Patent Publication Number: US-2022230138-A1

Title: Generating and providing collections of collaborative content items to teams of user accounts

Description:
BACKGROUND 
     Advancements in computing devices and networking technology have led to a variety of innovations in providing digital content to, and sharing digital content among, user accounts. For example, online digital content systems are now able to provide access to digital content items across many devices all over the world. Existing systems are also able to synchronize changes to shared digital content across the devices. Indeed, whether in education, employment, or other domains, modern online digital content systems are able to provide access to digital content for users to collaborate together across diverse physical locations and through a variety of computing devices. Despite these advances, however, existing digital content systems continue to suffer from a number of disadvantages, particularly in their flexibility, accuracy, and efficiency. 
     As just mentioned, existing digital content systems are often inflexible. More specifically, some existing systems rigidly require manual curation of relevant documents for providing to user accounts. Indeed, digital content is often fluid and changing (e.g., as users access and edit digital content items), and teams of user accounts are also constantly in flux as user accounts are added and removed from various teams over time (which often changes ownership of digital content as well). To maintain relationships between digital content and user accounts, existing systems rigidly rely on particular user accounts (e.g., specialized accounts) to manually curate the digital content to indicate which digital content items are germane to which user accounts (e.g., by manually creating and maintaining share folders). 
     Due at least in part to their inflexible nature, existing digital content systems inaccurately identify relevant digital content items to provide to user accounts. In particular, because of the onerous requirements of manual content curation in some existing systems, these existing systems often fall into disarray due to neglect. Thus, existing systems end up providing out-of-date, inaccurate information for surfacing relevant digital content to user accounts. In some cases, the existing system inaccuracies will result in providing irrelevant digital content to user accounts, while in other cases, these existing systems omit relevant digital content items when surfacing content to user accounts. 
     Additionally, many existing digital content systems inefficiently utilize computing resources such as processing time, processing power, and memory. For example, manually curating digital content and relationships between digital content and user accounts is time consuming and computationally expensive. Indeed, not only does the inflexibility of existing systems lead to inaccurate relationships between digital content items and user accounts, but such inflexibility further results in inefficient graphical user interfaces that often require an excessive number of user interactions for manual curation. For example, a existing system that requires manual content curation requires a client device or a server to process user interactions for indicating and updating relationships of each digital content item in relation to potentially large numbers of user accounts. Having to (frequently) navigate through the large numbers of digital content items to update germaneness relative to various user accounts in this way requires an excessive number of user interactions. Additionally, processing these user interactions wastes computing resources that could otherwise be avoided. 
     Thus, there are several disadvantages with regard to existing digital content systems. 
     SUMMARY 
     One or more implementations described herein provide benefits and solve one or more of the foregoing or other problems in the art by providing systems, methods, and non-transitory computer readable media that can generate and distribute team-specific collections of collaborative content items. In particular, the disclosed systems can determine relationships between teams of user accounts within an organizational ontology and various collaborative content items. Based on the determined relationships, the disclosed systems can identify which collaborative content items are relevant to which teams and can provide the collaborative content items to user accounts accordingly. As part of determining relationships between collaborative content items and user accounts (or teams of user accounts), the disclosed systems can determine various types of information pertaining to the collaborative content items. For example, the disclosed systems can determine access patterns, sharing patterns, types of activity (e.g., viewing vs. editing) performed, and other information associated with collaborative content items. Utilizing this information, the disclosed systems can generate team-specific collections of collaborative content items by identifying teams within an organizational ontology corresponding to the collaborative content items. In some embodiments, the disclosed systems can also provide graphical user interfaces for creating and managing team collections of collaborative content items. 
     Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of such example implementations. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       This disclosure will describe one or more implementations of the invention with additional specificity and detail by referencing the accompanying figures. The following paragraphs briefly describe those figures, in which: 
         FIG. 1  illustrates a schematic diagram of an example environment of a team content identification system in accordance with one or more embodiments; 
         FIG. 2  illustrates an overview of generating and providing a collaborative content item within a team content collection in accordance with one or more embodiments; 
         FIG. 3  illustrates generating a set of candidate collaborative content items in accordance with one or more embodiments; 
         FIG. 4A  illustrates identifying and analyzing access patterns of collaborative content items in accordance with one or more embodiments; 
         FIG. 4B  illustrates identifying and analyzing sharing patterns of collaborative content items in accordance with one or more embodiments; 
         FIG. 5  illustrates generating team scores for teams of an organizational ontology in accordance with one or more embodiments; 
         FIG. 6A  illustrates utilizing a team score generation model to generate team scores for content items in accordance with one or more embodiments; 
         FIG. 6B  illustrates updating team scores based on changes to information for content items in accordance with one or more embodiments; 
         FIG. 7  illustrates grouping collaborative content items within a team content collection based on semantic meaning in accordance with one or more embodiments; 
         FIG. 8A  illustrates an example team content collection interface in accordance with one or more embodiments; 
         FIG. 8B  illustrates an example organizational ontology interface in accordance with one or more embodiments; 
         FIG. 8C  illustrates an example collaborative team space interface in accordance with one or more embodiments; 
         FIG. 9  illustrates a schematic diagram of a team content identification system in accordance with one or more embodiments; 
         FIG. 10  illustrates a flowchart of a series of acts of generating and providing a team content collection including collaborative content items based on team scores in accordance with one or more embodiments; 
         FIG. 11  illustrates a block diagram of an exemplary computing device in accordance with one or more embodiments; and 
         FIG. 12  illustrates an example environment of a networking system having the team content identification system in accordance with one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     One or more implementations described herein provide benefits and solve one or more of the foregoing or other problems in the art with a team content identification system that can generate and provide team content collections to user accounts belonging to teams within an organizational ontology. In particular, the team content identification system can access an organizational ontology that includes various parent nodes and child nodes indicating relationships between teams of user accounts. In addition, the team content identification system can generate team content collections to provide to various teams within the organizational ontology. To generate a team content collection, the team content identification system can determine relationships between teams (or user accounts within teams) and collaborative content items. Based on the relationships, the team content identification system can identify which collaborative content items to provide to which teams within the organizational ontology. Thus, the team content identification system can generate team content collections including the collaborative content items and can provide the team content collections to user accounts belonging to the respective teams. 
     To illustrate by way of an example, the team content identification system can automatically (e.g., without user interaction) generate and provide a team content collection for onboarding a new team member. For instance, if a new user account is added to a particular team of user accounts within an organization, the team content identification system determines relationships between the new user account and a number of stored collaborative content items. Based on the relationships, the team content identification system further generates a team content collection to provide to the new user account, including collaborative content items arranged or grouped into sections based on subject matter or semantic meaning. In some embodiments, an addition (or alternatively) to grouping collaborative content item into sections, the team content identification system generates and assigns tags for the collaborative content items based on the relationships. Thus, the team content identification system identifies and provides necessary or relevant collaborative content items to the new user account automatically. This example is merely illustrative, and additional implementations and examples are possible, as described in more detail below. 
     As just mentioned, in one or more embodiments, the team content identification system accesses a repository of collaborative content items from which to generate team content collections. To elaborate, the team content identification system identifies a repository collaborative content items created by user accounts within a particular organization. From the repository of collaborative content items, the team content identification system further identifies candidate collaborative content items that are eligible to be selected for inclusion within a team content collection. For instance, the team content identification system identifies, as candidate collaborative content items, those content items that are publicly accessible, that do not contain sensitive or personal information, and that are accessible by at least a threshold number of user accounts (e.g., a threshold number of non-specialized accounts). As described in further detail below, the team content identification system further filters the candidate collaborative content items (e.g., based on access patterns, sharing patterns, content activity, and/or geographic information) to select those items to include within team content collections to provide to teams of user accounts. 
     In some embodiments, as part of generating and providing team content collections, the team content identification system accesses and analyzes an organizational ontology. In particular, the team content identification system can identify nodes that make up the organizational ontology and that indicate teams of user accounts within an organization. Indeed, the team content identification system can identify parent nodes and child nodes that indicate relationships between teams throughout the organizational ontology. Based on the arrangement of the nodes, the team content identification system can further determine relationships between user accounts (e.g., belonging to a common team or different teams). For example, the team content identification system can identify teams to which user accounts belong and can determine links between their respective teams (or nodes) within the organization ontology. In some embodiments, the team content identification system determines relationships between user accounts utilizing additional or alternative information, such as geographic information corresponding to user accounts and/or activity data indicating common projects associated with user accounts. 
     To identify collaborative content items to include within a team content collection, the team content identification system can determine relationships between user accounts and collaborative content items. More specifically, in some cases, the team content identification system determines team scores for each of a number of collaborative content items. For instance, the team content identification system selects a given collaborative content item and determines measures of correspondence between the collaborative content item and each of a plurality of teams. 
     To determine team scores, the team content identification system can utilize a team score generation model to process data such as i) access patterns of user accounts accessing the collaborative content item, ii) sharing patterns of user accounts sharing the collaborative content item, iii) activity data indicating quantities, frequencies, and/or types of activity user accounts perform in relation to the collaborative content item, and/or iv) geographic information associated with user accounts that access the collaborative content item. In some embodiments, the team content identification system compares the team scores to select one or more teams of user accounts for providing the collaborative content item (e.g., a team with a highest score or a number of teams that satisfy a team score threshold). Additional detail regarding determining team scores and selecting collaborative content items to include within a team content collection based on team scores is provided below with reference to the figures. 
     Not only does the team content identification system generate a team content collection including collaborative content items relevant to user accounts belonging to a particular team, but in some embodiments the team content identification system further organizes the team content collection into sections. To elaborate, the team content identification system can analyze collaborative content items that belong to a particular team content collection to cluster or group the collaborative content items into sections with respect to one another. For instance, the team content identification system can determine a semantic meaning associated with each collaborative content item and can group the collaborative content items based on semantic meaning. In some cases, the team content identification system implements a clustering algorithm to group collaborative content items together based on distances from each other within a vector space. In certain embodiments, the team content identification system groups collaborative content items together into sections based on additional or alternative information, such as access data (e.g., by grouping more frequently accessed collaborative content items together) or other information as will be described in further detail below. 
     As suggested above, the team content identification system provides several advantages over existing digital content systems. For example, embodiments of team content identification systems are more flexible than existing systems. In particular, unlike existing systems that rigidly require manual curation from user accounts (e.g., specialized user accounts), the team content identification system automatically (e.g., without user interaction) curates collaborative content items. More specifically, the team content identification system determines and flexibly adapts relationships between user accounts and collaborative content items based on access of, and interaction with, collaborative content items over time. Existing systems, by contrast, require user interaction to indicate (and constantly update) which content items are germane to which user accounts. 
     Due at least in part to its improved flexibility, the team content identification system can more accurately identify relevant collaborative content item to provide to user accounts compared to existing digital content systems. To elaborate, the team content identification system can analyze access patterns, sharing patterns, activity data, and geographic information to determine measures of correspondence between user accounts (or teams of user accounts) and collaborative content items. Existing systems, by contrast, often fall into disarray due to disuse over time as curators fail to maintain or update relationships as changes occur within content as well as user accounts. As access of, and activity within, collaborative content items changes over time, the team content identification system can further update access patterns, sharing patterns, and other information to maintain accurate measures of correspondence between user accounts (or teams of user accounts) and collaborative content items. 
     In addition to improved flexibility and accuracy, embodiments of the team content identification system further improve efficiency over existing digital content systems. Particularly, whereas existing systems process large number of user interactions with manual curation of digital content, the team content identification system can reduce the number of (or eliminate) user interactions required to determine and maintain relationships between user accounts and collaborative content items. Indeed, the team content identification system can provide efficient user interfaces that require fewer user interactions to access desired data and/or functionality. For instance, the team content identification system generates (e.g., automatically or without user interaction) team content collections and provides visual representations of team content collections which are selectable to access collaborative content item relevant to a particular user account (or team of user accounts). By providing team content collections with fewer user interactions, the team content identification system consumes fewer computing resources such as processing time, processing power, and memory. 
