In an example, a query for a generative artificial intelligence (AI) tool may be received from a client device. A database including query autosuggestion profiles may be accessed to identify a set of query autosuggestion profiles matching the query. A set of query autosuggestions may be generated based upon the query and the set of query autosuggestion profiles. An autosuggestion interface indicative of the set of query autosuggestions may be provided on the client device. In response to receiving a selection of a first query autosuggestion of the set of query autosuggestions via the autosuggestion interface, the generative AI tool may be used to generate a first content item based upon the first query autosuggestion. The first content item may be provided for presentation on the client device.
One or more computing devices, systems, and/or methods are provided. In an example, an email addressed to an email address associated with an email account is received. A request to display the email is received from a client device associated with the email account. In response to receiving the request to display the email, the email and a social interaction interface are displayed via an email interface on the client device. The social interaction interface includes one or more social interaction features. The one or more social interaction features include a commenting feature, a feedback feature and/or a chat feature.
H04L 51/42 - Aspects liés aux boîtes aux lettres, p. ex. synchronisation des boîtes aux lettres
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
3.
COMBINING VECTORS OUTPUT BY MULTIPLE DIFFERENT MECHANISMS FOR CONTENT ITEM RETRIEVAL
One or more systems and/or methods for combining vectors output by multiple different mechanisms for content item retrieval are provided. An image encoder may output a first set of vectors generated by an image model using an input image as input. A text encoder may output a second set of vectors generated by a text model using input text as input. A vector combination module may combine the first set of vectors and the second set of vectors to create a vector output. A weight is applied to the vector output to create a weighted output. An output vector is generated based upon a combination of the first set of vectors, the second set of vectors, and the weighted output. The output vector is used to query a catalog to identify a content item related to the input image and the input text.
Disclosed frameworks for generating an image including a salient object and a staged background include extracting a salient object from a source image and applying a generative model to the salient object to generate the image. According to some embodiments, extracting a salient object from a source image involves using salient object detection method to identify the relevant portions of the source image corresponding to the salient object. In some embodiments, the generative model is a generative adversarial network trained using a domain relevant dataset.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p. ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]Caractéristiques régionales saillantes
G06V 10/88 - Reconnaissance d’images ou de vidéos utilisant des moyens optiques, p. ex. filtres de référence, masques holographiques, filtres de domaine de fréquence ou filtres de domaine spatial
5.
METHOD AND SYSTEM FOR AUTOMATIC DETECTION AND PREVENTION OF QUALITY ISSUES IN ONLINE EXPERIMENTS
The present teaching relates to managing online experiments. In one example, a plurality of experiment layers is created with respect to a plurality of online users. Each experiment layer includes at least one experiment each of which includes one or more buckets associated with respective features to be experimented on. Each of the plurality of online users is assigned to a corresponding bucket in each experiment layer, such that the user is simultaneously associated with multiple experiments in different layers. User event data related to the plurality of experiment layers are collected from the plurality of online users. One or more contaminated buckets are automatically detected based on the user event data.
In an example, sets of event information associated with events may be identified. The events may include conversion events. The conversion events may comprise attributed and un-attributed conversion events, as well as aggregated conversion events. Machine learning model training may be performed, using a training set of attributable and un-attributable conversion events and a privacy bias in connection with the un-attributable conversion events, to generate a first machine learning model. Further machine learning model training may be performed, using the aggregated conversion events to generate a second machine learning model. Conversion probabilities associated with content items may be determined using the first and second machine learning models. Attributable content items may be selected for presentation via a client device based upon the conversion probabilities.
In an example, a prompt for a generative AI tool may be received from a client device. A first prompt modification profile matching the prompt may be identified. The prompt may be modified based upon the first prompt modification profile to generate an updated prompt. Using the generative AI tool, a first content item may be generated based upon the updated prompt. A first content modification profile matching at least a portion of the first content item may be identified. The first content item may be modified based upon the first content modification profile to generate an updated content item. The updated content item may be provided for presentation on the client device.
One or more systems and/or methods that provide configurable ad spaces based on configurable content position groups, rather than single content positions, and bid requests containing configuration information describing the position groups, as well as auctions configured to take advantage of the configurability provided by the use of position groups. In an example, a content service platform may receive position group requests and provide auctions mediated by one or more mediation protocols tailored to positions groups and optionally specified by the bid request.
In an example, sets of event information associated with events may be identified. The events may include conversion events having affinity between conversion actions and associated ad clicks and outlier conversion events. Outlier bias associated with the outlier conversion events may be determined for content items. Machine learning model training may be performed, using only the sets of event information associated with the conversion events having affinity between conversion actions and associated ad clicks, to generate a machine learning model. Conversion probabilities associated with content items may be determined using the machine learning model and the calculated biases. A content item may be selected for presentation via a client device based upon the conversion probabilities.
In an example, responsive to receiving a first content request, a first bid request indicative of a first bid floor may be generated. The first bid request may be transmitted to a first bidder. A first bid response may be received. The first bid response may indicate a first bid for submission to a first auction. Responsive to receiving a second content request, a second bid request indicating a second bid floor different than the first bid floor may be generated. The second bid request may be transmitted to the first bidder. A second bid response may be received. The second bid response may indicate a second bid for submission to a second auction. A bidder profile associated with the first bidder may be determined based upon the first bid floor, the second bid floor, the first bid, the second bid, a first auction result and/or a second auction result.
One or more computing devices, systems, and/or methods for determining whether requests for content are fraudulent are provided. A request for content may be received from a first device. A first user profile associated with the first device may be identified. The first user profile may comprise activity information associated with the first device, demographic information associated with the first device and/or interest information associated with the first device. A user profile database may be analyzed to identify a set of user profiles similar to the first user profile. A relevance score associated with the request for content may be generated based upon the resource, the set of user profiles and/or the first user profile. The relevance score may be compared with a threshold relevance to determine whether the request for content is fraudulent.
The disclosed systems and methods provide a novel framework that enables cost-effective, accurate and scalable detection and recognition of key events in sporting or live events. The framework functions by creating a domain-specific video dataset with frame level annotations (i.e., deep domain datasets) and then training a lightweight camera view classifier to detect camera views for a given video. The disclosed framework uses pre-trained pose estimation and panoptic segmentation models along with geometric rules as labeling functions to define scene types and derive frame level classification training data. According to some embodiments, disclosed frameworks may be used to identify key persons or events, select a thumbnail corresponding to a key person or event, generate personalized highlights to enhance user experience and social media promotions for a team, sport or players, and predict and select the best camera view sequence for automatic highlights generation.
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques
13.
SYSTEM AND METHOD FOR MINING DATA USING GENERATIVE AI
One or more computing devices, systems, and/or methods that provide an interactive reporting system for analyzing and reporting on campaign related data and mining insights using generative AI are provided. In an example, a user interface is configured to receive a natural language request and display a natural language response. A serving system is coupled to a data store housing campaign data sets and comprises a dedicated campaign large language model (LLM) configured to convert the natural language request to a suitable query capable of being run against the campaign data sets and to run the query against the data sets, to receive output from the query, to encode relevant query output together with the natural language request and/or the suitable query into an encoded output, and to decode the encoded output to generate a natural language response to the natural language request.
