Koninklijke Philips Corporation N.v.

Philips Lighting Holding B.v.

Trumpf Photonic Components Gmbh

Philips Photonics Gmbh

Versuni Holding B.v.

Gerben Kooijman

Okke Ouweltjes

Jan Frederik Suijver

Petrus Theodorus Jutte

Jeroen Herman Lammers

Karel Johannes Adrianus Van Den Aker

Ronaldus Maria Aarts

Franciscus Johannes Gerardus Hakkens

Christoph Dobrusskin

Felipe Maia Masculo

Mark Thomas Johnson

Vincent Jeanne

Johannes Hendrikus Maria Spruit

Alexander Marc Van Der Lee

Mart Kornelis-Jan Te Velde

Arnoldus Johannes Martinus Jozeph Ras

Joachim Wilhelm Hellmig

Michael Alex Van Hartskamp

Martin John Edwards

Yvonne Wendela Kruijt-Stegeman

Johannes Wilhelmus Weekamp

Frederik Jan De Bruijn

Koray Karakaya

Klaas Jacob Lulofs

Karl Catharina Van Bree

Adrienne Heinrich

Lutz Christian Gerhardt

Supriyo Chatterjea

Hendrikus Hubertus Petrus Gommans

Toon Hardeman

Armin Gerhard Kohlrausch

Egbertus Reinier Jacobs

Michiel Johannes Jongerius

Susanne Maaike Valster

Wilhelmus Andreas Marinus Arnoldus Maria Van Den Dungen

Lu Wang

Koen Theo Johan De Groot

Jasper De Vreede

Amir Hussein Rmaile

Marinus Karel Johannes De Jager

Shakith Devinda Fernando

Joris Adelbert Maria Nieuwendijk

John Taylor

Philip Preshaw

Cornelus Hendricus Bertus Arnoldus Van Dinther

Zhuangxiong Huang

Harmina Christina Zeijlstra

Job Lipsch

Jasper Van Dorp Schuitman

Rene Henk Van Der Woude

Wilhelmus Johannes Everardus Evers

Cornelis Willem Hameetman

Steven Charles Deane

Daniel Uhlmann

Jan Brouwer

Dionisio Massingo Numes

Filipe Maia Masculo

Priscilla BRANDÃO Silva

Stefano Johannes Van Den Elzen

Ming Zhang

John J Taylor

Skakith Devinda Fernando

Joris A M Nieuwendijk

René Henk Van Der Woude

Carl Glasse

Iain Leslie Campbell Chapple

Melissa Mackay Grant

A computer-implemented method of identifying a subject's body part on which a hand-held device is used to perform a personal care operation. The hand-held device comprises a head portion for contacting the subject's skin one or more sensors for measuring a parameter and generating measurement signals as the hand-held device is used on the subject's skin. The sensor(s) comprise(s) at least one of a skin contact sensor, skin tone sensor, and/or proximity sensor. The method includes obtaining a measurement signal from each sensor; analyzing the obtained measurement signal using a trained machine learning model (MLM) to identify the subject's body part that the hand-held device is used on. The trained MLM analyzes a plurality of measurements in a time window on each of the obtained measurement signals to identify the body part. An indication of the identified body part is then outputted.

Identifying a body part

According to an aspect, there is provided an apparatus for use with a treatment device. The treatment device is configured to apply a light pulse to skin of a subject to perform a treatment operation to hairs on the skin. The apparatus comprises a processing unit configured to receive one or more images of a first area of the skin from an imaging unit, wherein the imaging unit is arranged to obtain images of the skin of the subject; process the one or more images to determine whether the hairs on the first area of the skin have been treated with a light pulse based on a degree of at least one of carbonisation and curling of the hairs on the first area of the skin as shown in the one or more images; and output a first signal indicating whether the hairs on the first area of skin have been treated with a light pulse.