     As illustrated by the foregoing discussion, the present disclosure utilizes a variety of terms to describe features and benefits of the team content identification system. Additional detail is hereafter provided regarding the meaning of these terms as used in this disclosure. As used herein, the term “collaborative content item” (or simply “content item”) refers to a digital object or a digital file that includes information interpretable by a computing device (e.g., a client device) to present information to a user. A collaborative content item can include a file such as a digital document file, a digital image file, a digital audio file, a digital video file, a web file, or some other type of file or digital object. A collaborative content item can have a particular file type or file format, which may differ for different types of collaborative content items (e.g., digital documents or digital images). A collaborative content item can be editable or otherwise modifiable and can also be sharable from one user account (or client device) to another. In some cases, a collaborative content item is modifiable by multiple user accounts (or client devices) simultaneously and/or at different times. 
     As mentioned, the team content identification system can identify candidate collaborative content items from a repository of collaborative content item. As used herein, the term “candidate collaborative content item” refers to a content item that is eligible for inclusion within a team content collection. In some cases, a candidate collaborative content item includes a collaborative content item that is publicly accessible, that does not include personal information or sensitive information, and/or that is accessible by at least a threshold number of (non-specialized) user accounts. For example, a candidate collaborative content item can include a collaborative content item that has been previously accessed at least a threshold number of times by non-specialized user accounts and that is publicly accessible (e.g., not restricted to particular user accounts such as legal team user accounts, administrative user accounts, or executive user accounts). Additional detail about determining or identifying candidate collaborative content items is described more fully below. 
     Relatedly, the term “team content collection” refers to a collection of candidate collaborative content items selected for providing to a team of user accounts. In particular, a team content collection includes collaborative content items provided to a team with the highest team scores for the included collaborative content items (or with team scores that satisfy a threshold team score). In some embodiments, a team content collection includes multiple sections or groupings of collaborative content items, arranged according to subject matter, semantic meaning, recency of activity, frequency of activity, or another arrangement. 
     As mentioned, the team content identification system can determine team scores for various teams with respect to a particular collaborative content item. As used herein, the term “team score” refers to a score or a rating that indicates a measure of correspondence between a collaborative content item and a team of user accounts. For example, a team score includes a probability or a likelihood that a collaborative content item corresponds to a particular team of user accounts within an organization ontology. In some cases, a higher score indicates a stronger correspondence or a higher likelihood that the collaborative content item belongs to a particular team (e.g., should be included within a team content collection). 
     To determine a team score for a collaborative content item, in some embodiments, the team content identification system determines and processes an access pattern and/or a sharing pattern for the collaborative content item. As used herein, the term “access pattern” refers to a pattern of user accounts accessing a particular collaborative content item over a period of time. For example, an access pattern includes a historical record of user accounts previously accessing a collaborative content item and indicating the date and time of access, the user account which accessed, and from where or what device the access originated. In some cases, an access pattern is a pattern in that it repeats in the same or similar fashion over a particular period. For instance, an access pattern can include accesses at regular intervals and/or accesses in sporadic bursts with large numbers of accesses spread out in short timeframes. In certain embodiments, an access pattern indicates an access frequency or a number of times a collaborative content item is accessed over a particular time period (e.g., as an average). 
     In a similar sense, the term “sharing pattern” refers to a pattern of user accounts sharing a collaborative content item with one or more other user accounts. For example, a sharing pattern includes a historical record of user accounts previously sharing a collaborative content item and indicating the date and time of sharing, the user account which shared, the user accounts which received the shared collaborative content item, and where the sharing originated. In some embodiments, a sharing pattern indicates a sharing frequency for a collaborative content item, where the collaborative content item is shared a number of times over a particular time period (e.g., as an average). 
     In some embodiments, the team content identification system utilizes a team score generation model to generate a team score. As used herein, the term “team score generation model” refers to a machine learning model such as a neural network, a support vector machine, a decision tree, and/or a linear regression model that generates a team score to indicate correspondence between a collaborative content item and a team of user accounts. In some embodiments, a team score generation model refers to a neural network that processes input data such as an access pattern (or a sharing pattern) of a collaborative content item to generate a plurality of team scores for teams with user accounts that access (or share) the collaborative content item. The term “neural network” refers to a machine learning model that can be trained and/or tuned based on inputs to determine classifications or approximate unknown functions. For example, a neural network includes a model of interconnected artificial neurons (e.g., organized in layers) that communicate and learn to approximate complex functions and generate outputs (e.g., determinations of team scores) based on a plurality of inputs provided to the neural network. In some cases, a neural network refers to an algorithm (or set of algorithms) that implements deep learning techniques to model high-level abstractions in data. 
     Relatedly, the term “team” refers to a collection or a grouping of user accounts belonging to a particular node of an organizational ontology. In particular, a team includes user accounts associated with, or assigned to, a particular group or level within an organizational ontology. For example, a team can refer to a specialized team, an executive team, a programming team, a project-specific team, a staff team, a clerk team, an engineer team, or some other team. In some cases, a team is geographically based, where engineers in one geographic region (e.g., Japan) may be on a separate team from engineers in another geographic region (e.g., Germany). In one or more embodiments, a team includes user accounts within a threshold similarity of each other (e.g., based on geography, account activity, and/or accessed collaborative content items). In certain embodiments, a given team can include either specialized user accounts or non-specialized user accounts or both. 
     A “specialized user account” refers to a user account with special privileges and/or authorization to access restricted collaborative content items (e.g., collaborative content items that are not publicly accessible). Conversely, a “non-specialized user account” refers to a user account without special privileges and/or that cannot access restricted collaborative content items that are not publicly accessible. Thus, a collaborative content item that is “publicly accessible” is a collaborative content item that is accessible to non-specialized user accounts. In some embodiments, the team content identification system identifies a collaborative content item that includes “sensitive information,” which refers to information that is restricted from access by non-specialized accounts, personal and private to a particular user account, and/or designated as sensitive by one or more user accounts. 
     As mentioned above, the team content identification system accesses an organizational ontology to identify, and determine relationships between, teams of user accounts. As used herein, the term “organizational ontology” refers to a stratified or hierarchical arrangement of nodes or teams within an organization. For example, an organizational ontology includes a number of nodes that represent teams of an organization arranged according to relationships between the teams. In some embodiments, an organizational ontology includes parent nodes and child nodes, where a parent node has a leadership (or managerial or oversight) relationship with respect to its one or more child nodes. In some cases, user accounts belonging to a team of a parent node receive collaborative content items provided as part of a team content collection to child nodes, but user accounts of a child node do not receive team content collections of the parent node. 
     As also mentioned, in some embodiments, the team content identification system determines semantic meanings associated with collaborative content items. As used herein, the term “semantic meaning” refers to a word-based or language-based meaning, subject, or topic of a collaborative content item. In particular, a semantic meaning can include a tag or a label that indicates a subject matter associated with a collaborative content item. In some embodiments, the team content identification system determines a semantic meaning utilizing a neural network (or some other natural language processing model) to label a collaborative content item based on its text. In certain cases, a semantic meaning is derived from the title of a collaborative content item, while in other cases a semantic meaning is derived from the body of the collaborative content item as well. 
     Additional detail regarding the team content identification system will now be provided with reference to the figures. For example,  FIG. 1  illustrates a schematic diagram of an example system environment for implementing a team content identification system  102  in accordance with one or more implementations. An overview of the team content identification system  102  is described in relation to  FIG. 1 . Thereafter, a more detailed description of the components and processes of the team content identification system  102  is provided in relation to the subsequent figures. 
     As shown, the environment includes server(s)  104 , client devices  108   a - 108   n , and a network  112 . Each of the components of the environment can communicate via the network  112 , and the network  112  may be any suitable network over which computing devices can communicate. Example networks are discussed in more detail below in relation to  FIGS. 11-12 . 
     As mentioned, the example environment includes client devices  108   a - 108   n . Each of the client devices  108   a - 108   n  can be one of a variety of computing devices, including a smartphone, a tablet, a smart television, a desktop computer, a laptop computer, a virtual reality device, an augmented reality device, or another computing device as described in relation to  FIGS. 11-12 . The client devices  108   a - 108   n  can communicate with the server(s)  104  via the network  112 . For example, the client devices  108   a - 108   n  can receive user input from respective users interacting with the client devices  108   a - 108   n  (e.g., via the client application  110 ) to, for example, access, modify, share, or comment on digital content items and/or to interact with content item elements. In turn, the team content identification system  102  on the server(s)  104  can receive information relating to various interactions with collaborative content items and/or user interface elements based on the input received by the client devices  108   a - 108   n  (e.g., to access, edit, and/or share the collaborative content items). In some implementations, user accounts associated with particular client devices  108   a - 108   n  (or user accounts associated with a subset of the client devices  108   a - 108   n ) can belong to a particular team and have access to a common collaborative team space that includes team-specific collaborative content items. 
     As shown, the client devices  108   a - 108   n  can each include a client application  110 . In particular, the client application  110  may be a web application, a native application installed on the client devices  108   a - 108   n  (e.g., a mobile application, a desktop application, etc.), or a cloud-based application where all or part of the functionality is performed by the server(s)  104 . The client application  110  can present or display information, including a user interface such as a collaborative team space that includes elements for team-specific collaborative content items. Additionally, the client application  110  can present information in the form of collaborative content items and can facilitate user interaction with the collaborative content items to access, modify, share, and/or comment on the digital content items. Indeed, a user can interact with the client application  110  to provide user input to perform an operation as mentioned above. 
     As illustrated in  FIG. 1 , the example environment also includes the server(s)  104 . The server(s)  104  may generate, track, store, process, receive, and transmit electronic data, such as collaborative content items, access patterns, sharing patterns, team scores, and team content collections. For example, the server(s)  104  may receive data from the client devices  108   a - 108   n  in the form modifications, accesses, shares, or comments of collaborative content items. In addition, the server(s)  104  can transmit data to the client devices  108   a - 108   n  in the form of updates to a collaborative team interface depicting various changes based on the modifications, accesses, shares, and comments. Indeed, the server(s)  104  can communicate with the client devices  108   a - 108   n  to send and/or receive data via the network  112 . In some implementations, the server(s)  104  comprise(s) a distributed server where the server(s)  104  include(s) a number of server devices distributed across the network  112  and located in different physical locations. The server(s)  104  can comprise one or more content servers, application servers, communication servers, web-hosting servers, machine learning server, and other types of servers. 
     As shown in  FIG. 1 , the server(s)  104  can also include the team content identification system  102  as part of a content management system  106 . The content management system  106  can communicate with the client devices  108   a - 108   n  to perform various functions associated with the client application  110  such as managing an organizational ontology, managing collaborative content items including team content collections (e.g., by permitting and denying access to the client devices  108   a - 108   n  based on user account permissions), synchronizing modifications, comments, shares, and other information across the client devices  108   a - 108   n , and facilitating user interaction with the collaborative content items to modify, comment, and share the digital content items. In addition, the content management system  106  and/or the team content identification system  102  can access and utilize a team score generation model to generate team scores for teams of user accounts in relation to a particular collaborative content item. In some embodiments, the team content identification system  102  and/or the content management system utilize the database  114  to store and access information such as an organizational ontology, team content collections, collaborative content items, team scores, access patterns, sharing patterns, and other information. 
     Although  FIG. 1  depicts the team content identification system  102  located on the server(s)  104 , in some implementations, the team content identification system  102  may be implemented by (e.g., located entirely or in part) on one or more other components of the environment. For example, the team content identification system  102  may be implemented by the client devices  108   a - 108   n  and/or a third-party device. 
     In some implementations, though not illustrated in  FIG. 1 , the environment may have a different arrangement of components and/or may have a different number or set of components altogether. For example, the client devices  108   a - 108   n  may communicate directly with the team content identification system  102 , bypassing the network  112 . In addition, the environment can include the database  114  located external to the server(s)  104  (e.g., in communication via the network  112 ) or located on the server(s)  104  and/or located on the client devices  108   a - 108   n  and that stores collaborative content items. Additionally, the environment can include a team score generation model as part of the team content identification system  102 , stored within the database  114 , included as part of the client application  110 , or housed on the server(s)  104 . 
     As mentioned, the team content identification system  102  can generate a team content collection to provide to user accounts belonging to a particular team. In particular, the team content identification system  102  can identify and select one or more collaborative content items to include within the team content collection.  FIG. 2  illustrates an example sequence of acts that the team content identification system  102  performs to assign a collaborative content item to a particular team content collection to provide to a team of user accounts in accordance with one or more embodiments. The description of  FIG. 2  provides an overview of the example acts involved in selecting a team content collection (and a section within the team content collection) for a collaborative content item. Additional detail regarding the various illustrated acts is provided thereafter in relation to the subsequent figures. 
