Facilitating user input by predicting target storage locations

Abstract

A method, computer system, and computer program product for modifying a user interface. Attributes of a source object identified by a user are determined in connection with a user input for storing the source object. Attributes of one or more target storage locations are determined. A target storage location for storing the source object is predicted, along with a confidence value associated with the prediction. The prediction is made using a machine learning model that predicts the predicted target storage location and the associated confidence value based on the determined attributes of the source object. Usage patterns of the plurality of target storage locations are determined. The user interface is modified based on the predicted target storage location.

Description

Background Technology

[0001] This invention generally relates to the field of computing, and more particularly to predicting the target storage location of a source object in a user interface to facilitate user input.

[0002] User data can be stored on a user's device or on a remote storage system, which may be referred to here as a cloud storage device. Storing user data in a cloud storage device allows a user to access their data from multiple locations via multiple devices. Additionally, cloud storage devices are a reliable way to back up data. Users can access cloud storage systems through web browsers and / or applications on smartphones.

[0003] A user interface for storing user data can include a directory showing folders and files. Folders and files can be created, accessed, organized, and deleted by the user. If user data is stored on a user's device, all files and folders belong to the user of that device. Conversely, data stored in a cloud storage system can be shared with other users. Folders and files shown in a user interface for a cloud storage system can be accessed by a single user or a group of users. For example, if a user decides to share a file or folder with another user, the user interface for a cloud storage system provides a means to grant that other user access. For instance, a group of workers can collaborate by sharing work-related folders. When a user shares content with another user, the shared folder or file can appear in that user's directory along with a folder created by that user. An example of a cloud-based file storage and synchronization service is Google. Microsoft as well as

[0004] The user interface used to store user data allows for many different types of interaction. One such interaction is dragging and dropping source items, such as files, onto a target storage location, such as a folder. Summary of the Invention

[0005] Embodiments of the present invention disclose a method, computer system, and computer program product for analyzing results associated with storing a source object in one of one or more target storage locations, predicting a target storage location that a user might want, and modifying a user interface to help the user move the source object to the predicted target storage location.

[0006] Embodiments of the present invention relate to a computer-implemented method for modifying a user interface. The method may include determining attributes of a source object identified by a user by incorporating user input for storing a source object. Attributes of one or more target storage locations may be determined. Furthermore, the method may include predicting a target storage location for storing the source object and a confidence value associated with the prediction. A machine learning model predicts the target storage location and associated confidence value based on the determined attributes of the source object. Additionally, usage patterns for multiple target storage locations may be determined. The user interface may be modified based on the predicted target storage location.

[0007] In one embodiment, determining multiple target storage location usage patterns includes determining usage patterns for users and user locations.

[0008] In one embodiment, determining usage patterns for multiple target storage locations includes determining user usage patterns as well as the time and date entered by the user.

[0009] In one embodiment, determining usage patterns for multiple target storage locations includes determining usage patterns for a group of users.

[0010] In one embodiment, predicting a target storage location and an associated confidence value by a machine learning model based on attributes of a determined source object includes: predicting a target storage location and an associated confidence value based on attributes of one or more determined target storage locations. Additionally, the source object includes confidential data attributes, and the target storage location includes non-confidential access attributes.

[0011] In one embodiment, modifying the user interface based on the predicted target storage location includes providing a gravitational effect between the source object and the predicted target storage location.

[0012] In one embodiment, modifying the user interface based on the predicted target storage location includes: presenting a line between the source object and the target storage location in the user interface. The line is weighted by thickness according to a confidence level.

[0013] In one embodiment, modifying the user interface based on the predicted target storage location includes: presenting a line between the source object and the target storage location in the user interface; and labeling the line according to a confidence level. Attached Figure Description

[0014] These and other objects, features, and advantages of the present invention will become apparent from the following detailed description of its illustrative embodiments, which is taken in conjunction with the accompanying drawings. The various features in the drawings are not to scale, as the illustrations are provided for clarity and to assist those skilled in the art in understanding the invention in conjunction with the detailed description. In the drawings:

[0015] Figure 1A user interface presented on a display device according to at least one embodiment is shown.

