The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating function code and test code to determine that the function code satisfies an intent query. For example, the disclosed systems utilize a large language model to process an intent query and generate function code that defines one or more processes whose execution satisfies the intent query. In addition, the disclosed systems also utilize the large language model to generate test code that defines a function test for determining that the function code satisfies the intent query. Moreover, the disclosed systems can execute the test code to determine that the function code satisfies the intent query.
Methods and systems provide for dynamic contextual generation of creative content for product listings. In one embodiment, the system receives initial product facts for a product, user engagement data for a user of a platform, and one or more pieces of contextual information related to how the product will be viewed within the platform; uses this data to train a generative AI model for dynamic creative content generation for the listing; uses the trained generative AI model to dynamically generate creative content for the listing; displays the creative content for the listing on a client device associated with the user; receives feedback regarding user engagement with the creative content in terms of whether an engagement objective has been achieved; and refines the generative AI model based on the received feedback, including optimizing the generative AI model to generate or modify the creative content to achieve the engagement objective.
Disclosed herein is a system and method to determine whether to place an advertisement to a user requesting an address from the user. The system can iteratively determine multiple advertisement metrics of multiple advertisements to obtain multiple metrics. An advertisement metric among the multiple advertising metrics can indicate the value of placing the advertisement to the user. The system can rank multiple advertisements based on the multiple advertisement metrics and present a predetermined percentage of top-ranking advertisements among the multiple advertisements.
In some embodiments, a client application at a client device can receive, from a browser application at the client device, a first message including a unique identifier associated with a session of the browser application at a website associated with a content management system. The client application can extract the unique identifier from the first message, and establish a connection between the client application and the content management system by sending, from the client application to the content management system, a second message including the unique identifier. The client application can then receive, from the content management system through the connection, a third message relayed by the content management system from the website, where the third message is associated with the unique identifier.
Disclosed are systems, methods, and non-transitory computer-readable storage media for providing an embedded web view of a folder in a content management system on a web page. For example, a user can request from a content management system code for embedding a web view of a content item or group of content items (e.g., folder) into a web page. After the code is embedded into the webpage, the web page can present a web view of the content item or group of content items that is dynamically updated when the content item or group of content items is updated. Thus, the user is relieved of the burden of updating the web page with new links to reflect changes in content items stored in the online content management system.
Methods and systems provide for a unified presentation of cross-platform content to a user visiting a platform. In one embodiment, the system connects a client device associated with a user to a first content platform; receives a request from the client device to present content to the user at the first content platform; receives content associated with one or more additional content platforms; determines a subset of the content to present to the user; standardizes the subset of the content in a format to be used at the first content platform; presents the subset of the content to the user at the first content platform; processes a set of unified cross-platform metrics for user events related to the user interacting with the subset of the content at the first content platform; and provides a report of the set of unified cross-platform metrics for the user events.
Disclosed here is a system that can obtain attributes of an advertisement, where an attribute has a continuous value, and a range of acceptable values is uncertain. The system can create a file including contents that when provided to a predetermined function produce a value of the attribute. Based on the file, the system can generate values corresponding to the attributes. Based on the generated values, the system can create the advertisement. The system can obtain a response data to the created advertisement and can fit a multidimensional function to the attributes and the user response data. Based on the multidimensional function, the system can determine next values and next ranges, where the next values and the next ranges indicate an improvement in the response data.
This disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that can detect scene types across various portions of media content and display collections that organize segments (or portions) of media content (e.g., videos or images) according to the detected scene types for the media content files. For example, the disclosed systems can automatically identify content segments of media content that belong to one or more identified scene types and display the content segments organized by the different scene types. In order to determine the scene types for the content segments of the media content files, the disclosed systems can utilize machine learning that determines relevancies between data of the media content files and the scene types. Furthermore, the disclosed systems can display, within a GUI, the groupings of media content segments organized by the different scene types.
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The system receives a natural language-based input associated with a client device of a user. The system generates a search criterion for the received natural language-based input. The system, via the generative AI-bases search and retrieval system, generates a relevancy-ranked output listing of content items. The relevancy-ranked output listing content items responsive to the generated search criterion content items having an associated content identifier and a content description. The system generates a carousel display structure definition of the relevancy-ranked content items. The system transmits the carousel display structure definition of the relevancy-ranked content items and the content items to the client device. The client device renders, via a user interface, at least a portion of the relevancy-ranked content items.
G06F 16/2457 - Query processing with adaptation to user needs
G06F 7/08 - Sorting, i.e. grouping record carriers in numerical or other ordered sequence according to the classification of at least some of the information they carry
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for initiating, monitoring, and performing data backups from remote devices using web-based applications. For example, the disclosed systems receive a backup request from a remote device running a web-based application that does not include backup functionality, where the backup request triggers a data backup for data stored locally on a different client device. In some embodiments, the disclosed systems can establish a connection between the remote web-based application and a desktop application installed locally on the device to be backed up, where the connection facilitates triggering and managing a data backup executed by the desktop application via the remote web-based application. In some cases, the disclosed systems determine whether data backups are synchronous, asynchronous, and/or for user accounts within a team, and the disclosed systems facilitate data backups accordingly.
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
11.
USING GENERATIVE AI MODELS FOR CONTENT SEARCHING AND GENERATION OF CONFABULATED SEARCH RESULTS
Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The user search is provided to a generative AI based search sub-system and to a traditional search sub-system. A first search result listing is generated by the generative AI based subsystem, and a second search result listing is generated by the traditional search sub-system. The first search result listing and the second search result listing are aggregated together and provided for display to a user client device.
The present disclosure relates to systems, methods, and non-transitory computer-readable media for segmenting a digital video to segment a digital video by employing a chapterization approach to video transcripts based on contextual data from audio signals and video signals together. In some embodiments, the disclosed systems can extract various types of audio signals and various types of video signals from a digital video. From the extracted signals, the disclosed systems can determine a set of break points to segment a video transcript. In some embodiments, the disclosed systems can further recommend, via a notification on a client device, inserting corresponding breaks from the segmented transcript into the digital video.
G06V 20/40 - ScenesScene-specific elements in video content
G06T 7/90 - Determination of colour characteristics
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G10L 15/18 - Speech classification or search using natural language modelling
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
13.
