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, e.g. interconnection topology
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 16/26 - Visual data miningBrowsing structured data
G06F 16/958 - Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
G06F 40/143 - Markup, e.g. Standard Generalized Markup Language [SGML] or Document Type Definition [DTD]
G06N 3/06 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
G06N 3/086 - Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
G06N 3/126 - Evolutionary algorithms, e.g. genetic algorithms or genetic programming
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
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 - Matching criteria, e.g. proximity measures
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
G06F 18/2413 - Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
42 - Scientific, technological and industrial services, research and design
Goods & 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 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 11/36 - Prevention of errors by analysis, debugging or testing of software
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 or management of web site content, e.g. publishing, maintaining pages or automatic linking
G06Q 30/02 - MarketingPrice estimation or determinationFundraising
G06F 40/143 - Markup, e.g. Standard Generalized Markup Language [SGML] or Document Type Definition [DTD]
G06N 3/086 - Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 11/36 - Prevention of errors by analysis, debugging or testing of software
G06N 3/126 - Evolutionary algorithms, e.g. genetic algorithms or genetic programming
G06F 9/451 - Execution arrangements for user interfaces
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 - Scientific, technological and industrial services, research and design
Goods & 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 - Scientific, technological and industrial services, research and design
Goods & 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 - Scientific, technological and industrial services, research and design
Goods & 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.
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 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 3/00 - Input arrangements for transferring data to be processed into a form capable of being handled by the computerOutput arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
G06F 40/154 - Tree transformation for tree-structured or markup documents, e.g. XSLT, XSL-FO or stylesheets
G06N 3/063 - Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G06N 3/12 - Computing arrangements based on biological models using genetic models
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.
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06N 3/04 - Architecture, e.g. interconnection topology
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.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 11/36 - Prevention of errors by analysis, debugging or testing of software
G06N 3/12 - Computing arrangements based on biological models using genetic models
G06F 16/958 - Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
G06F 17/22 - Manipulating or registering by use of codes, e.g. in sequence of text characters
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 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 11/36 - Prevention of errors by analysis, debugging or testing of software
G06N 3/12 - Computing arrangements based on biological models using genetic models
G06F 9/451 - Execution arrangements for user interfaces
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 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 11/36 - Prevention of errors by analysis, debugging or testing of software
G06N 3/12 - Computing arrangements based on biological models using genetic models
G06F 9/451 - Execution arrangements for user interfaces
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 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 11/36 - Prevention of errors by analysis, debugging or testing of software
G06N 3/12 - Computing arrangements based on biological models using genetic models
G06F 9/451 - Execution arrangements for user interfaces
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.
