Abstract: A chatbot system for an interactive platform is disclosed. The chatbot system retrieves a conversation history of one or more conversations between a user and a chatbot from a conversation history datastore and generates one or more summarized memories using the conversation history. One or more moderated memories are generated using the summarized memories. The moderated memories are stored in a memories datastore. A user prompt is received, and a current conversation context is generated from a current conversation between the user and the chatbot. One or more memories are retrieved from the memories datastore using the current conversation context. An augmented prompt is generated using the user prompt and the one or more memories, which is communicated to a generative AI model. A response is received from the generative AI model to the augmented prompt, which is provided to the user.

Abstract: Aspects of the present disclosure involve a system comprising a storage medium storing a program and method for predicting a conversion rate. The program and method provide for receiving, from an advertisement service, a bid to display a first advertisement at a computing device; determining, in response to receiving the bid, a set of features that relate to the first advertisement; providing the set of features to a machine learning model configured to output a predicted conversion rate for the first advertisement, the machine learning model having been trained based on multi-task learning using plural sets of features corresponding to plural second advertisements, the plural sets of features being associated with both click-through conversions and view-through conversions; and determining, based on the output of the machine learning model with respect to the set of features, the predicted conversion rate for the first advertisement.

Abstract: A chatbot system for an interactive platform. The chatbot system receives a prompt from a user and determines an intent of the user using the prompt. If the chatbot system can't determine an intent, the chatbot communicates the prompt as a personality prompt to a generative AI model. If the chatbot system can determine an intent from the prompt, the chatbot system generates an API call to an additional service using the intent. The chatbot system uses values returned by the additional service to generate a hint prompt that is communicated to the generative AI model. The chatbot system receives a response from the generative AI model to the personality prompt or hint prompt and communicates the response to the user.

Abstract: A system for including a chatbot into a group chat session is provided. The system receives a chatbot mention message from a user in the group chat session. The chatbot mention message includes a chatbot prompt for a chatbot. The system generates a prompt using the chatbot mention message and communicates the response as a chatbot response message to each user in the group chat session.

Abstract: Disclosed are systems, methods, and computer-readable storage media to present content on an electronic display. In one aspect, a method includes identifying a first candidate content and a second candidate content for presentation on an electronic display, determining a first probability and a second probability that the first candidate content and the second candidate content respectively will elicit a particular type of input response, determining a first weight and a second weight based on the first probability and the second probability respectively, selecting either the first content or the second content based on the first weight and the second weight; and presenting the selected content on the electronic display.

Abstract: A content request is received from a device of a user. A plurality of candidate content items is identified. Each candidate content item has a bid value. A relevancy value is automatically generated for each candidate content item. The relevancy value indicates whether the candidate content item is likely to be skipped by the user. For each candidate content item, a combined value is automatically generated by adjusting the bid value using the relevancy value generated for the candidate content item. One or more candidate content items are automatically selected based on the combined value generated for each of the one or more candidate content items. The one or more selected candidate content items are automatically integrated into at least one placeholder area among one or more pre-selected content items as part of the aggregated content. The aggregated content is presented on the device of the user.

Abstract: A chatbot system for filtering conversation content. A chatbot system receives, from a client system, a prompt of a user during an interactive session. The chatbot system filters the prompt of the user based on a set of platform policies and generates a response based on the filtering of the prompt of the user, and communicates the response to the client system.

Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

Abstract: A machine learning engine identifies training data that includes historical user data and historical content data. A machine learning classifier is trained on the training data to generate a relevancy value for each of a plurality of given content items associated with a given user. The relevancy value for each given content item is indicative of a likelihood that the given user will perform a first user device input action and of a likelihood that the given user will perform a second user device input action, in response to being presented with the given content item. The machine learning classifier receives a plurality of candidate content items associated with a first user. The machine learning classifier generates a relevancy value for each candidate content item. At least one of the candidate content items is identified for inclusion in a first content collection based on the generated relevancy values.

Abstract: Disclosed are systems, methods, and computer-readable storage media to present content on an electronic display. In one aspect, a method includes identifying a first candidate content and a second candidate content for presentation on an electronic display, determining a first probability and a second probability that the first candidate content and the second candidate content respectively will elicit a particular type of input response, determining a first weight and a second weight based on the first probability and the second probability respectively, selecting either the first content or the second content based on the first weight and the second weight; and presenting the selected content on the electronic display.

Abstract: A surgical assembly and system are disclosed. The system comprises a first generator for generating a first signal for use in generating an electrical field proximate a site of a surgical procedure for removing particles suspended proximate the surgical site. The assembly further comprises a surgical tool comprising a tool-piece communicatively couplable with the first generator and a second generator, the second generator being arranged to generate a second signal for use in cutting or cauterizing biological tissue of the patient during the surgical procedure, the assembly further comprising a switching assembly for switching the application of the first signal and second signal to the tool-piece and a controller for controlling the application of the first signal and second signal to the tool-piece.

Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

Abstract: Machine learning architectures may predict the likelihood of interaction by users with content items that are accessible using a client application. The machine learning architectures may include one or more feature interaction layers that are coupled with one or more extraction layers. Content items may be selected to provide to users of the client application based on probabilities of users performing one or more actions with respect to the content items, where the probabilities for each action are determined by the machine learning architectures.

Abstract: Watersport board fins with fin retention systems and associated watersport boards and methods. A fin includes a hydrodynamic blade and a fin base extending from the hydrodynamic blade. The hydrodynamic blade defines a fin plane and includes a leading edge, a trailing edge, and a foil surface. The fin further includes a fin retention system with a selective retention system that includes a retainer and an actuator coupled to the retainer via a pivot shaft. The actuator forms a portion of the hydrodynamic blade when the selective retention system is in a locked configuration and is configured to rotate away from the fin plane when the selective retention system transitions from the locked configuration to an unlocked configuration. The retainer is configured to rotate away from the fin plane when the selective retention system transitions from the unlocked configuration to the locked configuration.

Abstract: A machine learning engine identifies training data that includes historical user data and historical content data. A machine learning classifier is trained on the training data to generate a relevancy value for each of a plurality of given content items associated with a given user. The relevancy value for each given content item is indicative of a likelihood that the given user will perform a first user device input action and of a likelihood that the given user will perform a second user device input action, in response to being presented with the given content item. The machine learning classifier receives a plurality of candidate content items associated with a first user. The machine learning classifier generates a relevancy value for each candidate content item. At least one of the candidate content items is identified for inclusion in a first content collection based on the generated relevancy values.

Abstract: A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.

Abstract: Disclosed are systems, methods, and computer-readable storage media to present content on an electronic display. In one aspect, a method includes identifying a first candidate content and a second candidate content for presentation on an electronic display, determining a first probability and a second probability that the first candidate content and the second candidate content respectively will elicit a particular type of input response, determining a first weight and a second weight based on the first probability and the second probability respectively, selecting either the first content or the second content based on the first weight and the second weight; and presenting the selected content on the electronic display.

Abstract: Disclosed are systems, methods, and computer-readable storage media to present content on an electronic display. In one aspect, a method includes identifying a first candidate content and a second candidate content for presentation on an electronic display, determining a first probability and a second probability that the first candidate content and the second candidate content respectively will elicit a particular type of input response, determining a first weight and a second weight based on the first probability and the second probability respectively, selecting either the first content or the second content based on the first weight and the second weight; and presenting the selected content on the electronic display.

Abstract: A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.

Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.