Patent Application
20210243869
The invention relates to a verification device, a lighting system, a lighting system verification method, and a computer readable medium.
Buildings with windows and/or other sources for natural light ingress receive enough daylight to partly illuminate the interior space. In modern lighting systems, the use of electrical lighting can be reduced to conserve energy. This may be done using so-called daylight controls. In such controls, the amount of electric lighting used is controlled based on the amount of daylight present to regulate the net illumination in the space. For instance, a daylight controlled lighting system can dim or switch off luminaires if the amount of available daylight is sufficiently high to meet illumination requirements. The monitoring of illumination is done using light sensors. Daylight controls lead to a reduction of electrical energy consumption due to lighting and can be used to reduce peaks in energy demand.
However, commissioning of daylight control systems can be quite complex in lighting systems [1]. As an example, daylight controls may be designed to operate in very specific manners. However due to installation and eventual lifetime of system operation, the controls may be misconfigured. For example, the building may be reconfigured, e.g., walls may be moved or removed without proper adjustment of the existing lighting system. As another example, the light sensors need to be properly calibrated. Improper calibration can cause under-illumination [2] or annoying light-level fluctuations that can lead to occupant frustration which may even result in occupants sabotaging the system by disabling controls, e.g., by blocking the light sensor.
There is thus a need for automated support to diagnose misconfigured of broken lighting systems. Fortunately, a current trend is that such lighting systems are becoming equipped with large numbers of sensors and are increasingly connected. For example, lighting data may be collected and stored in a backend database or Cloud, etc. Unfortunately, a problem observed by the inventors is that it often happens that not all relevant data is stored. For example, frequently only lighting energy consumption and occupation data is logged, without the light sensor data. There is thus a need for diagnosing or verifying a lighting system based on incomplete information.
In the art, different methods for daylight control are known. For example, in patent [3] more than one light sensor is used to improve calibration and reliability of daylight controls. Based on sensor data from two sensors (one at a luminaire operating in closed-loop and another located elsewhere operating in open-loop mode), improved calibration was proposed. These methods use additional sensors but do not address the problem of validating whether daylight control is working correctly, e.g., according to design intent, or of diagnosing other daylight control misconfigurations.
A verification device for verifying a lighting system (also simply referred to as a verification device, or verification device) is proposed as defined in the claims. The verification device can identify problems in the lighting system even though it has access only to partial information. For example, verification device may have access to energy consumption and occupancy information, but not to lighting data. Interestingly, the verification device computes an energy consumption estimate based on the partial information, and compares it to the actual energy consumption. If the estimate is too close, that is, if it is possible to estimate energy consumption well without having access to all of the factors that the verification device allegedly takes into account, then it is concluded that the lighting system might not be configured correctly or that there may be a malfunction.
Using a prototype of an embodiment, automated misconfigurations have indeed been detected on the basis of occupancy data, and limited configuration data. In particular, without access to data from light sensors, lighting systems have been identified in which it turned out that light sensor information was not taken into account.
In an embodiment, the lighting system is configured to control multiple luminaires at least partly in response to the occupancy sensors and partly in response to other factors. For example, the other factors may be lighting sensors. Interestingly, verification device may verify the configuration of the lighting system without access to the other factors. For example, in an embodiment, the verification device is configured to transmit a signal indicating a lack of dependency of the lighting system on lighting sensors.
The verification device is an electronic device. For example, it may be a computer, a server, etc.
In an embodiment of the verification device, the one or more luminaires are luminaires that are assigned to a same control zone.
An aspect of the invention concerns a lighting verification method. The method according to the invention may be implemented on a computer as a computer implemented method, or in dedicated hardware, or in a combination of both. Executable code for a method according to the invention may be stored on a computer program product. Examples of computer program products include memory devices, optical storage devices, integrated circuits, servers, online software, etc. Preferably, the computer program product comprises non-transitory program code stored on a computer readable medium for performing a method according to the invention when said program product is executed on a computer.
In a preferred embodiment, the computer program comprises computer program code adapted to perform all the steps of a method according to the invention when the computer program is run on a computer. Preferably, the computer program is embodied on a computer readable medium.
Another aspect of the invention provides a method of making the computer program available for downloading. This aspect is used when the computer program is available for downloading.