Determining whether hairs on an area of skin have been treated with a light pulse

Disclosed is a system, a kit, a use and an in vitro method for assessing whether a human patient suffering from periodontitis has mild periodontitis or advanced periodontitis. The system and method are based on the insight to apply a clustering technique to a reference set of patient data, and then further discriminating the sets of bio markers to be applied. The clusters are determined on the basis of the biomarkers Hepatocyte Growth Factor (HGF), Matrix metalloproteinase 8 (MMP8), and Matrix metalloproteinase 9 (MMP9). The actual classification of the patient is done on the basis of the measurement of the concentrations of either of two sets of biomarkers. For one cluster these are Interleukin-1β (IL1β), Interleukin-6 (IL6), and Collagen Telopeptide. For the other cluster, these are HGF and metallopeptidase inhibitor 1 (TIMP1).

Classification of periodontitis patients

A method () for determining a position and an orientation of a portion of a handheld personal care device relative to a user's body (), comprising: providing () a handheld personal care device comprising at least one energy source () and at least one detector (), both the at least one energy source and the at least one detector located within the handheld personal care device, the at least one energy source arranged at a first position and having a first orientation within the handheld personal care device; emitting () energy in a three dimensional space surrounding the handheld personal care device; detecting () a portion of the energy emitted; generating () sensor data; extracting () one or more features from the generated sensor data; and estimating () a position and an orientation of a portion of the handheld personal care device relative to the user's body based on the extracted one or more features.

Method and system for improved motion robustness during location measurement

Disclosed is an in vitro method for assessing whether a human patient suffering from periodontitis has mild periodontitis or advanced periodontitis. The method is based on the insight to determine a selection of three biomarker proteins. Accordingly, in a sample of saliva a patient suffering from periodontitis, the concentrations are measured of the proteins Interleukin-1β (IL-1β), Matrix metalloproteinase-9 (MMP-9) and at least one of the proteins: Interleukin-6 (IL-6), and Matrix metalloproteinase-3 (MMP-3). Based on the concentrations as measured, a value is determined reflecting the joint concentrations for said proteins. This value is compared with a threshold value reflecting in the same manner the joint concentrations associated with advanced periodontitis. The comparison allows assessing whether the testing value is indicative of the presence of advanced periodontitis or of mild periodontitis in said patient. Thereby, typically, a testing value reflecting a joint concentration below the joint concentration reflected by the threshold value is indicative for mild periodontitis in said patient, and a testing value reflecting a joint concentration at or above the joint concentration reflected by the threshold value, is indicative for advanced periodontitis in said patient.

Diagnostics of mild or advanced periodontitis based on salivary IL-1BETA and MMP-9

The invention describes a laser sensor or laser sensor module () using self-mixing interference for particle density detection, a related method of particle density detection and a corresponding computer program product. The invention further relates to devices comprising such a laser sensor or laser sensor module. It is a basic idea of the present invention to detect particles by means of self-mixing interference signals and determine a corresponding particle density. In addition at least a first parameter related to at least one velocity component of a velocity vector of the particles is determined in order to correct the particle density if there is the relative movement between a detection volume and the particles. Such a relative movement may for example be related to a velocity of a fluid transporting the particles (e.g. wind speed). Furthermore, it is possible to determine at least one velocity component of the velocity of the particles based on the self-mixing interference signals.

Laser sensor for particle detection

The present invention relates to a consumable recognition system for recognizing placement and/or type of consumable containing a food substance for the preparation of a beverage by use of a beverage dispenser. To enable the recognition of placement and/or type of consumable in a simple, foolproof and easily implementable way an embodiment of the system comprises a light source () for emitting light () to the consumable (), a light sensor () for sensing light () reflected from a reflection element () of the consumable to obtain a sensor signal, wherein the sensor signal depends on the position, orientation, fluorescence, phosphorescence and/or polarization characteristic of said reflection element, and a signal processor () for recognizing placement and/or type of consumable based on said sensor signal.

