Methods, systems, and apparatus, including computer programs encoded on computer storage media, for analyzing content. One of the methods includes obtaining a collection of content items, each content item including an image and associated metadata; generating a collection of attributes for each content item by analyzing the image and the associated metadata; assigning one or more attributes to each content item; generating a plurality of queries from the collection of attributes; and populating a plurality of collections, each collection having a topic corresponding to a query, wherein populating the collections comprises evaluating a relevance of each query-content item pair based on an overlap between attributes associated with the query and attributes assigned to the content item.
G06F 16/538 - Présentation des résultats des requêtes
G06F 16/535 - Filtrage basé sur des données supplémentaires, p. ex. sur des profils d'utilisateurs ou de groupes
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
2.
MULTI-OBJECT IMAGES AND COMPLEMENTARY OBJECT IMAGES RECOMMENDATION
Disclosed are systems and methods that connect an input image, such as an individual product image, to lifestyle images that include multiple products, and then to one or more complementary product images. For example, when a user provides or selects a product image, the disclosed implementations determine one or more lifestyle images of multiple products that include the product or include another, visually similar, product. Still further, the disclosed implementations may also determine, for each of the multiple products in the lifestyle image, a set of complementary product images that may work well together and with the product in the input image.
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 10/40 - Extraction de caractéristiques d’images ou de vidéos
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source
Disclosed are systems and methods for unified content item ranking across multiple output channels that provide personalized content item recommendations while adapting to different output channel contexts. A machine learning model receives four sets of data for each candidate content item: user engagement history data, content item popularity data, content item information data, and contextual relevance data. The contextual relevance data includes both a query context associated with an output channel and a content relevance based on existing content. The model generates engagement scores for multiple types of engagement actions, which are then weighted according to output channel-specific parameters to produce final ranking scores. By using a single unified model that automatically adapts its scoring behavior based on the specific output channel, the system reduces engineering complexity while maintaining optimized recommendations across diverse output channels such as style-based recommendations, topic-based landing pages, and personalized shopping interfaces.
H04N 21/25 - Opérations de gestion réalisées par le serveur pour faciliter la distribution de contenu ou administrer des données liées aux utilisateurs finaux ou aux dispositifs clients, p. ex. authentification des utilisateurs finaux ou des dispositifs clients ou apprentissage des préférences des utilisateurs pour recommander des films
H04N 21/258 - Gestion de données liées aux clients ou aux utilisateurs finaux, p. ex. gestion des capacités des clients, préférences ou données démographiques des utilisateurs, traitement des multiples préférences des utilisateurs finaux pour générer des données collaboratives
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
Advertising and promotional services; arranging and
conducting special events for commercial, promotional or
advertising purposes. Arranging and conducting of conferences in the field of
advertising, marketing, e-commerce, software, and mobile
applications; educational services, namely, conducting
programs in the field of advertising, marketing, e-commerce,
software, and mobile applications (term considered too vague
by the International Bureau pursuant to Rule 13 (2) (b) of
the Regulations).
Disclosed are implementations that enable the linking or connection of objects and different scenes in which those objects are represented. For example, a corpus of scenes (e.g., digital images) that include a representation of one or more objects may be processed using the disclosed implementations to segment from those scenes the individual objects represented in those scenes. The disclosed implementations may further determine clusters of visually similar object segments and form object clusters for those object segments. The scenes that include those object segments are also linked to the object cluster. With scenes linked to different object clusters, a user may select one or more query objects or a query scene and be presented with other scenes that include visually similar objects, even though the overall scenes may be visually different.
G06V 20/30 - ScènesÉléments spécifiques à la scène dans les albums, les collections ou les contenus partagés, p. ex. des photos ou des vidéos issus des réseaux sociaux
Disclosed are implementations that enable the linking or connection of objects and different scenes in which those objects are represented. For example, a corpus of scenes (e.g., digital images) that include a representation of one or more objects may be processed using the disclosed implementations to segment from those scenes the individual objects represented in those scenes. The disclosed implementations may further determine clusters of visually similar object segments and form object clusters for those object segments. The scenes that include those object segments are also linked to the object cluster. With scenes linked to different object clusters, a user may select one or more query objects or a query scene and be presented with other scenes that include visually similar objects, even though the overall scenes may be visually different.
G06V 20/30 - ScènesÉléments spécifiques à la scène dans les albums, les collections ou les contenus partagés, p. ex. des photos ou des vidéos issus des réseaux sociaux
Described is a system and method for enabling visual search for information. With each selection of a search term, additional search terms are dynamically selected and presented to the user in conjunction with results matching the currently selected search terms. Likewise, a selected search term may be tokenized and a graphical token presented to the user to represent the selected search term.
Described is a system and method for enabling dynamic selection of a search input. For example, rather than having a static search input box, the search input may be dynamically positioned such that it encompasses a portion of displayed information. For example, a user may touch a touch-based display using two fingers to invoke the dynamic search input and then determine a size and a position of the dynamic search input by moving their fingers on the display. An image segment that includes a representation of the encompassed portion of the displayed information is generated and processed to determine an object represented in the portion of the displayed information. Additional images with visually similar representations of objects are then determined and presented to the user.
G06F 16/532 - Formulation de requêtes, p. ex. de requêtes graphiques
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/04845 - 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 pour la transformation d’images, p. ex. glissement, rotation, agrandissement ou changement de couleur
G06F 3/0488 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
This disclosure describes, in part, systems and methods that enable users to manage, search for, share and discover objects based on a context of the object from the user's perspective. The same object may have vastly different meanings (context) to different individuals based on how they experience the object. Rather than managing objects solely based on information about the object, the implementations described allow users to specify a context for the object and manage objects based on that context. In addition, external sources may provide supplemental information about objects and/or representations of objects.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Advertising and marketing; Advertising and marketing consultancy; Market research in the nature of collecting data of household television viewing activity for television rating purposes; Analyzing and compiling data for measuring the performance of advertising campaigns Measuring television audience size and composition for others via electronic data collection; Software as a service (SAAS) services featuring software for data analysis and data reporting in the field of providing marketing and advertising services; Software as a service (SAAS) services featuring software for running marketing campaigns and visualizing aggregated and compiled advertising analytics
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Advertising and marketing; Advertising and marketing consultancy; Market research in the nature of collecting data of household television viewing activity for television rating purposes; Analyzing and compiling data for measuring the performance of advertising campaigns Measuring television audience size and composition for others via electronic data collection; Software as a service (SAAS) services featuring software for data analysis and data reporting in the field of providing marketing and advertising services; Software as a service (SAAS) services featuring software for running marketing campaigns and visualizing aggregated and compiled advertising analytics
12.
GENERATING CUSTOMIZED CONTENT USING A GENERATIVE MODEL
Disclosed are systems and methods that generate a natural language prompt that is configured to be processed by a generative model, such as a large language model (LLM), and includes certain user information to facilitate the determination and/or generation of customized content for users of an online platform. For example, textual information associated with certain user information may be extracted and aggregated and incorporated into one or more natural language prompts, which may be processed by a generative model, such as an LLM, to generate a particular output based on the type of customized content being sought and/or generated for the user. The output may then be processed to determine and/or generate the customized content or the user.
G06F 16/248 - Présentation des résultats de requêtes
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
13.
METHOD, MEDIUM, AND SYSTEM FOR FACILITATING PURCHASE OF OBJECTS
This disclosure describes systems and methods that facilitate purchase of objects from merchants. For example, a user may browse a website available from an object management service and identify objects that they desire to purchase. Rather than having to locate the seller of those objects to make a purchase, the implementations described herein facilitate a connection between the user and the merchant so that the merchant's sales are increased and the user is provided an efficient and safe shopping experience.
Described are systems and methods for determining complementary and/or matching objects based on an input query object. The described systems and methods can generate an embedding representative of the provided object, which can be transformed to generate a style embedding by a trained system, such as a machine learning system. The style embedding can then be used to identify one or more complementary objects from a corpus of classified objects. Aspects of the present disclosure also relate to creation of the training dataset, as well as training the machine learning system.
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
Systems and methods for generating user notifications to a set of users of a social networking service is presented. For each user of a set of users of the social networking service, one or more machine learning models selects an optimal notification channel, an optimal notification template, and optimal personalization content for configurable elements of a selected notification template. Each of these determinations/selections is made according to and based on a likelihood of increased user engagement with the social networking service. Upon determining the notification channel, notification template, and personalizations to the template, the notification is generated and sent to the corresponding user.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
(1) Advertising and promotional services; arranging and conducting special events for commercial, promotional or advertising purposes.