Further details, aspects, and embodiments of the invention will be described, by way of example only, with reference to the drawings. Elements in the Figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. In the Figures, elements which correspond to elements already described may have the same reference numerals. In the drawings,
While this invention is susceptible of embodiment in many different forms, there are shown in the drawings and will herein be described in detail one or more specific embodiments, with the understanding that the present disclosure is to be considered as exemplary of the principles of the invention and not intended to limit the invention to the specific embodiments shown and described.
In the following, for the sake of understanding, elements of embodiments are described in operation. However, it will be apparent that the respective elements are arranged to perform the functions being described as performed by them.
Further, the invention is not limited to the embodiments, and the invention lies in each and every novel feature or combination of features described herein or recited in mutually different dependent claims.
In an embodiment, the light sensor is a so-called ambient light sensor which measures the ambient light. However, one could also use a daylight light sensor which measures only light of daylight frequencies. We will assume the light sensors are ambient light sensors.
Lighting system 100 comprises a lighting controller 110. Lighting controller 110 is configured to receive sensor measurements from the occupancy and light sensors, and to generate a control signal based thereon. The control signal is sent to the luminaires. For example, lighting controller 110 may be configured to only turn the luminaires of a control zone on if at least one of the occupancy sensors assigned to the control zone detects occupancy in the area illuminated by the control zone, or, e.g., occupancy has been detected in a recent time-period, e.g., the last 5 minutes. When occupancy has been detected, the illumination level of the luminaires may be based on a feedback system. For example, controller 110 may control the dimming level of the luminaires in the control zone so that the light sensors measure a desired illumination level. Interestingly, this mechanism automatically takes into account illumination in the illumination area corresponding to the control zone that comes from other sources than the luminaires of the control zones, e.g., daylight, or light coming from other control zones. The desired illumination level, or the correspondence between sensors and luminaires may be stored in a storage of controller 110 (not shown separately in
In this embodiment, the control of the lights thus takes into account two factors: occupancy and the light level. There may be other factors that could be taken into account, e.g., time of day, type of occupation, user preference profiles, manual wall switches, etc.
Shown in
Note that in
Returning to
Lighting system 100 comprises an energy measurement device 115 arranged to measure the energy use of lighting system 100, e.g., of the luminaires. Energy measurement device 115 measures at some spatial resolution, e.g., at the level of control zones, and reports at some time resolution, e.g., every 5 minutes, every hour, etc. These resolutions may both be finer or coarser. For example, spatial resolution may be at the level of individual luminaires or at the level of multiple control zones, e.g., an entire floor. For example, energy measurement device 115 may report energy use of a period, e.g., the last 5 minutes in some unit of energy use, e.g., (kilo)watt, or some unit of energy, e.g., (kilo)watt-hours (kwh). Energy measurement device 115 may store, e.g., log the energy use itself or report it, say, to controller 110.
As shown in
Note that
Device 200 comprises a communication interface 210. Communication interface 210 is arranged to receive,
For example, the energy consumption may be of a particular control zone, and the occupancy data may be of the one or more occupancy sensors assigned to the same control zone. For example, the energy consumption may be of a particular luminaire, and the occupancy data may be of an occupancy sensor controlling that luminaire. In an embodiment, the verification device 200 does not receive data of the light sensors in lighting system 100. In an embodiment, verification device 200 computes an estimate of the energy use under the assumption that the other factors, e.g., the light sensors are not properly used. If the estimated energy consumption turns out to be a fair approximation of the actual energy consumption, it is concluded that the other factors are not properly taken into account. A likely cause may be that lighting system 100 is not configured to take daylight into account. For example, by accident a simpler configuration may be installed which is intended for lighting systems 100 which do not have light sensors. Alternatively, some of the light sensors may be disabled, e.g., by occupants of the building who for some reason dislike the lighting system.
The occupancy data and the energy consumption data may correspond to a same time period. For example, their collected information may be collected within said same time period, e.g. during daytime.
For example, occupancy data may be received at device 200 in a raw format, e.g., it may be a log of the messages sent by the occupancy sensor, possibly together with a timestamp. The occupancy data may also have been processed, e.g., by controller 110. For example, the occupancy information may contain which control zone was occupied at which times. For example, it may be assumed that a luminaire or entire control zone is turned on when a corresponding occupancy sensor detects occupancy, e.g., movement; it may further be assumed that they stay on for a period, say for at least 5 minutes.
