Abstract: Self-consumable portions generation techniques from a digital document are described. The self-consumable portions are generated based on a determination of an amount of resources available at a receiver device that is to receive the digital document. Examples of the resources include an amount of memory resources, processing resources, and/or network resources associated with the receiver device. The self-consumable portions, once generated, are separately renderable at the receiver device.

Abstract: Aspects of the preset document relate to social or group-based presentation of cryptographic objects. Cryptographic objects linked to members of a group are visually represented to each group member, such as in a “binder”-like presentation. Further, group activity related to the cryptographic objects is also presented to each group member. Specific object events of the group activity are detected based on searching an immutable cryptographic ledger. The object events are categorized according to value and volume of the cryptographic objects involved in the object events. With this event data (and event data for other related object events), event signifiers are assigned to object events and presented to group members. Example event signifiers include audio, persistent visuals, and/or dynamic visuals that are connected to visual representations of the cryptographic objects involved in the object events.

Abstract: Disclosed herein is a social media platform profile identification and social discovery feature. Disclosed social media networks enable introduction of users that may not otherwise know one another based on commonality between those users. Social media profiles are identified by digital objects instead of or in addition to more traditional indexing methods such as real names or screen names. Social discovery on a social network is performed via matching to similar behavior profiles in activity monitored by a block explorer. Machine learning models categorize behavior patterns observed by the block explorer into a machine recognized glossary. Social networks further recommend actions by users based on the monitored online behaviors of social connections.

Abstract: Disclosed herein is a time-based leaderboard that ranks users based on a length of time each user has controlled or possessed a given digital object. The leaderboard includes customization options for purposes of user identification and identity connected to social network objects. The leaderboard further uses a staking feature where users provide their digital objects to universal wallets to hold for a predetermined period based on smart contract limitations. Staking improves leaderboard position. The leaderboard further enables expression and displayed of staked digital objects despite the user no longer having actual possession of the digital object. A digital object generator builds unique digital objects based on the user specific input. The unique digital objects are part of a graphic presentation to users.

Abstract: Accordingly, systems and methods for purchasing, securing, and maintaining emoji sequences is presented herein. In one or more examples, an enterprise or user can generate or purchase an emoji sequence, which can include a sequence of emojis that can be used to uniquely identify an enterprise. Once the emoji sequence has been acquired, in one or more examples, the emoji sequence can have a digital token associated with it, wherein the digital token can be derived from the emoji sequence string, in a manner that is unique and deterministic.

Abstract: Disclosed herein are models used in digital object generation systems and methods. The models receive input data recognize elements of the received data for use in digital object generation. The models learn to recognize the data and features thereof by comparing normalized vectors of the received data and parameters. The models learn from extracted features of the input data or from the input data directly. The models identify features of the data for use in digital object generation based on distinctiveness, interoperability, or exclusivity features in the received data. The models provide an output for digital object generation that includes an amalgamation of features, recognized or extracted from the received data.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: Disclosed concepts relate to presenting interconnected animations in an object display interface that indicate dynamic events for related objects. A platform presents an interface that includes tiles for objects that belong to object sets (e.g., a non-fungible token (NFT) collection, a set of numbered items, a limited sneaker line). Each tile includes information individually specific to a corresponding object. A platform monitors for event data that results in a change to an aggregate parameter (e.g., a floor price, an event rate) for a given object set or that specifies one or more individual events for objects belonging to the given object set. Based on detecting the event data, the platform dynamically presents animations on certain tiles corresponding to objects of the given object set, and the animations indicate the change to the aggregate parameter or indicate the individual events. Contextual and multi-level information is thus conveyed without significant visual clutter.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique digital objects based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: Disclosed herein is a social media platform profile identification and social discovery feature. Disclosed social media networks enable introduction of users that may not otherwise know one another based on commonality between those users. Social media profiles are identified by digital objects instead of or in addition to more traditional indexing methods such as real names or screen names. Social discovery on a social network is performed via matching to similar behavior profiles in activity monitored by a block explorer. Machine learning models categorize behavior patterns observed by the block explorer into a machine recognized glossary. Social networks further recommend actions by users based on the monitored online behaviors of social connections.