DIRECTED ACYCLIC GRAPH FRAMEWORK FOR EXECUTING SEARCH FUNCTION OPERATIONS
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for performing a data search for a search request by utilizing a directed acyclic graph. For example, the disclosed systems receive a search request at a search engine of a content management system. In addition, the disclosed systems determine (e.g., in response to the search request) a node path from a directed acyclic graph that includes a plurality of interconnected nodes defining computer operations. Further, the determined node path includes a set of nodes that corresponds to the search request. Moreover, the disclosed systems perform the data search for the search request by executing operations defined by nodes along the node path within the directed acyclic graph.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and providing content update synopses using a large language model. In particular, in some embodiments, the disclosed systems determine, for a collaborative workspace of a content management system, activity metadata defining user account activity across a plurality of user accounts performing actions within the collaborative workspace. Further, the disclosed systems can generate, from the activity metadata, a text representation of the user account activity indicating the actions within the collaborative workspace that occur between a first timestamp and a second timestamp. Additionally, the disclosed systems can generate a summary generation prompt from the text representation of the user account activity. Moreover, the disclosed systems can generate an activity summary for the user account activity within the collaborative workspace between the first timestamp and the second timestamp by providing the summary generation prompt to a large language model.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for providing a streamed presentation of a video proxy referencing a cloud-based location of a digital video. For example, the disclosed systems receive a request to edit a digital video stored in a cloud-based location of a content management system. In addition, the disclosed systems generate a video proxy that include a reference to the cloud-based location of the digital video and modifies the video proxy in response to an editing operation. Further, the disclosed systems provide for display a streamed presentation of the video proxy depicting modifications defined by the editing operation.
G11B 27/031 - Electronic editing of digitised analogue information signals, e.g. audio or video signals
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
G06F 3/04847 - Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
16.
GENERATING AND PROVIDING SYNTHESIZED TASKS PRESENTED IN A CONSOLIDATED GRAPHICAL USER INTERFACE
The present disclosure relates to systems, non-transitory computer-readable media, and methods for collecting, organizing, and managing third-party content from multiple sources associated with a user account to present as synthesized tasks in a consolidated graphical user interface and minimize the distraction provided by multiple interfaces. In particular, in one or more embodiments, the disclosed systems analyze content from various web-based data sources, collect relevant content, create synthesized tasks associated with the relevant content, and present the relevant content to the user grouped into synthesized tasks in a single graphical user interface. Additionally, the disclosed systems can prioritize the generated synthesized tasks within the graphical user interface and provide productivity metrics based on the degree to which an associated user interacts with the synthesized tasks.
G06F 9/48 - Program initiatingProgram switching, e.g. by interrupt
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
G06F 9/451 - Execution arrangements for user interfaces
17.
INTELLIGENTLY GENERATING AND MANAGING THIRD-PARTY SOURCES WITHIN A CONTEXTUAL HUB
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating contextual hubs for organizing and presenting web-accessible content from third-party sources. In particular, the systems described herein can organize and manage within a contextual hub. For instance, the disclosed systems may perform actions on tabs based on analyzing usage signals associated with the tabs. Furthermore, the disclosed systems can organize contextually related content within contextual hubs. The disclosed systems may also facilitate collaboration between users within a contextual hub by synchronizing interactions with content within a contextual hub.
G06F 16/957 - Browsing optimisation, e.g. caching or content distillation
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
G06F 9/451 - Execution arrangements for user interfaces
G06F 16/954 - Navigation, e.g. using categorised browsing
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
G06F 16/958 - Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
H04L 67/125 - Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
This disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that can enable integration of functionalities for a content item search platform within a search platform prototyping tool. For example, the disclosed systems can utilize a search platform prototyping tool that enables a quick selection of a feature extraction function (or component). Based on the selected feature extraction function, the disclosed systems can prototype the feature extraction function on live data of a content management system to emulate a fully onboarded implementation of the feature extraction function (as a new functionality) on a search platform. Moreover, the disclosed systems can also display, within a search platform graphical user interface, selectable elements to display consolidated metadata associated with search results within a single user interface.
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating, arranging, and providing subgroupings of content items according to content attributes and dynamic facets. For example, the disclosed systems generate dynamic facets reflecting content attributes, where the dynamic facets are selectable interface elements for arranging content items into subgroups. In certain cases, as part of generating a subgrouping of content items, the disclosed systems can identify portions of content items (e.g., segments of a digital video or sections of a digital document) that correspond to a dynamic facet. The disclosed systems can also provide filtering options for creating or refining subgroupings from a content collection by, for example, filtering according to recency criteria such as file type, recency of interaction, collaborative user accounts, or others.
G06F 3/04842 - Selection of displayed objects or displayed text elements
G06F 3/04817 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
20.
Display screen or portion thereof with graphical user interface
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for utilizing a catalyst application to analyze digital content of another computer application to generate or extract tasks. The catalyst application can accompany and operate in conjunction with another computer application to extract data from the application and generate tasks. For example, the disclosed systems can scan displayed (or otherwise presented) digital content in an application and can generate a prompt for causing a large language model to identify or extract tasks from the digital content. Through the catalyst application, the disclosed systems can thus implement a large language model to generate a task list from the digital content of another computer application. The disclosed systems can further utilize the catalyst application to automatically execute or perform extracted tasks.
This disclosure describes systems that identify one or more models (e.g., large language models and/or virtual assistants) permitted to access content items stored for user accounts within a content management system. The disclosed systems can determine a model available to a user account within the content management system from among the one or more models. For example, the disclosed systems can determine one or more relationships between the user accounts within the content management system, large language models utilized by the user accounts, virtual assistants utilized by the user accounts, and content items accessed by the user accounts. The disclosed systems can determine the model for the user account according to the one or more relationships. The disclosed systems can provide a notification corresponding to the model via a user interface of a client device associated with the user account.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating a dynamic facet by using a large language model. For example, the disclosed systems extract raw facet data from a plurality of content items stored in a content management system. In addition, the disclosed systems determine one or more facet content groups by grouping the plurality of content items according to the raw facet data. Further, the disclosed systems generate a facet prompt from the one or more facet content groups. Moreover, the disclosed systems generate a dynamic facet by providing the facet prompt to a large language model.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating personal responses through retrieval-augmented generation. In particular, the disclosed systems can generate a query embedding from a query generated by an entity and determine data context specific to the entity by comparing the query embedding with a plurality of vectorized segments of content items associated with the entity. The disclosed systems can provide the data context to a large language model and generate a personalized response informed by the data context. Subsequently, the disclosed systems can provide the personalized response for display on a client device associated with the entity.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.
G06F 11/20 - Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements
G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
27.
SYNTHESIZING VISUALIZATIONS FOR CONTENT COLLECTIONS
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing synthetic visualizations representative of content collections within a content management system. In some cases, the disclosed systems generate a synthetic visualization based on content features that indicate relevance of content items with respect to a user account to emphasize more relevant content items within the synthetic visualization and/or to represent descriptive content attributes of the content items. For example, the disclosed systems can generate a synthetic phrase that represents a content collection and can further generate a synthetic visualization from the synthetic phrase utilizing a synthetic visualization machine learning model.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.
