Abstract: A method and system are disclosed for a novel architecture in which competing suggestions, possibly generated by competing systems, are selected by a Cognitive Unit (CU). The CU observes the user context and learns which contextual circumstances affect the user's cognitive behaviour. Majority of the traditional models trained over multiple users fail to represent the individual because (1) they ignore personal bias toward certain decisions and (2) they don't have complete visibility of all options available to users (i.e. from competitive systems). The invention is ideally suited to interact with several other products as more and more modern products are using AIs to drive the user experience. That shifts traditional HCI towards a novel form of interaction that we call human-AI interaction (HAII). When applied to user experience, predictive models make decisions on users' behalf attempting to minimise user interaction while guiding them toward the completion of predefined funnels.

Abstract: A method and system to generate vectors that represent linearly progressing entities like time are disclosed. Traditional methods of vectorisation account for semantic or associative similarity of the entities. Thus, vectors conveying semantic information do not convey structural relations between such entities. The method allows for the representation of such structural information, for example the months in a year. The vectors generated by the invention encode this relation between the months such that one can interpret the sequence of the months, the difference between then and their cyclic nature. The method works in a manner similar to a genetic code, where subsequent “child” vectors are generated by related “parents”, thus encoding the similarity and the distance of the sequential entities. An object of the inventions to allow algorithms in machine learning to easily learn over temporal entities its natural text.

Abstract: A method and system to generate vectors that represent linearly progressing entities like time are disclosed. Traditional methods of vectorisation account for semantic or associative similarity of the entities. Thus, vectors conveying semantic information do not convey structural relations between such entities. The method allows for the representation of such structural information, for example the months in a year. The vectors generated by the invention encode this relation between the months such that one can interpret the sequence of the months, the difference between then and their cyclic nature. The method works in a manner similar to a genetic code, where subsequent “child” vectors are generated by related “parents”, thus encoding the similarity and the distance of the sequential entities. An object of the inventions to allow algorithms in machine learning to easily learn over temporal entities its natural text.

Abstract: A method and system are disclosed for a novel architecture in which competing suggestions, possibly generated by competing systems, are selected by a Cognitive Unit (CU). The CU observes the user context and learns which contextual circumstances affect the user's cognitive behaviour. Majority of the traditional models trained over multiple users fail to represent the individual because (1) they ignore personal bias toward certain decisions and (2) they don't have complete visibility of all options available to users (i.e. from competitive systems). The invention is ideally suited to interact with several other products as more and more modern products are using AIs to drive the user experience. That shifts traditional HCI towards a novel form of interaction that we call human-AI interaction (HAII). When applied to user experience, predictive models make decisions on users' behalf attempting to minimise user interaction while guiding them toward the completion of predefined funnels.