The technology disclosed relates to a webinterface production and deployment system. In particular, it relates to a presentation module that applies a selected candidate individual to a presentation database to determine frontend element values corresponding to dimension values identified by the selected candidate individual, and which presents toward a user a funnel having the determined frontend element values.
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 16/26 - Exploration de données visuellesNavigation dans des données structurées
G06F 16/958 - Organisation ou gestion de contenu de sites Web, p. ex. publication, conservation de pages ou liens automatiques
G06F 40/143 - Balisage, p. ex. utilisation du langage SGML ou de définitions de type de document
G06N 3/06 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone
G06N 3/086 - Méthodes d'apprentissage en utilisant les algorithmes évolutionnaires, p. ex. les algorithmes génétiques ou la programmation génétique
G06N 3/126 - Algorithmes évolutionnaires, p. ex. algorithmes génétiques ou programmation génétique
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
2.
Implementing a graphical user interface to collect information from a user to identify a desired document based on dissimilarity and/or collective closeness to other identified documents
k documents of the selected grouping, (v) and dynamically displaying an identified subsequent document from the selected grouping in dependence on the set of liked documents and the set of disliked documents.
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G06F 18/23213 - Techniques non hiérarchiques en utilisant les statistiques ou l'optimisation des fonctions, p. ex. modélisation des fonctions de densité de probabilité avec un nombre fixe de partitions, p. ex. K-moyennes
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
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
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Web site optimization; consulting in the field of email marketing optimization; consulting services in the field of web site optimization; consultancy with regard to web site optimization Software as a service (SaaS) services featuring software which uses artificial intelligence for website optimization, e-mail marketing optimization, A/B testing, and multivariate testing; consulting services in the field of A/B testing and multivariate testing, namely, website usability testing services featuring A/B testing, and multivariate testing; consultancy with regard to webpage design
4.
Webinterface generation and testing using artificial neural networks
The technology disclosed relates to webinterface generation and testing to promote a predetermined target user behavior. In particular, the technology disclosed stores a candidate database having a population of candidate individuals. Each of the candidate individuals identify respective values for a plurality of hyperparameters of the candidate individual. The hyperparameters describe topology of a respective neural network and coefficients for interconnects of the respective neural network. The technology disclosed writes a preliminary pool of candidate individuals into the candidate individual population. The technology disclosed tests each of the candidate individuals in the candidate individual population. The technology disclosed adds to the candidate individual population new individuals based on the testing. The technology disclosed repeats the candidate testing and the addition of the new individuals.
G06N 3/06 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel
Roughly described, the technology disclosed provides a so-called machine-learned conversion optimization (MLCO) system that uses artificial neural networks and evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Neural networks are represented as genomes. Neural networks map user attributes from live user traffic to different dimensions and dimension values of output funnels that are presented to the users in real time. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well.
G06F 16/958 - Organisation ou gestion de contenu de sites Web, p. ex. publication, conservation de pages ou liens automatiques
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06F 40/143 - Balisage, p. ex. utilisation du langage SGML ou de définitions de type de document
G06N 3/086 - Méthodes d'apprentissage en utilisant les algorithmes évolutionnaires, p. ex. les algorithmes génétiques ou la programmation génétique
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel
G06N 3/126 - Algorithmes évolutionnaires, p. ex. algorithmes génétiques ou programmation génétique
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
Roughly described, a system for user identification of a desired document. A database is provided which identifies a catalog of documents in an embedding space, the database identifying a distance in the embedding space between each pair of documents corresponding to a predetermined measure of dissimilarity between the pair of documents. The system presents an initial collection of the documents toward the user, from an initial candidate space which is part of the embedding space. The system then iteratively refines the candidate space using geometric constraints on the embedding space determined in response to relative feedback by the user. At each iteration the system identifies to the user a subset of documents from the then-current candidate space, based on which the user provides the relative feedback. In an embodiment, these subsets of documents are more discriminative than the average discriminativeness of similar sets of documents in the then-current candidate space.