[0016] Figure 2 A user interface presented on a display device according to at least one embodiment is shown, the user interface including visual modifications to the user interface.

[0017] Figure 3 A user interface presented on a display device according to at least one embodiment is shown, the user interface including visual modifications to the user interface.

[0018] Figure 4 A user interface presented on a display device according to at least one embodiment is shown, the user interface including visual modifications to the user interface.

[0019] Figure 5 This is an operational flowchart illustrating, according to various embodiments, a process for predicting a target storage location that a user might want and modifying the user interface to help the user move a source object to the predicted target storage location.

[0020] Figure 6 This is a block diagram illustrating a computer system that can be used according to various embodiments.

[0021] Figure 7 yes Figure 6 A block diagram of the persistent storage device of a computer system. Detailed Implementation

[0022] The embodiments described below pertain to a system, method, and computer program product for predicting the target storage location of a source object in a user interface to facilitate user input. Thus, the described embodiments improve the technical field of user interfaces in computer systems. Furthermore, the described embodiments improve the technical field of data security in computer systems by reducing the possibility of unintentional disclosure of confidential data.

[0023] Figure 1 A user interface 102 presented on a display device according to at least one embodiment is shown. The display device may be combined with the following... Figure 6 and Figure 7 The computer system 600 described herein includes a display device 616. The computer system 600 includes various components described in more detail below, which enable the user interface 102 to perform the functions and operations described herein.

[0024] User interface 102 can receive input from various sources, including manual input from the user via a mouse, keyboard, touchscreen, or other input devices. User interface 102 can also output data from user interface 102 to display device 616, so that user interface 102 and any graphical elements in user interface 102 can be displayed to the user.

[0025] User interface 102 may include any number of graphical elements, such as folders 103-109 and files 110-111 and 113. Folders 103-109 are arranged in the left column of the active workspace 115. Folders 103-109 can be displayed in any order or position. Folder 112 and files 110-111 and 113 are described as icons in workspace 115. In this example, the icons for folders 112 and files 110-111 and 113 in the workspace are movable. Folders 112 and files 110-111 and 113 can be moved to any desired location in workspace 115. Additionally, folders 112 and files 110-111 and 113 can be moved into any of the folders 103-109 in the left column using a drag-and-drop action. Files 110-111 and 113 can also be moved into folder 112 using a drag-and-drop action. The "drag and drop" action copies a source item, such as a file, to a target storage location, such as a folder. It should be understood that embodiments of the invention can be practiced in many different types of user interfaces and are not limited to the illustrated example user interface 102.

[0026] Folder 112 and files 110-111 and 113 can be represented by graphical elements. Graphical elements represent computer files stored in volatile or non-volatile memory of computer system 600 and are displayed as icons indicating a specific type of file. Graphical elements can represent data files such as text documents, spreadsheets, email messages, calendar events, videos, financial account data, personal tax data, personal medical data, confidential business data, or folders containing any of these types of data files.

[0027] User interfaces allow for many different types of interactions. An example interaction is the "drag and drop" operation of copying a source item to a target storage location. However, users sometimes make seemingly minor mistakes that can have negative consequences. For instance, in a web application, a user might want to drag the expense report 110 document to their "Expenses" folder 104, but they might accidentally place it in a folder directly below "Friends" 105, as... Figure 1As shown, this can lead to documents being placed in the wrong location. Another example could be a user attempting to drag a PDF document to a file repository on a cloud storage system, such as the box.com website, but accidentally placing the PDF document into their music application, resulting in the file being copied to the music application. As yet another example, a user could drag a file containing confidential or personal data to a folder on a cloud storage system that is shared with someone who does not have permission or need to know the confidential or personal information. For users with limited mobility (e.g., tremors in the hand), these minor errors may occur more frequently than for other users, because disabled users cannot use pointing devices (e.g., a mouse, fingers) on a conventional or touchscreen display with sufficient precision to complete tasks such as moving files.