The present teaching relates to a method and system for selecting content. Upon receiving a request with an indication of a first piece of content for selecting one or more pieces of second content to be presented together with the first piece of content, a plurality of pieces of candidate second content are identified. At least one sentiment feature associated with the first piece of content is determined and the one or more pieces of second content are selected from the plurality of pieces of candidate second content based on the at least one sentiment feature of the first piece of content so that the one or more pieces of second content are emotionally compatible with the first piece of content. The one or more pieces of second content are sent in response to the request.
One or more computing devices, systems, and/or methods are provided. In an example, a first budget is determined for a first instance of a first time period. A pacing system, that is configured to pace spending over a time period based upon a budget, is controlled to spend on transmission of content to a first plurality of client devices using the first budget during the first instance of the first time period. Feedback indicating a rate of spending associated with the transmission of content to the first plurality of client devices is received. Using the feedback, the first budget is updated to determine a second budget for a second instance of the first time period. The pacing system is controlled to spend on transmission of content to a second plurality of client devices using the second budget during the second instance of the first time period.
Systems and methods are for identifying online advertisements to display to a user based on historical user data collected across the user's use of a plurality of Internet devices. One method includes receiving, over a network, a request for an advertisement to display at a first device of the user, the request including a unique identifier stored on the user's first device; accessing, in a database, a demographic or browsing history information generated from the user's use of a second device, the demographic or browsing history information being stored in the database in relation to the unique identifier; and identifying, based on the demographic or browsing history information, an advertisement to display at the user's first device. The demographic or browsing history information is synchronized based on the unique identifier being stored on the user's first device and the user's second device.
The present disclosure relates to systems and methods customizing electronic communications. A future event associated with a first user may be determined, and a second user that is associated with the first user may be identified. A plurality of communications involving the first user and the second user may be analyzed. A selection rule may be applied based on the analyzed plurality of communications, the selection rule identifying content from the database. Content from the database may be selected based on the application of the selection rule. An electronic message may be provided to the first user identifying the future event, and the selected content may be provided to the first user.
One or more systems and/or methods for providing conversion prospecting for dynamic content recommendation are provided. A model is trained, using positive events and negative events, to predict conversion-given-click probabilities of users and products. The model is utilized to generate a prediction of a conversion-given-click probability that a user will perform an action in relation to a product. The model is used to generate a bid for the user and the product based upon the conversion-given-click probability, a content provider bid for the product, and a target cost per action. The bid is used to determine whether the product is to compete in an auction hosted by a content serving platform that selects and transmits content items of products to devices for display to users.
Systems and methods are disclosed for protecting consumer privacy in an online advertising environment. A request may be received from a browser for a webpage along with a unique browser identifier. The browser may be provided a first portion of the webpage that is locally available. The unique browser identifier may be provided to at least one advertising entity, wherein the advertising entity determines an advertisement based, at least in part, on the unique browser identifier. The advertisement may be received from the advertising entity, and provided to the browser as a second portion of the webpage.
One or more systems and/or methods for providing trending prospecting for dynamic content recommendation are provided. A model is trained to predict eligibility scores of users to content items based upon popularity of the content items and similarities of the users with other users that have engaged with the content items. As part of training, positive and negative events are input into the model. For each user and content item pair, the model generates an eligibility score corresponding to a ratio between a number of users that are similar to a user and have engaged with the content item to a number of users that are similar to the user and are part of a user population represented by the positive events and the negative events. The eligibility scores are used to generate and train the model. The model is used to select and provide content items to computing devices.
Systems, apparatuses, and methods are provided for determining a bid value for placing an advertisement onto advertising space available through an electronic marketplace. A method is used for calculating the option value of maintaining the advertisement in the advertising space during one or more periods of time. The option value may be based on expected profits and the estimated future value of maintaining the advertisement. The option value may then be used to calculate the bid price for placing the advertisement.
Systems and methods are disclosed for model evaluation and performance-based selection. One method comprises receiving, by one or more processors, a model and corresponding configuration information, creating one or more model variations based on the model and corresponding configuration information, determining a model variation subset based on one or more evaluation scores of each of the one or more model variations, omitting the one or more model variations not included in the model variation subset, initiating one or more new model variations based on the model variation subset, determining a best model of the one or more new model variations based on one or more new model evaluation scores for each of the one or more new model variations, and omitting the one or more new model variations except the best model.
Systems, methods, and computer-readable media are disclosed for augmenting real-time bidding data with proprietary data. One method includes: receiving, at a server over an electronic communications network from a real-time impression bidder, a bid request or a request to augment a bid request with proprietary data; accessing, by the server from an internal database, proprietary data of a data augmenting service based on a user identifier of the bid request; determining, by the server, proprietary data to include in an augmented bid request based on at least one of the received bid request and the user identifier; formatting, by the server, the augmented bid request into a standardized, augmented bid request; and transmitting, by the server over the electronic communications network, the standardized, augmented bid request to the real-time impression bidder.
Systems and methods are disclosed for determining an estimate of available user impressions on a network, comprising receiving a request for an estimate of available user impressions for viewing one or more media elements on a network, the request comprising one or more viewer demographic group limitations. A request may be received to include deterministic users and probabilistic users in the estimate of available user impressions. A number of deterministic users may be determined based on query results from a deterministic user data set. A number of probabilistic users may be determined based on query results from a probabilistic user data set, and the estimate of available user impressions may be determined based on the number of deterministic users and the number of probabilistic users.
Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework for compiling, updating and dynamically managing a confidence graph for a user that leads to generation of a scored interest profile for the user that content providers can utilize as a basis for disseminating their proprietary digital content. The disclosed confidence graph provides a scored interest profile for each user that is based on authenticated user data derived from an inbox of the user. The confidence graph is not only derived from authenticated data, but is also dynamic and evolves simultaneously with changing user interests. Thus, digital content is selected and transmitted to users based on the current, real-time digital data reflecting their current interests as reflected by their inbox activity.
G06F 16/38 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06F 16/31 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/335 - Filtrage basé sur des données supplémentaires, p. ex. sur des profils d’utilisateurs ou de groupes
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
Systems and methods are provided for determining a quantity of network location visitors that are likely generated or encouraged by specific offline events. A corresponding number of leads may then be attributed to and associated with those specific events. Ongoing conversion activity of those visitors may be tracked and associated with the offline events. Conversions of those visitors may be attributed entirely or partially to one or more specific offline events. The effectiveness of each offline may then be evaluated based on aggregate lead and conversion information.
The present teaching relates to a fraud detecting system and method for providing protection against fraudulent advertisement requests. Upon receiving a request for an advertisement, the system extracts an identifier, associated with a source from which the request originates, included in the request. The system determines whether the extracted identifier is included in a list of designated identifiers, and when the identifier is included in the list, the system denies the request for the advertisement. When the identifier is not included in the list of designated identifiers, the system provides the advertisement in response to the request, and extracts a set of features from the request and other requests that originate from the source to determine whether the identifier associated with the source is to be included in the list of designated identifiers based on the set of features in accordance with one or more models.