Verification device 200 comprises an energy estimator 220. The latter may comprise or otherwise have access to, e.g., comprise, an optional lighting model 222. There is an advantage in using a lighting model 222, but in practice it has turned out that good results can also be obtained without one.
The execution of the verification device 200 is implemented in a processor circuit, examples of which are shown herein.
For example, the occupancy data may comprise multiple data messages comprising, say an occupancy sensor id, and a timestamp, which indicates that the occupancy sensor with that particularly id, detected occupancy at that particular timestamp. The timestamp may be added by lighting system 100, e.g., controller 110, or by the occupancy sensor. For example, the occupancy data may comprise multiple data messages which also comprise occupancy status, which indicate if the occupancy sensor detected occupancy or not. For example, the occupancy data may have been processed and comprise one or more data messages comprising, say, an occupancy sensor id, and a time period, which indicates that there was occupancy in that period. For example, the occupancy data may have been processed and comprise multiple data messages comprising, say, a control zone id and/or a luminaire id, and a time period, which indicates that there was occupancy in that period for that control zone and/or luminaire.
Device 200 may comprise an optional configuration storage 250. Information in configuration storage 250 may be provided by lighting system 100, e.g., controller 100 as well. The information may also be obtained from a third party. Configuration storage 250 comprises information about the configuration of lighting system 100. For example, this information may be obtained from the installer of lighting system 100. For example, configuration storage 250 may comprise an assignment of luminaires to occupancy sensors. In an embodiment, energy estimator 220 maps occupancy information to particular control zones and/or to luminaires using the assignment information.
Energy estimator 220 is configured to compute an energy consumption estimate for the one or more luminaires from the occupancy data. For example, the occupancy data may indicate occupancy of particular control zones. The occupancy data may have a time resolution, e.g., of per minute occupancy information. The energy estimate may have a similar resolution. There are various ways to compute an energy estimate.
In an embodiment, estimator 220 is configured to estimate which of the one or more luminaires are turned on from the occupancy data. For example, estimator 220 may estimate that the luminaires assigned to a particular occupancy sensor are turned on, whenever the occupancy sensor detects occupancy. Once it has been detected which luminaires or control zones are turned on, the energy consumption can be estimated. For example, the estimate may be the product of rated power consumption of the luminaire(s), also referred to as an energy rating, e.g., in kilowatt, and the time the luminaire(s) or control zones are turned on. For example, the energy rating may be obtained from the configuration store 250 as well. It is the aim to estimate the energy of the same number of devices as the received energy consumption. For example, if the energy consumption from system 100 is per control zone, then the energy consumption estimate is also estimated per control zone. In this case, a rated power consumption, i.e., an energy rating, per control zone may be stored in store 250. The energy use may initially be estimated per luminaire and then summed to obtain a per control zone estimate.
Returning to
For example, one may assume that a 60% dimmed luminaire has an energy rating which is 60% of its full energy rating. In embodiments such as these, if the estimated energy use were graphed a block-graph with more than two levels would emerge. An advantage of taking dimming levels into account is that a better estimate is obtained from occupancy alone. Using the estimate will allow a better distinction between differences caused by bad light sensor configuration and differences caused by the occupancy sensors themselves. It may be the case that some luminaires are not dimmable. In that case one could, e.g., use the dimming level for the dimmable luminaires, and assume that the other luminaires are at full power.
More generally, energy use is estimated based on occupancy data while assuming that the other factors are set to a fixed value. In case the other factors are light sensors, it may be assumed that the light sensors are set to a default value, in particular, to full darkness. This is a setting that would normally drive the lighting system to create as high light output.
In an embodiment, estimator 220 uses a data model 222. For example, the data model may be implemented in computer software stored in a memory of device 200 and running on a processor circuit of device 200. Ideally, the lighting model 222 is the same model as is used by controller 110. For example, the lighting model could take as input data of the occupancy sensors and the other factors, e.g., of the light sensors, and produces as output a control signal for the luminaires. For the other factors, e.g., of the light sensors fictional data may be used, in particular a constant value. The model may be used to compute an energy consumption estimate, by providing the model with the occupancy information, and with fixed values of the unknown factors, e.g., inputs of the data model corresponding to light sensors may be set to a value corresponding with maximum darkness, e.g., 0. For example, the lighting data may correspond to a dark room without illumination. Such a light sensor response may be measured.