(2) Arranging and conducting of conferences in the field of advertising, marketing, e-commerce, software, and mobile applications; educational services, namely, conducting programs in the field of advertising, marketing, e-commerce, software, and mobile applications (term considered too vague by the International Bureau pursuant to Rule 13 (2) (b) of the Regulations).
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
38 - Services de télécommunications
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Downloadable computer software for allowing users to
interact online with information and media content that
other users share; downloadable computer software that
allows users to discover, access and share information and
media in the field of goods, services, and experiences;
downloadable computer software for creating, uploading,
bookmarking, viewing, annotating, sharing, and discovering
data, information, and media content; downloadable mobile
applications for creating, uploading, bookmarking, viewing,
annotating, sharing, and discovering data, information, and
media content; downloadable computer software for connecting
social network users with businesses in the field of
facilitating business promotion; downloadable electronic
publications in the nature of journals, photographs, and
graphic art in the field of general human interest;
downloadable computer e-commerce software to allow users to
perform electronic business transactions via a global
computer network. Advertising and promotional services; advertising and
marketing services, namely, promoting the goods and services
of others; business data analysis; business monitoring and
consulting services, namely, tracking web sites and
applications of others to provide strategy, insight,
marketing, sales, operation, product design, particularly
specializing in the use of analytic and statistic models for
the understanding and predicting of consumers, businesses,
and market trends and actions; marketing services, namely,
promoting or advertising the goods and services of others;
promoting the goods and services of others by providing
hypertext links to the web sites of others; electronic
commerce services, namely, providing information about
products via telecommunication networks for advertising and
sales purposes. Electronic bulletin board services. Organizing and arranging exhibitions for entertainment
purposes; entertainment, namely, a continuing lifestyle and
cooking show broadcast over television, internet, and video
media; providing online non-downloadable photographs via a
website. Providing online non-downloadable computer software
platforms for creating, uploading, bookmarking, viewing,
annotating, sharing, and discovering data, information, and
media content; computer services, namely, creating an
on-line community for registered users to participate in
discussions, get feedback from their peers, form virtual
communities, and engage in social networking services in the
field of general interest; providing online non-downloadable
computer software for enabling users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content via a website; providing
online non-downloadable computer software platforms for
uploading, posting, showing, displaying, tagging, sharing
and transmitting messages, comments, multimedia content,
photos, pictures, images, text, information, and other
user-generated content; developing and hosting a server on a
global computer network for the purpose of facilitating
e-commerce via such a server; providing online
non-downloadable computer software platforms for
facilitating sharing and discovering information and media
content via mobile devices and local and global computer,
mobile, cellular, electronic, wireless, and data
communications networks. Online social networking services; online social networking
services in the field of commentary, comparison,
collaboration, consultation, evaluation, advice, discussion,
research, notification, reporting, identification,
information sharing, indexing, information location,
entertainment, pleasure, or general interest.
Systems and methods are presented for training a second machine learning model according to aspects of a trained first machine learning model. Processing features utilized by a training framework to train the first machine learning model are identified, and at least some of the processing features are combined with an initial set of training features to form updated training features. The updated training features are presented to a user for customization, resulting in customized training features. An executable training framework is configured with the customized training features and executed to train the second machine learning model.
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
Systems and methods are presented for training a second machine learning model according to aspects of a trained first machine learning model. Processing features utilized by a training framework to train the first machine learning model are identified, and at least some of the processing features are combined with an initial set of training features to form updated training features. The updated training features are presented to a user for customization, resulting in customized training features. An executable training framework is configured with the customized training features and executed to train the second machine learning model.
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
Described are systems and methods to determine a content item recommendation that is included in a content item bid request, recommending to a third party, a third party content item to include in a content item bid that is responsive to the bid request. The content item recommendation may indicate a particular third party product or third party content item that is predicted to perform well in a content item slot and for a specific user without disclosing user information to the third party.
Systems and methods for identifying relevant content within a corpus of visual content items in response to a user's text-based query are presented. In response to a text-based query, the query is mapped to a most-engaged content item of the corpus of visual content items included in responses to the query from a plurality of users. At least one text-based term associated with the most-engaged content item is identified and combined with the query from an expanded query. The expanded query is mapped to an interest node of an interest taxonomy and content items associated with the mapped interest node are identified. At least some of the content items associated with the mapped interest node are selected and returned as response content to the received query.
A computer system extracts product data from a website and correlates product records from multiple sources to one another as corresponding to the same product. A website is crawled efficiently by rendering webpages using a virtual browser that ignores blacklisted elements, extracts data from objects without rendering, and suppressing retrieval of remote resources. Data is extracted according to engine control statements including a selector and extractor. A website may be crawled repeatedly and changes in extracted data may be detected and flagged. Engine control statements may be automatically changed in response to detecting a change in the configuration of the website. Images of product records may be correlated with one another by first comparing text of the product records and selecting images for comparison based on composition. Images are compared using a machine learning model. Images determined to be similar may be presented to a human for a correlation decision.
Described are systems and methods of identifying complementary image segments and generating collages of the complementary image segments. Based on an initial image segment, the complementary image segments may first be determined. Then, a layout of the collage may be determined based on the initial image segment and the complementary image segments. The collage may then be generated using the initial image segment, the complementary image segments, and the layout. The origin information, such as the source image, source image location, etc., from which the extracted image segment is generated is maintained as metadata so that interaction with the extracted image segment on the collage can be used to determine and/or return to the origin of the extracted image segment. Collages may be updated, shared, adjusted, etc.
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 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
24.
DATA ANOMALY DETECTION USING A LARGE LANGUAGE MODEL
Disclosed are systems and methods that process a dataset to determine data anomalies in the dataset. The process may receive a query to create the dataset. At least one known data anomaly may be identified in the dataset. An algorithm that models a pattern of the dataset may be selected. The dataset, known data anomaly, and/or algorithm may be sent to a Large Language Model (LLM) with instructions to determine configuration information for data anomaly detection including at least one threshold that indicates additional anomalies in the dataset. The algorithm may create a reference dataset that is compared to the dataset to determine deviations. The threshold may determine which deviations indicate additional anomalies. The LLM may send configuration data, including at least the threshold, to an anomaly detection application, which may be configured with the configuration data and used to determine data anomalies in other, similar, datasets generated with the query or a similar query.
Systems and methods for identifying relevant content within a corpus of visual content items in response to a user's text-based query are presented. In response to a text-based query, the query is mapped to a most-engaged content item of the corpus of visual content items included in responses to the query from a plurality of users. At least one text-based term associated with the most-engaged content item is identified and combined with the query from an expanded query. The expanded query is mapped to an interest node of an interest taxonomy and content items associated with the mapped interest node are identified. At least some of the content items associated with the mapped interest node are selected and returned as response content to the received query.
Described are systems and methods for determining complementary and/or matching objects based on an input query object. The described systems and methods can generate an embedding representative of the provided object, which can be transformed to generate a style embedding by a trained system, such as a machine learning system. The style embedding can then be used to identify one or more complementary objects from a corpus of classified objects. Aspects of the present disclosure also relate to creation of the training dataset, as well as training the machine learning system.
G06V 10/22 - Prétraitement de l’image par la sélection d’une région spécifique contenant ou référençant une formeLocalisation ou traitement de régions spécifiques visant à guider la détection ou la reconnaissance
G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
Systems and methods are presented for training a second machine learning model according to aspects of a trained first machine learning model. Processing features utilized by a training framework to train the first machine learning model are identified, and at least some of the processing features are combined with an initial set of training features to form updated training features. The updated training features are presented to a user for customization, resulting in customized training features. An executable training framework is configured with the customized training features and executed to train the second machine learning model.
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
Systems and methods for responding to a subscriber's text-based request for content items are presented. In response to a request from a subscriber, word pieces are generated from the text-based terms of the request. A request embedding vector of the word pieces is obtained from a trained machine learning model. Using the request embedding vector, a set of content items, from a corpus of content items, is identified. At least some content items of the set of content items are returned to the subscriber in response to the text-based request for content items.