Systems and methods are disclosed for traffic filtration by content providers. One method includes receiving a content request from a device of a user; determining whether one or more container tags are associated with requested content; determining, prior to responding to the content request, whether the content request is by a user based on the content request and the one or more container tags; generating, prior to responding to the content request, an ad request based on the content request and the one or more container tags; determining, prior to responding to the content request, an ad request recipient based on the generated ad request and the one or more container tags; transmitting the ad request to the determined ad request recipient; and transmitting, over the electronic network to the device, a response to the content request when the content request is determined to be by a user.
In an example, a first content item is provided for display on a first client device. A first feedback signal is received. The first feedback signal is indicative of one or more first user reactions to display of the first content item on the first client device. The first content item is provided for display on a second client device. A second feedback signal is received. A first popularity score associated with the first content item is determined based upon the first feedback signal and the second feedback signal. A first popularity label is assigned to the first content item based upon the first popularity score. An enhanced content presentation interface including the first content item is generated based upon the first popularity label assigned to the first content item. The enhanced content presentation interface including the first content item is presented on a third client device.
H04N 21/431 - Génération d'interfaces visuellesRendu de contenu ou données additionnelles
H04N 21/239 - Interfaçage de la voie montante du réseau de transmission, p. ex. établissement de priorité des requêtes de clients
H04N 21/2668 - Création d'un canal pour un groupe dédié d'utilisateurs finaux, p. ex. en insérant des publicités ciblées dans un flux vidéo en fonction des profils des utilisateurs finaux
H04N 21/442 - Surveillance de procédés ou de ressources, p. ex. détection de la défaillance d'un dispositif d'enregistrement, surveillance de la bande passante sur la voie descendante, du nombre de visualisations d'un film, de l'espace de stockage disponible dans le disque dur interne
30.
COMPUTERIZED SYSTEMS AND METHODS FOR GROUP IDENTIFICATION AND CONTENT DELIVERY
Systems, methods, and computer-readable media are provided for group identification and content delivery. In accordance with one implementation, a computer-implemented method is provided that includes operations performed by at least one processor. The operations of the method include generating, based on identification data, an identification profile for each member of a set of members, the set of members being associated with a viewing group. The set of members may comprise all of the members of the viewing group or any subset of the members of the viewing group. The operations also include determining an individual score for each member of the set of members for each media file of a plurality of available media files based on the corresponding identification profile. Additionally, the operations include determining a group score for each of the plurality of available media files based on the corresponding individual score of each member.
Disclosed are systems and methods for suppressing audio leakage in watch-together sessions. In an embodiment, a device is provided comprising a speaker, a microphone, a media player configured to provide a first media content audio, and a peer to peer (“P2P”) communications engine including a local loopback module and a conferencing module. The local loopback module configured to receive the first media content audio, provide a second media content audio, and amplify, using the speaker, the second media content audio, and the conferencing module configured to capture, using the microphone, captured conference audio, the captured conference audio including the second media content audio, and transmit to a modified captured conference audio; wherein the P2P communications engine is configured to generate the modified captured conference audio by suppressing the second content audio from the captured conference audio.
One or more computing devices, systems, and/or methods are provided. In an example, a first bucket associated with a first profile and/or a second bucket associated with a second profile are configured. First processes of a first evaluation period may be assigned to the first bucket. The first processes may be performed according to the first profile associated with the first bucket. Second processes of the first evaluation period may be assigned to the second bucket. The second processes may be performed according to the second profile associated with the second bucket. Evaluation metrics associated with the first bucket and the second bucket may be determined based upon the first processes and the second processes. Based upon the evaluation metrics, the first bucket may be selected to be a production bucket during a second evaluation period following the first evaluation period.
Systems and methods are disclosed for online distribution of content by receiving, from a user's mobile device, a request for a web page hosted by a publisher's CMS; applying a rules engine to analyze a received URL according to a set of rules identifying one or more website types and/or referrers; if the received URL satisfies the rules engine, redirecting the received request to a syndication server system hosted within a global CDN; adding a URL of the web page to a missing content queue and redirecting the request to the publisher's CMS if the CDN syndication server does not contain a suitable mobile-formatted version of the web page; and delivering a package of binary compressed content of the web page to a stub page cached at the user's mobile device by the CDN syndication server, using recirculation and monetization components chosen by the publisher.
H04L 67/1087 - Réseaux de pairs [P2P] en utilisant les aspects inter-fonctionnels d’établissement de réseau
H04L 67/02 - Protocoles basés sur la technologie du Web, p. ex. protocole de transfert hypertexte [HTTP]
H04L 67/289 - Traitement intermédiaire fonctionnellement situé à proximité de l'application consommatrice de données, p. ex. dans la même machine, dans le même domicile ou dans le même sous-réseau
H04L 67/563 - Redirection de flux de réseau de données
H04L 67/565 - Conversion ou adaptation du format ou du contenu d'applications
H04L 67/5681 - Pré-extraction ou pré-livraison de données en fonction des caractéristiques du réseau
H04L 67/5682 - Politiques ou règles de mise à jour, de suppression ou de remplacement des données stockées
H04L 67/5683 - Stockage des données fournies par les terminaux des utilisateurs, c.-à-d. mise en antémémoire inversée
H04L 69/04 - Protocoles de compression de données, p. ex. ROHC
H04M 1/72445 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens de soutien local des applications accroissant la fonctionnalité pour donner accès à des applications de navigateur Internet
One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A request for content associated with a client device may be received. Bid values and/or click probabilities associated with content items may be determined. A probability of receiving a negative signal associated with a content item of the content items from the client device responsive to presenting the content item via the client device may be determined based upon a user profile associated with the client device. A content item score, of content item scores associated with the content items, may be generated based upon the probability, a click probability and/or a bid value associated with the content item. The content item may be selected from the content items for presentation via the client device based upon the content item scores. The content item may be transmitted to the client device.
The example embodiments are directed toward improvements in document classification. In an embodiment, a method is disclosed comprising generating a set of sentences based on a document; predicting a set of labels for each sentence using a multi-label classifier, the multi-label classifier including a self-attended contextual word embedding backbone layer, a bank of trainable unigram convolutions, a bank of trainable bigram convolutions, and a fully connected layer the multi-label classifier trained using a weakly labeled data set; and labeling the document based on the set of labels. The various embodiments can target multiple use cases such as identifying related entities, trending related entities, creating ephemeral timeline of entities, and others using a single solution. Further, the various embodiments provide a weakly supervised framework to train a model when a labeled golden set does not contain a sufficient number of examples.
One or more computing devices, systems, and/or methods for generating and providing recommendations of products are provided. For example, content is extracted from a message sent to a user. The content is evaluated to identify a product identifier corresponding to a product title of a product. If the product identifier is a truncated version of the product title, then a database of product titles and frequencies of occurrence of the product titles is used to complete the product title. A model is used to infer a product category for the product title. Matching scores are assigned to products within a product category based upon weighted attributes. A recommendation is provided to the user for a product having a matching score greater than a matching threshold.