Based on the inputs, the lighting model computes control signals for the luminaires. For example, the control signals may comprise dimming levels for the luminaires. From the dimming levels and the full energy rating, energy ratings for the dimmed luminaires may be computed. The lighting model 110 may take into account various factors which are not taken into account in the simpler estimations. For example, the data model may use different dimming levels at different times of the day. For example, the data model may use different dimming levels for areas with different types of activities; e.g., typing requires different illumination than say assembly. Finally, the energy use of the luminaires may be estimated from the dimming level, and if needed, summed, e.g., for control zones. An advantage of using a data model is that good estimates can be obtained of the energy. Unfortunately, the data model is often proprietary information, and not available to the verification device. Furthermore, installing the data model comes at additional complexity, which may be undesired especially if many lighting systems may need to be analyzed.
Verification device 200 comprises a comparator 230. Comparator 230 may have access to, e.g., comprise, an optional residual unit 232 and/or to an optional correlator unit 234. In an embodiment, comparator 230 is configured to compare the energy consumption estimate to the received energy consumption data and detect if a conformance therebetween is within a threshold. In other words, comparator 230 triggers if the energy estimate is too good. If a good estimate of energy consumption can be made on the basis of partial information, e.g., without taking the other factors, such as light sensors, into account, then apparently those other factors do not play a role after all. If a lighting system with light sensors is installed, yet its energy consumption may be estimated without it, it is a fair assumption that there is a problem with the light sensors, e.g., they are not taken into account or malfunction for some reason.
Device 200 comprises a signal generator 240 which may be trigged by comparator 230 in case a too good estimate is found. For example, signal generator 240 may be configured to transmit a signal indicating a lack of dependency of the lighting system on the other factors. For example, signal generator 240 may send an email, or a report, or an SMS or the like. Signal generator 240 may comprise a display, e.g., a monitor, for displaying the signal. For example, the signal may indicate which luminaire or which control zone seems to be independent of the other factors.
There are number of ways in which the energy estimate may be compared to an actual energy measurement. For example, comparator 230 may use the optional residual unit 232. Residual unit 232 is configured to compute a residual signal as the difference between the received energy consumption and the estimated energy consumption. Detecting a conformance within a threshold may then comprise detecting that the residual signal is below a threshold. For example, the estimate and measurement may be subtracted point wise, e.g., per time moment. For example, the measurement may be subtracted from the estimate, since often the latter is smaller than the former. However, this is not needed. The residual signal may be compared in absolute value, e.g., an absolute value operator may be applied to the difference signal to obtain the residual signal. Determining that the residual signal is below a threshold may comprise computing an integral of the residual signal over a time period, e.g., over a day, and comparing the integral with a threshold value. Determining that the residual signal is below a threshold may be done by determining that the residual signal is always below a threshold value, e.g., in absolute value. A residual signal may also be formed by dividing the estimated energy used by the actual energy use, or vice versa. For small values of the estimated energy used and/or the actual energy use this may set to a fixed, value, e.g., to 0.
The residual signals may be used for further verifications. For example, as part of the verifications, residual signals may be obtained for multiple control zones, or for multiple luminaires. Even if the difference of a residual signal is not below a threshold, further use of them may be made. For example, suppose two residual signals have been computed for two different sets of luminaires. In this case, a set may be, e.g., a single luminaire, or a single control zone, etc. For example, in an embodiment, comparator 230 is configured to use correlator 234. Correlator 234 is configured to compute a correlation between the two residual signals, and transmit the signal if the correlation is below a threshold, e.g., the absolute value of the correlation. The correlation may be computed over a time period, say a day, a week, etc.
For example, suppose that neither of the two signals is a too-good approximation of the actual energy use. As a result, the effect of the other factors, e.g., of the light sensors, has become even more pronounced in the residual signal. Such other factors are likely similar for similar luminaires/control zones, especially if they are near. Accordingly, if the residual signals have a low correlation one of the two may have a problem with the other factors. Correlation may be used with either type of residual signal, in particular with a difference and with a division type.