[0028] According to various embodiments of the present invention, predictive analysis of results associated with one or more targets is determined, and after the analysis, aspects of the user interface 102 can be modified to help the user move the source to the target that is classified as or predicted to be the user's expected target storage location.

[0029] In one embodiment, the user interface 102 is modified to allow the user to perceive the effects of gravity. If the user begins moving a graphical object along a path toward a predicted target, the system response to a given amount of user input pointing at the device can be increased. For example, in a drag-and-drop action where a file represented by an icon is moved to a folder, user input that would typically be converted into 100-pixel movement can be converted into 200-pixel icon movement in the UI when the movement is along a path toward the predicted target. Conversely, if the user begins moving the indicating device or graphical object along a path toward a less likely, desired target, the system response to a given amount of user input can be reduced. For example, in a drag-and-drop action moving a file icon to a less likely desired folder, user input that would typically be converted into 100-pixel movement can be converted into 50-pixel icon movement. In one embodiment, in these examples, the time required for an icon to move 200 or 50 pixels is the same as the time typically required for an icon to move 100 pixels. For example, if user input typically converts to 100-pixel movement in 0.1 seconds, then a conversion to 200 or 50 pixels will also take 0.1 seconds. In addition, the "standard" translation distance and "standard" time can be the default or user-preferred distance and time settings.

[0030] In one embodiment, a velocity effect (or a combination of velocity and gravity effects) may be employed. The time required to traverse a specific number of pixels in response to user input can be based on predictive analytics of results associated with one or more, or two or more, targets. For example, in a drag-and-drop action, the movement of a file icon along a path toward a predicted target causes the system to accelerate the movement of the graphical object, bringing it to the predicted target in a shorter amount of time than would typically take in response to the same user input without a velocity effect. For instance, user input that would typically translate to 100 pixels moving in 0.2 seconds when moving along a path toward a predicted target can be translated into an icon moving in 0.1 seconds in the UI. Conversely, the movement of a pointing device or graphical object corresponding to a file along a path toward a less likely expected target causes the system to slow down the movement of the icon on the screen, making it take a significantly longer time to bring the icon to the predicted target compared to the time would typically take in response to the same amount of user input without a velocity effect. For example, when motion moves along a path toward a predicted target, what would typically be translated as 100 pixels of user input moving in 0.2 seconds can be translated into an icon in the UI that moves in 0.4 seconds.

[0031] As further described below, the proportions of items can be learned and adjusted over time on an individual basis (Mary or John's preference), on a larger group (the tendency of all pediatricians), or on a group of unaffiliated individuals.

[0032] refer to Figure 2 In one embodiment, one or more lines are drawn in the user interface between a file icon representing the source object of the drag-and-drop action and a graphical object corresponding to a possible target storage location, such as a folder. The line thickness can be weighted, and the lines can be marked with probability or confidence levels to guide the user to the most appropriate target location. Figure 2As depicted in the example, line 220 between file 210 (the source object) and the expense folder 104 (the first possible target storage location) is given a high weight and labeled with a 70% probability or confidence level. Line 222 between file 210 and the marketing folder 106 (the second possible target storage location) is lightly weighted and labeled with a 20% probability or confidence level. Line 224 between file 210 and the training folder 108 (the third possible target storage location) is unweighted and labeled with a 10% probability or confidence level. Lines may not be drawn for possible target storage locations associated with probability or confidence levels below a threshold (e.g., below 10%). For example, lines for possible target storage locations 103, 105, 107, 109, and 112 are not shown. These lines can be used to help or guide the file icon along a path toward the predicted target.

