Nokia Technologies Oy

Istvan Beszteri

Mikko Lonnfors

Eki Monni

Mika Kasslin

Janne Marin

Teemu Ilmari Savolainen

Mikko Terho

Olli Alanen

Leo Karkkainen

Ari Aarnio

Markus Isomäki

Leo Kärkkäinen

Minna Hellstrom

Lauri Laitinen

Oleg Chistyakov

Jyri HÄMÄLÄINEN

Minna HELLSTRÖM

Mikko Lönnfors

A method comprising: determining, relative to a display, a point of view of a user of the display; controlling content displayed in the display in dependence upon the determination of the point of view of the user of the display.

Controlling content displayed in a display

According to an example embodiment, a method is provided, the method comprising receiving, in a user device, over a first wireless link from a wearable device, a beaconing message that includes emergency information concerning a user wearing the wearable device, initiating, in the user device, in response to said beaconing message, an emergency call to a destination that hosts an emergency service entity, and relaying, by the user device, the emergency information received in the beaconing message from the wearable device to the emergency service entity via a second wireless link.

Emergency data delivery

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

A method comprising: identifying characteristic changes in radio environment arising from a person; and using the identified characteristic changes in radio environment arising from a person to predict when a person approaches a display apparatus and to pre-fetch data for the person.

Controlling display of data to a person via a display apparatus

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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Espoo, Finland

Istvan Beszteri