In an embodiment, the configuration storage 250 stores a distance indication from the one or more luminaires to a window, the two sets having a distance indicating within a threshold. For example, the distance indication may be the row number, e.g., the row number counting from or towards a window, e.g., as in
The comparator 230 may use further information, e.g., external lighting data. The external lighting data may be obtained from an optional further sensor 236. The further sensor may be a building light sensor, e.g., arranged outside of the building. The external lighting data may be blinds information, which may be taken from a blinds system. The blind information is correlated to the amount of light that is received at a window. A residual signal, especially corresponding to a luminaire or control zone close to a window is expected to correlate high with the external lighting data. For example, in an embodiment the comparator is configured to select a control zone located close to a window, receive external lighting information, e.g., from a building sensor and compute a correlation. If the correlation is low, there may be a problem.
In the various embodiments of device 200, the communication interface may be selected from various alternatives. For example, the interface may be a network interface to a local or wide area network, e.g., the Internet, a storage interface to an internal or external data storage, an application interface (API), etc.
The verification device may have a user interface, which may include well-known elements such as one or more buttons, a keyboard, display, touch screen, etc. The verification device may also have a user interface. The user interface may be arranged for accommodating user interaction for performing a verification action, e.g., uploading or receiving energy and occupation information and/or receiving information signals indicating possible problems with the lighting system 100.
Verification device 200 may comprise a configuration storage 250 and may also comprise other storage, e.g., for storing the received data, for storing computer software implementing a verification method, and the like. The storage, e.g., storage 250 may be implemented as an electronic memory, say a flash memory, or magnetic memory, say hard disk or the like. The storage may comprise multiple discrete memories together making up the storage. The storage may also be a temporary memory, say a RAM. In the case of a temporary storage, the storage may contain means to obtain data before use, say by obtaining them over an optional network connection.
Typically, the verification device 200 comprises a microprocessor (not separately shown in
In an embodiment, verification device 200 comprises a communication interface circuit, an energy estimator circuit, a comparator circuit and a signal generator circuit, and optionally a lighting model circuit, a residual unit circuit and/or a correlator unit circuit. The circuits implement the corresponding units described herein. The circuits may be a processor circuit and storage circuit, the processor circuit executing instructions represented electronically in the storage circuits.
A processor circuit may be implemented in a distributed fashion, e.g., as multiple sub-processor circuits. A storage may be distributed over multiple distributed sub-storages. Part or all of the memory may be an electronic memory, magnetic memory, etc. For example, the storage may have volatile and a non-volatile part. Part of the storage may be read-only.
Further embodiments are illustrated with reference to
Zone 1: daylight controls enabled
Zone 2: daylight controls enabled
. . .
Zone 10: daylight controls enabled
The numbers in the curly braces refer to IDs of luminaires and/or sensors. Configuration store 450 also stores that for all these zones daylight controls are enabled. However, this may be faulty, and can be verified by verification device 440.
In an embodiment, verification device 440 may be configured for one or more of the following: obtaining a residual of installed power*occupancy and lighting energy consumed at different time instants at different building hierarchical levels e.g., at luminaire and control area levels; correlating residual signals across different logically neighborhood luminaires and/or control zones; correlating residual signals across different logically neighborhood luminaires and/or control zones with external environmental data like daylight availability for example measured by a building sensor and/or other building system data like blind configurations; using some of these options to diagnose and validate daylight controls, e.g., in relation to the intended design configuration.
The occupancy sensors and light sensors may be used in the lighting systems to adapt to presence and daylight conditions. The output of one or more sensors is used to control a group of luminaires that form a control zone. This is depicted in
Denote Ek(ti−tj) to be the lighting energy consumption for the k-th luminaire/control zone over time interval ti−tj. Let Pk be the installed power for the k-th luminaire/control zone, and Ok(ti−tj) be the representative occupancy over the time interval ti−tj. The quantity Pk*Ok(ti−tj) is an example of estimated lighting energy consumption taking in to account the effect of occupancy controls. For example, control zones 401-410 may have index 1-10.