G06F 9/50 - Allocation of resources, e.g. of the central processing unit [CPU]
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
G06F 11/20 - Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements
30.
DYNAMICALLY SELECTING ARTIFICIAL INTELLIGENCE MODELS AND HARDWARE ENVIRONMENTS TO EXECUTE TASKS
The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for capturing snapshots of digital content displayed on a client device and searching through the captured content. The disclosed systems provide a search function for effectively traveling back in time to identify digital content previously displayed on a client device. The disclosed systems provide options for capturing snapshots of content displayed on a display screen, extracting data from the snapshots, and storing the snapshots for use when populating search results. The disclosed systems utilize machine learning models to extract text and/or to generate text versions of snapshots including extracted text, descriptions of images, transcripts of videos, and/or textual summaries from displayed documents or webpages. In response to a search query, the disclosed systems can produce search results that include digital videos including captured snapshots of content displayed by a client device over time.
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
33.
MACHINE LEARNING RECOMMENDATION ENGINE FOR CONTENT ITEM DATA ENTRY BASED ON MEETING MOMENTS AND PARTICIPANT ACTIVITY
A content management system obtains at least a portion of a meeting transcript based on an audio stream of a meeting attended by a plurality of users, the meeting transcript obtained in an ongoing manner as words are uttered during the meeting. The content management system detects text entered by a user of the plurality of users into a content item during the meeting. The content management system matches the detected text to at least part of the at least the portion of the meeting transcript. The content management system provides the at least part of the at least the portion of the meeting transcript to the user as a suggested subsequent text.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for the intelligent generation and completion of digital documents. For example, the disclosed systems provide a document creation interface for entering inputs to generate and modify digital documents. In some instances, the disclosed systems receive a document generation prompt, generate a digital document using a large language model, and provide an indication of the digital document within the document creation interface. Moreover, the disclosed systems can further generate a document summary of the digital document and provide the document summary for a recipient device via a document review interface. Additionally, the disclosed systems can further generate a suggested document modification element and modify the digital document in response to a user interaction with the suggested document modification element using the large language model.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for the intelligent generation and completion of digital documents. For example, the disclosed systems provide a document creation interface for entering inputs to generate and modify digital documents. In some instances, the disclosed systems receive a document generation prompt, generate a digital document using a large language model, and provide an indication of the digital document within the document creation interface. Moreover, the disclosed systems can further generate a document summary of the digital document and provide the document summary for a recipient device via a document review interface. Additionally, the disclosed systems can further generate a suggested document modification element and modify the digital document in response to a user interaction with the suggested document modification element using the large language model.
A client application sends an application programming interface call to an interface of a content management system. The call specifies one or more content item search criteria. Sending the call causes the content management system to perform a lookup in a content item index to identify at least one content item that satisfies the one or more content item search criteria. Based on sending the call, the client application receives from the content management system a suggestion to attach the at least one content item to a text being displayed by at the computing system.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for editing and collaborating with digital videos through interactions with video transcripts. For example, the disclosed systems can provide a user interface for interacting with a video transcript associated with a digital video. Based on interacting with the video transcript, the disclosed systems can perform editing operations and/or collaborating operations in relation to the digital video. For instance, the disclosed systems can edit a digital video at a video portion corresponding to transcript location where a user interaction occurs within a video transcript.
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing intelligent insights for video calls and other virtual meetings. In some embodiments, the disclosed systems analyze stored meeting data from past video calls and other virtual meetings to generate intelligent insights for an upcoming video call. The disclosed systems also generate and provide intelligent insights or coaching tools for ongoing video calls. As part of the intelligent coaching tools for ongoing video calls, the disclosed systems can generate predictions for accomplishing target goals for the video calls. Further, the disclosed systems can generate intelligent insights or coaching tools after video calls take place.
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing intelligent insights for video calls and other virtual meetings. In some embodiments, the disclosed systems analyze stored meeting data from past video calls and other virtual meetings to generate intelligent insights for an upcoming video call. The disclosed systems also generate and provide intelligent insights or coaching tools for ongoing video calls. As part of the intelligent coaching tools for ongoing video calls, the disclosed systems can generate predictions for accomplishing target goals for the video calls. Further, the disclosed systems can generate intelligent insights or coaching tools after video calls take place.
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing intelligent insights for video calls and other virtual meetings. In some embodiments, the disclosed systems analyze stored meeting data from past video calls and other virtual meetings to generate intelligent insights for an upcoming video call. The disclosed systems also generate and provide intelligent insights or coaching tools for ongoing video calls. As part of the intelligent coaching tools for ongoing video calls, the disclosed systems can generate predictions for accomplishing target goals for the video calls. Further, the disclosed systems can generate intelligent insights or coaching tools after video calls take place.
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing intelligent insights for video calls and other virtual meetings. In some embodiments, the disclosed systems analyze stored meeting data from past video calls and other virtual meetings to generate intelligent insights for an upcoming video call. The disclosed systems also generate and provide intelligent insights or coaching tools for ongoing video calls. As part of the intelligent coaching tools for ongoing video calls, the disclosed systems can generate predictions for accomplishing target goals for the video calls. Further, the disclosed systems can generate intelligent insights or coaching tools after video calls take place.
A client application sends an application programming interface call to an interface of a content management system. The call specifies one or more content item search criteria. Sending the call causes the content management system to perform a lookup in a content item index to identify at least one content item that satisfies the one or more content item search criteria. Based on sending the call, the client application receives from the content management system a suggestion to attach the at least one content item to a text being displayed by at the computing system.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for performing a data search for a search request by utilizing a directed acyclic graph. For example, the disclosed systems receive a search request at a search engine of a content management system. In addition, the disclosed systems determine (e.g., in response to the search request) a node path from a directed acyclic graph that includes a plurality of interconnected nodes defining computer operations. Further, the determined node path includes a set of nodes that corresponds to the search request. Moreover, the disclosed systems perform the data search for the search request by executing operations defined by nodes along the node path within the directed acyclic graph.
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and searching a hybrid search index. In some embodiments, the disclosed systems generate a hybrid search index that comprises one or more content items stored at a content management system or at external network locations linked to the content management system via software connectors along with world state data associated with the one or more content items. The disclosed systems can generate a search result from the hybrid search index in response to receiving a search query of the hybrid search index. In some cases, the disclosed systems can rank one or more content items included in the search result based on observation layer data of the one or more content items.