G06F 16/483 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
38 - Services de télécommunications
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
(1) Downloadable computer software for allowing users to interact online with information and media content that other users share; downloadable computer software that allows users to discover, access and share information and media in the field of goods, services, and experiences; downloadable computer software for creating, uploading, bookmarking, viewing, annotating, sharing, and discovering data, information, and media content; downloadable mobile applications for creating, uploading, bookmarking, viewing, annotating, sharing, and discovering data, information, and media content; downloadable computer software for connecting social network users with businesses in the field of facilitating business promotion; downloadable electronic publications in the nature of journals, photographs, and graphic art in the field of general human interest; downloadable computer e-commerce software to allow users to perform electronic business transactions via a global computer network. (1) Advertising and promotional services; advertising and marketing services, namely, promoting the goods and services of others; business data analysis; business monitoring and consulting services, namely, tracking web sites and applications of others to provide strategy, insight, marketing, sales, operation, product design, particularly specializing in the use of analytic and statistic models for the understanding and predicting of consumers, businesses, and market trends and actions; marketing services, namely, promoting or advertising the goods and services of others; promoting the goods and services of others by providing hypertext links to the web sites of others; electronic commerce services, namely, providing information about products via telecommunication networks for advertising and sales purposes.
(2) Electronic bulletin board services.
(3) Organizing and arranging exhibitions for entertainment purposes; entertainment, namely, a continuing lifestyle and cooking show broadcast over television, internet, and video media; providing online non-downloadable photographs via a website.
(4) Providing online non-downloadable computer software platforms for creating, uploading, bookmarking, viewing, annotating, sharing, and discovering data, information, and media content; computer services, namely, creating an on-line community for registered users to participate in discussions, get feedback from their peers, form virtual communities, and engage in social networking services in the field of general interest; providing online non-downloadable computer software for enabling users to create, upload, bookmark, view, annotate, share and discover data, information and media content via a website; providing online non-downloadable computer software platforms for uploading, posting, showing, displaying, tagging, sharing and transmitting messages, comments, multimedia content, photos, pictures, images, text, information, and other user-generated content; developing and hosting a server on a global computer network for the purpose of facilitating e-commerce via such a server; providing online non-downloadable computer software platforms for facilitating sharing and discovering information and media content via mobile devices and local and global computer, mobile, cellular, electronic, wireless, and data communications networks.
(5) Online social networking services; online social networking services in the field of commentary, comparison, collaboration, consultation, evaluation, advice, discussion, research, notification, reporting, identification, information sharing, indexing, information location, entertainment, pleasure, or general interest.
Disclosed are implementations that enable the linking or connection of objects and different scenes in which those objects are represented. For example, a corpus of scenes (e.g., digital images) that include a representation of one or more objects may be processed using the disclosed implementations to segment from those scenes the individual objects represented in those scenes. The disclosed implementations may further determine clusters of visually similar object segments and form object clusters for those object segments. The scenes that include those object segments are also linked to the object cluster. With scenes linked to different object clusters, a user may select one or more query objects or a query scene and be presented with other scenes that include visually similar objects, even though the overall scenes may be visually different.
G06V 20/30 - ScènesÉléments spécifiques à la scène dans les albums, les collections ou les contenus partagés, p. ex. des photos ou des vidéos issus des réseaux sociaux
31.
DATA EXTRACTION APPROACH FOR RETAIL CRAWLING ENGINE
A computer system extracts product data from a website and correlates product records from multiple sources to one another as corresponding to the same product. A website is crawled efficiently by rendering webpages using a virtual browser that ignores blacklisted elements, extracts data from objects without rendering, and suppressing retrieval of remote resources. Data is extracted according to engine control statements including a selector and extractor. A website may be crawled repeatedly and changes in extracted data may be detected and flagged. Engine control statements may be automatically changed in response to detecting a change in the configuration of the website. Images of product records may be correlated with one another by first comparing text of the product records and selecting images for comparison based on composition. Images are compared using a machine learning model. Images determined to be similar may be presented to a human for a correlation decision.
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
Advertising and promotional services; Arranging and conducting special events for commercial, promotional or advertising purposes Arranging and conducting of conferences in the field of advertising, marketing, e-commerce, software, and mobile applications; Educational services, namely, conducting programs in the field of advertising, marketing, e-commerce, software, and mobile applications
33.
END-TO-END TRAINED GENERATIVE SLATE RECOMMENDATION MODEL
Described is an end-to-end generative recommendation model configured to determine a slate of content items to present to a user and the training thereof. The slate recommendation model may employ a generative model (e.g., generative transformer-based model, etc.) that is trained employing a multi-stage training approach. First, a generative model may be trained to learn a sequence model configured to generate a sequence of content items. The trained model may then be fine-tuned to better learn a distribution of slate recommendations. After fine-tuning of the model, a reward model may be trained based on one or more objectives. The reward model may be employed using a reinforcement learning technique or direct preference optimization technique to further fine-tune the model to bias the slate recommendations in view of the one or more objectives.
Described are systems and methods that suggest candidate recipients to receive an object identifier from a first user. For example, a first user, independent of a conversation with a second user, may select, close-up, or otherwise view an object identifier. The system, upon detecting the interaction with the object identifier by the first user, may determine one or more second users to suggest to the first user as candidate recipients with which the first user can share the viewed object identifier. The systems and methods may also suggest object identifiers to share with a selected recipient and/or determine two or more users that have a common connection and share an object identifier related to the connection with each of those users.
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06F 16/907 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
Described are systems and methods to extract image segments from an image and include those extracted image segments in a collage. The origin information, such as the source image, source image location, etc., from which the extracted image segment is generated is maintained as metadata so that interaction with the extracted image segment on the collage can be used to determine and/or return to the origin of the extracted image segment. Collages may be updated, shared, adjusted, etc., by the creator of the collage or other users.
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
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 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
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
36.
SYSTEM AND METHODS TO RECOMMEND ITEMS THAT INCREASE LONG-TERM USER SATISFACTION
Described are systems and methods that resolve the shortcomings of existing recommendation systems that focus on immediate user reward rather than long-term user satisfaction. The disclosed implementations increase user session duration and user session depth during a session by selecting items that incent the user to remain engaged and participating in the session, rather than optimizing for immediate user metrics.
Systems and methods for automatically placing a taxonomy candidate within an existing taxonomy are presented. More particularly, a neural taxonomy expander (a neural network model) is trained according to the existing, curated taxonomic hierarchy. Moreover, for each node in the taxonomic hierarchy, an embedding vector is generated. A taxonomy candidate is received, where the candidate is to be placed within the existing taxonomy. An embedding vector is generated for the candidate and projected by a projection function of the neural taxonomy expander into the taxonomic hyperspace. A set of closest neighbors to the projected embedding vector of the taxonomy candidate is identified and the closest neighbor of the set is assumed as the parent for the taxonomy candidate. The taxonomy candidate is added to the existing taxonomic hierarchy as a child to the identified parent node.
A user preference hierarchy is determined from user response to images. Images may be tagged using machine learning models trained to determine values for images. Products are clustered according to product vectors. Images of products within a cluster are clustered according to composition and groups of images are selected from image clusters for soliciting feedback regarding user preference for products of a cluster. Feedback is used to train a user preference model to estimate affinity for a product vector. A user may provide feedback regarding a price point and products are weighted according to a distribution about the price point. The distribution may be asymmetrical according to direction of movement of the price point. Filters may be dynamically defined and presented to a user based on popularity and frequency of occurrence of attribute-value pairs of search results and based on feedback regarding the search results.
Systems and method for determining a topic cohesion measurement between a content item and a hyperlinked landing page are presented. In one embodiment, a plurality of content item signals is generated for the content item and a corresponding plurality of signals are generated for the hyperlinked landing page. An analysis of the corresponding signals is conducted to determine a measurement of topic cohesion, a topic cohesion score, between the content item and the hyperlinked landing page. A cohesion predictor model is trained to generate the predictive topic cohesion score between an input content item and a hyperlinked landing page. Upon a determination that the topic cohesion score is less than a predetermined threshold, remedial actions are taken regarding the hyperlink of the content item. Alternatively, positive actions may be carried out, including promoting the content item to others, associating advertisements with the content item, and the like.