Compute the residual rk(ti−tj)=Pk*Ok(ti−tj)−Ek(ti−tj). A daylight control is expected to be active if the residual exceeds a certain threshold value. If the value is below the threshold, various context data may be checked to further verify if there are problems with the daylight control. For example, level/type of daylight ingress, blind configurations (e.g. are blinds down), external daylight conditions, before establishing that daylight controls are not functioning properly or disabled. If this is not the design intent for daylight controls, then feedback is provided to a lighting configuration system to fix this. The interval ti-tj may have a duration of a few minutes or an hour, etc. These residuals are computed over sufficiently long periods of time, e.g., weeks or months so that the different effects of the environment (like weather, blinds) are taken in to account. A second confirmation may be made by correlating the residual with external data sources that capture variation of daylight representative in the said space.
Now consider two logically adjacent luminaires/zones indexed m and n, or having a similar distance indication, etc. It would be expected that if daylight control is enabled that the residual signals are high and correlated. This corr({rm(ti−tj)}, {m(ti−tj)}) is a feature that may be used for diagnosing whether daylight control is properly functioning. In
In an embodiment, a collection of these features may further be used in a machine learning routine. In a training phase, the feature set may be computed and labelled (e.g. feature set for properly functioning daylight control, feature set for daylight control disabled, feature set for miscellaneous daylight control misconfigurations). In a subsequent online phase, the learnt machine learning classifier is then used for classifying the state of daylight controls. For example, the classifier may receive as input energy consumption over a period, and occupation data over the same period. During training of the classifier, also the input is provided if daylight controls are enabled or not. It is however, noted that good results with detecting configuration problems have been achieved even without machine learning.
Many different ways of executing the method are possible, as will be apparent to a person skilled in the art. For example, the order of the steps can be varied or some steps may be executed in parallel. Moreover, in between steps other method steps may be inserted. The inserted steps may represent refinements of the method such as described herein, or may be unrelated to the method. For example, steps 610, 620 and 630 may be executed, at least partially, in parallel. Moreover, a given step may not have finished completely before a next step is started.
A method according to the invention may be executed using software, which comprises instructions for causing a processor system to perform method 600. Software may only include those steps taken by a particular sub-entity of the system. The software may be stored in a suitable storage medium, such as a hard disk, a floppy, a memory, an optical disc, etc. The software may be sent as a signal along a wire, or wireless, or using a data network, e.g., the Internet. The software may be made available for download and/or for remote usage on a server. A method according to the invention may be executed using a bitstream arranged to configure programmable logic, e.g., a field-programmable gate array (FPGA), to perform the method.
It will be appreciated that the invention also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the invention into practice. The program may be in the form of source code, object code, a code intermediate source, and object code such as partially compiled form, or in any other form suitable for use in the implementation of the method according to the invention. An embodiment relating to a computer program product comprises computer executable instructions corresponding to each of the processing steps of at least one of the methods set forth. These instructions may be subdivided into subroutines and/or be stored in one or more files that may be linked statically or dynamically. Another embodiment relating to a computer program product comprises computer executable instructions corresponding to each of the means of at least one of the systems and/or products set forth.
For example, in an embodiment, the light system verification device may comprise a processor circuit and a memory circuit, the processor being arranged to execute software stored in the memory circuit. For example, the processor circuit may be an Intel Core i7 processor, ARM Cortex-R8, etc. In an embodiment, the processor circuit may be ARM Cortex M0. The memory circuit may be an ROM circuit, or a non-volatile memory, e.g., a flash memory. The memory circuit may be a volatile memory, e.g., an SRAM memory. In the latter case, the device may comprise a non-volatile software interface, e.g., a hard drive, a network interface, etc., arranged for providing the software.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments.
In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. Use of the verb ‘comprise’ and its conjugations does not exclude the presence of elements or steps other than those stated in a claim. The article ‘a’ or ‘an’ preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
In the claims references in parentheses refer to reference signs in drawings of exemplifying embodiments or to formulas of embodiments, thus increasing the intelligibility of the claim. These references shall not be construed as limiting the claim.
| Number | Date | Country | Kind |
|---|---|---|---|
| 17195722.8 | Oct 2017 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2018/077116 | 10/5/2018 | WO | 00 |
