by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned classification unit is It estimates the user's emotions and adjusts the classification criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned classification unit is When classifying files, consider file relationships to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned classification unit is When classifying files, consider file size and format to improve classification efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned classification unit is It estimates the user's sentiment and adjusts the tagging method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned classification unit is When classifying files, the system improves classification accuracy by referencing the user's past classification history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned classification unit is When classifying files, analyze users' social media activity and assign relevant tags. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is The system estimates user sentiment and adjusts the duplicate file detection criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is When detecting duplicate files, the accuracy of the detection is improved by considering not only the file content but also the file metadata. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is When detecting duplicate files, the detection priority is determined by considering the frequency of file access. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is It estimates the user's sentiment and adjusts how duplicate files are displayed based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is When detecting duplicate files, the system improves detection accuracy by referencing the user's past file operation history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is When detecting duplicate files, the system prioritizes detection based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, We estimate user sentiment and adjust the criteria for deletion suggestions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When suggesting deletion, we analyze the frequency of file use and importance to improve the accuracy of the suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a deletion suggestion, the priority of the suggestion is determined by considering the relevance of the files. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, The system estimates the user's sentiment and adjusts how deletion suggestions are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When suggesting deletion, the system improves the accuracy of the suggestion by referencing the user's past file operation history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When submitting a deletion proposal, the user's geographical location is taken into consideration to determine the priority of the proposal. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes the contents of the file, A classification unit that classifies and tags files based on the content analyzed by the aforementioned analysis unit, A detection unit scans the files classified by the classification unit and detects duplicate files, The system includes a proposal unit that proposes the deletion of duplicate and unnecessary files detected by the detection unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyzes digital data such as document files, image files, and video files. The system according to feature 1.

3. The aforementioned classification unit is The system automatically categorizes files and assigns appropriate tags based on the analysis results. The system according to feature 1.

4. The detection unit is Scan files in storage and identify duplicate files. The system according to feature 1.

5. The aforementioned proposal section is, It analyzes file usage frequency and importance, and suggests deleting files deemed unnecessary. The system according to feature 1.

6. The aforementioned proposal section is, It provides a user interface that allows users to easily search for files and quickly find the data they need. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing file contents, consider the file's creation date and last modified date to improve the accuracy of the analysis. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing the contents of a file, utilize the file's metadata to improve the efficiency of the analysis. The system according to feature 1.

10. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

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