Systems and methods for automatically placing a taxonomy candidate within an existing taxonomy are presented. More particularly, a neural taxonomy expander (a neural network model) is trained according to the existing, curated taxonomic hierarchy. Moreover, for each node in the taxonomic hierarchy, an embedding vector is generated. A taxonomy candidate is received, where the candidate is to be placed within the existing taxonomy. An embedding vector is generated for the candidate and projected by a projection function of the neural taxonomy expander into the taxonomic hyperspace. A set of closest neighbors to the projected embedding vector of the taxonomy candidate is identified and the closest neighbor of the set is assumed as the parent for the taxonomy candidate. The taxonomy candidate is added to the existing taxonomic hierarchy as a child to the identified parent node.
Described are systems and methods for implementing a recommendation system that is configured and/or optimized to determine recommended content items based on a long-term objective. The exemplary recommendation system may be generated based on a mapping between content items and the long-term objective. The mapping between the content items and the long-term objective may be determined based on mappings utilizing various interim metrics, which may be determined using trained models configured to predict a respective target variable based on respective inputs. The mapping may also be generated using alignment scores and/or a large language model.
Disclosed are systems and methods that process a selection of a plurality of content items through a Large Language Model (“LLM”) and determine, based on that processing, other content items to present or recommend. For example, one or more non-text content item may be processed to generate one or more captions descriptive of the non-text content item(s). The captions may then be processed by a LLM to determine a narrative description of the content items and the narrative description may be used as a text-based query to determine recommended content items.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
38 - Services de télécommunications
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Downloadable computer software for allowing users to interact online with information and media content that other users share; Downloadable computer software that allows users to discover, access and share information and media content in the fields of goods, services, and experiences; Downloadable computer software for creating, uploading, bookmarking, viewing, annotating, sharing, and discovering data, information, and media content; Downloadable mobile applications for creating, uploading, bookmarking, viewing, annotating, sharing, and discovering data, information, and media content; Downloadable computer software for connecting social network users with businesses in the field of facilitating business promotion; Downloadable electronic publications in the nature of journals, downloadable photographs, and downloadable graphic art reproductions in the field of general human interest; Downloadable computer e-commerce software to allow users to perform electronic business transactions via a global computer network Advertising and promotional services; Advertising and marketing services, namely, promoting the goods and services of others; Business data analysis; Business monitoring and consulting services, namely, tracking web sites and applications of others to provide strategy, insight, marketing, sales, operation, product design, particularly specializing in the use of analytic and statistic models for the understanding and predicting of consumers, businesses, and market trends and actions; Marketing services, namely, promoting or advertising the goods and services of others; Promoting the goods and services of others by providing hypertext links to the web sites of others; Electronic commerce services, namely, providing information about products via telecommunication networks for advertising and sales purposes Electronic bulletin board services Organizing and arranging exhibitions for entertainment purposes; Entertainment, namely, a continuing lifestyle and cooking show broadcast over television, internet, and video media over the internet; Providing a website featuring non-downloadable photographs; on-line journals, namely, blogs in the field of general human interest Providing online non-downloadable computer software platforms for creating, uploading, bookmarking, viewing, annotating, sharing, and discovering data, information, and media content; Computer services, namely, creating an on-line community for registered users to participate in discussions, get feedback from their peers, form virtual communities, and engage in social networking services in the field of general interest; Providing a website featuring technology that enables users to create, upload, bookmark, view, annotate, share, and discover data, information, and media content; Providing online non-downloadable computer software platforms for uploading, posting, showing, displaying, tagging, sharing, and transmitting messages, comments, multimedia content, photos, pictures, images, text, information, and other user-generated content being multimedia content; Developing and hosting a server on a global computer network for the purpose of facilitating e-commerce via such a server; Providing online non-downloadable computer software platforms for facilitating the sharing and access of information and media content via mobile devices and local and global computer, mobile, cellular, electronic, wireless, and data communications networks Online social networking services; Online social networking services in the field of commentary on multimedia content, field of comparison of multimedia content, field of collaboration of users, field of consultation on multimedia content, field of evaluation of multimedia content, field of advice on multimedia content, field of discussion groups, field of research on multimedia content, field of notification of multimedia content, field of reporting of multimedia content, field of identification of multimedia content, field of information sharing, field of indexing of multimedia content, field of information location, field of entertainment, and field of general interest
44.
GENERATING CONTENT BASED ON TEXT AND SUPPLEMENTAL INFORMATION
Described are systems and methods for providing a content generation service that may be configured to generate content, such as digital images and the like, based on both a textual input and supplemental information. The content generation service may include one or more trained machine learning models that may be configured to generate an image based on a textual input and one or more supplemental information input(s). The supplemental information may include, for example, user information, content information, etc. and can provide context, preferences, or any other additional information beyond the textual input that may be used to generate the content. Further, the content generation service and/or the user may be able to assign weights to each of the textual input and the supplemental information input in connection with the generation of the content.
Described are systems and methods to determine body types of individuals represented in content items. The determined body types may be associated with the content items to facilitate indexing, filtering, diversifying, etc. of the content items based on the determined body types. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more body types of individuals represented in each content item while eliminating the need for performing image pre-processing prior to determination of the body type(s) presented in the content item.
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
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06T 7/90 - Détermination de caractéristiques de couleur
46.
Associating user-provided content items to interest nodes
System and methods are presented for associating a user-posted content item with an interest node of an interest taxonomy. A corpus of content items and an interest taxonomy are maintained. The interest taxonomy comprises interest nodes organized in a hierarchical organization, each node having a text label descriptive of the interest node. Additionally, the content items of the corpus are associated with one or more interest nodes of the interest taxonomy. Upon receiving a user-posted content item, feature sets of the received content item are generated, these feature sets based on features and/or aspects of the received content item. After generating at least one feature set, the at least one feature set is provided to an interest prediction model that generates candidate interest nodes for the user-posted content item. At least some of the candidate interest nodes are associated with the user-posted content item in the corpus.
According to aspects of the disclosed subject matter, a taste graph comprising likely content collection nodes with corresponding likely digital content items is generated through one or more analyses of a corpus of content collections that is maintained by the online content service. As should be understood, this corpus of content collections is comprised of a plurality of curated content collections, with each content collection comprising a plurality of digital content items. With this taste graph available, as a user generates (or in response to a user generating) a content collection of digital content items, reference can be made to the taste graph to identify one or more digital content items that may be added to the content collection, where the one or more digital content items have a probabilistic likelihood of being complimentary and/or compatible with the other digital content items of the content collection.
G06F 16/957 - Optimisation de la navigation, p. ex. mise en cache ou distillation de contenus
G06Q 30/0201 - Modélisation du marchéAnalyse du marchéCollecte de données du marché
G06Q 30/0282 - Notation ou évaluation d’opérateurs commerciaux ou de produits
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
Described are systems and methods for generating a debiased training dataset and training one or more stages of a multi-stage recommendation system and/or service using the debiased training dataset. Rather than generating training datasets from the output data generated by the multi-stage recommendation system and/or data served by later stages of the multi-stage recommendation system, debiased training datasets may be generated from data that was served by the particular stage of the multi-stage recommendation system that is to be trained using the generated debiased training dataset. The debiased training dataset may be generated by generating pseudo-labels for each data record and comparing the generated pseudo-labels against two thresholds to generate a binary classification of the data records.
Described is a system and method for enabling visual search for information. With each selection of a search term, additional search terms are dynamically selected and presented to the user in conjunction with results matching the currently selected search terms. Likewise, a selected search term may be tokenized and a graphical token presented to the user to represent the selected search term.
Systems and methods for determining one or more topics that may be associated and/or grounded with a node of a taxonomy to facilitate the creation and/or modification of the taxonomy. The one or more topics can be determined by generating two layers of associations and/or groundings. In a first layer, tokens can be associated and/or grounded in a corpus of queries, and in a second layer, topics can be associated and/or grounded in the tokens. The topics can then be associated with and/or grounded in nodes of a taxonomy which can facilitate access to content items stored and maintained by an online service. Further, in exemplary implementations where the content items include associations and/or mappings to the corpus of queries, the nodes of the taxonomy (which are associated with one or more topics) can be transitively mapped to the content items.
Described are systems and methods that suggest candidate recipients to receive an object identifier from a first user. For example, a first user, independent of a conversation with a second user, may select, close-up, or otherwise view an object identifier. The system, upon detecting the interaction with the object identifier by the first user, may determine one or more second users to suggest to the first user as candidate recipients with which the first user can share the viewed object identifier. The systems and methods may also suggest object identifiers to share with a selected recipient and/or determine two or more users that have a common connection and share an object identifier related to the connection with each of those users.