     As illustrated in  FIG. 2 , the team content identification system  102  performs an act  202  to determine collaborative content items within a repository. In particular, the team content identification system  102  obtains a corpus of collaborative content items created by user accounts associated with a particular organization (e.g., a company or some other entity). For example, the team content identification system  102  accesses a repository of collaborative content items (e.g., within the database  114 ) to identify those collaborative content items that were created by user accounts within a certain organizational ontology (e.g., an organization ontology associated with the company or entity). As shown, the team content identification system  102  identifies five collaborative content items, three created by a first user account, and two created by a second user account. 
     As further illustrated in  FIG. 2 , the team content identification system  102  performs an act  204  to identify candidate collaborative content items. More specifically, the team content identification system  102  selects candidate collaborative content items from among the collaborative content items within the repository associated with the organization. For example, the team content identification system  102  filters out those collaborative content items that are not eligible for inclusion within a team content collection and identifies those collaborative content items that are eligible for inclusion within a team content collection. 
     In some embodiments, the team content identification system  102  utilizes one or more filtering criteria for identifying candidate collaborative content items. For instance, the team content identification system  102  identifies collaborative content items that are publicly accessible (e.g., accessible to anyone or to most people within the organization). For example, the team content identification system  102  identifies collaborative content items that are open for anyone, including non-specialized user accounts, to access. In some embodiments, the team content identification system  102  removes from consideration those collaborative content items that have restricted access (e.g., as part of a legal team, an executive team, or a financial team that maintain private content) and are therefore not publicly accessible, or at least not publicly accessible to non-specialized user accounts. 
     In addition (or alternatively), the team content identification system  102  identifies, as candidate collaborative content items, collaborative content items that are accessible to at least a threshold number of non-specialized user accounts. Particularly, the team content identification system  102  identifies collaborative content items that non-specialized user accounts can access, view, edit, and/or share. In some cases, the team content identification system  102  identifies collaborative content items that are not only accessible to at least a threshold number of non-specialized user accounts but that have also been previously accessed by at least a threshold number of non-specialized user accounts (e.g., within a certain time period). Thus, the team content identification system  102  filters out those collaborative content items that are not accessible to, or that have not been previously accessed by, at least the threshold number of non-specialized user accounts. 
     In one or more embodiments, the team content identification system  102  identifies, as candidate collaborative content items, collaborative content items that do not include sensitive information. To elaborate, the team content identification system  102  analyzes or processes the collaborative content items to determine whether or not a given collaborative content item includes sensitive information. In some cases, the team content identification system  102  utilizes natural language processing, a machine learning model (e.g., a neural network), regular expressions, or other systems or methods to identify sensitive information within a collaborative content item (e.g., personal information, financial information, medical information, or other types of confidential, privileged, or otherwise sensitive information). In some cases, the team content identification system  102  identifies as sensitive only those types of information type were specifically designated by a user account as sensitive. In any event, the team content identification system  102  filters out collaborative content items that include sensitive information. 
     In the example of  FIG. 2 , the team content identification system  102  analyzes two collaborative content items. For the first collaborative content item (e.g., the digital image), the team content identification system  102  determines that the collaborative content item is accessible (or has been previously accessed) by only two user accounts. Based on a threshold of three non-specialized user accounts, for example, the team content identification system  102  determines that the digital image is not a candidate collaborative content item. For the digital document, however, the team content identification system  102  determines that the digital document is accessible (or has been previously accessed) by three non-specialized user accounts. Thus, based on this determination (and/or determinations pertaining to the lack of sensitive information and public accessibility) the team content identification system  102  determines that the digital document is a candidate collaborative content item. 
     As further illustrated in  FIG. 2 , in some embodiments, the team content identification system  102  performs an act  206  to analyze an access pattern for each candidate collaborative content item. In particular, the team content identification system  102  analyzes an access pattern that indicates dates and times of access of the candidate collaborative content item(s) by user accounts belonging to different teams of an organizational ontology. For example, the team content identification system  102  determines which user accounts access the candidate collaborative content item(s) and when such access occurs. 
     Based on the access pattern, the team content identification system  102  determines access frequencies with which particular user accounts (or teams) access the candidate collaborative content item(s). Additionally (or alternatively), as part of analyzing the access pattern, the team content identification system  102  determines a total number of previous accesses associated with a particular team, a recency of each access, a number of accesses for each team within a given time period, and/or any other statistical or analytical information describing the access pattern(s). As shown in  FIG. 2 , the team content identification system  102  determines that various user accounts accessed the digital document on 11/1, 11/2, and 11/3 at various times between 1:00 and 1:05. 
     Based on analyzing the access pattern, the team content identification system  102  determines a team (or teams) to which the candidate collaborative content item(s) belongs. To elaborate, in some embodiments, the team content identification system  102  performs an act  208  to generate team scores for the candidate collaborative content item(s) based at least in part on the access pattern determined in act  206 . Indeed, the team content identification system  102  generates team scores to select one or more teams to receive a team content collection that includes the candidate collaborative content item(s). More specifically, the team content identification system  102  analyzes the access pattern to generate team scores for one or more teams of users based on accesses by user accounts belonging to the different teams. As shown, the team content identification system  102  generates team scores for each team within an organization ontology including seven teams. Indeed, the team content identification system  102  generates team scores for each team that accessed the candidate collaborative content item(s) to select a team to receive the candidate collaborative content item(s) (as part of a team content collection). 
     To generate the team scores, the team content identification system  102  utilizes a team score generation model. More particularly, the team content identification system  102  utilizes a team score generation model to generate team scores from the access pattern of the candidate collaborative content item(s). In some embodiments, the team content identification system  102  provides the access pattern as input into the team score generation model, whereupon the team score generation model generates a plurality of team scores by comparing accesses of user accounts belonging to various teams. For example, the team content identification system  102  utilizes the team score generation model to compare access frequencies, access volumes (e.g., total numbers of accesses or numbers of accesses within a given time period), and/or access recencies. In certain embodiments, the team content identification system  102  generates team scores on a scale from 0 to 1, or from 1 to 10, or from 1 to 100 (e.g., as a probability or likelihood of belonging to a particular team). In some cases, higher team scores indicate stronger measures of correspondence between a team and the candidate collaborative content item(s). 
     In one or more embodiments, the team content identification system  102  generates team scores based on factors other than, or in addition to, access patterns. For example, in some embodiments, the team content identification system  102  utilizes sharing patterns, geographic information, and/or user activity information pertaining to the candidate collaborative content item(s) to generate team scores for that item. Regarding sharing patterns, the team content identification system  102  determines and/or accesses a sharing pattern for the candidate collaborative content item(s) indicating numbers, frequencies, and/or recencies of instances of user accounts sharing the candidate collaborative content item(s) with other user accounts. Indeed, the team content identification system  102  determines which teams have shared the candidate collaborative content item(s) more frequently, more recently, more overall, and/or more within a particular time period. 
     In some embodiments, the team content identification system  102  utilizes geographic information to determine or generate team scores. For example, the team content identification system  102  determines a geographic region where the candidate collaborative content item(s) originated (e.g., a geographic region of a user account that created the collaborative content item). In addition, the team content identification system  102  determines geographic regions associated with user accounts to determine geographic relationships with the candidate collaborative content item(s). In some embodiments, the team content identification system  102  determines geographic relationships between user accounts as well. For instance, if user accounts from a team in Germany frequently access the candidate collaborative content item(s), then the team content identification system  102  determines a higher team score for other user accounts or teams in Germany (e.g., a new user account joining a team in Germany). 
     In certain embodiments, the team content identification system  102  utilizes activity information as part of determining or generating team scores. In particular, the team content identification system  102  determines activity and types of activity performed by user accounts with respect to the candidate collaborative content item(s). For example, the team content identification system  102  determines that a first user account accessed the candidate collaborative content item(s) but only viewed the collaborative content item, while a second user account accessed and edited the candidate collaborative content item(s). In some embodiments, the team content identification system  102  determines higher team scores for teams that perform certain types of activity (e.g., editing or modifying the candidate collaborative content item(s)) and determines lower team scores for teams that perform other types of activity (e.g., viewing the candidate collaborative content item(s)). 
     In one or more embodiments, the team content identification system  102  generates or determines team scores based on a combination of one or more of the above factors. Specifically, the team content identification system  102  can generate team scores from a combination (e.g., weighted combination) of an access pattern, a sharing pattern, activity information, and/or geographic information. For example, the team content identification system  102  determines and assigns weights based on a default setting (e.g., a heaviest weight for access patterns, a next-heaviest weight for sharing patterns, a third-heaviest weight for activity information, and a lightest weight for geographic information) or based on a user indication from a user account (e.g., a specialized user account or a team leader user account). Based on one or more of these factors, as shown in the example of  FIG. 2 , the team content identification system  102  generates team scores indicating: a “6” team score for the overall parent node team (e.g., the team that encompasses the entire organization); “9” team score and a “4” team score for the child nodes; and teams scores of “4,” “7,” “2,” and “3” for the grandchild nodes (e.g., nodes that are in a layer of the organizational ontology below the child nodes, or that are child nodes of the other child nodes). Additional detail regarding the organizational ontology depicted by the branching tree of nodes is provided below with specific reference to  FIG. 5 . 
     As illustrated in  FIG. 2 , the team content identification system  102  further performs an act  210  to select team(s) for the candidate collaborative content item(s). More specifically, the team content identification system  102  determines to provide the candidate collaborative content item(s) to one or more teams from among the organizational ontology. For instance, the team content identification system  102  determines to include the candidate collaborative content item(s) within a team content collection provided to users belonging to one or more teams. 
     In certain embodiments, the team content identification system  102  selects a single team (or user accounts belonging to a single team) for the candidate collaborative content item(s). In particular, the team content identification system  102  identifies a team with a highest team score from among the plurality of teams within the organizational ontology as the team to receive the candidate collaborative content item(s). For example, the team content identification system  102  identifies a team with the most accesses, the most frequent accesses, the most recent accesses, the most shares, the most frequent shares, and/or the most recent shares as the team to receive the candidate collaborative content item(s) (e.g., as part of a team content collection). As an example of such a case, as shown in  FIG. 2 , the team content identification system  102  selects the team or node with the team score of “9,” which is highest among the illustrated nodes. 
     In other embodiments, the team content identification system  102  selects multiple teams (or user accounts belonging to multiple teams) to receive the candidate collaborative content item(s). For example, the team content identification system  102  compares the team scores against a threshold team score. For example, the team content identification system  102  determines a threshold team score and identifies teams that satisfy the threshold team score as teams to receive the candidate collaborative content item(s) (e.g., as part of a team content collection). As shown in  FIG. 2 , the team content identification system  102  utilizes a threshold team score of “7” and thus selects the two nodes in the organizational ontology with team score that satisfy the threshold score (e.g., those with team scores of “9” and “7”). In some cases, the team content identification system  102  determines the threshold team score, while in other cases, the team content identification system  102  receives an indication to set the threshold team score (e.g., from a specialized user account). 
     In certain embodiments, the team content identification system  102  determines that no team satisfies the threshold team score for the candidate collaborative content item. In these embodiments, the team content identification system  102  determines to omit (or not provide) the candidate collaborative content item from any or all team content collections. In some cases, however, if the team content identification system  102  determines that no team satisfies the threshold team score, then the team content identification system  102  lowers the threshold team score. 
     In one or more embodiments, the team content identification system  102  determines that a certain team satisfies threshold team scores for a large number of candidate collaborative content items (e.g., more than a threshold number of collaborative content items). In these or other embodiments, the team content identification system  102  determines a maximum number of collaborative content items to provide to user accounts of the certain team. For example, the team content identification system  102  identifies those collaborative content items for which the team scores are highest and provides only those collaborative content items to the team, up to the maximum number. 
     Additionally or alternatively, the team content identification system  102  can determine that a minimum number of collaborative content items are provided to a team (or provided to each team). Thus, if a team has team scores that fail to satisfy any threshold team scores for all collaborative content items, the team content identification system  102  nevertheless provides a minimum number of collaborative content items to the team. For instance, the team content identification system  102  identifies a number of candidate collaborative content items for which the team scores highest, and the team content identification system  102  provides that number (e.g., the top 3) of collaborative content items to user accounts of the team. 