[0033] refer to Figure 3 In one embodiment, one or more target storage locations that have been identified as potential targets may be outlined or otherwise visually highlighted to emphasize their relevance. As an example, Figure 3 The cost folder 104 is shown outlined with a thick line. Highlighting can take the form of changing the color of the target folder for emphasis. Highlighting can also take the form of an animated icon representing the target folder, such as blinking or flashing. The outline or visual highlighting can be triggered when the confidence interval associated with the prediction target is within a threshold level, such as a 90% confidence level.

[0034] refer to Figure 4 In one embodiment, if an "error" is detected—that is, if the probability or confidence level of the target is below a threshold—a confirmation dialog box can be presented. Action buttons can be provided in the dialog box to allow the user to correct the error. Figure 4 As depicted in the example, file 410 has been moved to the friends folder 105 in a drag-and-drop action. However, in this example, the probability or confidence level that the friends folder 105 is the folder the user wants is below a threshold, indicating that the movement of file 410 may have been an error. For example, the threshold could be set to 50%, and the probability or confidence level that file 410 is intended for the friends folder 105 could be only 10%. Under these conditions, dialog box 422 will be displayed. If the user does not intend to move file 410 to the friends folder 105, i.e., the determined 10% probability or confidence level is accurate, the user can select the "No" button to "undo" the operation. On the other hand, if the friends folder 105 is indeed intended, the user can select the "Yes" button. In one embodiment, if the user does not select anything within a certain period of time, such as 5 seconds, the dialog box may disappear automatically.

[0035] In one embodiment, the threshold used to infer an error and display a dialog box can be an absolute probability or a confidence value. In another embodiment, the threshold can be a relative probability or confidence value combined with an absolute value. When two or more targets have confidence values ​​that are not significantly different, the dialog box can advise the user to be cautious rather than inferring an "error" by requesting confirmation. For example, suppose the folder with the highest confidence value has a confidence value greater than 50% (the second threshold), then suppose the first threshold is defined as being no more than 10% lower than the target folder with the highest confidence value. For illustration, suppose the confidence values ​​of three folders A, B, and C are 61%, 54%, and 38%, respectively. The user action of moving the source file to the target folder B will not be identified as an error because the confidence value of folder A, with the highest confidence value, is greater than 50%, and the confidence value of target folder B is less than 10% lower than the confidence value of target folder A (61% - 54% = 7%). However, because the confidence values ​​of the two or more targets do not differ by more than the first threshold, the dialog box requests confirmation.

[0036] Now for reference Figure 5 The operation flowchart illustrates an exemplary process 500 for analyzing the consequences associated with storing a source object in a target storage location, predicting the user's likely expected target storage location, and modifying the user interface to help the user move the source object to the predicted target storage location.

[0037] At 502, one or more attributes of a source object, such as a file, are determined. The source object can be selected by a user using a user input device in any suitable manner, such as a mouse, finger, or voice command. Attributes may include titles and metadata such as author, tags, and file type (e.g., DOCX, MP3, XLSX, PPT). Attributes may include whether the document is locked for writing, reading, or both. Attributes can be determined using natural language processing of the text contained in the source object itself, as well as in the title and metadata. Any suitable natural language processing tool can be used. In one embodiment, the natural language processing at 502 can use the Natural Language Toolkit (NLTK) to process the textual information of the source object to extract words from the source object. NLTK is a suite of libraries and programs for symbolic and statistical natural language processing of English text, developed by Steven Bird and Edward Loper of the Department of Computer and Information Sciences at the University of Pennsylvania. In another embodiment, IBM... AI services. For example, natural language processing can be used to identify attributes such as keywords, categories, topics, sentiment, and entities.

[0038] Attributes extracted using natural language processing are not limited to linguistic data, i.e., text. Attributes extracted at position 502 can include numeric data, including social security numbers, account numbers, and currency values. Additionally, numeric attributes can include age, height, and weight, as well as medical test results. Some values ​​may be confidential.