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06F 16/907 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
52.
RECOMMENDATION CAMPAIGNS BASED ON PREDICTED SHORT-TERM USER BEHAVIOR AND PREDICTED LONG-TERM USER BEHAVIOR
Described are systems and methods for generating recommendation campaigns that optimize for both a desired short-term user behavior and a desired long-term user behavior. In comparison to existing techniques that focus on targeting advertisements or recommendations to specific individuals with a single goal of receiving an interaction with the advertisement from that individual (i.e., a desired short-term behavior), the disclosed implementations consider the long-term user behavior, such as increased visits to a website during a long-term rage, and generate a recommendation campaign that also optimizes for that desired long-term user behavior.
Described are systems and methods for determining and/or generating a queue of content items to improve the playback experience of the content items for a user. The content items may be obtained by a client device from an online service in response to a query, a request for content items, etc. Relevance rankings and/or scores associated with the content items may be replaced and/or augmented with a playability score, which can represent a quality of the playback experience associated with the content item. The playability score may be aggregated with the relevance and/or user engagement score to determine an overall playback score for each content item. The content items may be ranked, ordered, arranged, and/or presented in accordance with the overall playback scores associated with the content items to facilitate an improved playback experience for a user associated with the client device.
H04N 21/232 - Opération de récupération de contenu au sein d'un serveur, p. ex. lecture de flux vidéo du réseau de disques
H04N 21/2387 - Traitement de flux en réponse à une requête de reproduction par un utilisateur final, p. ex. pour la lecture à vitesse variable ("trick play")
H04N 21/25 - Opérations de gestion réalisées par le serveur pour faciliter la distribution de contenu ou administrer des données liées aux utilisateurs finaux ou aux dispositifs clients, p. ex. authentification des utilisateurs finaux ou des dispositifs clients ou apprentissage des préférences des utilisateurs pour recommander des films
H04N 21/472 - Interface pour utilisateurs finaux pour la requête de contenu, de données additionnelles ou de servicesInterface pour utilisateurs finaux pour l'interaction avec le contenu, p. ex. pour la réservation de contenu ou la mise en place de rappels, pour la requête de notification d'événement ou pour la transformation de contenus affichés
Systems and methods are presented for inferring an embedding vector of an item of a first type into the embedding space. Upon receiving a first time for which there is no embedding vector, documents of a document corpus that include (co-occurrence) both the received item and other items of the same type are identified. Of those other items that have embedding vectors, those embedding vectors are retrieved and averaged. The resulting averaged embedding vector is established as an inferred embedding vector for the received item.
Described are systems and methods for generating recommendation campaigns that optimize for both a desired short-term user behavior and a desired long-term user behavior. In comparison to existing techniques that focus on targeting advertisements or recommendations to specific individuals with a single goal of receiving an interaction with the advertisement from that individual (i.e., a desired short-term behavior), the disclosed implementations consider the long-term user behavior, such as increased visits to a website during a long-term rage, and generate a recommendation campaign that also optimizes for that desired long-term user behavior.
Systems and methods for generating embeddings for nodes of a corpus graph are presented. More particularly, operations for generation of an aggregated embedding vector for a target node is efficiently divided among operations on a central processing unit and operations on a graphic processing unit. With regard to a target node within a corpus graph, processing by one or more central processing units (CPUs) is conducted to identify the target node's relevant neighborhood (of nodes) within the corpus graph. This information is prepared and passed to one or more graphic processing units (GPUs) that determines the aggregated embedding vector for the target node according to data of the relevant neighborhood of the target node.
G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
G06F 16/9536 - Personnalisation de la recherche basée sur le filtrage social ou collaboratif
G06F 18/211 - Sélection du sous-ensemble de caractéristiques le plus significatif
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
Systems and methods for generating user notifications to a set of users of a social networking service is presented. For each user of a set of users of the social networking service, one or more machine learning models selects an optimal notification channel, an optimal notification template, and optimal personalization content for configurable elements of a selected notification template. Each of these determinations/selections is made according to and based on a likelihood of increased user engagement with the social networking service. Upon determining the notification channel, notification template, and personalizations to the template, the notification is generated and sent to the corresponding user.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
Example order management systems and methods are described. In one implementation, a catalog ingestion system receives data associated with multiple products offered by multiple brands. A commerce management system receives a customer order from a customer. The customer order includes at least a portion of the multiple products offered by the multiple brands. The commerce management system splits the customer order into multiple brand orders such that each brand order is associated with products in the customer order from a particular brand. An integration platform fulfills the brand orders by submitting the brand orders to the associated brands.
Systems and methods for responding to a subscriber's text-based request for content items are presented. In response to a request from a subscriber, word pieces are generated from the text-based terms of the request. A request embedding vector of the word pieces is obtained from a trained machine learning model. Using the request embedding vector, a set of content items, from a corpus of content items, is identified. At least some content items of the set of content items are returned to the subscriber in response to the text-based request for content items.
G06F 16/483 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.
Systems and methods for determining whether a linked content page may include spamming, malicious, and/or otherwise undesirable content. The linked content page may be crawled, scraped, and/or parsed to extract various information associated with the text, media items, and/or structure of the linked content page. The text, media, and/or structure information may be analyzed and processed to generate one or more textual features, media features, and/or structural features, which may then be processed by a trained machine learning model to determine whether the content page includes spamming, malicious, and/or otherwise undesirable content.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing content. One of the methods includes providing a plurality of image content items to an application interface of a client device for presentation; receiving a user selection of a particular image content item of the plurality of presented image content items; and responsive to the selection, providing a combination of native content and third party content associated with the selected image content item, wherein the native content includes a close up view of the selected image content item and the third party content includes a third party webpage.
G06F 17/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
G06F 3/04845 - 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 pour la transformation d’images, p. ex. glissement, rotation, agrandissement ou changement de couleur
G06F 3/0485 - Défilement ou défilement panoramique
Systems and methods for determining one or more topics that may be associated and/or grounded with a node of a taxonomy to facilitate the creation and/or modification of the taxonomy. The one or more topics can be determined by generating two layers of associations and/or groundings. In a first layer, tokens can be associated and/or grounded in a corpus of queries, and in a second layer, topics can be associated and/or grounded in the tokens. The topics can then be associated with and/or grounded in nodes of a taxonomy which can facilitate access to content items stored and maintained by an online service. Further, in exemplary implementations where the content items include associations and/or mappings to the corpus of queries, the nodes of the taxonomy (which are associated with one or more topics) can be transitively mapped to the content items.
Systems and method for determining a topic cohesion measurement between a content item and a hyperlinked landing page are presented. In one embodiment, a plurality of content item signals is generated for the content item and a corresponding plurality of signals are generated for the hyperlinked landing page. An analysis of the corresponding signals is conducted to determine a measurement of topic cohesion, a topic cohesion score, between the content item and the hyperlinked landing page. A cohesion predictor model is trained to generate the predictive topic cohesion score between an input content item and a hyperlinked landing page. Upon a determination that the topic cohesion score is less than a predetermined threshold, remedial actions are taken regarding the hyperlink of the content item. Alternatively, positive actions may be carried out, including promoting the content item to others, associating advertisements with the content item, and the like.
This disclosure describes systems and methods that facilitate the generation of recommendations by traversing a graph. Walks that traverse the graph may be initiated from a plurality of different nodes in the node graph. In order to give greater or lesser weight to particular nodes, the walks may have different lengths depending on the nodes from which they are initiated, or an unequal amount of walks may be distributed between nodes from which walks are initiated. A plurality of walks through a node graph may be tracked, and visit counts or scores for nodes in the node graph may be determined. For example, scores may be increased for nodes that are visited by a walk initiated from a first node and a second walk initiated from a second node, or scores may be decreased for nodes that are not visited by a first walk initiated from a first node and a second walk initiated from a second node. Content corresponding to nodes may be recommended based on the scores or visit counts.