One or more computing devices, systems, and/or methods are provided. In an example, an email addressed to an email address associated with an email account is received. A request to display the email is received from a client device associated with the email account. In response to receiving the request to display the email, the email and a social interaction interface are displayed via an email interface on the client device. The social interaction interface includes one or more social interaction features. The one or more social interaction features include a commenting feature, a feedback feature and/or a chat feature.
G06F 17/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
H04L 51/42 - Aspects liés aux boîtes aux lettres, p. ex. synchronisation des boîtes aux lettres
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.
Systems and methods are disclosed for accessing first party Internet cookies. One method includes receiving, over an electronic network, a request for an electronic advertisement or an Internet cookie, wherein the request is received in response to a user requesting content from a subdomain of an advertising entity domain; and sending, to a device associated with the user, an Internet cookie having the subdomain of the advertising entity domain. The method further includes receiving, from the device associated with the user, a request for content from the subdomain of the advertising entity domain, wherein the request is received in response to the user requesting content from a domain of an online publisher; and accessing data stored in the Internet cookie sent to the device associated with the user.
The disclosed systems and methods provide a novel framework that provides mechanisms for enabling message senders to dictate, control and/or create dynamic immersive content consumption experiences for recipients of their messages. The disclosed framework provides message senders with previously non-existent functionality to control the experience and environment within which their messages are consumed. Conventional systems provide recipients with capabilities to consume messages and/or supplemental content; however, these capabilities are driven and controlled by the hosting messaging platform and/or the third party entity that is availed opportunities to provide supplemental content. The disclosed framework prevents such experiences by providing capabilities to the message sender that involves control not only over the experience of the sender's messages but also control over the experience of the environment in which the messages are consumed by their recipients.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
The disclosed systems and methods provide a cloud-based framework for the creation and/or enhancement of high quality media content for dissemination over a computerized network. The disclosed framework is configured to operate in accordance with at least one of a content creation, content enhancement and/or rendering template. Such templates can function as rules engines that call specific types of ML or AI algorithms or routines that modify the input data according to the type of template being used. Thus, the disclosed framework can be utilized for creating new high-quality media, improving upon existing media and/or reformatting media for rendering upon dissemination.
In an example, sets of event information associated with events may be identified. The events may include intentional click events, accidental click events and/or skip events. Accidental click probabilities associated with the accidental click events and/or the skip events may be determined. Machine learning model training may be performed, using the sets of event information associated with the events and labels associated with the events, to generate a first machine learning model. The labels may include second labels associated with the intentional click events and/or third labels associated with the accidental click events and/or the skip events. The second labels may correspond to an intentional click classification. The third labels may be based upon the accidental click probabilities. Click probabilities associated with content items may be determined using the first machine learning model. A content item may be selected for presentation via a client device based upon the click probabilities.
A computer-implemented method provides features of a point based subscription plan to a user of a product. The method comprises providing actions to a user device of the user, each action having an associated point value, receiving an indication that the user has completed at least one action, determining a number of earned points by the user based on the at least one completed action and the corresponding associated point value, storing the number of earned points by the user in a user profile, determining whether a total number of earned points is equal to or greater than a threshold number of points, and providing the features of the point-based subscription plan to the user, upon determining that the total number of earned points is equal to or greater than the threshold number of points.
One or more computing devices, systems, and/or methods for determining whether requests for content are fraudulent are provided. A request for content may be received from a first device. A first user profile associated with the first device may be identified. The first user profile may comprise activity information associated with the first device, demographic information associated with the first device and/or interest information associated with the first device. A user profile database may be analyzed to identify a set of user profiles similar to the first user profile. A relevance score associated with the request for content may be generated based upon the resource, the set of user profiles and/or the first user profile. The relevance score may be compared with a threshold relevance to determine whether the request for content is fraudulent.
One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A first content item may be transmitted to a first set of client devices. A first request for content associated with a first client device of a second set of client devices may be received. A first bid value associated with a second content item may be selected. The first bid value may be modified based upon a second bid value associated with the first content item to generate a third bid value associated with the second content item. The second content item may be selected from a first plurality of content items for presentation via the first client device based upon a plurality of bid values having the third bid value. The second content item may be transmitted to the first client device.
Methods are disclosed that include receiving electronic content data associated with an electronic content provider comprising: a content campaign identification associated with a virtual token, and provider-identified platform identifications that identify platforms on which the content campaign may be initiated; receiving input from the user comprising: a user identification, a virtual token identification, a campaign identification that identifies the content campaign associated with the virtual token, and user-identified platform identifications that identify the platforms on which the user consents to be targeted by the content campaign; determining, based on the received electronic content data and user data, the content campaign and platforms the user consents to; and generating a notification to initiate a content campaign targeting the user on the platforms on which the user consents to be targeted; and outputting the generated notification to the platforms on which the user consents to be targeted.
A method includes receiving inputs from a plurality of data providers reflecting the plurality of data providers' respective parameters for providing message channel data; receiving inputs from a plurality of data recipients reflecting the data recipients' respective parameters for receiving message channel data; identifying, using a matching engine, message channel data that matches the parameters of at least one of the plurality of data providers and the parameters of at least one of the data recipients; processing the message channel data in accordance with the parameters of the data providers, the processing step comprising segmenting the message channel data into permitted data segments and non-permitted data segments based on the data providers' parameters; delivering the permitted data segments to the identified data recipients in accordance with their respective parameters; and initiating a request for compensation of the identified data providers in accordance with their respective parameters.
Systems and methods are disclosed for opting-out of targeted advertising in online advertising environments. One method includes receiving an opt-out verification request, the opt-out verification including an IP address of a user device and HTTP header fields of an HTTP request of the user device; determining a geographic area of the user device based on the IP address of the user device; accessing an opt-out database having entries of user devices that opted-out of receiving targeted advertising based on at least one HTTP header field of the HTTP request; determining whether the user device has opted-out of receiving targeted advertising based on the determined geographic area of the user device, the HTTP header fields of the HTTP quest, and the entries of user devices of the opt-out database; and transmitting a verification acknowledgement when the user device is determined to have opted-out of receiving targeted advertising.
One or more systems and/or methods for combining vectors output by multiple different mechanisms for content item retrieval are provided. An image encoder may output a first set of vectors generated by an image model using an input image as input. A text encoder may output a second set of vectors generated by a text model using input text as input. A vector combination module may combine the first set of vectors and the second set of vectors to create a vector output. A weight is applied to the vector output to create a weighted output. An output vector is generated based upon a combination of the first set of vectors, the second set of vectors, and the weighted output. The output vector is used to query a catalog to identify a content item related to the input image and the input text.