[0039] Additionally, computer vision techniques can be used in step 502 to extract attributes from images. The image can be included within a source object, or the image itself can be the source object. In one embodiment, a convolutional neural network can be used to classify or identify objects in an image. Therefore, computer vision techniques can be used to classify images as containing information identifying specific individuals. Images are not limited to traditional photographs but can also include medical images, such as X-rays or ultrasound images. Some images may be confidential.

[0040] Furthermore, at position 502, attributes can be extracted from the audio file using computer speech recognition technology. The audio file can be included in the source object, or the audio file itself can be the source object. Audio files containing speech can be processed using speech recognition tools (such as IBM...). The speech-to-text service converts the audio file to text. The resulting text file can be processed using natural language processing tools as described above. Some audio files may be confidential, such as transcripts of meetings discussing proprietary or trade-secret information. Audio files containing music are tagged using user-specified attributes. For example, a user can specify that all audio files containing music are personal. In some embodiments, music recognition tools, such as… It can be used in conjunction with song fingerprints to identify attributes such as song title, album, recording artist, and year.

[0041] At 504, at least one attribute of the target storage location (e.g., a folder) is determined. In some embodiments, attributes of two or more target storage locations are determined. Any attribute that is possible for the source object can be an attribute of the target storage location. However, the attributes of the target storage location can be more general and granular than many attributes of the source object. For example, attributes of subject, person, project, group, category, or class. For example, attributes of the target storage location include attributes of personal or private attributes, such as personal expenses, friends, professional contacts, music, entertainment, personal photos, resume, personal calendar, and personal communications. Attributes can also be work-related: employees belonging to a team or business unit, customers, business expenses, business needs, and various forms of work products. The attributes of the target storage location can include whether it is explicitly designated as public or private, and which users have permission to access the target. The attributes of the target storage location can be determined using any of the tools and techniques described above for determining attributes of the source object.

[0042] At point 506, a prediction is made for each possible target storage location. Machine learning models, such as random forests or neural network models, can be used to predict or classify the target storage location desired by the user for the source object. The target storage location can be classified based on whether the attributes of the source object are related to the attributes of the target storage location. Each classification using the machine learning model can produce an associated confidence interval. Furthermore, the machine learning model can determine the consequences associated with storing the source object in the target storage location. An example of a determined consequence is that data corruption will occur if a source object containing confidential or sensitive information is copied to a folder that can be accessed by another user without needing or being permitted to know that information. For example, such unintentional data corruption can occur in an environment where folders and files are displayed in the user interface of a cloud storage system that can be accessed by a single user or a group of users. Another example of a determined consequence is that an inappropriate source object unintentionally stored in the target storage location cannot be located later. For example, storing work files in a personal folder, or alternatively, storing personal files in a work folder, can make the file difficult to locate. As yet another example, storing personal medical records in a personal folder used for tax purposes will result in the medical records being difficult to locate at a later time.

[0043] In addition to machine learning models for source objects used to classify target storage locations according to user expectations based on source object attributes, standalone machine learning models, such as neural networks, can classify attributes extracted from source objects based on whether the source object contains confidential or sensitive information. For example, a machine learning model can classify attributes extracted from source objects related to a specific person as public, personal, or sensitive personal information. Furthermore, a machine learning model can classify attributes extracted from source objects related to an entity as public, proprietary, or trade secret information. The determination of whether the obtained source object contains confidential or sensitive information can itself be treated as an attribute used by a master machine learning model that classifies the target storage location according to user expectations for the source object based on source object attributes.

[0044] The main machine learning model, which categorizes target storage locations based on source object attributes, can be adjusted or improved over time by incorporating usage patterns received by the model. Therefore, the prediction made at time 506 for each possible target storage location can be based on information obtained at the instance at time 508 prior to time 506.