G06Q 30/0201 - Modélisation du marchéAnalyse du marchéCollecte de données du marché
G06F 16/487 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des informations géographiques ou spatiales, p. ex. la localisation
Methods, systems, and apparatus, including computer programs encoded on computer storage media for receiving a set of images at a social media system, wherein each image includes one or more recognized features associated with one or more lightness values; indexing each image using the one or more recognized features and the associated range of lightness values; receiving a query; determining a first group of images that is responsive to the query; determining that the query triggers a lightness filter to be displayed on the user device; providing the first group of images for display on a user interface with one or more lightness filter indicators; and in response to a user selection of one of the one or more lightness filter indicators: filtering the first group of images to determine a filtered group of images, and updating the images provided for display according to the filtered group of images.
Disclosed are implementations that enable the linking or connection of objects and different scenes in which those objects are represented. For example, a corpus of scenes (e.g., digital images) that include a representation of one or more objects may be processed using the disclosed implementations to segment from those scenes the individual objects represented in those scenes. The disclosed implementations may further determine clusters of visually similar object segments and form object clusters for those object segments. The scenes that include those object segments are also linked to the object cluster. With scenes linked to different object clusters, a user may select one or more query objects or a query scene and be presented with other scenes that include visually similar objects, even though the overall scenes may be visually different.
G06V 20/30 - ScènesÉléments spécifiques à la scène dans les albums, les collections ou les contenus partagés, p. ex. des photos ou des vidéos issus des réseaux sociaux
Described are systems and methods for determining and/or generating a queue of content items to improve the playback experience of the content items for a user. The content items may be obtained, for example, by a client device from an online service in response to a query, a request for content items, and the like. Relevance rankings and/or scores associated with the content items may be replaced and/or augmented with a playability score, which can represent a quality of the playback experience associated with each content item. The playability score for each content item may be aggregated with the relevance and/or user engagement score associated with each content item to determine an overall playback score for each content item. The content items may be ranked, ordered, arranged, and/or presented in accordance with the overall playback scores associated with the content items to facilitate an improved playback experience for a user associated with the client device.
H04N 21/232 - Opération de récupération de contenu au sein d'un serveur, p. ex. lecture de flux vidéo du réseau de disques
H04N 21/2387 - Traitement de flux en réponse à une requête de reproduction par un utilisateur final, p. ex. pour la lecture à vitesse variable ("trick play")
H04N 21/25 - Opérations de gestion réalisées par le serveur pour faciliter la distribution de contenu ou administrer des données liées aux utilisateurs finaux ou aux dispositifs clients, p. ex. authentification des utilisateurs finaux ou des dispositifs clients ou apprentissage des préférences des utilisateurs pour recommander des films
H04N 21/472 - Interface pour utilisateurs finaux pour la requête de contenu, de données additionnelles ou de servicesInterface pour utilisateurs finaux pour l'interaction avec le contenu, p. ex. pour la réservation de contenu ou la mise en place de rappels, pour la requête de notification d'événement ou pour la transformation de contenus affichés
A user preference hierarchy is determined from user response to images. Images may be tagged using machine learning models trained to determine values for images. Products are clustered according to product vectors. Images of products within a cluster are clustered according to composition and groups of images are selected from image clusters for soliciting feedback regarding user preference for products of a cluster. Feedback is used to train a user preference model to estimate affinity for a product vector. A user may provide feedback regarding a price point and products are weighted according to a distribution about the price point. The distribution may be asymmetrical according to direction of movement of the price point. Filters may be dynamically defined and presented to a user based on popularity and frequency of occurrence of attribute-value pairs of search results and based on feedback regarding the search results.
G06F 16/20 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données structurées, p. ex. de données relationnelles
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
Systems and methods are presented for inferring an embedding vector of an item of a first type into the embedding space. Upon receiving a first time for which there is no embedding vector, documents of a document corpus that include (co-occurrence) both the received item and other items of the same type are identified. Of those other items that have embedding vectors, those embedding vectors are retrieved and averaged. The resulting averaged embedding vector is established as an inferred embedding vector for the received item.
Described is a system and method for enabling visual search for information. With each selection of a search term, additional search terms are dynamically selected and presented to the user in conjunction with results matching the currently selected search terms. Likewise, a selected search term may be tokenized and a graphical token presented to the user to represent the selected search term.
Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.
This disclosure describes systems and methods for establishing promotions for sellers and promoting images of items to users on behalf of sellers. A management service receives a source location identifier from a seller, processes images stored in an image data store to determine images that include the source location identifier in the corresponding image information and includes those images in a promotion that is established for the seller. Likewise, the management service may determine users that have previously interacted with the images and include those users in the promotion campaign.
Methods, systems and apparatus, including computer programs encoded on computer storage media for receiving a set of images at a social media system, wherein each image includes one or more recognized features associated with one or more lightness values; indexing each image using the one or more recognized features and the associated range of lightness values; receiving a query; determining a first group of images that is responsive to the query; determining that the query triggers a lightness filter to be displayed on the user device; providing the first group of images for display on a user interface with one or more lightness filter indicators; and in response to a user selection of one of the one or more lightness filter indicators: filtering the first group of images to determine a filtered group of images, and updating the images provided for display according to the filtered group of images.
This disclosure describes systems and methods that facilitate the generation of recommendations by traversing a graph. Walks that traverse the graph may be initiated from a plurality of different nodes in the node graph. In order to give greater or lesser weight to particular nodes, the walks may have different lengths depending on the nodes from which they are initiated, or an unequal amount of walks may be distributed between nodes from which walks are initiated. A plurality of walks through a node graph may be tracked, and visit counts or scores for nodes in the node graph may be determined. For example, scores may be increased for nodes that are visited by a walk initiated from a first node and a second walk initiated from a second node, or scores may be decreased for nodes that are not visited by a first walk initiated from a first node and a second walk initiated from a second node. Content corresponding to nodes may be recommended based on the scores or visit counts.
G06F 7/00 - Procédés ou dispositions pour le traitement de données en agissant sur l'ordre ou le contenu des données maniées
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
G06Q 30/0201 - Modélisation du marchéAnalyse du marchéCollecte de données du marché
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06F 16/435 - Filtrage basé sur des données supplémentaires, p. ex. sur des profils d'utilisateurs ou de groupes
G06F 16/487 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des informations géographiques ou spatiales, p. ex. la localisation
Systems and methods are presented for inferring an embedding vector of an item of a first type into the embedding space. Upon receiving a first time for which there is no embedding vector, documents of a document corpus that include (co-occurrence) both the received item and other items of the same type are identified. Of those other items that have embedding vectors, those embedding vectors are retrieved and averaged. The resulting averaged embedding vector is established as an inferred embedding vector for the received item.
Described are systems and methods to determine advertisements to be presented to a user. To determine the advertisements to be presented to the user, the described systems and methods utilize localized contextual information to select the advertisements and the relative positioning of the advertisements to be presented, so as to select and present more relevant advertisements based on the content that is surrounding and proximate to the presentation of the selected advertisements.
Described are systems and methods for providing a sequential trained machine learning model that may be configured to generate a user embedding that is representative of the user and is configured to predict a plurality of the user's actions over a period of time. The exemplary sequential trained machine learning model may be employed, for example, in connection with recommendation, search, and/or other services. Exemplary embodiments of the present disclosure may also employ the user embeddings generated by the exemplary sequential trained machine learning model in connection with one or more conditional retrieval systems that may include an end-to-end learned model, which are configured to generate updated user embeddings based on the user embeddings generated by the exemplary sequential trained machine learning model and certain contextual information.
Described is a system and method for enabling visual search for information. With each selection of an object included in an image, additional images that include visually similar objects are determined and presented to the user.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06F 16/248 - Présentation des résultats de requêtes
G06F 16/56 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données d’images fixes en format vectoriel
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 16/738 - Présentation des résultats des requêtes
Described are systems and methods for providing a multi-tasked trained machine learning model that may be configured to generate product embeddings from multiple types of product information. The exemplary product embeddings may be generated for a corpus of products (e.g., products included in a product catalog, etc.) based on both image information and text information associated with each respective product. Accordingly, the generated product embeddings may be compatible with learned representations of the different types of product information (e.g., image information, text information, etc.) and may be used to create a product index, which can be used to determine and serve product recommendations in connection with multiple different recommendation services that may be configured to receive different types of inputs (e.g., a single image, multiple images, text-based information, etc.).