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and visualizing effectivity scores from features extracted for different data of a time period. The disclosed systems generate a meeting effectivity score, a flow state effectivity score, and/or a work about work effectivity score that reflect measures of effectiveness or productivity. To generate the effectivity scores, the disclosed systems can leverage software connectors that extract features relevant to the respective effectivity scores for inputting into effectivity-score-generating models. Based on the effectivity scores, the disclosed systems can further generate recommendations for improving one or more of the effectivity scores. The disclosed systems can also generate and provide graphical visualizations of effectivity scores for display on a client device, together with recommendations for improving the effectivity scores.
The present technology is a cache that provides an implied guarantee that data returned in response to a query is current. The cache uses two techniques together to guarantee that data returned in response to a query from the cache is current. The first technique is a guaranteed clock value. The is a clock value for which all changes that have occurred before it have been written to the authoritative database. Any query having a clock value greater than the guaranteed clock value is invalid. The second technique is a data invalidation technique, where the cache repeatedly polls the authoritative database for information about changes and invalidates objects associated with changes. If no change has happened, the cache can confidently conclude it is up to date since the cache knows that it is aware of all changes as of the guaranteed clock value.
The present technology provides a solution that can bring organization to the accumulated data and can surface insights derived from an analysis of stored objects. More specifically, the present technology is directed to an object analysis and classification service which is a configurable platform for identifying objects relevant to a topic and providing analysis of the objects relevant to that topic. The object analysis and classification service can include one or more technologies for identifying objects relevant to a topic. The topic can be configured by a user account or can be a predefined topic selected by the user account. The topic can include parameters that can be used by the one or more technologies for identifying objects relevant to a topic and to provide insights defined by the topic.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for integrating a content management system plugin with a large language model. In particular, in some embodiments, the disclosed systems integrate, into a computing environment of a large language model, a content management system plugin comprising computer code for an application programming interface (API) that facilitates operations by the large language model on content items stored in a content management system. Moreover, in some embodiments, the disclosed systems receive, from the large language model via the content management system plugin, a function call requesting performance of an API operation on a content item stored in the content management system. Furthermore, in some implementations, the disclosed systems execute the API operation on the content item stored in the content management system in response to the function call from the content management system plugin integrated within the large language model.
The present technology addresses a need in the art for providing additional context to a recipient receiving a shared object(s). Sharing messages are generally limited to a brief statement indicating that a user account has shared the object(s). The present technology can automatically create a summary for an object(s) to be shared. Moreover, the present technology provides a user interface that is part of a sharing process for the creation of the summary so that the creation of the summary is very convenient for the user, and can even be completely automatic. Additionally, the present technology includes carefully engineered prompts that result in summaries that are appropriate for the sharing context in which they are intended.
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
A system may obtain performance signals associated with at least one of a plurality of servers used in connection with performing a task. A system may provide the performance signals to a machine learning model. A system may receive as output from the machine learning model a health metric related to the at least one of the plurality of servers. A system may determine whether the health metric meets a migration condition. A system may, responsive to the health metric meeting the migration condition, initiate a reallocation of resources for performing the task, wherein the reallocation of resources includes migration of responsibility for performing the task to at least one other server of the plurality of servers.
H04L 67/1008 - Server selection for load balancing based on parameters of servers, e.g. available memory or workload
H04L 43/0817 - Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning
51.
SYSTEM AND METHOD FOR DETECTING PROMPT INJECTION ATTACKS TO LARGE LANGUAGE MODELS
A computing system receives a prompt to be provided as input to a large language model. The computing system generates generating an input string to the large language model by appending a plurality of contexts to the prompt. The plurality of contexts defines rules for the large language model to follow when generating the prompt. The plurality of contexts includes a negative context. Based on the prompt and the plurality of contexts, the computing system generates an attention matrix representing relationships between the prompt and the plurality of contexts. The computing system provides the attention matrix to a trained neural network to determine a likelihood that the prompt is malicious. Responsive to determining that the prompt is likely a malicious prompt, the computing system initiates a remedial action.
Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The system receives a natural language-based input associated with a client device of a user. The system generates a search criterion for the received natural language-based input. The system, via the generative AI-bases search and retrieval system, generates a relevancy-ranked output listing of content items. The relevancy-ranked output listing content items responsive to the generated search criterion content items having an associated content identifier and a content description. The system causes portions of the relevancy-ranked output listing to be rendered at the client device of the user.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for using a large language model and digital content items (e.g., digital files) stored in a content management system to generate workflow outputs for external computing platforms. For instance, in some embodiments, the disclosed systems receive a workflow request that includes a natural language description of an objective to be accomplished via a workflow. The disclosed systems determine an action plan for completing the workflow and executes the action plan by accessing digital content items (e.g., digital files) stored in a content management system and extracting or generating data from the contents of the digital content items. Further, the disclosed systems use a large language model to generate a workflow output that is provided to an external computing platform for use.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for decomposing high-order objectives to implement automated or semi-automated changes to software applications. In particular, in one or more embodiments, the disclosed systems receive, from a client device, a high-order objective to be achieved within a computing system. In addition, in some embodiments, the disclosed systems determine, from the high-order objective, a set of sub-processes available to the computing system that combine to accomplish the high-order objective. Moreover, in some implementations, the disclosed systems generate, for a sub-process from among the set of sub-processes, a logic breakdown comprising a description of the sub-process and its predicted effect toward the high-order objective. Furthermore, in some embodiments, the disclosed systems provide the logic breakdown for display via the client device.
A content management system synchronizes content items across client computing systems connected by a network. Each client device has a storage allocation for synchronized shared content items. If the storage allocation for shared content items on a client device is exceeded by the request to add or edit a content item such that it is enlarged, or open a large content item remote to the client device, a client application or the host of content management system selects content items to remove from residence on the client device but keep remotely on content management system. Upon removal of the selected content items, the client application creates shadow items, representing the content item but only containing the metadata of the content item. This creates sufficient space for the initial request to be completed while maintaining user access to all synchronized shared content items.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 16/16 - File or folder operations, e.g. details of user interfaces specifically adapted to file systems
G06F 16/178 - Techniques for file synchronisation in file systems
H04L 67/1095 - Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
The present technology pertains to displaying granular viewer interactions for how viewers are viewing shared content items, such as videos, over time. A sharing and tracking service of a sharing platform associated with or managed by a content management system provides the granular viewer interactions of viewer interactions that retrace a full journey of views of shared content items in a content item interaction analytics timeline. The content item interaction analytics timeline may provide a visual representation of how the viewer interacts with the content item (e.g., how the user is scrolling through the different segments) throughout the viewing.