Systems and methods are provided for determining a quantity of network location visitors that are likely generated or encouraged by specific offline events. A corresponding number of leads may then be attributed to and associated with those specific events. Ongoing conversion activity of those visitors may be tracked and associated with the offline events. Conversions of those visitors may be attributed entirely or partially to one or more specific offline events. The effectiveness of each offline may then be evaluated based on aggregate lead and conversion information.
A facility for processing a search query is described. The facility identifies one or more items that satisfy the query, at least one of which is a media sequence. For each identified media sequence, the facility identifies an advertising message based upon the contents of the query. In response to the query, the facility returns a search result that indicates the identified items. When one of the identified media sequences is selected in the search result, the selected media sequence is provided in conjunction with the advertising message identified for it.
One or more computing devices, systems, and/or methods are provided. In an example, an internet resource identification item associated with one or more internet resources may be identified. User activity information associated with a plurality of events may be analyzed to determine a plurality of sets of text associated with the internet resource identification item, wherein each set of text of the plurality of sets of text is associated with an event, of the plurality of events, associated with an internet resource of the one or more internet resources. A plurality of term representations may be determined based upon the plurality of sets of text. A user intention-based representation associated with the internet resource identification item may be generated based upon the plurality of term representations. A content item may be selected for presentation via a client device based upon the user intention-based representation.
The present disclosure relates to systems and methods customizing electronic communications. A future event associated with a first user may be determined, and a second user that is associated with the first user may be identified. A plurality of communications involving the first user and the second user may be analyzed. A selection rule may be applied based on the analyzed plurality of communications, the selection rule identifying content from the database. Content from the database may be selected based on the application of the selection rule. An electronic message may be provided to the first user identifying the future event, and the selected content may be provided to the first user.
One or more computing devices, systems, and/or methods for isolated budget utilization are provided. A first budget pacing component is assigned to control bidding by a first content serving component for a set of content items. A second budget pacing component is assigned to control bidding by a second content serving component for the set of content items. The first budget pacing component controls the bidding by the first content serving component according to a first portion of a content item budget based upon a traffic share of the first content serving component. The second budget pacing component controls the bidding by the second content serving component according to a second portion of the content item budget based upon a traffic share of the second content serving component.
In an example, a companion user account is generated without a user specifying a username of the companion user account, wherein the companion user account is different than a primary user account of the user. A first interface, that provides access to resources associated with the primary user account, is displayed via a client application. A second interface, that provides access to resources associated with the companion user account, is displayed via the client application. A content targeting profile associated with the companion user account is generated based upon activity associated with the companion user account and/or one or more interests selected by the user. A subset of content items is selected, from among a plurality of content items and based upon the content targeting profile, for presentation via the second interface associated with the companion user account. A content item of the subset of content items is displayed via the second interface.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
H04L 51/42 - Aspects liés aux boîtes aux lettres, p. ex. synchronisation des boîtes aux lettres
H04L 51/21 - Surveillance ou traitement des messages
H04L 51/063 - Adaptation du contenu, p. ex. remplacement d'un contenu inapproprié
H04L 51/52 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel pour la prise en charge des services des réseaux sociaux
56.
Systems and methods for providing targeted content across user channels
A method of providing targeted content across user channels includes receiving a plurality of device activity events on a user device, storing the received plurality of device activity events, creating an audience population based on the stored device activity events, determining links for additional secondary user devices based on the user device, creating a cross-channel advertising campaign targeting the determined audience population, and running the cross-channel advertising campaign based on the device activity and the determined additional secondary user devices.
Systems and methods are disclosed for associating a plurality of Internet-enabled devices with a common user profile for targeting Internet content or advertising. One method includes: receiving, from a plurality of Internet-enabled devices, a plurality of requests for electronic content or advertising; extracting, from each of the plurality of requests, a source IP address and a unique identifier associated with the respective Internet-enabled device; for each source IP address for which requests were received over a predetermined time period from a number of Internet-enabled devices below a threshold number of devices, identifying each possible pair of devices from which requests were received; and for each possible pair of devices, calculating a probability that the pair of devices are owned or operated by a common user.
H04W 4/21 - Signalisation de servicesSignalisation de données auxiliaires, c.-à-d. transmission de données par un canal non destiné au trafic pour applications de réseaux sociaux
58.
Systems and methods for ad-supported mobile data plans or in-app purchases
Methods are disclosed for providing an ad-supported mobile data plan, where ad display may be tied to data usage levels and user input. A method includes receiving, using at least one processor, user interaction with advertisement content displayed on a device; retrieving, using the at least one processor, a data usage limit associated with the device; and causing a change in the data usage limit based on the user interaction with the advertisement content displayed on the device.
Systems, apparatuses, and methods are provided for determining a bid value for placing an advertisement onto advertising space available through an electronic marketplace. A method is used for calculating the option value of maintaining the advertisement in the advertising space during one or more periods of time. The option value may be based on expected profits and the estimated future value of maintaining the advertisement. The option value may then be used to calculate the bid price for placing the advertisement.
Methods, systems, and computer-readable media are disclosed for utilizing unused network capacity for prefetch requests. One method includes: receiving, over a network, network traffic information from a network provider of the network; determining a threshold value for prefetch request fulfillment based on the received network traffic information; receiving, over the network, a plurality of prefetch requests from an application running on a mobile device connected to the network of the network provider; determining, for each prefetch request of the plurality of prefetch requests, a score for the prefetch request based on the received plurality of prefetch requests; and responding to, for each prefetch request of the plurality of prefetch requests, the prefetch request based on the determined threshold value and the determined score for the prefetch request.
G06F 15/173 - Communication entre processeurs utilisant un réseau d'interconnexion, p. ex. matriciel, de réarrangement, pyramidal, en étoile ou ramifié
H04L 43/0817 - Surveillance ou test en fonction de métriques spécifiques, p. ex. la qualité du service [QoS], la consommation d’énergie ou les paramètres environnementaux en vérifiant la disponibilité en vérifiant le fonctionnement
H04L 43/0876 - Utilisation du réseau, p. ex. volume de charge ou niveau de congestion
One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.
Systems and methods are disclosed for online distribution of content. One method includes: receiving, from a first content publisher, a request to publish, on a web page of a second content publisher in a network of publishers, a link to or preview of content of the first content publisher, wherein the request includes at least one parameter associated with at least one attribute of a desired viewer of the link to or preview of content; receiving, from the second content publisher in the network, a request for a link to or preview of content of a publisher in the network, the request including at least one identifier associated with a prospective viewer of a web page of the second content publisher; and determining whether to display a link to or preview of content of the first content publisher on the web page of the second content publisher.
Systems and methods are disclosed for associating a plurality of Internet-enabled devices with a common user profile for targeting Internet content or advertising. One method includes: receiving, from a plurality of Internet-enabled devices, a plurality of requests for electronic content or advertising; extracting, from each of the plurality of requests, a source IP address and a unique identifier associated with the respective Internet-enabled device; identifying each possible pair of devices from which requests were received; calculating for each possible pair of devices a probability that the pair of devices are owned or operated by a common user; and prompting a user to either confirm a characteristic of a prior browsing session or to log-in to an account associated with the common user based on a comparison of the calculated probability to one or more thresholds.
