Koninklijke Philips Corporation N.v.

Philips Lighting Holding B.v.

Johanna Maria De Bont

Saeed Reza Bagheri

Alexandre Georgievich Sinitsyn

Ingrid Christina Maria Flinsenberg

Parikshit Shah

Nevenka Dimitrova

Lucas Jacobus Franciscus Geurts

Nilanjana Banerjee

Pei-Yin Chao

Johan Partomo Djajadiningrat

Yee Him Cheung

Alexander Ryan Mankovich

Vartika Agrawal

Andreas Henricus Elisabeth Lamers

Jozef Hieronymus Maria Raijmakers

Balaji Srinivasan Santhanam

In patient cohort identification, clustering () of patients is performed using a patient comparison metric dependent on a set of features (). Information is displayed on sample patients who are similar or dissimilar to a query patient according to the clustering. User inputted comparison values are received comparing the sample patients with the query patient. The set of features and/or feature weights are adjusted to generate an adjusted patient comparison metric having improved agreement with the user inputted comparison values. The clustering is repeated using the adjusted patient comparison metric. A patient cohort is identified from a cluster () containing the query patient produced by the last clustering repetition. The information on the sample patients may be shown by simultaneously displaying two or more graphical modality representations () each plotting the sample patients and the query patient against two or more features of the modality.

Relevance feedback to improve the performance of clustering model that clusters patients with similar profiles together

Alighting system may include: at least one controller which may be configured to: obtain lighting logging data including feature information related to features of the lighting system and obtained from a plurality of feature spaces; determine lighting prediction data which predicts at least one component failure in the lighting system at a future time in accordance with the lighting logging data and include at least one complex feature; model predicted component failures which are predicted to occur at a future time in accordance with the lighting prediction data and maintenance cost; and/or store the predicted component failures model in a memory.

Intelligent lighting system with predictive maintenance scheduling and method of operation thereof

A lighting system which includes at least one controller which is configured to: obtain lighting logging information related to operation of a lighting system including a plurality of lamps from a lighting logging data portion; model lamp failures in the lighting system in accordance with the lighting logging information in the lighting system and maintenance cost, the model having a time range; predict failures in the lighting system at future times at least in part in accordance with the prediction model; form a graphical user interface (GUI) which includes a graphical depiction of the model; and render the GUI on a rendering device.

A method of navigating, organizing, browsing, searching, and selecting information objects and/or content in a ring based structure is provided wherein the relation between rings are clear and easily grasped. The method is particularly well suited for navigating in media content. The method comprises locating a number of information objects, displaying one or more of the information objects on a display in a ring based structure comprising one or more rings, and changing the size of a specific ring by selecting the specific ring. By this enlargement, the visibility of information objects comprised in the specific ring is increased, and the individual information objects visually present in the ring, may readily be selected from the enlarged ring.

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Eindhoven, Netherlands

Johanna Maria De Bont