Disclosed are systems, methods, and non-transitory computer-readable storage media for managing projects in a content management system. For example, the content management system can create a project folder (e.g., shared folder) for managing data associated with a project. The project folder can be shared with content management system users (e.g., project members) who are contributors to the project. The content management system can store project data (e.g., content items, communications, comments, tasks, etc.) related to the project in the project folder. When the project folder is selected by a user, the content management system can generate a project folder view that presents the project data associated with the project folder and/or project in a convenient and easy to access graphical user interface. The content management system can aggregate project data from various content items associated with the project and present the project data in a single graphical user interface.
G06F 16/16 - File or folder operations, e.g. details of user interfaces specifically adapted to file systems
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and displaying a preview tile. In particular, in one or more embodiments, the disclosed systems select content item segments from a content item stored for a content management system and generate a preview tile by generating hotspot frames that reflect digital content in the content items segments. Further, in one or more embodiments, the disclosed systems provide the preview tile on a client device and, based on an input location relative to the preview tile, determine a hotspot frame to display within the preview tile.
Systems and methods for creating and accessing content items are provided. In some examples, a method can include receiving a user selection of an interface element located in a persistent user interface of an operating system desktop of a client device associated with a user account, identifying, based on a query to a meeting service, a meeting associated with the user account, displaying, in response to the user selection of the interface element, a set of user options for the meeting, the set of user options comprising an option to access a content item for the meeting, receiving a user selection of the option to access the content item for the meeting, and accessing, in response to the user selection, the content item.
H04L 65/401 - Support for services or applications wherein the services involve a main real-time session and one or more additional parallel real-time or time sensitive sessions, e.g. white board sharing or spawning of a subconference
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06Q 10/1093 - Calendar-based scheduling for persons or groups
H04L 12/18 - Arrangements for providing special services to substations for broadcast or conference
60.
TECHNIQUES FOR CAPTURING AND DISPLAYING PARTIAL MOTION IN VIRTUAL OR AUGMENTED REALITY SCENES
The present disclosure relates to techniques for capturing and displaying partial motion in VAR scenes. VAR scenes can include a plurality of images combined and oriented over any suitable geometry. Although VAR scenes may provide an immersive view of a static scene, current systems do not generally support VAR scenes that include dynamic content (e.g., content that varies over time). Embodiments of the present invention can capture, generate, and/or share VAR scenes. This immersive, yet static, view of the VAR scene lacks dynamic content (e.g., content which varies over time). Embodiments of the present invention can efficiently add dynamic content to the VAR scene, allowing VAR scenes including dynamic content to be uploaded, shared, or otherwise transmitted without prohibitive resource requirements. Dynamic content can be captured by device and combined with a preexisting or simultaneously captured VAR scene, and the dynamic content may be played back upon selection.
Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The user search is provided to a generative AI based search sub-system and to a traditional search sub-system. A first search result listing is generated by the generative AI based subsystem, and a second search result listing is generated by the traditional search sub-system. The first search result listing and the second search result listing are aggregated together and provided for display to a user client device.
Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The system receives a natural language-based input associated with a client device of a user. The system generates a search criterion for the received natural language-based input. The system, via the generative AI-bases search and retrieval system, generates a relevancy-ranked output listing of content items. The relevancy-ranked output listing content items responsive to the generated search criterion content items having an associated content identifier and a content description. The system generates a carousel display structure definition of the relevancy-ranked content items. The system transmits the carousel display structure definition of the relevancy-ranked content items and the content items to the client device. The client device renders, via a user interface, at least a portion of the relevancy-ranked content items.
G06F 16/2457 - Query processing with adaptation to user needs
G06F 7/08 - Sorting, i.e. grouping record carriers in numerical or other ordered sequence according to the classification of at least some of the information they carry
Computer-implemented techniques encompass using distinct machine learning sub-models to score respective types of candidate content for the purpose of providing personalized content suggestions to end-users of a content management system. The relevancy scores generated by the distinct sub-models are mapped to expected end-user interaction scores of the candidate content scored. Content suggestions are provided at end-users' computing devices where the suggested content is selected from the candidate content based on the expected end-user interaction scores of the candidate content. For each distinct sub-model, a normalizing mapping function is solved using an optimizer that maps the relevancy scores generated by the sub-model for the candidate content to expected end-user interaction scores for the candidate content. The expected end-user interaction scores are comparable across the distinct sub-models and can be used to rank content suggestions across the distinct sub-models.
A system and method are provided for automatically selecting a subset of photos to be backed up by an intelligent photo upload service provided by a content management system. The intelligent photo upload service is provided to a client application on a client device that uses one or more machine-learning models to determine which photos have the one or more attributes. The photos that have been determined to have the one or more attributes may be uploaded. Once uploaded the one or more attributes may be used to label the respective photos with metadata indicating the presence of the one or more attributes. The one or more attributes may be used to filter photos at the content management system.
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
G06V 20/30 - ScenesScene-specific elements in albums, collections or shared content, e.g. social network photos or video
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
The present technology is directed to a notification manager which provides a consolidated notification assistant interface for managing notifications received across a plurality of sources, such as applications and channels. The notification manager can group similar notifications in the consolidated notification assistant interface. The notification manager can automatically respond to some notifications, and provide a status regarding whether a responsive notification has been sent, or whether a responsive notification has been prepared that needs review, etc. For example, the notification manager can propose responsive notifications to some notifications, and provide a call to action for the user account to review the proposed responsive notification and to send the proposed responsive notification. The user associated with the user account can interact with the notification manager by giving natural language instructions by interacting with a chatbot to instruct the notification manager to take actions.
H04L 51/02 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
H04L 51/216 - Handling conversation history, e.g. grouping of messages in sessions or threads
Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The user search is provided to a generative AI based search sub-system and to a traditional search sub-system. A first search result listing is generated by the generative AI based subsystem, and a second search result listing is generated by the traditional search sub-system. The first search result listing and the second search result listing are aggregated together and provided for display to a user client device.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating context engine outputs by utilizing an interpreter purpose-built to execute code generated by large language models. For example, the disclosed systems generate computer code executable for responding to a query by utilizing a large language model. In addition, the disclosed systems execute the model-generated computer code utilizing an interpreter integrated with the context engine that further includes swappable logic interchangeable across multiple executors. Moreover, the disclosed systems can further generate as part of executing the computer code utilizing the interpreter, a first context engine output by implementing the interpreter at a first executor. Additionally, the disclosed systems can further generate a second context engine output by implementing the interpreter at a second executor.