     As another example, the team content identification system  102  selects teams associated with a parent node of a selected node. For instance, the team content identification system  102  selects a node with a highest team score and further identifies a parent node of the highest-scoring node to receive the candidate collaborative content item(s). Indeed, in certain embodiments, user accounts associated with a parent node may need access to candidate collaborative content items of user accounts they oversee or manage (e.g., those in child nodes). Thus, the team content identification system  102  selects a team corresponding to a node that is a parent to a highest-scoring (or otherwise selected) node. In some cases, the opposite situation may be applicable, where the team content identification system  102  selects a user account of a child node of a selected parent node to receive the candidate collaborative content item(s). 
     As further illustrated in  FIG. 2 , in some embodiments, the team content identification system  102  performs an act  212  to assign a grouping for the candidate collaborative content item(s). More specifically, the team content identification system  102  determines a grouping or a section within a team content collection where the candidate collaborative content item(s) belongs. For example, the team content identification system  102  determines a semantic meaning associated with the candidate collaborative content item(s) and assigns the candidate collaborative content item(s) to a section corresponding to the semantic meaning. In some embodiments, the team content identification system  102  utilizes natural language processing or some other language-based machine learning model to determine semantic meaning from the title of the candidate collaborative content item(s) (and/or other portions such as the body of the candidate collaborative content item(s), the metadata associated with the candidate collaborative content item(s), etc.). For instance, the team content identification system  102  identifies most common words, a most commonly used set of words, and/or syntactic similarity of the words. 
     Indeed, in some cases, the team content identification system  102  analyzes a team content collection to cluster (e.g., via a clustering algorithm) candidate collaborative content items into groupings or sections based on semantic meaning. In addition, based on determining the semantic meaning of the new collaborative content item, the team content identification system  102  determines where to place the collaborative content item within the team content collection for providing to user accounts of the one or more selected teams. As shown, the team content identification system  102  determines a semantic meaning of “Meeting Minutes” for the candidate collaborative content item(s), and therefore includes the candidate collaborative content(s) item within a “Meeting Minutes” section of a team content collection. The team content identification system  102  thus provides the team content collection, including the candidate collaborative content item(s), to user accounts of the selected team(s). 
     As mentioned above, the team content identification system  102  can identify candidate collaborative content items. In particular, the team content identification system  102  can filter or refine a plurality of collaborative content items from a repository to identify collaborative content items that are eligible for inclusion within a team content collection.  FIG. 3  illustrates filtering or identifying candidate collaborative content items based on various filtering criteria or factors in accordance with one or more embodiments. 
     As illustrated in  FIG. 3 , the team content identification system  102  accesses a repository of collaborative content items  302 . In particular, the team content identification system  102  accesses the database  114  to identify the repository of collaborative content items  302  stored within the database  114 . For example, the team content identification system  102  identifies collaborative content items created, generated, edited, accessed, or otherwise associated with user accounts of a particular organization. In some embodiments, the team content identification system  102  accesses an organization-specific repository of digital images that includes collaborative content items created by user accounts within a particular organizational ontology. 
     In addition, the team content identification system  102  filters out certain collaborative content items to identify and select a set of candidate collaborative content items from among the repository of collaborative content items  302 . Specifically, the team content identification system  102  utilizes one or more determinations to identify those collaborative content items that are eligible and/or those collaborative content items that are ineligible for inclusion within a team content collection. As illustrated, the filtering process includes four acts  304 - 310  as factors for filtering collaborative content items. In some embodiments, the team content identification system  102  utilizes one or more of the described filtering factors (not necessarily all four) and does so in any order. 
     For example, the team content identification system  102  performs an act  304  to identify publicly accessible collaborative content items. Particularly, the team content identification system  102  identifies those collaborative content items that are publicly accessible from among the repository of collaborative content items  302 . For instance, the team content identification system  102  determines (levels of) accessibility associated with each collaborative content item within the repository of collaborative content items  302 . 
     Based on determining accessibility, the team content identification system  102  identifies collaborative content items that are publicly accessible and identifies collaborative content items that are not publicly accessible. Specifically, the team content identification system  102  identifies a publicly accessible collaborative content item by determining that the collaborative content item is accessible to non-specialized user accounts within an organizational ontology. Indeed, the team content identification system  102  determines that the collaborative content item is not limited to user accounts of a legal team, an executive team, a financial team, or some other set of specialized user accounts. In some embodiments, the team content identification system  102  identifies a publicly accessible collaborative content item as a collaborative content item that is accessible by at least a threshold number (e.g., three, five, or ten) of non-specialized user accounts. 
     As further illustrated in  FIG. 3 , the team content identification system  102  performs an act  306  to determine numbers of accesses of collaborative content items. In particular, the team content identification system  102  determines how many times each collaborative content item within the repository of collaborative content items  302  is, or has been previously, accessed (e.g., in total or by non-specialized user accounts only). In certain embodiments, the team content identification system  102  determines a number of accesses within a particular time period (e.g., within the past two weeks, the past six months, or the past year) or determines. 
     In these or other embodiments, the team content identification system  102  determines which user accounts accessed the collaborative content item. For example, the team content identification system  102  determines whether specialized user accounts or non-specialized user accounts access the collaborative content item, and how many times user accounts of each type accessed the collaborative content item. In some cases, the team content identification system  102  further identifies collaborative content items that have been accessed at least a threshold number of times (e.g., by non-specialized user accounts). 
     In some embodiments, the team content identification system  102  determines information other than (or in addition to) numbers of accesses. For example, the team content identification system  102  determines a recency of access for each collaborative content item and/or frequencies of access for each collaborative content item. In certain cases, the team content identification system  102  determines that collaborative content items that have been accessed within a threshold recency (e.g., the last hour, the last day, or the last month) are eligible for inclusion within a team content collection. In these or other cases, the team content identification system  102  determines that collaborative content items that have been accessed with at least a threshold frequency are eligible for inclusion within a team content collection. In one or more embodiments, the team content identification system  102  excludes from consideration those collaborative content items that have not been accessed within a threshold recency and/or with at least a threshold frequency. 
     As further illustrated in  FIG. 3 , the team content identification system  102  performs an act  308  to determine which user accounts access collaborative content items. More specifically, the team content identification system  102  determines which user accounts access which collaborative content items. For a given collaborative content item, for instance, the team content identification system  102  analyzes access information to determine which user accounts accessed the collaborative content item and when. 
     In some embodiments, the team content identification system  102  identifies specialized user accounts and/or non-specialized user accounts that access a collaborative content item. For example, the team content identification system  102  identifies collaborative content items that only specialized user accounts access. In addition, the team content identification system  102  identifies collaborative content items that non-specialized user accounts access (in addition or alternatively to specialized user accounts). 
     In certain embodiments, the team content identification system  102  determines teams associated with accessing user accounts. To elaborate, the team content identification system  102  detects an access from a first user account and another access from a second user account. The team content identification system  102  further determines a team associated with the first user account and determines another team associated with the second user account. Thus, the team content identification system  102  identifies teams of user accounts that access collaborative content items. By determining which user accounts access a collaborative content item (and when), the team content identification system  102  determines relationships between user accounts (or teams of user accounts) and the collaborative content item. 
     As shown in  FIG. 3 , the team content identification system  102  also performs an act  310  to identify sensitive collaborative content items. More particularly, the team content identification system  102  identifies those collaborative content items that contain sensitive information and/or that are designated as sensitive (e.g., by a specialized user account or a creator user account). For example, the team content identification system  102  identifies as sensitive collaborative content items associated with certain teams within an organizational ontology. Such teams include legal teams, executive teams, financial teams, and personal records teams, though not necessarily all collaborative content items associated with these teams are sensitive. In some cases, the team content identification system  102  identifies collaborative content items accessible exclusively by user accounts within one or more of these certain teams as sensitive. 
     In some cases, the team content identification system  102  identifies an indication (e.g., a tag or a label) designating a collaborative content item as sensitive. For example, a creator user account or a specialized user account can label a collaborative content item as sensitive to prevent the team content identification system  102  from sharing or surfacing the collaborative content item with other user accounts. In some cases, the team content identification system  102  analyzes a collaborative content item (e.g., utilizing a neural network or some other machine learning model) to determine that the collaborative content item includes sensitive information. For instance, the team content identification system  102  recognizes certain words in titles (or elsewhere) such as “private” or “sensitive” or “personal” and determines to exclude the collaborative content item from any team content collections. In some embodiments, the team content identification system  102  identifies as sensitive personal information relating to finances, addresses, or other private information. 
     In some embodiments, the team content identification system  102  utilizes a combination of two or more of the acts  304 - 310  to identify candidate collaborative content items. For example, the team content identification system  102  determines numbers (and/or frequencies and/or recencies) of access of user accounts associated with particular teams. Indeed, the team content identification system  102  determines which teams access a collaborative content item the most overall, the most within a given time period, the most recently, and/or the most frequently. In this way, the team content identification system  102  determines relationships between teams of user accounts and collaborative content items. As another example of combining more than one of the acts  304 - 310  together, the team content identification system  102  identifies publicly accessible collaborative content items that are also accessible (or previously accessed) by at least a threshold number of non-specialized user accounts. 
     As further illustrated in  FIG. 3 , the team content identification system  102  identifies candidate collaborative content items  312 . In particular, the team content identification system  102  generates, identifies, or determines a set of candidate collaborative content items  312  from the repository of collaborative content items  302  stored within the database  114 . Indeed, by utilizing the filtering process described above, including one or more of the acts  304 - 310 , the team content identification system  102  identifies those collaborative content items that are eligible for inclusion within a team content collection. For instance, the team content identification system  102  identifies, as candidate collaborative content items, non-sensitive collaborative content items which are publicly accessible and that have been accessed by non-specialized user accounts at least a threshold number of times. In some cases, the team content identification system  102  filters out ineligible collaborative content items. In these or other cases, the team content identification system  102  further filters out collaborative content items that are designated as irrelevant by a user account (or by a threshold number of user accounts). 
     As mentioned above, the team content identification system  102  can generate or identify access patterns associated with collaborative content items. In particular, the team content identification system  102  can identify different access patterns for different collaborative content items, where each access pattern indicates a historical trend of access of a given collaborative content item.  FIG. 4A  illustrates an access pattern  406  corresponding to a collaborative content item  402  and an access pattern  408  corresponding to a collaborative content item  404  in accordance with one or more embodiments. 
     As illustrated in  FIG. 4A , the team content identification system  102  identifies or generates the access pattern  406  for the collaborative content item  402 . In particular, the team content identification system  102  analyzes access data or access information associated with the collaborative content item  402 . From the access information, the team content identification system  102  identifies user accounts (e.g., non-specialized user accounts and/or specialized user accounts) that access the collaborative content item  402  and when the accesses occur. In some embodiments, the team content identification system  102  also determines or identifies teams associated with user accounts that access the collaborative content item  402 . The team content identification system  102  thus generates the access pattern  406  that indicates times of user accounts accessing the collaborative content item  402 . As shown, the access pattern  406  shows a burst-like trend of access by several user accounts in a periodic fashion, with particular heavy access time periods that are spread out with little or no access between them. 
     From the access pattern, the team content identification system  102  determines which team or teams are most strongly related to the collaborative content item  402 . For example, the team content identification system  102  identifies those teams with user accounts that access the collaborative content item  402  the most (or the most frequently or the most recently or the most within a certain time period). Indeed, the team content identification system  102  utilizes the access pattern to determine measures of correspondence between the collaborative content item  402  and various teams of an organizational ontology based on numbers, frequencies, and/or recencies of access (e.g., a weighted combination of numbers, frequencies, and recencies). 
     As also illustrated in  FIG. 4A , the team content identification system  102  identifies or generates the access pattern  408  for the collaborative content item  404 . In particular, the team content identification system  102  analyzes access information associated with the collaborative content item  404  to identify user accounts that access the collaborative content item  404 . As shown, the team content identification system  102  generates the access pattern  408  that is spread out at regular intervals (e.g., less burst-like than the access pattern  406 ) with different user accounts accessing the collaborative content item  404  at different times. 
     As mentioned above in relation to the collaborative content item  402 , the team content identification system  102  also determines measures of correspondence between the collaborative content item  404  and teams of user accounts. Particularly, the team content identification system  102  utilizes the access pattern  408  to determine measures of correspondence for one or more teams based on numbers, frequencies, and/or recencies of access by user accounts belonging to the team(s). In one or more embodiments, for a given collaborative content item (e.g., the collaborative content item  404  or the collaborative content item  402 ), the team content identification system  102  generates team scores for teams of an organizational ontology based at least in part on an access pattern of the collaborative content item (e.g., the access pattern  408  or the access pattern  406 ). 