[0045] At point 508, usage patterns can be determined. User usage patterns are determined based on multiple destination location decisions. In other words, usage patterns can be determined when a user selects a specific target storage location for a source object with a specific set of attributes, and then repeats that selection for one or more second source objects with similar attribute sets. Usage patterns can be used as "ground truth" training data for training a master machine learning model that categorizes target storage locations as user expectations for the source object. As an example, the machine learning model could learn over time that documents with titles including "Expenses" or documents describing a set of dollar expense amounts are submitted to the "Expenses" folder.

[0046] Usage patterns do not need to be determined from multiple destination location decisions made by a single user. In some embodiments, usage patterns can be based on patterns of user groups. For example, usage patterns can be based on a group of employees working in a particular business unit or having the same or similar job descriptions, such as a group of pediatricians. As another example, usage patterns can be “crowded,” for example, based on usage patterns of a group of non-affiliated individuals participating via a website hosting a public folder directory or multiple websites holding similar folder directories.

[0047] Determining usage patterns can also take into account the user's location, time of day, days of week, or a combination of location and time factors. For example, a pattern where a user primarily uses their "Friends" folder for work on weekends when they are at home can be detected. On weekdays, when a user is in their office location, they almost never use their "Friends" folder.

[0048] At point 510, the user interface can be modified based on the predicted target storage location. For example, in a drag-and-drop scenario, determining the target storage location might be what the user desires—increasing the gravitational pull (attraction) between the source object and the target. The user interface could then gently nudge the source document toward the target in a manner similar to a "grab" action or as described elsewhere in this document. The user can still override the gravitational effect, but depending on the confidence level of the trained model in understanding the user's expectations, the gravitational effect of the source object toward the target storage location can be increased. If the confidence level of the trained model is low relative to the UI action, the gravitational effect can remain neutral. The gravitational relationship of items can be adjusted on a personalized basis based on the preferences of individual users or larger user groups.

[0049] Information about how well a modified user interface performs can be collected. For example, at 510, the user interface can be annotated or enhanced in a specific way; for instance, the storage location of a target identified as a possible objective can be outlined in green to emphasize its relevance. If the objective is also displayed in green, the emphasis can be subtle and negligible. Information can be collected by asking users to determine their satisfaction with a particular annotation or enhancement. Information can also be collected indirectly by inferring how well the modified user interface performs based on the number of “errors” caused when using the interface—that is, when using the enhanced interface, the source object is copied to an unexpected folder. For example, an error can be inferred from a user performing a subsequent copy from an unexpected folder to the expected folder, or from a user receiving a rejection confirmation input in response to a dialog box asking for confirmation. Information can be collected from a single user or multiple users. The collected information can be used to annotate or enhance the user interface in different ways to provide more effective enhancements for specific users or groups of users or larger user groups. As an example, a machine learning model can learn that when an objective is displayed in green, it should outline it with a contrasting color, such as red or purple, to emphasize its relevance. Alternatively, the model can learn that the user is partially or completely colorblind and does not emphasize relevance to general colors or specific colors. As another example, a machine learning model can learn that when a target is displayed in green, it should also be presented with a line drawn in the user interface between the source object and the target's storage location. As yet another example, lines can be drawn in the UI with specific weights. The model can learn from the collected information that weighted lines labeled with probability or confidence levels produce fewer errors than unlabeled weighted lines.

[0050] Embodiments of the present invention can be applied to storage locations, and more generally, to any type of classification where one or more possible categories exist. For example, in some examples, a “target storage location” could be a repository or archive system (with document preservation and destruction rules) in an enterprise content management system that maintains documents for future retrieval.