The present disclosure is directed toward systems, methods, and non-transitory computer-readable media for generating context engine outputs by utilizing an interpreter purpose-built to execute code generated by large language models. For example, the disclosed systems generate computer code executable for responding to a query by utilizing a large language model. In addition, the disclosed systems execute the model-generated computer code utilizing an interpreter integrated with the context engine that further includes swappable logic interchangeable across multiple executors. Moreover, the disclosed systems can further generate as part of executing the computer code utilizing the interpreter, a first context engine output by implementing the interpreter at a first executor. Additionally, the disclosed systems can further generate a second context engine output by implementing the interpreter at a second executor.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating composite actions for a user account. In particular, in one or more embodiments, the disclosed systems determine a set of tasks performable by the user account using software tools on a client device. In some embodiments, the disclosed systems generate a task initialization prompt to provide to a large language model. Additionally, in some implementations, the disclosed systems generate a composite action comprising a hybridized combination of the set of tasks performable by the user account along with a set of content items relevant to the set of tasks. Moreover, in some embodiments, the disclosed systems provide access to the composite action and the set of content items via a user interface of the client device. Furthermore, in some implementations, the disclosed systems generate and insert predicted content into a content item without user input.
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing an aggregated feed interface that includes or presents aggregate summaries for thread data across multiple data feeds of a user account. In some embodiments, the disclosed systems generate an aggregate summary by extracting digital communications and other thread data from a variety of data feeds across multiple computer applications and by utilizing a summary generation model to generate an aggregate summary that combines and condenses the thread data from the different data feeds. For example, the disclosed systems determine topics associated with thread data from various data feeds and generates different aggregate summaries for different topics. The disclosed systems can further rank aggregate summaries and/or other digital communications to present within the aggregated feed interface according to an account-specific ranking algorithm resulting a customized per-account presentation of the aggregated feed interface.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating composite actions for a user account. In particular, in one or more embodiments, the disclosed systems determine a set of tasks performable by the user account using software tools on a client device. In some embodiments, the disclosed systems generate a task initialization prompt to provide to a large language model. Additionally, in some implementations, the disclosed systems generate a composite action comprising a hybridized combination of the set of tasks performable by the user account along with a set of content items relevant to the set of tasks. Moreover, in some embodiments, the disclosed systems provide access to the composite action and the set of content items via a user interface of the client device. Furthermore, in some implementations, the disclosed systems generate and insert predicted content into a content item without user input.
A system receives a request for a data block, in which the data block corresponds to a set of fragments, and the data block can be fully reconstructed using a threshold number of the set of fragments. A first subset of the set of fragments are stored in a first storage system, and a second subset of the set of fragments are stored in a second storage system characterized by a higher average latency than the first storage system. A system, responsive to receiving the request for the data block, requests the first subset of the set of fragments. A system receives at least the threshold number of the first subset of the fragments. A system reconstructs the data block using the received fragments of the first subset of the fragments. A system provides the reconstructed data block.
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing an aggregated feed interface that includes or presents aggregate summaries for thread data across multiple data feeds of a user account. In some embodiments, the disclosed systems generate an aggregate summary by extracting digital communications and other thread data from a variety of data feeds across multiple computer applications and by utilizing a summary generation model to generate an aggregate summary that combines and condenses the thread data from the different data feeds. For example, the disclosed systems determine topics associated with thread data from various data feeds and generates different aggregate summaries for different topics. The disclosed systems can further rank aggregate summaries and/or other digital communications to present within the aggregated feed interface according to an account-specific ranking algorithm resulting a customized per-account presentation of the aggregated feed interface.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for modifying a fillable digital document. In particular, the disclosed systems can receive a user interaction requesting to populate one or more aggregated data fields in a fillable digital document. In response to the request, the field object generation system can determine the data relevant to one or more aggregated data fields in the fillable digital document by utilizing a large language model to process one or more source content items for a user account. Further the systems and generate a field object from the data relevant to one or more aggregated data field and modify the fillable digital document by including the field object in the fillable digital document.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating content-item-specific large language model responses from content items by segmenting a content item and selecting relevant sections of the content item to provide to a large language model to generate a corresponding output. In particular, in one or more embodiments, the disclosed systems can generate a text representation that includes a plurality of text segments each comprising a number of tokens of the text representation. Further, the systems can extract, from the plurality of text segments, segment-specific text embeddings that correspond to respective portions of the text representation of the content item. Additionally, the systems can determine a segment-specific text embedding corresponding to a model output request. Moreover, the systems can generate a model output by passing a text segment corresponding to the segment-specific text embedding to a large language model together with the model output request.
Methods and systems provide for dynamic contextual generation of creative content for product listings. In one embodiment, the system receives initial product facts for a product, user engagement data for a user of a platform, and one or more pieces of contextual information related to how the product will be viewed within the platform; uses this data to train a generative AI model for dynamic creative content generation for the listing; uses the trained generative AI model to dynamically generate creative content for the listing; displays the creative content for the listing on a client device associated with the user; receives feedback regarding user engagement with the creative content in terms of whether an engagement objective has been achieved; and refines the generative AI model based on the received feedback, including optimizing the generative AI model to generate or modify the creative content to achieve the engagement objective.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and providing customized content labels as elements for seamless integration within a graphical interface. For instance, the disclosed systems provide generative options utilizing contextual data to more effectively incorporate a content label (textual and/or visual) based on the surrounding graphical elements, the functionality of the label within the interface, and the purpose of the label or interface. In this way, the disclosed systems generate contextual labels with appropriate textual content and that are appropriately sized, styled, and positioned based on their relevance within the context of the graphical interface.
Methods and systems provide for rich media presentation of recommendations in generative media. In one embodiment, the system presents, via a trained generative AI, a set of media content to a user in a communication session within a platform, the media content including a number of sorted recommended items; monitors and quantifies one or more user responses from the user to the presented media content and one or more associated generative responses from the trained generative AI; based on the monitoring and quantifying, detects one or more mentions of the user to one of the plurality of sorted recommended items; generates, from the one or more detected mentions, one or more labeled training examples; and further trains the trained generative AI based on the one or more labeled training examples to improve the presentation of the media content in future communication sessions.
Methods and systems provide for cross-relevant refinement of generative artificial intelligence models for creative content across multiple platforms. In one embodiment, the system receives creative content related to a first product listing for a product within a first platform, user engagement data for a user of a second platform, and one or more pieces of contextual information; trains a refinement of a second generative AI model for dynamic creative content generation for a modified version of the first product listing for the second platform; generates and displays one or more pieces of creative content for a second product listing to be published on the second platform; receives feedback regarding user engagement with the pieces of creative content in terms of whether an engagement objective has been achieved; and refines the first generative AI model and the second generative AI model via a network of cross-refinement.