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing content. One of the methods includes receiving a target image at an image platform; determining one or more images related to the target image; determining queries associated with the related images; deriving candidate keywords from the determined queries; filtering the candidate keywords to select one or more keywords; and providing the image to one or more users in response to respective incoming queries based on the one or more keywords.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 16/248 - Présentation des résultats de requêtes
Described is a system and method for enabling dynamic selection of a search input. For example, rather than having a static search input box, the search input may be dynamically positioned such that it encompasses a portion of displayed information. An image segment that includes a representation of the encompassed portion of the displayed information is generated and processed to determine an object represented in the portion of the displayed information. Additional images with visually similar representations of objects are then determined and presented to the user.
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06F 16/532 - Formulation de requêtes, p. ex. de requêtes graphiques
G06F 16/538 - Présentation des résultats des requêtes
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06F 3/04812 - Techniques d’interaction fondées sur l’aspect ou le comportement du curseur, p. ex. sous l’influence de la présence des objets affichés
G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
G06F 3/04886 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels par partition en zones à commande indépendante de la surface d’affichage de l’écran tactile ou de la tablette numérique, p. ex. claviers virtuels ou menus
Described are systems and methods to extract image segments from an image and include those extracted image segments in a collage. The origin information, such as the source image, source image location, etc., from which the extracted image segment is generated is maintained as metadata so that interaction with the extracted image segment on the collage can be used to determine and/or return to the origin of the extracted image segment. Collages may be updated, shared, adjusted, etc., by the creator of the collage or other users.
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
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 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
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
Described are systems and methods to extract image segments from an image and include those extracted image segments in a collage. The origin information, such as the source image, source image location, etc., from which the extracted image segment is generated is maintained as metadata so that interaction with the extracted image segment on the collage can be used to determine and/or return to the origin of the extracted image segment. Collages may be updated, shared, adjusted, etc., by the creator of the collage or other users.
Systems and methods for responding to a subscriber's text-based request for content items are presented. In response to a request from a subscriber, word pieces are generated from the text-based terms of the request. A request embedding vector of the word pieces is obtained from a trained machine learning model. Using the request embedding vector, a set of content items, from a corpus of content items, is identified. At least some content items of the set of content items are returned to the subscriber in response to the text-based request for content items.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 16/483 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
Systems and methods for identifying content for an input query are presented. A mapping model is trained to map elements of an input query embedding vector for a received query into one or more elements of a destination embedding vector. In response to receiving an input query, an input query embedding vector is generated that projects into an input query embedding space. The input query embedding vector is processed by the mapping model to map the input query embedding vector into one or more elements of a destination embedding vector in a destination embedding space, resulting in a partial destination embedding vector. Items of a corpus of content are projected into the destination embedding space and the partial destination embedding vector is also projected into the destination embedding space. A similarity measure determines the most-similar items to the partial destination embedding vector and at least some of the most-similar items are returned in response to the input query.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Computer software, namely, software that allows users to
interact online with information and media content that
other users share, and software that allows users to
discover, access and share information about, and media
content concerning, goods, services, and experiences;
computer software and software applications that enable
electronic communications network users to create, upload,
bookmark, view, annotate, and share data, information and
media content; software, downloadable or prerecorded, in the
nature of a mobile application that enables electronic
communications network users to create, upload, bookmark,
view, annotate, share and discover data, information and
media content; software downloadable via electronic
communications networks and wireless devices that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content; software to facilitate
business promotion, connecting social network users with
businesses; downloadable electronic publications in the
nature of blogs, photographs, and graphic art in the field
of general human interest; computer e-commerce software to
allow users to perform electronic business transactions via
a global computer network; software applications that enable
electronic communications network users to create, upload,
bookmark, view, annotate, and share data, information and
media content; software, downloadable or prerecorded, in the
nature of a mobile application that enables electronic
communications network users to create, upload, bookmark,
view, annotate, share and discover data, information and
media content; software downloadable via electronic
communications networks and wireless devices that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content. Advertising and promotional services; advertising and
marketing services, namely, promoting the products and
services of others; business data analysis; business
monitoring and consulting services, namely, data and
behavior analysis to provide strategy, insight, and
marketing guidance, and for analyzing, understanding and
predicting behavior and motivations, and market trends;
promoting the goods and services of others by means of
operating an online platform with hyperlinks to the
resources of others; providing consumer product information
relating to consumer, business, and industrial goods of
others via an online searchable database; electronic
commerce services, namely, providing information about
products via telecommunication networks for advertising and
sales purposes. Electronic bulletin board services. Providing a platform featuring technology that enables
internet users to create, upload, bookmark, view, annotate,
share and discover data, information and multimedia content;
computer services, namely, creating an online community for
registered users to participate in discussions, get feedback
from their peers, form virtual communities, and engage in
social networking services in the field of general interest;
providing online non-downloadable software that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content via a website; providing a
platform featuring non-downloadable software that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content; hosting an interactive
platform and online non-downloadable software for uploading,
posting, showing, displaying, tagging, sharing and
transmitting messages, comments, multimedia content, photos,
pictures, images, text, information, and other
user-generated content; developing and hosting a server on a
global computer network for the purpose of facilitating
e-commerce via such a server; platform and facility for
mobile device communication, namely, providing
non-downloadable software that facilitates sharing and
discovering information and media content via mobile
devices; platform and facility for networked communications,
namely, providing non-downloadable software that facilitates
sharing and discovering information and media content via
local and global computer, mobile, cellular, electronic,
wireless, and data communications networks. Providing online social networking services for purposes of
commentary, comparison, collaboration, consultation,
evaluation, advice, discussion, research, notification,
reporting, identification, information sharing, indexing,
information location, entertainment, pleasure, or general
interest.
A computer system extracts product data from a website and correlates product records from multiple sources to one another as corresponding to the same product. A website is crawled efficiently by rendering webpages using a virtual browser that ignores blacklisted elements, extracts data from objects without rendering, and suppressing retrieval of remote resources. Data is extracted according to engine control statements including a selector and extractor. A website may be crawled repeatedly and changes in extracted data may be detected and flagged. Engine control statements may be automatically changed in response to detecting a change in the configuration of the website. Images of product records may be correlated with one another by first comparing text of the product records and selecting images for comparison based on composition. Images are compared using a machine learning model. Images determined to be similar may be presented to a human for a correlation decision.
A computer system extracts product data from a website and correlates product records from multiple sources to one another as corresponding to the same product. A website is crawled efficiently by rendering webpages using a virtual browser that ignores blacklisted elements, extracts data from objects without rendering, and suppressing retrieval of remote resources. Data is extracted according to engine control statements including a selector and extractor. A website may be crawled repeatedly and changes in extracted data may be detected and flagged. Engine control statements may be automatically changed in response to detecting a change in the configuration of the website. Images of product records may be correlated with one another by first comparing text of the product records and selecting images for comparison based on composition. Images are compared using a machine learning model. Images determined to be similar may be presented to a human for a correlation decision.
Systems and methods for de-serializing one or more data elements of a serialized structured data record are presented. In response to a request to de-serialize one or more data elements of a serialized structured data record of a first type, the location of the serialized structured data record is determined within a data file containing a plurality of serialized structured data records. Locations of the data of the one or more data elements are determined and the data of the one or more data elements is retrieved. The data is de-serialized and stored in corresponding data elements in an instantiated structured data record of the first type. The one or more data elements that are de-serialized are fewer than all of the data elements of the serialized structured data record.