Abstract: Methods and systems for generating a non-fungible token (NFT) based on an input of an existing NFT. The methods and systems include receiving NFT inputs into a one-way function; and generating an NFT output based on the NFT inputs such that the inputs cannot be identified by inverting the one-way function with the NFT output. The methods and systems provide for extracting features of the input via a few-shot model and identifying features from the extracted features for use in the generation of the NFT output. The identification is based upon distinctiveness, interoperability and/or exclusivity of the extracted features. The identified features are amalgamated via an artistic model in which the amalgamation includes matching elements of the identified features. The methods and systems also provide for encoding the matching elements to a cryptographic token connected to a smart contract that is associated with the NFT output.

Abstract: Disclosed herein is a social media platform profile identification and social discovery feature. Disclosed social media networks enable introduction of users that may not otherwise know one another based on commonality between those users. Social media profiles are identified by digital objects instead of or in addition to more traditional indexing methods such as real names or screen names. Social discovery on a social network is performed via matching to similar behavior profiles in activity monitored by a block explorer. Machine learning models categorize behavior patterns observed by the block explorer into a machine recognized glossary. Social networks further recommend actions by users based on the monitored online behaviors of social connections.

Abstract: Disclosed herein is digital object generator that makes uses a one-way function to generate unique extended reality environments based on the user specific input. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: Disclosed herein are methods, systems and non-transitory computer-readable medium for connecting users in a social networking platform. The methods include generating unique digital objects for digital inputs of each user and determining a graphical commonality between two or more unique digital objects to form a social connection between at least two users in a computer construct.

Abstract: A digital object generation platform generates a digital object associated with an event. Ownership of the digital object is assigned to a first cryptographic wallet based on attendance of a first user associated with the first cryptographic wallet at the event. When the platform detects a transfer of ownership of the digital object associated with the event to a second cryptographic wallet, the platform performs an analysis to determine attendance of a second user associated with the second cryptographic wallet at the event. The digital object is deactivated in response to the analysis returning a result indicating the second user did not attend the event.

Abstract: Disclosed herein is digital object generator that makes uses biometric data to generate unique digital objects based on the user specific input. In some embodiments, the biometric data is captures in real-time such as gestures or heart rate captures by wearable devices or mobile device cameras. Also, disclosed herein is a system and a method establishing a set of input types including any combination of cryptographic protocols, biometric data, image data, or multimedia data, and parameters handled by a digital object generator. Features of the input are first extracted via a few-shot convolutional neural network model, then evaluated weight and integrated fit. The resulting digital object includes a user decipherable output such as a visual representation, an audio representation, or a multimedia representation that includes recognizable elements from the user specific input.

Abstract: Disclosed herein is a social media platform profile identification feature. Social media profiles are identified by digital objects instead of or in addition to more traditional indexing methods such as real names or screen names. Associating a digital object as a Social Media avatar, or as a profile detail enables the social media platform to index the digital object and enable users to search for the subject user with the digital object or descriptions thereof. A digital object generator builds unique digital objects based on the user specific input. The unique digital objects are part of a graphic presentation to users.

Abstract: A digital object generation platform processes a request to generate a digital object associated with an event according to criteria associated with the event for generating the digital object. If the criteria are satisfied, the platform generates the digital object based on digital media associated with the event. A user decipherable representation of the digital object is encoded to a smart contract.

Abstract: Disclosed herein is a social media network interface feature that ranks/sorts users based on a past interaction with creators of cryptographic objects. The cryptographic objects are managed by a disparate set of platforms with varied rules and customs with regard to original creators. While initial movement of a cryptographic object is a controlled event, where the creator of the object is enabled to choose the relevant platform used, subsequent movement does not typically have the same controls. Based on the ranking/sorting the users are enabled to retroactively address past interaction with the creators of the cryptographic objects. A digital object generator builds unique digital objects based on the user specific input. The unique digital objects are part of a graphic presentation to users.

Abstract: Embodiments disclosed herein relate to user-centric security classifications of cryptographic token sets that represent a prediction of whether a token set is malicious. A user-centric security classification of a token set is generated according to user behaviors that characterize users involved with the token set, including users holding tokens of the token set and users creating tokens of the token set. A model is trained to recognize user behaviors of certain users whose involvement with the token set increase the likelihood that the token set is non-malicious and secure. Such user behaviors that the model is trained to recognize include high time-rank behaviors, or holding onto cryptographic tokens for extended lengths of time. Upon generation of a user-centric security classification for a token set, graphical displays of tokens or of the token set are updated to reflect the user-centric security classification.