Methods and systems provide for dynamically optimized recommendations in generative media. In one embodiment, the system receives, through a conversational interface, input submissions from a user engaging in a conversation with a generative artificial intelligence (AI) system; generates, via the generative AI system, a search query for a search engine backend of the platform; sends the search query to the search engine backend of the platform to retrieve at least a subset of a prompt as input to the generative AI system, the subset of the prompt including a sorted list of search results from the search engine backend; processes the prompt to generate a set of personalized recommendations for the user; and presents, within the platform presented at the client device, the set of personalized recommendations for the user, the presentation incorporating media content representing at least a portion of the search result items.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for improving digital transcripts of a meeting based on user information. For example, a digital transcription system creates a digital transcription model to automatically transcribe audio from a meeting based on documents associated with meeting participants, event details, user features, and other meeting context data. In one or more embodiments, the digital transcription model creates a digital lexicon based on the user information, which the digital transcription system uses to generate the digital transcript. In some embodiments, the digital transcription model trains and utilizes a digital transcription neural network to generate the digital transcript.
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and searching a hybrid search index. In some embodiments, the disclosed systems generate a hybrid search index that comprises one or more content items stored at a content management system or at external network locations linked to the content management system via software connectors along with world state data associated with the one or more content items. The disclosed systems can generate a search result from the hybrid search index in response to receiving a search query of the hybrid search index. In some cases, the disclosed systems can rank one or more content items included in the search result based on observation layer data of the one or more content items.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a context transformer engine, a smart topic agent, and a large language model to generate a smart topic output. In particular, in one or more embodiments, the disclosed systems generate a smart topic output from a transcript of a video call. In some embodiments, the disclosed systems provide a smart topic interface that provides the smart topic output on a client device and receives selections of smart topic elements. In one or more embodiments, the disclosed systems generate a combined smart topic from transcripts of video calls in which client devices that participated are associated with a collaborating user account group.
G10L 15/18 - Speech classification or search using natural language modelling
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G10L 15/183 - Speech classification or search using natural language modelling using context dependencies, e.g. language models
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G10L 15/30 - Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a context transformer engine, a smart topic agent, and a large language model to generate a smart topic output. In particular, in one or more embodiments, the disclosed systems generate a smart topic output from a transcript of a video call. In some embodiments, the disclosed systems provide a smart topic interface that provides the smart topic output on a client device and receives selections of smart topic elements. In one or more embodiments, the disclosed systems generate a combined smart topic from transcripts of video calls in which client devices that participated are associated with a collaborating user account group.
A collaborative content management system (CMS) is disclosed herein for generating templates for received documents. The disclosed CMS recognizes that a document selected by a user for processing was previously processed by the CMS and that a user has previously added particular overlaid fillable fields to the document. When the determination is made, the system generates a recommendation to create a template of the document with the previously added overlaid fillable fields. In some embodiments, the CMS makes the recommendation to generate a template when the user creates, in a received document, identical overlaid fillable fields or field types to those created in the previously processed document.
A collaborative content management system identifies an application installed on one or more client devices that is susceptible to an attack by using the API calls of the application. The collaborative content management system obtains API calls made by the application and derives API call features. The collaborative content management system inputs the API call features into a machine learning model and receives, as output from the model, a determination of whether the set of API calls represents a C2 attack. In some embodiments, the collaborative content management system, responsive to determining that the set of API calls represents a C2 attack, may take a security action.
The system obtains performance signals associated with respective hard disks of a volume of hard disks including a plurality of hard disks that are dedicated to activities of a service. The system determines a volume failure prediction for the volume of hard disks by, for each respective hard disk of the volume of hard disks, determining a hard disk failure prediction. The system determines a hard disk failure prediction by: inputting the respective performance signals into a supervised machine learning model; and receiving as output from the machine learning model the hard disk failure prediction for the respective hard disk. The system based on the received outputs, determines that the volume failure prediction is associated with a migration condition. The system, responsive to determining that the volume failure prediction is associated with the migration condition, migrates data from the volume of hard disks to a second volume of hard disks.
Disclosed are systems, methods, and non-transitory computer-readable storage media for managing projects using references between the project and project items. Project items can be, for example, synchronized content items, collaborative content items, other projects, folders, tasks, user accounts, etc. The content management system can create a project identifier for managing data and/or people associated with a project. In various implementations, the content management system can store references between the project and project items in one or more folders associated with the project, in a database, in content item metadata, etc. In some implementations, the storage location of a content item does not affect whether it is associated with the project. When a project is selected by a user, the content management system can generate project view that presents various project items associated with the project in a convenient and easy to access graphical user interface.
G06Q 10/101 - Collaborative creation, e.g. joint development of products or services
G06F 3/04817 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
G06F 3/0482 - Interaction with lists of selectable items, e.g. menus
G06F 16/16 - File or folder operations, e.g. details of user interfaces specifically adapted to file systems
G06F 16/25 - Integrating or interfacing systems involving database management systems
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
G06Q 10/06 - Resources, workflows, human or project managementEnterprise or organisation planningEnterprise or organisation modelling
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
89.
GENERATING AND MANAGING MULTILOCATIONAL DATA BLOCKS
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and managing multilocational data blocks, generating and summarizing content blocks within a virtual space interface, and generating and providing a content block browser as part of a virtual space platform. In some embodiments, the disclosed systems generate a multilocational data block that includes a block identifier that is tied to a source identifier for embedding digital content from a network location indicated by the source identifier. The disclosed systems can also generate block summaries from content blocks for presenting and modifying digital content embedded within the content blocks via block identifiers and source identifiers. In some embodiments, the content block system can provide a content-block-based web browser in the form of a virtual space that includes embedded content blocks that integrate webpage functionality.
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
G06F 3/06 - Digital input from, or digital output to, record carriers
G06Q 10/101 - Collaborative creation, e.g. joint development of products or services
Techniques for learning and using content type embeddings. The content type embeddings have the useful property that a distance in an embedding space between two content type embeddings corresponds to a semantic similarity between the two content types represented by the two content type embeddings. The closer the distance in the space, the more the two content types are semantically similar. The farther the distance in the space, the less the two content types are semantically similar. The learned content type embeddings can be used in a content suggestion system as machine learning features to improve content suggestions to end-users.
G06F 16/957 - Browsing optimisation, e.g. caching or content distillation
G06F 16/178 - Techniques for file synchronisation in file systems
G06F 16/2457 - Query processing with adaptation to user needs
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
91.
PROCESSING ELECTRONIC SIGNATURE DATA IN A COLLABORATIVE ENVIRONMENT
A collaborative content management system (CMS) is disclosed herein for processing electronic signature data in a collaborative environment and performing actions based on the received data. The CMS may receive a selection of a content item to be electronically signed by one or more users. Upon receipt of the selection, the CMS may generate a fillable form and send the fillable form to be electronically filled out signed. When each user fills out and signs the form, the CMS may receive that form data and aggregate the form data with previously received form data. The CMS may determine whether the aggregated form data meets one or more conditions stored within the CMS and perform appropriate actions based on one or more conditions that are met.
