Nokia Technologies Oy

Minna Hellstrom

Mikko Terho

Mikko Lonnfors

Istvan Beszteri

Eki Monni

Leo Karkkainen

Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation. Co-occurrence data may be stored in the experience matrix may be updated by a method comprising determining a first word based on a state of a system and/or based on a physical quantity detected by a sensor, forming a first word group comprising the first word and a second word, associating the first word and the second word with a common sparse vector, associating the first word words with a first vector of a matrix, associating the second word with a second vector of the matrix, modifying the first vector of the matrix by adding contribution of the common sparse vector to the first vector of the matrix, and modifying the second vector of the matrix by adding contribution of the common sparse vector to the second vector of the matrix.

System, a data structure for controlling the system, and a method for updating the data structure

The invention relates to predictive browsing. A set of words for use with an experience matrix are formed, wherein the words are descriptive of a context of a system such as a current web page, and wherein said experience matrix comprises sparse vectors associated with words. At least a part of at least one sparse vector of said experience matrix is accessed to form a prediction output, and suggestions of web pages are provided to a user in response to said prediction output.

Predictive service access

The invention relates to forming a prediction using an experience matrix, a matrix based on sparse vectors such as random index vectors. At least a part of a first experience matrix and at least a part of at least a second experience matrix are caused to be combined () to obtain a combined experience matrix. The experience matrices comprise sparse vectors or essentially similar vectors in nature, and said experience matrices comprise information of at least one system, for example contexts of a system. At least a part of at least one sparse vector of the combined experience matrix is accessed to form a prediction output (), and a system is controlled () in response to said prediction output.

Adaptive system

Co-occurrence data representing e.g. preferences and facts observed in a plurality of situations may be stored in a matrix as combinations of high-dimensional sparse vectors. The matrix may be called e.g. as an experience matrix. The data stored in the experience matrix may be subsequently utilized e.g. for predicting a preference of a user in a new situation. A prediction may be determined by a method comprising providing a query comprising one or more query words, accessing the experience matrix containing co-occurrence data stored as vectors of the experience matrix, determining a first auxiliary vector by identifying a vector of the experience matrix associated with a first query word, forming a query vector by using the first auxiliary vector, and determining the prediction by comparing the query vector with the vectors of the experience matrix.

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Tuusula, Finland

Minna Hellstrom