A method for finding a best solution to a problem is provided. The method includes evolving candidate individuals in a candidate pool by testing each candidate individual of the candidate individuals to obtain test results, assigning a performance measure to each of the tested candidate individuals in dependence upon the test results, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from parent candidate individuals remaining in the candidate pool, and repeating the evolution steps to evolve the candidate individuals in the candidate pool. The method further includes selecting, as a winning candidate individual, a candidate individual from the candidate pool having a best probability to beat a predetermined score, the probability to beat the predetermined score being determined in dependence upon a Bayesian posterior probability distribution of a particular candidate individual.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Web site optimization; consulting in the field of email marketing optimization; consulting services in the field of web site optimization; consultancy with regard to webpage design in the nature of web site optimization Software as a service (SaaS) services featuring software which uses artificial intelligence for website optimization, e-mail marketing optimization, A/B testing, and multivariate testing; consulting services in the field of A/B testing and multivariate testing, namely, website usability testing services featuring A/B testing, and multivariate testing
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Web site optimization; consulting in the field of email marketing optimization; consulting services in the field of web site optimization; consultancy with regard to webpage design in the nature of web site optimization Software as a service (SaaS) services featuring software which uses artificial intelligence for website optimization, e-mail marketing optimization, A/B testing, and multivariate testing; consulting services in the field of A/B testing and multivariate testing, namely, website usability testing services featuring A/B testing, and multivariate testing
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Web site optimization; consulting in the field of email marketing consulting; consulting services in the field of web site optimization Software as a service (SaaS) services featuring software which uses artificial intelligence for website optimization, e-mail marketing optimization, A/B testing, and multivariate testing; website usability testing services featuring A/B testing, and multivariate testing; consultancy with regard to webpage design
11.
Intelligently driven visual interface on mobile devices and tablets based on implicit and explicit user actions
A method for identifying a desired document is provided to include forming K clusters of documents and, for each cluster: for each respective document of the cluster determining a sum of distances between (i) the respective document and (ii) each of the other documents of the cluster; and identifying a medoid document of the cluster as the document of the cluster having the smallest sum of determined distances of all of the documents of the cluster. The method also includes selecting M representative documents for each cluster, identifying for dynamic display toward the user K groupings of documents, wherein each of the K groupings of documents identifies the selected M representative documents of a corresponding cluster, and, in response to user selection of one of the K groupings of documents, identifying for dynamic display toward the user P documents of the cluster that corresponds to the selected grouping.
G06F 16/335 - Filtrage basé sur des données supplémentaires, p. ex. sur des profils d’utilisateurs ou de groupes
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
12.
Autonomous configuration of conversion code to control display and functionality of webpage portions
The technology disclosed is generally directed to massively multivariate testing, conversion rate optimization, and product recommendation and, in particular, directed to automatically and autonomously placing conversion code (e.g., scripts) in webpages of a host website without requiring any affirmative action on the part of the host. The conversion code modifies display and functionality of a particular portion of a host webpage without modifying other portions of the host webpage. The conversion code is placed by a website modification service which is limitedly authorized by the host to modify only the particular portion of the host webpage under a product recommendation and/or conversion rate optimization scheme.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06F 3/00 - Dispositions d'entrée pour le transfert de données destinées à être traitées sous une forme maniable par le calculateurDispositions de sortie pour le transfert de données de l'unité de traitement à l'unité de sortie, p. ex. dispositions d'interface
G06F 40/154 - Transformation en arborescence pour documents en configuration arborescente ou balisés, p. ex. langages XSLT, XSL-FO ou feuilles de style
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
G06N 3/12 - Agencements informatiques fondés sur des modèles biologiques utilisant des modèles génétiques
The technology disclosed relates to neural network-based systems and methods of preparing a data object creation and recommendation database. Roughly described, it relates to, for each of a plurality of preliminary data object images, providing a representation of the image in conjunction with a respective conformity parameter indicating level of conformity of the image with a predefined goal, training a neural network system with the preliminary data object image representations in conjunction with their respective conformity parameters, to evaluate future data object image representations for conformity with the predefined goal, selecting a subset of secondary data object image representations, from a provided plurality of secondary data object image representations, in dependence upon the trained neural network system, and storing the image representations from the selected subset of secondary data object image representations in a tangible machine readable memory for use in a data object creation and recommendation system.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
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
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
14.