     In certain embodiments, the team content identification system  102  compares access patterns of different collaborative content items. For example, the team content identification system  102  compares the burst-like access pattern  406  of the collaborative content item  402  with the regular access pattern  408  of the collaborative content item  404 . Based on the comparison, the team content identification system  102  identifies collaborative content items to include within, or exclude from, certain team content collections (or certain sections of a team content collection). For example, the access pattern  406  may indicate that the collaborative content item  402  belongs in a section corresponding to a particular periodic meeting where access spikes around meeting time. 
     Indeed, in some cases, collaborative content items that are accessed more regularly are more likely candidates for a section of a team content collection where, for example, user accounts of a team access the collaborative content item  404  regularly. However, in certain cases, the collaborative content item  402  with burst-like access pattern  410  is included within a particular section specific to a certain time-sensitive event or project, such as a section for a certain (e.g., periodic) meeting or project. 
     As further mentioned above, the team content identification system  102  can generate or identify sharing patterns associated with collaborative content items. In particular, the team content identification system  102  can generate different sharing patterns for different collaborative content items based on sharing information indicating user accounts sharing collaborative content items with other user accounts.  FIG. 4B  illustrates a sharing pattern  410  associated with the collaborative content item  402  and a sharing pattern  412  associated with the collaborative content item  404  in accordance with one or more embodiments. 
     As illustrated in  FIG. 4B , the team content identification system  102  identifies or generates the sharing pattern  410  for the collaborative content item  402 . In particular, the team content identification system  102  analyzes sharing information associated with the collaborative content item  402  that indicates user accounts sharing the collaborative content item  402  with other user accounts and when the sharing occurs. For example, the team content identification system  102  identifies a user account that shares the collaborative content item  402  and further identifies a user account that receives the shared collaborative content item  402 . 
     In some embodiments, the team content identification system  102  generates the sharing pattern  410  that indicates user accounts sharing and receiving the collaborative content item  402  at particular times. As shown, the sharing pattern  410  indicates a burst-like sharing trend, with heavy sharing activity occurring sporadically over particular time periods, and where little or no sharing occurs between these time periods. 
     From the sharing pattern  410 , the team content identification system  102  determines measures of correspondence between the collaborative content item  402  and teams of user accounts within an organizational ontology. For example, the team content identification system  102  identifies teams with user accounts that share and/or receive the collaborative content item  402  the most. In some cases, the team content identification system  102  determines teams that share the collaborative content item  402  the most within a given time period, the most frequently, and/or the most recently (or some weighted combination of the above). Thus, the team content identification system  102  determines measures of correspondence between the collaborative content item  402  and various teams, where teams that share (and/or receive) the collaborative content item  402  more (or more frequently or more recently) have a stronger measure of correspondence. 
     As also illustrated in  FIG. 4B , the team content identification system  102  identifies or generates the sharing pattern  412  for the collaborative content item  404 . In particular, the team content identification system  102  analyzes sharing information for the collaborative content item  404  to determine user accounts that share and/or receive the collaborative content item  404  and when the sharing and/or receiving occurs. As shown, the sharing pattern  412  is more regular than the sharing pattern  410 , with sharing occurring at evenly spread intervals. Based on the sharing pattern  412 , the team content identification system  102  determines measures of correspondence between the collaborative content item  404  and teams associated with user accounts that share and/or receive the collaborative content item  404 . For instance, the team content identification system  102  determines measures of correspondence based on numbers, frequencies, and/or recencies of sharing and/or receiving the collaborative content item  404 . 
     In some embodiments, the team content identification system  102  determines that the collaborative content item  404  is eligible for inclusion within a certain team content collection (or a certain section of a team content collection) based on the sharing pattern  412 . For instance, the team content identification system  102  determines that, due to its regular sharing pattern  412 , the collaborative content item  404  is a good fit for a section for a team project that has regular sharing between members of a certain team. Similarly, the team content identification system  102  determines that the collaborative content item  402  is a good candidate for a section of a team content collection corresponding to a period meeting or some other event that results in spikes in shares between user accounts of a team. In some cases, the team content identification system  102  compares the sharing pattern  410  with the sharing pattern  412  to determine which collaborative content item belongs in which team content collection or in which section of a team content collection. 
     As mentioned above, the team content identification system  102  can generate team scores that indicate measures of correspondence between a collaborative content item and a team of user accounts. In particular, for a given collaborative content item (e.g., the collaborative content item  402  or the collaborative content item  404 ), the team content identification system  102  can generate team scores for a plurality of teams from an organizational ontology based on an access pattern, a sharing pattern, and/or other factors as well.  FIG. 5  illustrates generating team scores corresponding to the collaborative content item  402  for various teams within an organizational ontology  504  in accordance with one or more embodiments. 
     As illustrated in  FIG. 5 , the team content identification system  102  accesses an organizational ontology  504  associated with a particular organization or a particular group of teams. For example, the team content identification system  102  determines a hierarchical or stratified arrangement of teams within an organization to generate or identify the organizational ontology  504 , where teams are placed in nodes indicating parent-child relationships (e.g., where a team of a parent node has some supervisory role in relation to a team of a child node). As illustrated, the node  506  is the parent of the node  508  which is the parent of the node  510  and the node  512 . In some cases, a user account can belong to more than one team or node. For example, a user account can belong to multiple teams, such as those corresponding to the node  510  and to the node  508 . 
     From the organizational ontology  504 , the team content identification system  102  determines relationships between teams and/or between user accounts. For instance, the team content identification system  102  determines that the user account within the node  506  has a supervisory relationship with the other user accounts within the organizational ontology  504 . In some cases, the team content identification system  102  determines that the node  506  is a specialized node or that the user account within the node  506  is a specialized user account. Indeed, the team content identification system  102  determines that user accounts within nodes higher (e.g., in layers above other nodes) in the organizational ontology  504  have specializedial or supervisory relationships in relation to user accounts in lower nodes. 
     As illustrated in  FIG. 5 , the team content identification system  102  further determines team scores for the teams within the organizational ontology  504 . More specifically, the team content identification system  102  utilizes a team score generation model  502  to generate the team scores for each of the teams. As shown, the team content identification system  102  utilizes the team score generation model  502  to generate team scores in relation to the collaborative content item  402 . Indeed, the team content identification system  102  utilizes the team score generation model  502  to generate team scores based on an access pattern of the collaborative content item  402 , a sharing pattern of the collaborative content item  402 , activity within the collaborative content item  402 , and/or geographic information corresponding to the collaborative content item  402  and/or user accounts interacting with the collaborative content item  402 . 
     As just mentioned, the team content identification system  102  generates team scores based on an access pattern. In particular, the team content identification system  102  determines numbers, frequencies, and/or recencies of access of the collaborative content item  402  associated with the teams of the organizational ontology  504 . For example, the team content identification system  102  determines how many times (and how recently and how frequently) user accounts from each team access the collaborative content item  402 . In some cases, the team content identification system  102  combines the accesses from user accounts of a certain team to generate the team score for the team. For instance, across user accounts for a given team, the team content identification system  102  totals a number of accesses, averages a recency of access, and/or averages a frequency of accesses of user accounts from the team to determine a team score. 
     In some embodiments, the team content identification system  102  weights access by different user accounts differently. For example, the team content identification system  102  identifies user accounts with viewing permission and further identifies user accounts with editing permission. In some cases, the team content identification system  102  generates higher team scores for teams with user accounts that access the collaborative content item  402  with editing permission, as compared to teams with user accounts the collaborative content item  402  with only viewing permission. 
     In addition (or alternatively) to utilizing an access pattern, in some cases, the team content identification system  102  utilizes a sharing pattern to determine or generate team scores. Particularly, the team content identification system  102  determines numbers, frequencies, and/or recencies of sharing the collaborative content item  402 . For example, the team content identification system  102  determines how many times (and how recently and how frequently) user accounts from the various teams of the organizational ontology  504  share and/or receive the collaborative content item  402 . In some cases, the team content identification system  102  combines the shares from user accounts of a certain to generate the team score for the team. For instance, the team content identification system  102  totals a number of shares, averages a recency of shares, and/or averages a frequency of shares across the user accounts of a given team to determine a team score. 
     Additionally, the team content identification system  102  determines activity associated with the collaborative content item  402  to generate team scores. More specifically, the team content identification system  102  determines activity performed by user accounts within, or in relation to, the collaborative content item  402 . For example, the team content identification system  102  determines whether user accounts access the collaborative content item  402  to merely view the collaborative content item  402  or to edit or otherwise manipulate the collaborative content item  402 . In some embodiments, in determining team scores, the team content identification system  102  weights more heavily user accounts that edit the collaborative content item  402  as compared to user accounts that merely view the collaborative content item  402 . 
     In certain described embodiments, the team content identification system  102  utilizes geographic information to determine or generate team scores. In particular, the team content identification system  102  determines geographic information related to the collaborative content item  402  and/or to the user accounts within the organizational ontology  504 . For example, the team content identification system  102  determines geographic locations (e.g., countries, states, or regions) where certain teams and/or user accounts originate. In addition, the team content identification system  102  determines a geographic location where the collaborative content item  402  originates (e.g., a geographic location of a user account that created the collaborative content item  402 ). 
     Based on the geographic information, the team content identification system  102  generates team scores for the teams of the organizational ontology  504 . For instance, the team content identification system  102  identifies common geographic locations among user accounts of a given team that access, share, and/or edit the collaborative content item  402 . Indeed, the team content identification system  102  generates higher team scores for teams that access the collaborative content item  402  from common geographic locations. In some cases, the team content identification system  102  generates higher team scores for teams that access the collaborative content item  402  from the same (or similar) geographic location where the collaborative content item  402  originates. 
     In one or more embodiments, the team content identification system  102  combines the access pattern, the sharing pattern, the activity information, and/or the geographic information to generate team scores. To elaborate, the team content identification system  102  combines the access pattern, the sharing pattern, the activity information, and/or the geographic information in a weighted combination to determine teams scores for teams of the organizational ontology. In some cases, the team content identification system  102  utilizes the team score generation model  502  to combine the access pattern, the sharing pattern, the activity information, and/or the geographic information. For instance, the team content identification system  102  utilizes the team score generation model  502  to generate predictions or likelihoods that the collaborative content item  402  belongs, or is germane, to the various teams. 
     As illustrated in  FIG. 5 , the team content identification system  102  generates a team score of 6 for the team corresponding to the node  506 , a 9 for the team corresponding to the node  508 , a 4 for the team corresponding to the node  510 , and a 7 for the team corresponding to the node  512 . Based on the illustrated team scores, the team content identification system  102  identifies one or more teams to receive a team content collection that includes the collaborative content item  402 . Indeed, the team content identification system  102  generates a team content collection (including the collaborative content item  402 ) to provide to a highest-scoring team (e.g., corresponding to the node  508 ) or to teams that satisfy a threshold team score. 
     As mentioned above, the team content identification system  102  can utilize the team score generation model  502  to generate team scores. In particular the team content identification system  102  can generate team scores by inputting information into the team score generation model  502 . Indeed, based on input information the team score generation model  502  generates predictions or likelihoods that various teams correspond to a collaborative content item.  FIG. 6A  illustrates generating a team score utilizing the team score generation model  502  in accordance with one or more embodiments. 
     As illustrated in  FIG. 6A , the team content identification system  102  accesses an access pattern  602  from the database  114 . Indeed, the team content identification system  102  identifies an access pattern  602  for a particular collaborative content item (e.g., the collaborative content item  402 ) stored within the database  114 . In addition, the team content identification system  102  inputs the access pattern  602  into the team score generation model  502 . In turn, the team score generation model  502  generates team scores for various teams based on the information within the access pattern  602 . 
     In particular, the team content identification system  102  utilizes the team score generation model  502  to generate team scores based on comparing access frequencies indicated by the access pattern  602 . For example, the team content identification system  102  utilizes the team score generation model  502  to determine an access frequency of a first team  604  from among an organizational ontology. In addition, the team content identification system  102  utilizes the team score generation model  502  to determine an access frequency of other teams  606  within the organizational ontology. 