[0051] Figure 6This is a block diagram illustrating a computer system that can be used as a client, server, or host computer according to various embodiments. As shown, the computer system 600 includes a processor unit 611, a memory unit 612, persistent storage 613, a communication unit 614, an input / output unit 615, a display 616, and a system bus 610. Computer programs are typically stored in persistent storage 613 until they need to be executed, at which point the programs are brought into memory unit 612 so that they can be directly accessed by the processor unit 611. The processor unit 611 selects a portion of memory unit 612 for reading and / or writing by using the address given to memory unit 612 along with read and / or write requests. Typically, reading and interpreting the encoded instructions at an address causes the processor 611 to fetch subsequent instructions at a subsequent address or some other address. The processor unit 611, memory unit 612, persistent storage 613, communication unit 614, input / output unit 615, and display 616 are interconnected via system bus 610.

[0052] Figure 7 yes Figure 6 A block diagram of persistent storage device 613 of computer system 600. In various embodiments, persistent storage device 613 may store computer-readable program instructions in the form of programs or modules required to perform various aspects of the disclosed embodiments. Persistent storage device 613 may store source attribute determination program 714, which may be used to determine one or more attributes of a source object (e.g., a file). Aspects of program 714 are further described with respect to operation 502. Additionally, persistent storage device 613 may store target attribute determination program 716, which may be used to determine one or more attributes of a target storage location. Aspects of program 716 are further described with respect to operation 504. Persistent storage device 613 may also store target prediction program 718, which may be used to predict whether a target storage location is likely to be a user-expected location of a particular source object. Aspects of program 718 are further described with respect to operation 506. Furthermore, persistent storage device 613 may store user pattern determination program 720, which may be used to determine usage patterns. Aspects of program 720 are further described with respect to operation 508. Additionally, persistent storage device 613 may store user interface modification program 722, which can be used to modify the user interface based on one or more predicted target storage locations. Aspects of program 722 are further described with respect to operation 510.

[0053] It should be understood that Figure 6 and 7 This is merely an illustration of an implementation and does not imply any limitation on the environments in which different embodiments may be implemented. Many modifications can be made to the described environment based on design and implementation requirements.

[0054] This document discloses detailed embodiments of the claimed structures and methods; however, it is to be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods, which can be implemented in various forms. The invention can be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments.

[0055] This invention can be a system, method, and / or computer program product at any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to perform aspects of the invention.

[0056] Computer-readable storage media can be tangible devices capable of retaining and storing instructions used by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices such as punch cards or recessed structures with instructions recorded thereon, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0057] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a suitable computing / processing device, or via a network, such as the Internet, a local area network (LAN), a wide area network (WAN), and / or a wireless network, to an external computer or external storage device. The network may include copper cables, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to a computer-readable storage medium within the respective computing / processing device.

[0058] Computer-readable program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​(including object-oriented programming languages ​​such as Smalltalk, C++, etc.) and procedural programming languages ​​(such as the "C" programming language or similar programming languages). The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of this invention, electronic circuits, including, for example, programmable logic circuits, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may execute computer-readable program instructions to personalize the electronic circuits by utilizing the status information of the computer-readable program instructions.

[0059] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0060] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / actions specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other devices to operate in a particular manner, such that the computer-readable storage medium in which the instructions are stored includes an article of writing comprising instructions for implementing aspects of the functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0061] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer-implemented process, such that the instructions, which execute on the computer, other programmable apparatus or other device, perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0062] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of instructions comprising one or more executable instructions for implementing a specified logical function. In some alternative embodiments, the functions indicated in the blocks may occur in a non-consecutive order as shown in the figures. For example, two blocks shown consecutively may actually be executed substantially simultaneously, or these blocks may sometimes be executed in reverse order, depending on the functions involved. It will also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action or executes a combination of dedicated hardware and computer instructions.

[0063] Various embodiments of the invention have been described for illustrative purposes, but are not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein has been chosen to best explain the principles of the embodiments, their practical application, or improvements to existing technologies in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A computer-implemented method for modifying a user interface, comprising: The attributes of the source object identified by the user are determined by combining user input used to store the source object; Determine the attributes of one or more target storage locations; Predicting a target storage location for storing the source object and a confidence value associated with the prediction, wherein the machine learning model predicts the target storage location and the associated confidence value based on the determined attributes of the source object; Determine the usage patterns of multiple target storage locations; as well as Modifying the user interface based on the predicted target storage location includes: presenting a line between the source object and the target storage location in the user interface, wherein the line is weighted by thickness according to a confidence level.

