Network server arrangement for processing non-parametric, multi-dimensional, spatial and temporal human behavior or technical observations measured pervasively, and related method for the same

Abstract

This invention generally discusses wireless devices, servers and communications networks. In particular the invention pertains to performing observations in one or more mobile terminals and processing and distributing the related data in a server side system through layered data processing activities, and conversion of non- parametric data into parameterized form through the utilization of statistical filtering and semantic data structures. It is further explained how such multi-layer, parametrized data can be utilized for predictive purposes, and how feedback loops can be built with the physical world to improve future predictions. The invention is applicable in various applications, for example in systems where precise digital profiles of users need to be built on a continuous basis, and such profiles need to be dynamically linked to one or several actions triggered by emerging characteristics in the data. The multi-layer approaches makes it possible to structure output statistics into continuous and standardized, periodic datasets, even though input data is highly unorganized, non-chronological, and sporadic. Similarly, the invention describes how the multi-layer data storage structure and chosen statistical operations make it possible to build virtually an infinite number of further aggregations and averages based on the output data streams.

Description

technical field [0001] The present invention relates generally to wireless devices and communication networks. In particular, but not exclusively, the present invention relates to the processing and distribution of data related to observations performed in one or more mobile devices by hierarchical data processing activities and transformation of non-parametric data into parametric form in a server-side system , the transformation includes the utilization of applicable techniques such as statistical filtering and semantic data structures. Background technique [0002] Increasingly, data can be collected from mobile devices such as mobile terminals such as smartphones, and transactional feeds can be created based on associated observations. However, these feeds are not self-contained in exhaustively or even adequately characterizing the mobile device user under consideration, although they can undeniably tell about relevant, e.g. transaction-oriented, time-dependent (point-i...

AI Technical Summary

Problems solved by technology

[0003] Second, when behavioral data or technical observations need to be processed, multiple factors (such as processing speed, memory requirements, or general availability of historical data and making it available for complex further processing or statistical analysis) are considered ), current database and data processing solutions are not optimized for

[0004] Third, despite the fact that in principle there is a vast amount of information available about people's lives, contemporary systems unfortunately mostly do not consider the link between historical data / models and real-time data (i.e., practical applications) and fail to Confirm that their technical implementation is feasible given widely available databases, storage and data processing hardware

Images