     The team content identification system  102  further utilizes the team score generation model  502  to compare the access frequency of the first team  604  with the access frequency of other teams  606 . Indeed, the team content identification system  102  determines a difference, or a delta, between access frequencies of the first team and other teams. In this way, the team content identification system  102  determines how frequently user accounts within the first team access the collaborative content item as compared to user accounts of other teams. From the comparison, the team content identification system  102  utilizes the team score generation model  502  to generate a team score for the first team  608 . For instance, the team content identification system  102  determines that the first team accesses the collaborative content item more frequently or less frequently than the other teams (e.g., on average) and generates a team score accordingly. As shown, the team content identification system  102  generates a team score of “6” for the first team based on comparing access frequencies. 
     In a similar fashion, the team content identification system  102  utilizes the team score generation model  502  to generate team scores for other teams as well. In particular, the team content identification system  102  generates team scores for other teams within an organizational ontology. For example, in generating a team score for a second team, the team content identification system  102  utilizes the team score generation model  502  to determine an access frequency of the second team to compare against an access frequency of other teams (including the first team). 
     In this fashion, the team content identification system  102  generates a team score for each team within an organizational ontology, by comparing access frequencies of a given team against access frequencies of all other teams within the organizational ontology. In some cases, the team content identification system  102  determines higher team scores for teams with larger deltas or differences between the team&#39;s access frequency and the access frequency of other teams. 
     In some embodiments, the team content identification system  102  utilizes information other than (or in addition to) the access pattern  602  to input into the team score generation model  502 . For example, the team content identification system  102  inputs a sharing pattern, whereupon the team score generation model  502  determines a sharing frequency of a particular team to compare against sharing frequencies of other teams in determining a team score. In some cases, the team score generation model  502  generates team scores based on activity within a given collaborative content item and/or geographic information as well. 
     As mentioned, the team content identification system  102  can modify or update team scores based on new or updated information. In particular, the team content identification system  102  can detect or identify changes to collaborative content item information such as access information, sharing information, activity information, and/or geographical information.  FIG. 6B  illustrates updating a team score based on changes or updates to information for a collaborative content item in accordance with one or more embodiments. 
     As illustrated in  FIG. 6B , the team content identification system  102  the team content identification system  102  inputs the access pattern  602  into the team score generation model  502 . In turn, the team score generation model  502  generates the team score for the first team  608 , as described above. Additionally, the team content identification system  102  identifies, receives, or detects updated access data  610 . More specifically, the team content identification system  102  continuously monitors the collaborative content item and tracks accesses (and shares and activities) of the collaborative content item. 
     Upon detecting a new access (or upon detecting a threshold number of new accesses), the team content identification system  102  identifies the updated access data  610  that indicates the new access of the collaborative content item by a particular user account. In addition, the team content identification system  102  modifies or updates the access pattern  602  to incorporate the new access. Further, the team content identification system  102  inputs the modified access pattern into the team score generation model  502 , whereupon the team score generation model  502  generates updated team scores (e.g., for each team within an organizational ontology). In some embodiments, the team content identification system  102  determines the updated access data  610  based on a threshold amount of time elapsing. For instance, the team content identification system  102  periodically checks for new access information at regular intervals (e.g., hourly, daily, or weekly). 
     In certain embodiments, the team content identification system  102  updates sharing information. For example, the team content identification system  102  detects a new instance (or a threshold number of new instances) of one or more user accounts sharing a collaborative content item. In some cases, the team content identification system  102  periodically checks for new sharing information at regular intervals. Upon detecting new sharing information, the team content identification system  102  updates or modifies a sharing pattern for the collaborative content item, inputs the modified sharing pattern into the team score generation model  502 , and generates updated team scores. 
     In one or more embodiments, the team content identification system  102  updates activity information (e.g., in addition to access information and sharing information). For example, the team content identification system  102  detects a new instance (or a threshold number of new instances) of one or more user accounts editing a collaborative content item. In some cases, the team content identification system  102  periodically checks for new activity information at regular intervals. Upon detecting new activity information, the team content identification system  102  inputs the modified activity information into the team score generation model  502  and generates updated team scores. 
     As a result of updating team scores, the team content identification system  102  can update which team(s) receive a team content collection including a particular collaborative content item. For example, the team content identification system  102  includes the collaborative content item within a team content collection provided to a first team based on initial team scores. As accesses, shares, and activity changes over time, however, the team content identification system  102  updates the team scores and determines to include the collaborative content item within a team content collection provided to a second team instead. In some cases, the team content identification system  102  thus removes or revokes the collaborative content item from team content collection provided to the first team. 
     In some embodiments, the team content identification system  102  trains, or learns parameters for, the team score generation model  502 . For example, the team content identification system  102  learns parameters for the team score generation model  502  that result in accurate team scores. To train the team score generation model  502 , the team content identification system  102  utilizes a set of training data or sample data. In particular, the team content identification system  102  inputs a sample access pattern (or a sample sharing pattern) into the team score generation model  502 , whereupon the team score generation model  502  generates a predicted team score. 
     In addition, the team content identification system  102  compares the predicted team score with a ground truth team score (e.g., a ground truth team score that is associated with the sample pattern). For instance, the team content identification system  102  utilizes a loss function to determine an error or a measure of loss associated with the team score generation model  502  that reflect the discrepancy between the predicted team score and the ground truth team score. Further, the team content identification system  102  back propagates to modify parameters of the team score generation model  502 . For example, the team content identification system  102  modifies weights or parameters associated with various layers and/or neurons of the team score generation model  502  to reduce or minimize the measure of loss. 
     The team content identification system  102  can repeat this process of inputting sample patterns, generating predicted team scores, comparing the predicted team scores with ground truth team scores, and modifying parameters for many iterations or epochs. Over many iterations, the team content identification system  102  continues to modify the parameters until the team score generation model  502  generates accurate team scores (e.g., where the error satisfies a threshold measure of error). 
     In one or more embodiments, the team content identification system  102  updates parameters for the team score generation model  502  based on user input. For example, as user accounts interact with collaborative content items by adding collaborative content items to team content collections, removing collaborative content items from team content collections, sharing collaborative content items, accessing collaborative content items, and/or performing other actions, the team content identification system  102  adjusts parameters for the team score generation model  502 . In some cases, the team content identification system  102  modifies the parameters to learn which user actions indicate higher degrees of relevance (and therefore higher team scores) and which user actions indicate lower degrees of relevance (and therefore lower team scores). For instance, the team content identification system  102  modifies parameters to cause the team score generation model  502  to generate lower team scores for teams that remove collaborative content items from team content collections. As another example, the team content identification system  102  modifies parameters to generate higher team scores for teams that add, share, and/or access collaborative content items. 
     As mentioned above, the team content identification system  102  can include a collaborative content item within a team content collection provided to a team of user accounts. In particular, the team content identification system  102  can includes the collaborative content item within a particular section of a team content collection.  FIG. 7  illustrates determining a section of a team content collection for a collaborative content item in accordance with one or more embodiments. 
     As illustrated in  FIG. 7 , the team content identification system  102  determines sections for each collaborative content item included within the team content collection  714 . To elaborate, the team content identification system  102  segments, partitions, or clusters the collaborative content items within the team content collection  714  into various sections. As shown, the team content identification system  102  determines four sections for the team content collection  714 : i) “Meeting Minutes,” ii) “Current Project,” iii) “Team Members,” and iv) “Schedule.” 
     In some embodiments, the team content identification system  102  determines the different sections based on semantic meanings associated with different collaborative content items. In particular, the team content identification system  102  analyzes the collaborative content item  702 , the collaborative content item  704 , the collaborative content item  706 , and the collaborative content item  708  to determine semantic meanings for each one. 
     For instance, the team content identification system  102  utilizes natural language processing and/or a neural network to determine semantic meanings for collaborative content items. Specifically, the team content identification system  102  analyzes content within a collaborative content item to determine its semantic meaning. For the collaborative content item  702 , for instance, the team content identification system  102  analyzes the digital image to classify, or determine a semantic label for, any objects or scenes depicted in the digital image. For the collaborative content item  704 , the team content identification system  102  analyzes the digital video to classify any objects or scenes within the digital video. Further, for the collaborative content item  706 , the team content identification system  102  analyzes the text of the digital document to determine its semantic meaning. In some cases, the team content identification system  102  determines a semantic meaning from a title of a collaborative content item. In other cases, the team content identification system  102  utilizes other portions of content in addition (or alternatively) to the title. In these or other cases, the team content identification system  102  clusters collaborative content items based on type, where digital images are in one section, digital videos are in another section, a first type of digital document (e.g., a spreadsheet) is in yet another section, and a second type of digital document (e.g., a text editing document) is in a further section. 
     In one or more embodiments, the team content identification system  102  determines clusters or groups of collaborative content items within a team content collection based on their semantic meaning. For example, the team content identification system  102  determines that the collaborative content item  702 , the collaborative content item  704 , and the collaborative content item  706  all belong to the team content collection  714 . In addition, the team content identification system  102  utilizes a clustering algorithm (e.g., a nearest neighbor algorithm) to group collaborative content items into sections. For instance, the team content identification system  102  utilizes a neural network to generate vector representations of the collaborative content items (or vector representations of the semantic meanings) and utilizes a clustering algorithm to cluster the vector representations within a vector space  710 . 
     As illustrated in  FIG. 7 , the team content identification system  102  clusters vector representations of the collaborative content item  702 , the collaborative content item  704 , the collaborative content item  706 , and other collaborative content items. In particular, the team content identification system  102  determines distances between the vector representations within the vector space  710  and groups the vectors into clusters such as the cluster  712  based on the distances (e.g., where the distances represent measures of similarity between collaborative content items). In some embodiments, each cluster within the vector space  710  represents a different section within the team content collection  714 . 
     Upon analyzing a new collaborative content item to include within the team content collection  714 , such as the collaborative content item  708 , the team content identification system  102  determines a semantic meaning for the collaborative content item  708  (e.g., “Meeting Minutes”). From the semantic meaning, the team content identification system  102  generates a vector representation for the collaborative content item  708  and determines distances from the vector representation to other vector representations within the vector space  710 . As shown, the team content identification system  102  groups the collaborative content item  708  into the cluster  712 , which corresponds to the section “Meeting Minutes.” Thus, the team content identification system  102  includes the collaborative content item  708  in the “Meeting Minutes” section and provides the team content collection  714  to a team (or teams) of user accounts (based on team scores). 
     As mentioned above, the team content identification system  102  can generate and provide a team content collection interface. In particular, the team content identification system  102  can present, or cause a client device (e.g., the client device  108   a  and/or the client device  108   n ) to present, a team content collection interface that includes a team content collection and various interface elements for interacting with the team content collection.  FIG. 8A  illustrates the client device  108   a  displaying a team content collection interface in accordance with one or more embodiments. 
     As illustrated in  FIG. 8A , the team content collection interface includes a team content collection element  802  that is selectable to view different sections within the team content collection (e.g., the team content collection  714 ). In some embodiments, each separate section of a team content collection is selectable. For example, upon receiving user interaction selecting a particular section (e.g., “Meeting Minutes”), the team content identification system  102  causes the client device  108   a  to display collaborative content items within the selected section. As shown, the team content collection interface includes three different “Meeting Minutes” collaborative content items, for “Nov  1 ,” “Nov  2 ,” and “Nov  3 .” 
     As also illustrated in  FIG. 8A , the team content collection interface includes a create new binder element  804  selectable to create a new team content collection. For instance, the team content identification system  102  receives an indication of user interaction with the create new binder element  804  and guides a user through a new binder creation wizard in response. In some cases, the team content identification system  102  asks the user to identify user accounts, teams, and/or collaborative content items to include within the team content collection. 
     Additionally, the team content collection interface includes a new section element  806  selectable to create a new section with a current team content collection. More specifically, the team content identification system  102  receives user interaction with the new section element  806  and generates a new section within the current team content collection. For example, the team content identification system  102  prompts a user to indicate a semantic meaning associated with the new section and further identifies other collaborative content items with similar semantic meanings to include within the new section. 
     In one or more embodiments, the team content identification system  102  provides additional or alternative user interfaces. For example, the team content identification system  102  provides an organizational ontology interface for navigating among teams and corresponding team content collections of an organizational ontology.  FIG. 8B  illustrates an organizational ontology interface in accordance with one or more embodiments. 