This disclosure describes systems and methods that facilitate purchase of objects from merchants. For example, a user may browse a website available from an object management service and identify objects that they desire to purchase. Rather than having to locate the seller of those objects to make a purchase, the implementations described herein facilitate a connection between the user and the merchant so that the merchant's sales are increased and the user is provided an efficient and safe shopping experience.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Computer software, namely, software that allows users to
interact online with information and media content that
other users share, and software that allows users to
discover, access and share information about, and media
content concerning, goods, services, and experiences;
computer software and software applications that enable
electronic communications network users to create, upload,
bookmark, view, annotate, and share data, information and
media content; software, downloadable or prerecorded, in the
nature of a mobile application that enables electronic
communications network users to create, upload, bookmark,
view, annotate, share and discover data, information and
media content; software downloadable via electronic
communications networks and wireless devices that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content; software to facilitate
business promotion, connecting social network users with
businesses; downloadable electronic publications in the
nature of blogs, photographs, and graphic art in the field
of general human interest; computer e-commerce software to
allow users to perform electronic business transactions via
a global computer network; software applications that enable
electronic communications network users to create, upload,
bookmark, view, annotate, and share data, information and
media content; software, downloadable or prerecorded, in the
nature of a mobile application that enables electronic
communications network users to create, upload, bookmark,
view, annotate, share and discover data, information and
media content; software downloadable via electronic
communications networks and wireless devices that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content. Advertising and promotional services; advertising and
marketing services, namely, promoting the products and
services of others; business data analysis; business
monitoring and consulting services, namely, data and
behavior analysis to provide strategy, insight, and
marketing guidance, and for analyzing, understanding and
predicting behavior and motivations, and market trends;
promoting the goods and services of others by means of
operating an online platform with hyperlinks to the
resources of others; providing consumer product information
relating to consumer, business, and industrial goods of
others via an online searchable database; electronic
commerce services, namely, providing information about
products via telecommunication networks for advertising and
sales purposes. Electronic bulletin board services. Providing a platform featuring technology that enables
internet users to create, upload, bookmark, view, annotate,
share and discover data, information and multimedia content;
computer services, namely, creating an online community for
registered users to participate in discussions, get feedback
from their peers, form virtual communities, and engage in
social networking services in the field of general interest;
providing online non-downloadable software that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content via a website; providing a
platform featuring non-downloadable software that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content; hosting an interactive
platform and online non-downloadable software for uploading,
posting, showing, displaying, tagging, sharing and
transmitting messages, comments, multimedia content, photos,
pictures, images, text, information, and other
user-generated content; developing and hosting a server on a
global computer network for the purpose of facilitating
e-commerce via such a server; platform and facility for
mobile device communication, namely, providing
non-downloadable software that facilitates sharing and
discovering information and media content via mobile
devices; platform and facility for networked communications,
namely, providing non-downloadable software that facilitates
sharing and discovering information and media content via
local and global computer, mobile, cellular, electronic,
wireless, and data communications networks. Providing online social networking services for purposes of
commentary, comparison, collaboration, consultation,
evaluation, advice, discussion, research, notification,
reporting, identification, information sharing, indexing,
information location, entertainment, pleasure, or general
interest.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Computer software, namely, software that allows users to
interact online with information and media content that
other users share, and software that allows users to
discover, access and share information about, and media
content concerning, goods, services, and experiences;
computer software and software applications that enable
electronic communications network users to create, upload,
bookmark, view, annotate, and share data, information and
media content; software, downloadable or prerecorded, in the
nature of a mobile application that enables electronic
communications network users to create, upload, bookmark,
view, annotate, share and discover data, information and
media content; software downloadable via electronic
communications networks and wireless devices that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content; software to facilitate
business promotion, connecting social network users with
businesses; downloadable electronic publications in the
nature of blogs, photographs, and graphic art in the field
of general human interest; computer e-commerce software to
allow users to perform electronic business transactions via
a global computer network. Advertising and promotional services; advertising and
marketing services, namely, promoting the products and
services of others; business data analysis; business
monitoring and consulting services, namely, data and
behavior analysis to provide strategy, insight, and
marketing guidance, and for analyzing, understanding and
predicting behavior and motivations, and market trends;
promoting the goods and services of others by means of
operating an online platform with hyperlinks to the
resources of others; providing consumer product information
relating to consumer, business, and industrial goods of
others via an online searchable database; electronic
commerce services, namely, providing information about
products via telecommunication networks for advertising and
sales purposes. Electronic bulletin board services. Providing a platform featuring technology that enables
internet users to create, upload, bookmark, view, annotate,
share and discover data, information and multimedia content;
computer services, namely, creating an online community for
registered users to participate in discussions, get feedback
from their peers, form virtual communities, and engage in
social networking services in the field of general interest;
providing online non-downloadable software that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content via a website; providing a
platform featuring non-downloadable software that enables
electronic communications network users to create, upload,
bookmark, view, annotate, share and discover data,
information and media content; hosting an interactive
platform and online non-downloadable software for uploading,
posting, showing, displaying, tagging, sharing and
transmitting messages, comments, multimedia content, photos,
pictures, images, text, information, and other
user-generated content; developing and hosting a server on a
global computer network for the purpose of facilitating
e-commerce via such a server; platform and facility for
mobile device communication, namely, providing
non-downloadable software that facilitates sharing and
discovering information and media content via mobile
devices; platform and facility for networked communications,
namely, providing non-downloadable software that facilitates
sharing and discovering information and media content via
local and global computer, mobile, cellular, electronic,
wireless, and data communications networks. Providing online social networking services for purposes of
commentary, comparison, collaboration, consultation,
evaluation, advice, discussion, research, notification,
reporting, identification, information sharing, indexing,
information location, entertainment, pleasure, or general
interest.
Disclosed are systems and methods that authenticate non-fungible tokens (“NFT”) and/or digital data represented by or pointed to by an NFT. In some implementations, authentication may be with respect to an existing NFT. In other implementations, authentication may be with respect to an NFT that is being created. The disclosed implementations may compare a candidate and/or candidate NFT data with existing NFTs and/or existing NFT data to determine if the candidate NFT and/or candidate NFT data is similar to other NFTs and/or other NFT data of another NFT, which may exist on any of many different blockchains.
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 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 9/06 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p. ex. système DES
H04L 9/30 - Clé publique, c.-à-d. l'algorithme de chiffrement étant impossible à inverser par ordinateur et les clés de chiffrement des utilisateurs n'exigeant pas le secret
Disclosed are systems and methods that authenticate non-fungible tokens ("NFT") and/or digital data represented by or pointed to by an NFT. In some implementations, authentication may be with respect to an existing NFT. In other implementations, authentication may be with respect to an NFT that is being created. The disclosed implementations may compare a candidate and/or candidate NFT data with existing NFTs and/or existing NFT data to determine if the candidate NFT and/or candidate NFT data is similar to other NFTs and/or other NFT data of another NFT, which may exist on any of many different blockchains.
Systems and methods for identifying content for an input query are presented. A mapping model is trained to map elements of an input query embedding vector for a received query into one or more elements of a destination embedding vector. In response to receiving an input query, an input query embedding vector is generated that projects into an input query embedding space. The input query embedding vector is processed by the mapping model to map the input query embedding vector into one or more elements of a destination embedding vector in a destination embedding space, resulting in a partial destination embedding vector. Items of a corpus of content are projected into the destination embedding space and the partial destination embedding vector is also projected into the destination embedding space. A similarity measure determines the most-similar items to the partial destination embedding vector and at least some of the most-similar items are returned in response to the input query.
Systems and methods are presented for adding system-identified content to a user's content space based on information identified from the user's email account. In operation, after obtaining user authorization to access email items of an email account, the email items are iteratively processed. In processing each of the email items, one or more topics of a currently processed email item are identified. Based on the identified topics and user preferences, one or more content items may be identified for addition to the user's online content space. Additionally, the information identified from processing the one or more email items is used to update the user's preferences.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
98.
Matching user provided representations of items with sellers of those items
This disclosure describes systems and methods for matching user provided images that include representations of items with sellers of those items. A management service, as described herein, may provide a web site where users can post images, view images, share images, correspond with other users, etc. The management service may identify items represented in the images and determine one or more sellers that offer those items for sale. When another user requests to view the user provided image, the image, seller information identifying the seller determined to sell the item represented in the image, and/or a purchase control that may be selected by a user to initiate a purchase with the seller is presented.
Systems and methods for responding to a subscriber's text-based request for content items are presented. In response to a request from a subscriber, word pieces are generated from the text-based terms of the request. A request embedding vector of the word pieces is obtained from a trained machine learning model. Using the request embedding vector, a set of content items, from a corpus of content items, is identified. At least some content items of the set of content items are returned to the subscriber in response to the text-based request for content items.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 16/483 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
Systems and methods for predictively identifying content sources as likely activity subscribers to an online service is presented. In view of a corpus of activity data describing activity of users of an online service, an analysis of this corpus of activity data is carried out to identify one or more content sources that are not activity subscribers but whose content is posted on the online service. A machine learning model is employed to predictively identify at least some content sources that are not activity subscribers of the online service that could likely become activity subscribers. A first content source of the one or more content sources is identified as a likely potential activity subscriber according to the trained machine learning model and the first content source is notified with information for becoming an activity subscriber.