2. The computer-implemented method of claim 1, wherein the determination of the multiple target storage location usage patterns is determined for the user and the user's location.

3. The computer-implemented method of claim 1, wherein the determination of the multiple target storage location usage patterns is based on the user and the time and date input by the user.

4. The computer-implemented method of claim 1, wherein the determination of a user's multiple target storage location usage patterns is determined for a group of users.

5. The computer-implemented method of claim 1, wherein predicting the target storage location and associated confidence value by a machine learning model based on the determined attributes of the source object further comprises: The predicted target storage location and associated confidence value are predicted based on the determined attributes of the one or more target storage locations. The source object includes confidential data attributes, and the target storage location includes non-confidential access attributes.

6. The computer-implemented method of claim 1, wherein modifying the user interface based on the predicted target storage location further comprises: Provides the gravitational effect between the source object and the predicted target storage location.

7. A computer system for verifying data written to magnetic tape, comprising: One or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more tangible storage media, the program instructions being executable by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: The attributes of the source object identified by the user are determined by combining user input used to store the source object; Determine the attributes of one or more target storage locations; Predicting a target storage location for storing the source object and a confidence value associated with the prediction, wherein the machine learning model predicts the target storage location and the associated confidence value based on the determined attributes of the source object; Determine the usage patterns of multiple target storage locations; and Modifying the user interface based on the predicted target storage location includes: visually highlighting the target storage location based on the confidence level of the prediction.

8. The computer system of claim 7, wherein the determination of the multiple target storage location usage patterns is determined with respect to the user and the user's location.

9. The computer system of claim 7, wherein the determination of the multiple target storage location usage patterns is made in relation to the user and the time and date input by the user.

10. The computer system of claim 7, wherein the determination of a user's multiple target storage location usage patterns is made for a group of users.

11. The computer system of claim 7, wherein predicting the target storage location and associated confidence value by a machine learning model based on the determined attributes of the source object further comprises: The predicted target storage location and associated confidence value are predicted based on the determined attributes of the one or more target storage locations. The source object includes confidential data attributes, and the target storage location includes non-confidential access attributes.

12. The computer system of claim 7, wherein modifying the user interface based on the predicted target storage location further comprises: Provides the gravitational effect between the source object and the predicted target storage location.

13. A computer program product for verifying data written to magnetic tape, comprising: One or more non-transient computer-readable storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions being executable by a processor to cause the processor to perform a method comprising the following steps: The attributes of the source object identified by the user are determined by combining user input used to store the source object; Determine the attributes of one or more target storage locations; Predicting a target storage location for storing the source object and a confidence value associated with the prediction, wherein the machine learning model predicts the target storage location and the associated confidence value based on the determined attributes of the source object; Determine the usage patterns of multiple target storage locations; as well as Modifying the user interface based on the predicted target storage location includes: presenting a line between the source object and the target storage location in the user interface, wherein the line is marked according to a confidence level.

14. The computer program product of claim 13, wherein the determination of the multiple target storage location usage patterns is determined with respect to the user and the user's location.

15. The computer program product of claim 13, wherein the determination of the multiple target storage location usage patterns is determined in relation to the user and the time and date input by the user.

16. The computer program product of claim 13, wherein the determination of a user's multiple target storage location usage patterns is determined for a group of users.

17. The computer program product of claim 13, wherein predicting the target storage location and associated confidence value by a machine learning model based on the determined attributes of the source object further comprises: The predicted target storage location and associated confidence value are predicted based on the determined attributes of the one or more target storage locations. The source object includes confidential data attributes, and the target storage location includes non-confidential access attributes.

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