     As illustrated in  FIG. 8B , the client device  108   a  displays an organizational ontology interface that includes a visual representation of an organizational ontology  808 . The team content identification system  102  receives indications of user interaction selecting a node (e.g., the node  810 ) from among the nodes of the organizational ontology  808 . In response, the team content identification system  102  causes the client device  108   a  to display the selected node  810  and any team content collections associated with the selected node. Based on user interaction selecting a team content collection, the team content identification system  102  provides a team content collection interface, as described above in relation to  FIG. 8A . In some embodiments, the team content identification system  102  provides the organizational ontology interface only to specialized user accounts. For instance, the team content identification system  102  provides a portion of an organizational ontology that includes a node of the specialized user account and any nodes branching from the node of the specialized user account (e.g., those nodes that the specialized user account has permission to access). 
     In certain embodiments, the team content identification system  102  generates and provides recommendations or suggestions to create a new team content collection and/or a new section within a team content collection. To elaborate, the team content identification system  102  identifies particular user accounts accessing certain collaborative content items, and rather than automatically generating a team content collection, the team content identification system  102  provides suggestions to generate a team content collection specific to a particular team or currently accessed collaborative content item.  FIG. 8C  illustrates a collaborative team space interface including a new team content collection suggestion  812  and a new section suggestion  814  in accordance with one or more embodiments. 
     As illustrated in  FIG. 8C , the client device  108   a  displays a collaborative team space interface for “Project Athos.” Within the interface, a user can access various collaborative content item in folders for Project Athos. The team content identification system  102  enables multiple user accounts within a team to access the same collaborative team space interface to access and edit collaborative content items for Project Athos. 
     In some cases, the team content identification system  102  determines that at least a threshold number of user accounts access Project Athos, that at least a threshold number of accesses of collaborative content items within Project Athos have occurred, and/or that Project Athos includes at least a threshold number of collaborative content items. Based on one or more of these determinations, the team content identification system  102  generates and provides the project-specific new team content collection suggestion  812  for display. In some cases, the team content identification system  102  provides the new team content collection suggestion  812  to a specialized user account within Project Athos to create a team content collection. 
     As further illustrated in  FIG. 8C , the client device  108   a  displays the new section suggestion  814 . In particular, the team content identification system  102  causes the client device  108   a  to display the new section suggestion  814  within the collaborative team space interface. In some embodiments, the team content identification system  102  analyzes a set of collaborative content items within Project Athos to determine semantic meanings. In addition, the team content identification system  102  determines that the collaborative content items belong in a common section within a team content collection. Thus, the team content identification system  102  generates and provides the new section suggestion  814  for display. 
     As shown, the team content identification system  102  determines (e.g., from the titles) that the digital documents “Coding Tips and Tricks,” “How to Program with Our Team,” and “Rules for Coding” all share a semantic meaning of “Coding Rules.” Thus, the team content identification system  102  generates the new section suggestion  814  to suggest creating a section for “Coding Rules” within a team content collection. 
     Additionally, the team content identification system  102  determines to provide a team content collection to particular user accounts belonging to teams with certain team scores. For example, the team content identification system  102  identifies a new user account (e.g., a user account onboarding to a team working on Project Athos) and provides a team content collection to the new user account. Specifically, the team content identification system  102  identifies collaborative content items to include within a team content collection by generating team scores for collaborative content items, as described. For instance, based on team scores for collaborative content items within Project Athos, the team content identification system  102  determines to provide a team content collection to the new user account. 
     Looking now to  FIG. 9 , additional detail will be provided regarding components and capabilities of the team content identification system  102 . Specifically,  FIG. 9  illustrates an example schematic diagram of the team content identification system  102  on an example computing device  900  (e.g., one or more of the client devices  108   a - 108   n  and/or the server(s)  104 ). As shown in  FIG. 9 , the team content identification system  102  may include a collaborative content item manager  902 , a team score manager  904 , a team content collection manager  906 , a user interface manager  908 , and a storage manager  910 . The storage manager  910  can operate in conjunction with one or more memory devices (e.g., the database  912 ) that store various data such as a repository of collaborative content items, user accounts, a team score generation model, and/or other information. 
     As just mentioned, the team content identification system  102  includes a collaborative content item manager  902 . In particular, the collaborative content item manager  902  manages, maintains, accesses, stores, detects, determines, generates, provides, and/or identifies collaborative content items. For example, the collaborative content item manager  902  manages collaborative content items for an organization, storing and distributing collaborative content items among user accounts for collaboration, sharing, viewing, and editing. In addition, the collaborative content item manager  902  identifies candidate collaborative content items that are eligible for inclusion within a team content collection. Specifically, the collaborative content item manager  902  filters out ineligible collaborative content items and identifies collaborative content items that are publicly accessible, have been accessed by non-manager at least a threshold number of times, and/or that do not include sensitive information. 
     As illustrated, the team content identification system  102  also includes a team score manager  904 . In particular, the team score manager  904  manages, determines, maintains, generates, or identifies team scores for teams of user accounts within an organizational ontology. For example, the team score manager  904  utilizes a team score generation model to generate team scores associated with a collaborative content item. The team score manager  904  generates team scores based on access data, sharing data, activity data, and/or geographic data. Based on the team scores, the team score manager  904  identifies one or more teams for providing a team content collection. 
     Indeed, the team content identification system  102  further includes a team content collection manager  906 . In particular, the team content collection manager  906  manages, maintains, generates, determines, provides, distributes, or identifies team content collections. For example, the team content collection manager  906  selects a team to receive a team content collection including one or more collaborative content items. In some embodiments, the team content collection manager  906  selects collaborative content items to include within a team content collection based on team scores and further groups collaborative content items into sections within a team content collection. 
     As further illustrated in  FIG. 9 , the team content identification system  102  includes a user interface manager  908 . In particular, the user interface manager  908  manages, maintains, provides, presents, displays, or causes a client device to present or display one or more user interfaces. For example, the user interface manager  908  provides a team content collection interface, a collaborative team space interface, and/or an organizational ontology interface, as described above. In addition, the user interface manager  908  receives indications of user interaction with one or more selectable elements within user interfaces and communicates with the other components of the team content identification system  102  to perform functions described herein. 
     As shown, the team content identification system  102  further includes a storage manager  910 . The storage manager  910  operates in conjunction with or include one or more memory devices such as the database  912  (e.g., the database  114 ) that store various data such as a repository of digital content items and digital content editing operations. The storage manager  910  (e.g., via a non-transitory computer memory/one or more memory devices) stores and maintains data associated with managing, maintaining, organizing, and providing collaborative content items, determining team scores, and generating team content collections (e.g., within the database  912 ). In some cases, the database  912  stores a repository of collaborative content items and a team score generation model. 
     In one or more implementations, each of the components of the team content identification system  102  are in communication with one another using any suitable communication technologies. Additionally, the components of the team content identification system  102  can be in communication with one or more other devices including one or more client devices described above. It will be recognized that although the components of the team content identification system  102  are shown to be separate in  FIG. 9 , any of the subcomponents may be combined into fewer components, such as into a single component, or divided into more components as may serve a particular implementation. Furthermore, although the components of  FIG. 9  are described in connection with the team content identification system  102 , at least some of the components for performing operations in conjunction with the team content identification system  102  described herein may be implemented on other devices within the environment. 
     The components of the team content identification system  102  can include software, hardware, or both. For example, the components of the team content identification system  102  can include one or more instructions stored on a computer-readable storage medium and executable by processors of one or more computing devices (e.g., the computing device  900 ). When executed by one or more processors, the computer-executable instructions of the team content identification system  102  can cause the computing device  900  to perform the methods described herein. Alternatively, the components of the team content identification system  102  can comprise hardware, such as a special purpose processing device to perform a certain function or group of functions. Additionally or alternatively, the components of the team content identification system  102  can include a combination of computer-executable instructions and hardware. 
     Furthermore, the components of the team content identification system  102  performing the functions described herein may, for example, be implemented as part of a stand-alone application, as a module of an application, as a plug-in for applications including content management applications, as a library function or functions that may be called by other applications, and/or as a cloud-computing model. Thus, the components of the team content identification system  102  may be implemented as part of a stand-alone application on a personal computing device or a mobile device. 
       FIGS. 1-9 , the corresponding text, and the examples provide a number of different systems and methods for generating and providing team content collections including relevant collaborative content items to teams of user accounts. In addition to the foregoing, implementations can also be described in terms of flowcharts comprising acts steps in a method for accomplishing a particular result. For example,  FIG. 10  illustrates a series of acts  1000  for generating and providing team content collections including relevant collaborative content items to teams of user accounts. 
     While  FIG. 10  illustrates acts according to one implementation, alternative implementations may omit, add to, reorder, and/or modify any of the acts shown in  FIG. 10 . The acts of  FIG. 10  can be performed as part of a method. Alternatively, a non-transitory computer readable medium can comprise instructions, that when executed by one or more processors, cause a computing device to perform the acts of  FIG. 10 . In still further implementations, a system can perform the acts of  FIG. 10 . 
     As illustrated in  FIG. 10 , series of acts  1000  may include act  1010  of identifying a set of candidate collaborative content items. In particular, the act  1010  involves identifying, from a repository of collaborative content items, a set of candidate collaborative content items accessible to user accounts associated with an organizational ontology indicating relationships between a plurality of teams of user accounts. For example, the act  1010  involves selecting, from the repository of collaborative content items, collaborative content items that are publicly accessible and that have been accessed by at least a threshold number of non-specialized user accounts. In some cases, the act  1010  involves excluding, from the set of candidate collaborative content items, collaborative content items that are not publicly accessible, that have not been accessed by at least the threshold number of non-specialized user accounts, or that include sensitive information. 
     As shown, the series of acts  1000  also includes an act  1020  of determining team scores for a collaborative content item corresponding to a plurality of teams. In particular, the act  1020  involves determining, for a collaborative content item within the set of candidate collaborative content items and utilizing a team score generation model, team scores corresponding to the plurality of teams within the organizational ontology. For example, the act  1020  involves identifying an access pattern for the collaborative content item indicating access of the collaborative content item by user accounts within the plurality of teams and generating the team scores utilizing the team score generation model to determine, from the access pattern, measures of correspondence between the collaborative content item and each of the plurality of teams. Determining an access pattern can involve determining access frequencies of the collaborative content item from user accounts within respective teams from among the plurality of teams. 
     In some cases, the act  1020  involves identifying a sharing pattern for the collaborative content item indicating sharing of the collaborative content item by user accounts within the plurality of teams. Additionally, the act  1020  involves utilizing the team score generation model to analyze the sharing pattern. Identifying the sharing pattern can involve determining frequencies of user accounts within respective teams sharing the collaborative content item. The act  1020  can involve identifying an access pattern for the collaborative content item indicating access of the collaborative content item by user accounts within the plurality of teams and utilizing a team score generation model to generate, from the access pattern, measures of correspondence between the collaborative content item and respective teams from the plurality of teams. 
     Further, the series of acts  1000  includes an act  1030  of incorporating the collaborative content item into a team content collection. In particular, the act  1030  involves, based on the team scores, incorporating the collaborative content item into a team content collection provided to user accounts belonging to a selected team from among the plurality of teams. 
     In some embodiments, the series of acts  1000  includes an act of comparing the team scores for the plurality of teams. Additionally, the series of acts  1000  includes an act of, based on comparing the team scores, providing the team content collection including the collaborative content item to only the user accounts belonging to the selected team. For example, the series of acts  1000  includes an act of comparing the team scores for the plurality of teams to determine that the selected team has a highest team score from among the plurality of teams. In addition, the series of acts  1000  includes an act of, based on determining that the selected team has the highest team score, providing the team content collection including the collaborative content item to only user accounts belonging to the selected team. 
     In some cases, the series of acts  1000  includes an act of determining that a team score associated with the selected team satisfies a threshold team score as well as an act of identifying an additional team from among the plurality of teams with an additional team score that satisfies the threshold team score. Further, the series of acts  1000  includes an act of providing the team content collection including the collaborative content item to the user accounts belonging to the selected team and to additional user accounts belonging to the additional team based on the team score and the additional team score satisfying the threshold team score. For example, the series of acts  1000  includes an act of comparing the team scores for the plurality of teams with a threshold team score and an act of determining that the selected team belongs to a set of teams that satisfy the threshold team score. Further, the series of acts  1000  includes an act of providing the team content collection including the collaborative content item to user accounts belonging to the set of teams. 