Consumable recognition system, set of consumables and beverage dispenser

A method () for determining a user's compliance with a guided cleaning session during use of an oral cleaning device () includes the steps of: (i) providing () an oral cleaning device including a sensor (), a guidance generator (), and a controller (); (ii) providing (), by the guidance generator, a guided cleaning session to the user; (iii) generating (), at a first location during the guided cleaning session, sensor data from the sensor indicating a position or motion of the oral cleaning device; (iv) comparing () the generated sensor data to expected sensor data for the first location; and (v) generating (), based on the comparison, an estimate of the user's compliance with the guided cleaning session.

Method and system for determining compliance with a guided cleaning session

A coffee grinder having a grinding tool () for grinding coffee beans, a motor () for driving the grinding tool (), a stall detector () for detecting a stall condition of the grinding tool () and/or of the motor (), and a reversal unit () configured to temporarily reverse a direction of rotation () of the grinding tool () and/or of the motor () if the stall detector () detects a stall condition. Further, a corresponding coffee grinding method and a coffee machine having a coffee grinder are presented.

Coffee grinder and coffee grinding method

The invention describes a laser sensor module () which is adapted to detect or determine at least two different physical parameters by means of self-mixing interference by focusing a laser beam to different positions. Such a laser sensor module () may be used as an integrated sensor module, for example, in mobile devices (). The laser sensor module () may be used as an input device and in addition as a sensor for detecting physical parameters in an environment of the mobile communication device (). One physical parameter in the environment of the mobile communication device () may, for example, be the concentration of particles in the air (air pollution, smog . . . ). The invention further describes a related method and computer program product.

Laser sensor for multi parameter detection

An optical particle sensor has at least first and second threshold settings applied to an optical sensor or a sensor signal to obtain first and second optical sensor readings. The first and second optical sensor readings are processed to determine a parameter which is dependent on a type of pollution event. The parameter is used to determine from at least one of the first and second optical sensor readings a mass of all particles below a first particle size. In this way the mass of all particles below a desired size can be evaluated, even though the optical sensor may not be responsive to the smallest particles.

Optical particle sensor and sensing method

The present invention relates to a consumable recognition system for recognizing placement and/or type of consumable containing a food substance for the preparation of a beverage by use of a beverage dispenser. To enable the recognition of placement and/or type of consumable in a simple, foolproof and easily implementable way the system comprises a plurality of magnetic and/or electric field elements () for separately sensing a magnetic and/or electric field, wherein said consumable comprises one or more magnetically and/or electrically conductive elements () and the sensed magnetic fields depend on at least one characteristic of the one or more magnetically and/or electrically conductive elements, a driver () for providing drive currents to said magnetic and/or electric field elements, and a signal processor () for recognizing placement and/or type of consumable based on the sensed magnetic and/or electric fields.

A consumable recognition system for recognizing placement and/or type of a consumable containing a food substance, for the preparation of a beverage using a beverage dispenser, includes a light sensor configured to sense light reflected from a reflection element of the consumable during a relative movement between the consumable and the light sensor to obtain a sensor signal. The sensor signal depends on the position, reflectivity and/or pattern of the reflection element. A movable sensor carrier is configured to carry the light sensor which is configured to sense light reflected from the reflection element during a movement of the sensor carrier. A signal processor is configured to recognize placement and/or type of consumable based on the sensor signal.

Consumable recognition system and beverage dispenser

A room divider () for dividing a room into two sub-portions (R, R) and for attenuating sound (S, S) travelling between the two sub-portions is provided. The room divider comprises hollow cylindrical elements () arranged periodically for dividing the room into the two sub-portions. At least some of the hollow cylindrical elements have a cylindrical shell () with at least one slit () extending in an axial direction () of the shell. The shell extends continuously along the perimeter of the corresponding hollow cylindrical element from one side () of the at least one slit to another side () of the at least one slit. Each of the at least one slit faces in a local elongation direction () of the room divider for increasing acoustic symmetry with respect to the two sub-portions. The use of destructive interference and resonance to attenuate sound allows for a less bulky/heavy acoustically absorbing room divider.

Acoustically absorbing room divider

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Eindhoven, Netherlands

Gerben Kooijman