A content management system synchronizes content items across client computing systems connected by a network. Each client device has a storage allocation for synchronized shared content items. If the storage allocation for shared content items on a client device is exceeded by the request to add or edit a content item such that it is enlarged, or open a large content item remote to the client device, a client application or the host of content management system selects content items to remove from residence on the client device but keep remotely on content management system. Upon removal of the selected content items, the client application creates shadow items, representing the content item but only containing the metadata of the content item. This creates sufficient space for the initial request to be completed while maintaining user access to all synchronized shared content items.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 16/16 - File or folder operations, e.g. details of user interfaces specifically adapted to file systems
G06F 16/178 - Techniques for file synchronisation in file systems
H04L 67/1095 - Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
93.
BACKUP FEATURE PROVIDED BY BIDIRECTIONAL SYNCHRONIZED CONTENT MANAGEMENT SYSTEM
The present technology is directed to providing a backup service utilizing a bi-directional synchronization architecture. In order to support both a backup service and a bi-directional synchronization service, the present technology can utilize a special object called an anchor that only permits unidirectional synchronization. Additionally, the present technology separates the backup service from the bi-directional synchronization service. This separation includes utilizing a separate backup directory and bi-directional synchronization directory on a client device, which in turn benefits from the use of a separate instance of the synchronization service on the client device. Further, at the content management system various steps are taken to separate the backup directory from the bi-directional synchronization directory to ensure no objects from the backup directory appear in a user interface related to bi-directional synchronization, and vice versa.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
G06F 16/25 - Integrating or interfacing systems involving database management systems
94.
PER-NODE METADATA FOR CUSTOM NODE BEHAVIORS ACROSS PLATFORMS
Technologies for implementing customized behaviors for content items are provided. An example method can include receiving, from a user account registered with a content management system, a request to access a content item managed by the content management system for the user account, the content item having one or more behaviors configured for an attribute associated with the content item and/or the content item associated with the attribute; obtaining, from a representation of a remote state of content items associated with the user account, metadata defining the attribute associated with the content item; based on the metadata, determining the one or more behaviors configured for the attribute and/or the content item associated with the attribute; and applying the one or more behaviors to the content item.
G06F 16/178 - Techniques for file synchronisation in file systems
G06F 16/13 - File access structures, e.g. distributed indices
G06F 16/16 - File or folder operations, e.g. details of user interfaces specifically adapted to file systems
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
Embodiments relate to identifying an error in a form that is incorrectly filled out and apply corrections to the form. A module may identify inputs of a form (e.g., webpage) and apply an autofill template to the identified inputs, where the autofill template specifies input types of the identified inputs and content to be applied to the input types. The module may automatically detect an input error. The module may, responsive to detecting the input error, receive a correction indication specifying a first input of the form (e.g., webpage) and an input type of the first input. The module may update the autofill template according to the correction indication. The module may apply the updated autofill template to the first input of the form (e.g., webpage).
Embodiments relate to improved classification techniques for classifying form (e.g., webpage) forms and inputs for password auto fill applications. A module may detect a client device accessing a form (e.g., webpage) and may identify inputs of the form (e.g., webpage). The module may group two or more of the identified inputs into a bundle and apply the bundle to one or more machine learned bundle classifier models configured to determine a form type of the bundle. The module may also apply the bundle to the selected input classifier model. The module may automatically fill one or more inputs of the form (e.g., webpage) according to the input types identified by the selected input classifier model.
Systems or methods for generating a plurality of different custom links that have different viewing options for viewing the same shared content item in customized content item players and displaying engagement analytics for how viewers are viewing shared content items, such as videos, are disclosed. A custom link to view the shared content item is generated in association with settings that are customized by an administrator. Once the custom link is accessed, a customized content item player is generated based on the settings. Once the content item is being played in a customized content item player, the engagement analytics are populated in real-time in a graphical display. The different engagement levels and graphical representations of segments of viewing are distinguished based on the engagement levels.
Disclosed are systems, methods, and non-transitory computer-readable storage media for shared folder backed integrated workspaces. In some implementations, a content management system can provide a graphical user interface (GUI) that integrates communications and content management into a single user interface. The user interface can include mechanisms that allow a user to provide input to generate a new workspace. The user interface can provide a mechanism to allow a user to view conversations related to the workspace and/or content items associated with the workspace. The user interface can present representations of content items associated with the workspace and allow the user to provide input to generate, view, edit, and share content items associated with the workspace.
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
G06F 3/04817 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 3/04842 - Selection of displayed objects or displayed text elements
G06F 3/04847 - Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
G06F 16/16 - File or folder operations, e.g. details of user interfaces specifically adapted to file systems
G06F 16/178 - Techniques for file synchronisation in file systems
G06F 16/25 - Integrating or interfacing systems involving database management systems
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
H04L 12/18 - Arrangements for providing special services to substations for broadcast or conference
H04L 41/22 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
H04L 51/046 - Interoperability with other network applications or services
H04L 51/08 - Annexed information, e.g. attachments
H04L 51/216 - Handling conversation history, e.g. grouping of messages in sessions or threads
H04L 51/224 - Monitoring or handling of messages providing notification on incoming messages, e.g. pushed notifications of received messages
H04L 51/42 - Mailbox-related aspects, e.g. synchronisation of mailboxes
H04L 51/48 - Message addressing, e.g. address format or anonymous messages, aliases
H04L 51/52 - User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail for supporting social networking services
H04L 51/56 - Unified messaging, e.g. interactions between e-mail, instant messaging or converged IP messaging [CPM]
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing an intelligent assistant interface that integrates with a large language model and a knowledge graph to adaptively change its appearance for presenting and interacting with different content items from various sources. In some embodiments, the disclosed systems provide an intelligent assistant interface that includes a set of interface elements selectable to interact with a large language model. Based on an input intent, the disclosed systems can utilize the large language model to analyze a knowledge graph for accessing and/or generating content items for display within the intelligent assistant interface.
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a content stack utilizing one or more machine-learning models. In some implementations, the disclosed systems generate and provide, to a user account, a content stack that includes content items corresponding to a topic prompt for the user account. For instance, in some implementations, the disclosed systems utilize content-based signals and account-based signals to generate an account-specific stack formulation graph that represents a plurality of content items and relationships of the content items with each other and with the user account. Additionally, in some implementations, the disclosed systems analyze the account-specific stack formulation graph to generate a content stack from the plurality of content items, the content stack comprising a set of content items corresponding to the topic prompt.