     The series of acts  1000  can include an act of determining a semantic meaning associated with the collaborative content item. In addition, the series of acts  1000  includes an act of comparing the semantic meaning of the collaborative content item with semantic meanings associated with groups of collaborative content items within the team content collection. The series of acts  1000  can also include an act of placing the collaborative content item into a section of the team content collection corresponding to the semantic meaning (e.g., based on the comparison). 
     In some embodiments, the series of acts  1000  includes an act of grouping collaborative content items within the team content collection according to semantic similarities between the collaborative content items within the team content collection. Additionally, the series of acts  1000  includes acts of determining a semantic meaning associated with the collaborative content item and identifying, for placing the collaborative content item within the team content collection, a group of collaborative content items corresponding to the semantic meaning. Further, the series of acts  1000  can include determining the team scores based on determining a type of activity performed by user accounts that access the collaborative content item. Further still, the series of acts  1000  can include acts of receiving a request to add a new user account to the selected team from among the plurality of teams and providing the team content collection to the new user account in response to the request. 
     Embodiments of the present disclosure may comprise or utilize a special purpose or general-purpose computer including computer hardware, such as, for example, one or more processors and system memory, as discussed in greater detail below. Implementations within the scope of the present disclosure also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. In particular, one or more of the processes described herein may be implemented at least in part as instructions embodied in a non-transitory computer-readable medium and executable by one or more computing devices (e.g., any of the media content access devices described herein). In general, a processor (e.g., a microprocessor) receives instructions, from a non-transitory computer-readable medium, (e.g., a memory, etc.), and executes those instructions, thereby performing one or more processes, including one or more of the processes described herein. 
     Computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that store computer-executable instructions are non-transitory computer-readable storage media (devices). Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, implementations of the disclosure can comprise at least two distinctly different kinds of computer-readable media: non-transitory computer-readable storage media (devices) and transmission media. 
     Non-transitory computer-readable storage media (devices) includes RAM, ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM), Flash memory, phase-change memory (“PCM”), other types of memory, other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. 
     A “network” is defined as one or more data links that enable the transport of electronic data between computer systems and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a transmission medium. Transmissions media can include a network and/or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. Combinations of the above should also be included within the scope of computer-readable media. 
     Further, upon reaching various computer system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to non-transitory computer-readable storage media (devices) (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computer system RAM and/or to less volatile computer storage media (devices) at a computer system. Thus, it should be understood that non-transitory computer-readable storage media (devices) can be included in computer system components that also (or even primarily) utilize transmission media. 
     Computer-executable instructions comprise, for example, instructions and data which, when executed by a processor, cause a general-purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. In some implementations, computer-executable instructions are executed on a general-purpose computer to turn the general-purpose computer into a special purpose computer implementing elements of the disclosure. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, or even source code. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the described features or acts described above. Rather, the described features and acts are disclosed as example forms of implementing the claims. 
     Those skilled in the art will appreciate that the disclosure may be practiced in network computing environments with many types of computer system configurations, including, personal computers, desktop computers, laptop computers, message processors, hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile telephones, PDAs, tablets, pagers, routers, switches, and the like. The disclosure may also be practiced in distributed system environments where local and remote computer systems, which are linked (either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links) through a network, both perform tasks. In a distributed system environment, program modules may be located in both local and remote memory storage devices. 
     Implementations of the present disclosure can also be implemented in cloud computing environments. In this description, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources. For example, cloud computing can be employed in the marketplace to offer ubiquitous and convenient on-demand access to the shared pool of configurable computing resources. The shared pool of configurable computing resources can be rapidly provisioned via virtualization and released with low management effort or service provider interaction, and then scaled accordingly. 
     A cloud-computing model can be composed of various characteristics such as, for example, on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, and so forth. A cloud-computing model can also expose various service models, such as, for example, Software as a Service (“SaaS”), Platform as a Service (“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computing model can also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, and so forth. In this description and in the claims, a “cloud-computing environment” is an environment in which cloud computing is employed. 
       FIG. 11  illustrates a block diagram of exemplary computing device  1100  that may be configured to perform one or more of the processes described above. One will appreciate that server(s)  104 , the client devices  108   a - 108   n , and/or the computing device  900  may comprise one or more computing devices such as computing device  1100 . As shown by  FIG. 11 , computing device  1100  can comprise processor  1102 , memory  1104 , storage device  1106 , I/O interface  1108 , and communication interface  1110 , which may be communicatively coupled by way of communication infrastructure  1112 . While an exemplary computing device  1100  is shown in  FIG. 11 , the components illustrated in  FIG. 11  are not intended to be limiting. Additional or alternative components may be used in other implementations. Furthermore, in certain implementations, computing device  1100  can include fewer components than those shown in  FIG. 11 . Components of computing device  1100  shown in  FIG. 11  will now be described in additional detail. 
     In particular implementations, processor  1102  includes hardware for executing instructions, such as those making up a computer program. As an example and not by way of limitation, to execute instructions, processor  1102  may retrieve (or fetch) the instructions from an internal register, an internal cache, memory  1104 , or storage device  1106  and decode and execute them. In particular implementations, processor  1102  may include one or more internal caches for data, instructions, or addresses. As an example and not by way of limitation, processor  1102  may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in memory  1104  or storage device  1106 . 
     Memory  1104  may be used for storing data, metadata, and programs for execution by the processor(s). Memory  1104  may include one or more of volatile and non-volatile memories, such as Random Access Memory (“RAM”), Read Only Memory (“ROM”), a solid state disk (“SSD”), Flash, Phase Change Memory (“PCM”), or other types of data storage. Memory  1104  may be internal or distributed memory. 
     Storage device  1106  includes storage for storing data or instructions. As an example and not by way of limitation, storage device  1106  can comprise a non-transitory storage medium described above. Storage device  1106  may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storage device  1106  may include removable or non-removable (or fixed) media, where appropriate. Storage device  1106  may be internal or external to computing device  1100 . In particular implementations, storage device  1106  is non-volatile, solid-state memory. In other implementations, Storage device  1106  includes read-only memory (ROM). Where appropriate, this ROM may be mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. 
     I/O interface  1108  allows a user to provide input to, receive output from, and otherwise transfer data to and receive data from computing device  1100 . I/O interface  1108  may include a mouse, a keypad or a keyboard, a touch screen, a camera, an optical scanner, network interface, modem, other known I/O devices or a combination of such I/O interfaces. I/O interface  1108  may include one or more devices for presenting output to a user, including, but not limited to, a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., display drivers), one or more audio speakers, and one or more audio drivers. In certain implementations, I/O interface  1108  is configured to provide graphical data to a display for presentation to a user. The graphical data may be representative of one or more graphical user interfaces and/or any other graphical content as may serve a particular implementation. 
     Communication interface  1110  can include hardware, software, or both. In any event, communication interface  1110  can provide one or more interfaces for communication (such as, for example, packet-based communication) between computing device  1100  and one or more other computing devices or networks. As an example and not by way of limitation, communication interface  1110  may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI. 
     Additionally or alternatively, communication interface  1110  may facilitate communications with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, communication interface  1110  may facilitate communications with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination thereof. 
     Additionally, communication interface  1110  may facilitate communications various communication protocols. Examples of communication protocols that may be used include, but are not limited to, data transmission media, communications devices, Transmission Control Protocol (“TCP”), Internet Protocol (“IP”), File Transfer Protocol (“FTP”), Telnet, Hypertext Transfer Protocol (“HTTP”), Hypertext Transfer Protocol Secure (“HTTPS”), Session Initiation Protocol (“SIP”), Simple Object Access Protocol (“SOAP”), Extensible Mark-up Language (“XML”) and variations thereof, Simple Mail Transfer Protocol (“SMTP”), Real-Time Transport Protocol (“RTP”), User Datagram Protocol (“UDP”), Global System for Mobile Communications (“GSM”) technologies, Code Division Multiple Access (“CDMA”) technologies, Time Division Multiple Access (“TDMA”) technologies, Short Message Service (“SMS”), Multimedia Message Service (“MIMS”), radio frequency (“RF”) signaling technologies, Long Term Evolution (“LTE”) technologies, wireless communication technologies, in-band and out-of-band signaling technologies, and other suitable communications networks and technologies. 
     Communication infrastructure  1112  may include hardware, software, or both that couples components of computing device  1100  to each other. As an example and not by way of limitation, communication infrastructure  1112  may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination thereof. 
       FIG. 12  is a schematic diagram illustrating environment  1500  within which one or more implementations of the team content identification system  102  can be implemented. Team content identification system  1202  may generate, store, manage, receive, and send digital content (such as collaborative content items). For example, team content identification system  1202  may send and receive digital content to and from client devices  1206  by way of network  1204 . In particular, team content identification system  1202  can store and manage a collection of digital content. Team content identification system  1202  can manage the sharing of digital content between computing devices associated with a plurality of users. For instance, team content identification system  1202  can facilitate a user sharing a digital content with another user of team content identification system  1202 . 
     In particular, team content identification system  1202  can manage synchronizing digital content across multiple client devices  1206  associated with one or more users. For example, a user may edit digital content using client device  1206 . The team content identification system  1202  can cause client device  1206  to send the edited digital content to team content identification system  1202 . Team content identification system  1202  then synchronizes the edited digital content on one or more additional computing devices. 
     In addition to synchronizing digital content across multiple devices, one or more implementations of team content identification system  1202  can provide an efficient storage option for users that have large collections of digital content. For example, team content identification system  1202  can store a collection of digital content on team content identification system  1202 , while the client device  1206  only stores reduced-sized versions of the digital content. A user can navigate and browse the reduced-sized versions (e.g., a thumbnail of a digital image) of the digital content on client device  1206 . In particular, one way in which a user can experience digital content is to browse the reduced-sized versions of the digital content on client device  1206 . 
     Another way in which a user can experience digital content is to select a reduced-size version of digital content to request the full- or high-resolution version of digital content from team content identification system  1202 . In particular, upon a user selecting a reduced-sized version of digital content, client device  1206  sends a request to team content identification system  1202  requesting the digital content associated with the reduced-sized version of the digital content. Team content identification system  1202  can respond to the request by sending the digital content to client device  1206 . Client device  1206 , upon receiving the digital content, can then present the digital content to the user. In this way, a user can have access to large collections of digital content while minimizing the amount of resources used on client device  1206 . 
     Client device  1206  may be a desktop computer, a laptop computer, a tablet computer, a personal digital assistant (PDA), an in- or out-of-car navigation system, a handheld device, a smart phone or other cellular or mobile phone, or a mobile gaming device, other mobile device, or other suitable computing devices. Client device  1206  may execute one or more client applications, such as a web browser (e.g., Microsoft Windows Internet Explorer, Mozilla Firefox, Apple Safari, Google Chrome, Opera, etc.) or a native or special-purpose client application (e.g., Dropbox Paper for iPhone or iPad, Dropbox Paper for Android, etc.), to access and view content over network  1204 . 
     Network  1204  may represent a network or collection of networks (such as the Internet, a corporate intranet, a virtual private network (VPN), a local area network (LAN), a wireless local area network (WLAN), a cellular network, a wide area network (WAN), a metropolitan area network (MAN), or a combination of two or more such networks) over which client devices  1206  may access team content identification system  1202 . 
     In the foregoing specification, the present disclosure has been described with reference to specific exemplary implementations thereof. Various implementations and aspects of the present disclosure(s) are described with reference to details discussed herein, and the accompanying drawings illustrate the various implementations. The description above and drawings are illustrative of the disclosure and are not to be construed as limiting the disclosure. Numerous specific details are described to provide a thorough understanding of various implementations of the present disclosure. 
     The present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. The described implementations are to be considered in all respects only as illustrative and not restrictive. For example, the methods described herein may be performed with less or more steps/acts or the steps/acts may be performed in differing orders. Additionally, the steps/acts described herein may be repeated or performed in parallel with one another or in parallel with different instances of the same or similar steps/acts. The scope of the present application is, therefore, indicated by the appended claims rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope. 
     The foregoing specification is described with reference to specific exemplary implementations thereof. Various implementations and aspects of the disclosure are described with reference to details discussed herein, and the accompanying drawings illustrate the various implementations. The description above and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of various implementations. 
     The additional or alternative implementations may be embodied in other specific forms without departing from its spirit or essential characteristics. The described implementations are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.