H04L 67/02 - Protocoles basés sur la technologie du Web, p. ex. protocole de transfert hypertexte [HTTP]
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p. ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
H04L 67/04 - Protocoles spécialement adaptés aux terminaux ou aux réseaux à capacités limitéesProtocoles spécialement adaptés à la portabilité du terminal
H04L 67/146 - Marqueurs pour l'identification sans ambiguïté d'une session particulière, p. ex. mouchard de session ou encodage d'URL
Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of impression indications associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine win probabilities and/or expected bid surpluses associated with multiple shaded bid values based upon one or more feature parameters, of the feature parameters, associated with the second set of features. A shaded bid value for submission may be determined based upon the win probabilities and/or the expected bid surpluses.
One or more computing devices, systems, and/or methods are provided. In an example, an internet resource identification item associated with one or more internet resources may be identified. User activity information associated with a plurality of events may be analyzed to determine a plurality of sets of text associated with the internet resource identification item, wherein each set of text of the plurality of sets of text is associated with an event, of the plurality of events, associated with an internet resource of the one or more internet resources. A plurality of term representations may be determined based upon the plurality of sets of text. A user intention-based representation associated with the internet resource identification item may be generated based upon the plurality of term representations. A content item may be selected for presentation via a client device based upon the user intention-based representation.
One or more computing devices, systems, and/or methods are provided. Shaded bid values may be determined and/or submitted to one or more auction modules for participation in auctions. Auction information including at least one of minimum bid values to win associated with the auctions, sets of features associated with the auctions, the shaded bid values associated with the auctions, unshaded bid values associated with the auctions, etc. may be stored in a database. A machine learning model may be trained using a loss function and/or the auction information to generate a first machine learning model with feature parameters associated with features. A bid request, indicative of a second set of features, may be received. The first machine learning model may be used to determine a shaded bid value for submission based upon one or more first feature parameters, of the feature parameters, associated with the second set of features.
One or more computing devices, systems, and/or methods for generating dynamic content item recommendations are provided. Content item information, extracted from message data, is aggregated to calculate popularity and attributes of content items. The content items are ranked based upon the popularity and attributes to generate a ranked list of content items. Exploration traffic is served utilizing a set of eligible content items selected from the ranked list of content items. An eligible content item is promoted for participation in auctions for serving non-exploration traffic based upon the eligible content item being served a threshold number of times.
Systems and methods are disclosed for processing electronic content, such as text, videos, and images. According to certain embodiments, user interactions with electronic content may be tracked over a plurality of modalities, such as web pages, email, mobile applications, and social media. The tracked user interactions may include copy/paste events, explicit user highlighting, social sharing, and user voting. Key passages of electronic content may be identified based on the tracked user interactions and ranked against one another. Ranking of passages may be based, for example, on a raw or normalized score for the identified key passages. Alternatively, the ranking of a passage may be based on a ratio of user interactions with the passage to total views of the electronic text containing the passage. One or more of the identified key passages (e.g., the highest ranked passages) may be published to one or more applications.
Allocating bids for providing content within a segmented campaign is controlled to ensure that an event rate associated with the provided content meets or exceeds a threshold rate. A campaign-level event rate, associated with the provided content, is estimated and provided as a feedback signal. This feedback signal is employed to dynamically update bid allocations for each of the segments, which in turn varies the number or rate of provided impressions and events. Such feedback enables the control of the campaign-level rate and ensures that the campaign-level rate meets or exceeds the rate threshold. To control these rates, the number of total impressions, as well as the number of associated events, is temporally sampled across the campaign segments. Based on the number of impressions and events, the campaign-level event rate is estimated and employed as the feedback signal. Updating the bid allocations may be based on the Beta Distribution.
One or more systems and/or methods for product similarity detection and recommendation are provided. Users may view articles and/or other content that includes images depicting products that may be of interest to the users. These images are processed using image processing functionality such as computer vision to identify the products depicted by the images. A vector embedding model is used to generate product vector representations of the products. Catalog items that are available from a catalog to supplement the articles and other content may be processed to generate catalog item vector representations. When content (an article) with an image depicting a product is to be displayed to the user, similarity between a product vector representation of the product and the catalog item vector representations is determined in order to identify and display catalog items depicting products that are similar to the product depicted by the image in the content.
Systems and methods are disclosed for determining segments of online users from a correlated dataset. One method includes receiving, over a network, a plurality of datasets including user-related data of a plurality of users, each dataset being transmitted from a data owner; correlating, by at least one processor, the plurality of datasets into a correlated dataset; receiving a segmentation request for determining a plurality of users that qualify for a segment, the segmentation request including a set of segment rules to apply to the correlated dataset; determining, by accessing the correlated dataset, whether each user of the plurality of users qualifies for the segment based on the segment rule; and storing an indication of the segment in the correlated dataset for each user determined to qualify for the segment.
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
G06F 12/14 - Protection contre l'utilisation non autorisée de mémoire
Disclosed are systems and methods for improving interactions with and between computers in content providing, searching and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel framework for performing automatic detection of surfaces in video frames resulting in the creation of a seamless in-video ad experience for viewing users. The disclosed framework operates by leveraging available surfaces in videos to show advertisements in compliance with publisher protection, compliance and policy in a fully automatic, end-to-end solution. The disclosed framework evidences a streamlined, automatic and computationally efficient process(es) that modifies digital content at the surface level within the frames of the content in compliance with the digital rights of the owners of the content being merged via the disclosed augmentation.
The present teaching relates to generating an updated model related to advertisement selection. In one example, a request is obtained for updating a model to be utilized for selecting an advertisement. A plurality of copies of the model is generated. The model is pre-selected based on a performance metric related to advertisement selection. Based on each of the plurality of copies, a candidate model is created by modifying one or more parameters of the copy of the model to create a plurality of candidate models. One of the plurality of candidate models is selected based on the performance metric. The steps of generating, creating, and selecting are repeated until a predetermined condition is met. The model is updated with the latest selected candidate model when the predetermined condition is met.
One or more computing devices, systems, and/or methods are provided. In an example, an email addressed to an email address associated with an email account is received. A request to display the email is received from a client device associated with the email account. In response to receiving the request to display the email, the email and a social interaction interface are displayed via an email interface on the client device. The social interaction interface includes one or more social interaction features. The one or more social interaction features include a commenting feature, a feedback feature and/or a chat feature.
G06F 17/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
H04L 51/42 - Aspects liés aux boîtes aux lettres, p. ex. synchronisation des boîtes aux lettres
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
One or more computing devices, systems, and/or methods for content cache invalidation using cache tags are provided. A first proxy hop may receive a request from a client device for a content object originating from a content source. A cache tagging script is executed to add a query parameter to the request to create a modified request. The query parameter specifies a cache tag version for the content object. The modified request is transmitted through one or more subsequent proxy hops to the content source to retrieve the content object. The content object, tagged with a cache tag specifying the cache tag version, is cached as a cached content object within a cache. The cache tag is associated with an expiration timestamp after which the cached content object is designated to be invalid.