MACHINE LEARNING BASED WEBINTERFACE GENERATION AND TESTING SYSTEM
Roughly described, the technology disclosed provides a so-called machine learned conversion optimization (MLCO) system that uses evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Website funnels with a single webpage or multiple webpages are represented as genomes. Genomes identify different dimensions and dimension values of the funnels. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well. Each webpage is tested only to the extent that it is possible to decide whether it is promising, i.e., whether it should serve as a parent for the next generation, or should be discarded.
Roughly described, the technology disclosed provides a so-called machine learned conversion optimization (MLCO) system that uses evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Website funnels with a single webpage or multiple webpages are represented as genomes. Genomes identify different dimensions and dimension values of the funnels. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well. Each webpage is tested only to the extent that it is possible to decide whether it is promising, i.e., whether it should serve as a parent for the next generation, or should be discarded.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel
G06N 3/12 - Agencements informatiques fondés sur des modèles biologiques utilisant des modèles génétiques
G06F 16/958 - Organisation ou gestion de contenu de sites Web, p. ex. publication, conservation de pages ou liens automatiques
G06F 17/22 - Manipulation ou enregistrement au moyen de codes, p.ex. dans une séquence de caractères de texte
Roughly described, the technology disclosed provides a so-called machine-learned conversion optimization (MLCO) system that uses artificial neural networks and evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Neural networks are represented as genomes. Neural networks map user attributes from live user traffic to different dimensions and dimension values of output funnels that are presented to the users in real time. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel
G06N 3/12 - Agencements informatiques fondés sur des modèles biologiques utilisant des modèles génétiques
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
Roughly described, the technology disclosed provides a so-called machine learned conversion optimization (MLCO) system that uses evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Website funnels with a single webpage or multiple webpages are represented as genomes. Genomes identify different dimensions and dimension values of the funnels. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well. Each webpage is tested only to the extent that it is possible to decide whether it is promising, i.e., whether it should serve as a parent for the next generation, or should be discarded.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel
G06N 3/12 - Agencements informatiques fondés sur des modèles biologiques utilisant des modèles génétiques
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
Roughly described, the technology disclosed provides a so-called machine-learned conversion optimization (MLCO) system that uses artificial neural networks and evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Neural networks are represented as genomes. Neural networks map user attributes from live user traffic to different dimensions and dimension values of output funnels that are presented to the users in real time. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel
G06N 3/12 - Agencements informatiques fondés sur des modèles biologiques utilisant des modèles génétiques
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
A method for identifying a desired document is provided to include calculating a Prior probability score for each document of a candidate list including a portion of documents of an embedding space, the Prior probability score indicating a preliminary probability, for each document of the candidate list, that the document is the desired document, and identifying an initial (i=0) collection of N0>1 candidate documents from the candidate list in dependence on the calculated Prior probability scores, the initial collection of candidate documents having fewer documents than the candidate list. The method further includes, for each i'th iteration in a plurality of iterations, beginning with a first iteration (i=1) and in response to user selection of an i'th selected document from the (i−1)'th collection of candidate documents, identifying an i'th collection of Ni>1 candidate documents from the candidate list in dependence on Posterior probability scores.
Roughly described, a system for user identification of a desired document. A database is provided which identifies a catalog of documents in an embedding space, the database identifying a distance in the embedding space between each pair of documents corresponding to a predetermined measure of dissimilarity between the pair of documents. The system presents an initial collection of the documents toward the user, from an initial candidate space which is part of the embedding space. The system then iteratively refines the candidate space using geometric constraints on the embedding space determined in response to relative feedback by the user. At each iteration the system identifies to the user a subset of documents from the then-current candidate space, based on which the user provides the relative feedback. In an embodiment, these subsets of documents are more discriminative than the average discriminativeness of similar sets of documents in the then-current candidate space.