The present teaching relates to displaying ads. An explore/exploit layer (EEL) is provided at frontend ad serving engine for storing combination distributions with respect to multiple ads. Each ad has multiple attributes. Each attribute can be instantiated using one of multiple assets. The frontend ad serving engine requests a recommended ad for bidding an ad display opportunity in a slot of a webpage viewed by a user on a user device. The recommended ad is one of the multiple ads. When the auction is successful, a combination of assets for the ad is drawn from the combination distributions in EEL and each of the assets instantiates a corresponding attribute of the ad. The combination is transmitted to the user device to render the ad.
The disclosed systems and methods provide a novel framework that enables cost-effective, accurate and scalable detection and recognition of key events in sporting or live events. The framework functions by creating a domain-specific video dataset with frame level annotations (i.e., deep domain datasets) and then training a lightweight camera view classifier to detect camera views for a given video. The disclosed framework uses pre-trained pose estimation and panoptic segmentation models along with geometric rules as labeling functions to define scene types and derive frame level classification training data. According to some embodiments, disclosed frameworks may be used to identify key persons or events, select a thumbnail corresponding to a key person or event, generate personalized highlights to enhance user experience and social media promotions for a team, sport or players, and predict and select the best camera view sequence for automatic highlights generation.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques
78.
Computerized system and method for image creation using generative adversarial networks
Disclosed frameworks for generating an image including a salient object and a staged background include extracting a salient object from a source image and applying a generative model to the salient object to generate the image. According to some embodiments, extracting a salient object from a source image involves using salient object detection method to identify the relevant portions of the source image corresponding to the salient object. In some embodiments, the generative model is a generative adversarial network trained using a domain relevant dataset.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p. ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]Caractéristiques régionales saillantes
G06V 10/88 - Reconnaissance d’images ou de vidéos utilisant des moyens optiques, p. ex. filtres de référence, masques holographiques, filtres de domaine de fréquence ou filtres de domaine spatial
79.
Computerized system and method for suppressing audio leakage
The present disclosure provides novel systems and methods of suppressing audio leakage in watch-together sessions. In an embodiment, a device is provided comprising a speaker, a microphone, a media player configured to provide a first media content audio, and a peer to peer (“P2P”) communications engine including a local loopback module and a conferencing module. The local loopback module configured to receive the first media content audio, provide a second media content audio, and amplify, using the speaker, the second media content audio, and the conferencing module configured to capture, using the microphone, captured conference audio, the captured conference audio including the second media content audio, and transmit to a modified captured conference audio; wherein the P2P communications engine is configured to generate the modified captured conference audio by suppressing the second content audio from the captured conference audio.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
In an example, a system, a method and/or an apparatus are provided. A first power distribution component and a second power distribution component are connected to a busway system including a busway. Whether first electrical power of the first power distribution component and second electrical power of the second power distribution component meet one or more conditions is determined. In response to a condition of the one or more conditions not being met, supply of electrical power from the second power distribution component to the busway system is inhibited.
One or more computing devices, systems, and/or methods for generating and providing recommendations of products are provided. For example, content is extracted from a message sent to a user. The content is evaluated to identify a product identifier corresponding to a product title of a product. If the product identifier is a truncated version of the product title, then a database of product titles and frequencies of occurrence of the product titles is used to complete the product title. A model is used to infer a product category for the product title. Matching scores are assigned to products within a product category based upon weighted attributes. A recommendation is provided to the user for a product having a matching score greater than a matching threshold.
The example embodiments are directed toward improvements in document classification. In an embodiment, a method is disclosed comprising generating a set of sentences based on a document; predicting a set of labels for each sentence using a multi-label classifier, the multi-label classifier including a self-attended contextual word embedding backbone layer, a bank of trainable unigram convolutions, a bank of trainable bigram convolutions, and a fully connected layer the multi-label classifier trained using a weakly labeled data set; and labeling the document based on the set of labels. The various embodiments can target multiple use cases such as identifying related entities, trending related entities, creating ephemeral timeline of entities, and others using a single solution. Further, the various embodiments provide a weakly supervised framework to train a model when a labeled golden set does not contain a sufficient number of examples.
In an example, sets of event information associated with events may be identified. The events may include intentional click events, accidental click events and/or skip events. Accidental click probabilities associated with the accidental click events and/or the skip events may be determined. Machine learning model training may be performed, using the sets of event information associated with the events and labels associated with the events, to generate a first machine learning model. The labels may include second labels associated with the intentional click events and/or third labels associated with the accidental click events and/or the skip events. The second labels may correspond to an intentional click classification. The third labels may be based upon the accidental click probabilities. Click probabilities associated with content items may be determined using the first machine learning model. A content item may be selected for presentation via a client device based upon the click probabilities.
One or more computing devices, systems, and/or methods for defect detection are provided. An image, depicting an object for evaluation to determine whether the object has a defect, is inputted into a segmentation model to identify an object region of interest of the object. An object region area of the object region of interest is calculated. A convex hull area of a convex hull encompassing the object region of interest is calculated. A ratio of the object region area to the convex hull area is determined. The ratio is compared to a threshold to determine whether the object has the defect or does not have the defect.
One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A request for content associated with a client device may be received. Bid values and/or click probabilities associated with content items may be determined. A probability of receiving a negative signal associated with a content item of the content items from the client device responsive to presenting the content item via the client device may be determined based upon a user profile associated with the client device. A content item score, of content item scores associated with the content items, may be generated based upon the probability, a click probability and/or a bid value associated with the content item. The content item may be selected from the content items for presentation via the client device based upon the content item scores. The content item may be transmitted to the client device.
Techniques for assigning users to buckets for use in bucket experiments are disclosed. Disclosed systems and methods provide systems and methods for making automatic bucket assignments using Nearest Neighbor Matching (NNM). In one embodiment, an iterative approach is used in assigning users to buckets, such that in a given iteration selected users are assigned to a number of buckets, the selected users being an initial user selected from a pool of users and other users selected using pairwise distances associated with the initial user and the other users.
Systems and methods are disclosed for executing an online auction of diverse online advertisements. One method includes receiving inventory information for serving ads on a publisher web page, the inventory information including at least a first ad configuration different from a second ad configuration, each of the first and second ad configurations defining one or both of an ad size and an ad attribute; receiving or generating a plurality of ad bids to serve ads in the first ad configuration and to serve ads in the second ad configuration; and awarding impressions to one or both of the bids to serve ads in the first ad configuration and the second ad configuration, in a proportion based at least in part on a revenue amount associated with the bids to serve ads in the first ad configuration and bids to serve ads in the second ad configuration.
Systems and methods are disclosed for attributing web traffic to an advertising spot. The method may include receiving traffic data for a web page from a server associated with an advertiser and receiving, from a log provider, a log of a plurality of advertising spots related to the advertiser. A duration of time as a peak may be designated to identify the amount of traffic that is attributable to the one of the plurality of advertising spots.
Systems and methods are disclosed for online distribution of content based on a user sentiment index. The method may include receiving, over a network and from a user device, one or more user generated inputs and calculating the user sentiment index based on the one or more user generated inputs. The method may also include receiving, over the network, from a content or advertising provider, instructions on publishing content or advertising to a webpage based on the calculated user sentiment index, and publishing content for display on user devices over the network based on a comparison of the calculated user sentiment index and the received instructions.
Systems and methods are provided for tracking sharing of an electronic content. An exemplary method may include receiving a request to access content associated with a web address by a user. Based on a unique identity assigned to the user and the web address, a unique tracking web address may be generated. This tracking web address may be shared with additional users. As other users request content associated with the tracking web address, information regarding the sharing of the electronic content may be determined and stored, allowing for tracking of sharing behavior of users.
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/958 - Organisation ou gestion de contenu de sites Web, p. ex. publication, conservation de pages ou liens automatiques
H04L 43/10 - Surveillance active, p. ex. battement de cœur, utilitaire Ping ou trace-route
H04L 67/02 - Protocoles basés sur la technologie du Web, p. ex. protocole de transfert hypertexte [HTTP]
One or more computing devices, systems, and/or methods are provided. In an example, a dataset associated with a plurality of dimensions and a plurality of metrics is identified. A subset of dimensions of the plurality of dimensions and a subset of metrics of the plurality of metrics are selected based upon historical dataset queries. A plurality of sets of results is generated, using the dataset, based upon the subset of dimensions and the subset of metrics. A plurality of significance scores is determined based upon the plurality of sets of results. One or more first sets of results of the plurality of sets of results are selected based upon the plurality of significance scores.
One or more computing devices, systems, and/or methods are provided. A request for content associated with a device and/or a set of request information associated with the request for content may be received. A content item may be transmitted to the device. A set of client information associated with the device may be received. The set of client information may be analyzed to determine a fraudulence label associated with the request for content. Fraud detection information generated based upon the set of request information, the set of client information and/or the fraudulence label may be stored in a fraud detection database. A second request for content associated with a second device and/or a second set of request information associated with the second request for content may be received. A second fraudulence label may be determined based upon the second set of request information and/or the fraud detection database.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
93.
SYSTEM AND METHOD FOR APPROXIMATING NUMERICAL FEATURES VIA CUBIC SPLINES AND APPLICATIONS THEREOF
The present teaching relates to method, system, medium, and implementations for approximating a non-linear relationship between a numerical feature and an output of a model. A value of a numerical feature is received and is transformed, via a transform function, into a transformed value within a fixed range. With respect to each of a plurality of basis functions used for approximating the non-linear relationship, a respective basis function value of the basis function is computed based on the transformed value. An approximated value of the non-linear numeric feature is generated based on a sum of the plurality of basis function values weighted respectively by each corresponding one of a set of the weights, obtained via machine learning.
G06N 7/08 - Agencements informatiques fondés sur des modèles mathématiques spécifiques utilisant des modèles de chaos ou des modèles de systèmes non linéaires
A novel framework optimizes SQL queries that are generated from a templated virtual sematic layer. The framework introduces the use of a virtual semantic layer into database management systems' operations, whereby templated SQL queries can be rewritten according to a determined and measured nesting, dimensional structure that produces an optimized search system. This enables templated SQL fragments to be translated for query optimization, thereby reducing the drain on a database's resources and minimizing a query's impact on the database's performance.
One or more computing devices, systems, and/or methods for providing comparable items for a query item are provided. A query is constructed based upon a set of similarity characteristics and a set of difference characteristics specified for a query item. The query is executed to identify a set of query item results comprising comparable items having characteristics similar to the set of similarity characteristics and characteristics different from the set of difference characteristics. The set of query item results are provided as query results for the query.
The disclosed systems and methods provide a novel framework that provides mechanisms for enabling message senders to dictate, control and/or create dynamic immersive content consumption experiences for recipients of their messages. The disclosed framework provides message senders with previously non-existent functionality to control the experience and environment within which their messages are consumed. Conventional systems provide recipients with capabilities to consume messages and/or supplemental content; however, these capabilities are driven and controlled by the hosting messaging platform and/or the third party entity that is availed opportunities to provide supplemental content. The disclosed framework prevents such experiences by providing capabilities to the message sender that involves control not only over the experience of the sender's messages but also control over the experience of the environment in which the messages are consumed by their recipients.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
H04L 51/08 - Informations annexes, p. ex. pièces jointes
One or more computing devices, systems, and/or methods are provided. In an example, purchase data associated with users may be determined. The purchase data may be indicative of purchases by users from entities. The purchase data may be analyzed to determine purchase metrics associated with the users. The purchase metrics may be analyzed to determine sets of groups of users associated with the entities. One or more groups of users, of the sets of groups of users, that include the user may be determined. Content may be selected for presentation via a first device associated with the first user based upon the one or more groups of users.
One or more computing devices, systems, and/or methods for implementing a model for serving exploration traffic are provided. An amount of spend by a content provider to provide content items of the content provider through a content serving platform to client devices of users is determined. A number of exploration impressions of users viewing exploration content items of the content provider over a timespan is determined. A return on exploration impression metric is determined for the content provider based upon a ratio of the amount of spend to the number of exploration impressions. The return on exploration metric is used to rank available exploration content items of content providers for serving exploration traffic.
G06Q 30/0273 - Détermination des frais de publicité
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06Q 10/0637 - Gestion ou analyse stratégiques, p. ex. définition d’un objectif ou d’une cible pour une organisationPlanification des actions en fonction des objectifsAnalyse ou évaluation de l’efficacité des objectifs
99.
COMPUTERIZED SYSTEM AND METHOD FOR ON-DEVICE CONTENT PERSONALIZATION
The disclosed systems and methods provide a novel framework that provides on-device functionality to user devices for localized content ranking, modification and rendering. The disclosed systems and methods provide functionality for on-device personalization in a real-time, secure and network anonymous manner. Rather than exposing a user's data to the network for content tailoring, the disclosed framework performs the ranking and content manipulation locally on the user's device. The disclosed framework enables locally (on-device) built, updated and hosted user profiles to be used to tailor received content for display on a user device. This ensures the integrity of the personalization while maintaining security for the user's personalized data and activities.
Systems and methods are disclosed for managing online advertising data secure sharing. One method includes receiving, at a server, a request for proprietary data from a data consumer, the request including a data consumer identifier; retrieving, from a database of proprietary data, proprietary data based on the request; determining, by the server, whether the retrieved proprietary data is at least one of: designated to be processed and designated to have privileges set; processing, by the server, the proprietary data when the server determines the proprietary data is designated to be processed; setting one or more privileges to the proprietary data using the certificate associated with the data consumer identifier when the server determines the proprietary data is designated to have privileges set; encrypting the proprietary data using the certificate associated with the data consumer identifier; and transmitting the encrypted proprietary data to the data consumer.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06F 21/10 - Protection de programmes ou contenus distribués, p. ex. vente ou concession de licence de matériel soumis à droit de reproduction
