Automatic control of a function of a motor vehicle

Abstract

A method (200) for controlling a function of a motor vehicle (105) comprises steps of capturing (205) a linguistic description of a conditional function execution; wherein the description comprises a condition and a function of the motor vehicle (105) to be executed if the condition is fulfilled; determining (220) a first embedding based on the condition by means of an LLM (130); determining (235) parameters of a situation of the motor vehicle (105); determining (240) a second embedding based on the parameters; and automatically executing (250) the function if the first embedding has a predetermined similarity to the second embedding (245).

Description

[0001] The present invention relates to the control of a function of a motor vehicle. In particular, the invention relates to the automatic control of such a function.

[0002] A motor vehicle includes numerous functions that can be controlled by a person on board. Examples of functions include playing a predefined piece of music on an entertainment system or adjusting a ventilation system (heating, venting, air conditioning, HVAC). Controlling the function may require adjusting multiple parameters and may therefore be complex.

[0003] To facilitate control of such a function, the use of a voice assistant has been proposed. A person on board the vehicle can name a predetermined function and specify a parameter to be adjusted. The function can then be controlled accordingly. However, voice control of the function can also be complex and thus time-consuming for the person.

[0004] It has also been proposed to automatically control a motor vehicle function when a predetermined condition is met. In practice, defining such a condition has proven to be complex and error-prone. Often, the function is mistakenly controlled or not controlled based on a person's intention.

[0005] Document DE 10 2022 122 039 A1 discloses a computer-implemented method for determining a parameterization of a building services installation, comprising: receiving an environmental condition state detected by means of at least one sensor device; determining a number of environmental condition states stored in a database which satisfy a similarity criterion with regard to the detected environmental condition state; and if the number is less than a predetermined number threshold, determining the parameterization of the building services installation based on at least one predefined parameterization rule; or if the number is greater than or equal to the predetermined number threshold, determining the parameterization of the building services installation based on a machine learning model.

[0006] Document US 2005 / 0 165 707 A1 discloses a rule-based system that uses a specification similar to that used in previous systems, such as OPS5, but does not require the computational complexity of implementations such as Rete. In some such implementations, matching applicable rules based on a set of facts is simplified by providing pre-assigned storage locations for the Boolean values ​​of the condition elements for each rule. The Boolean values ​​are set by directly processing each of the facts. These pre-assigned storage locations are arranged to enable efficient evaluation of the overall condition of each of the rules, in a manner that is significantly more efficient than implementations of previous rule-based systems.

[0007] Document US 2014 / 0 082 501 A1 discloses a context-aware service provisioning method and apparatus that detects the user context and executes an action corresponding to the user context according to a user-defined rule, and interactively returns the execution result to the user. The method for providing a context-aware service includes receiving user input, wherein the user input is at least one of a text input or a voice input, identifying a rule comprising a condition and an action corresponding to the condition based on the received user input, activating the rule to detect a context matching the condition of the rule, and executing the action corresponding to the condition when the context is detected.

[0008] Document DE 10 2018 206 366 A1 discloses a method for controlling a vehicle function. In the method, an input signal is provided, wherein the input signal is representative of a user input. Depending on the input signal, a rule is determined, wherein the rule is representative of a rule for executing a vehicle function. The rule comprises a trigger factor, a parameter, and the vehicle function. The parameter is representative of a parameter for controlling the vehicle function, and the trigger factor is representative of a factor for controlling the vehicle function depending on the parameter. The rule is saved so that the rule can be used to control the vehicle function.

[0009] One object underlying the present invention is to provide an improved technology for automatically controlling a function on board a motor vehicle. The invention achieves this object by means of the subject matter of the independent claims. Subclaims specify preferred embodiments.

[0010] According to a first aspect of the present invention, a method for controlling a function of a motor vehicle comprises steps of capturing a linguistic description of a conditional function execution; wherein the description comprises a condition and a function of the motor vehicle to be executed if the condition is fulfilled; determining a first embedding based on the condition by means of an LLM; determining parameters of a situation of the motor vehicle; determining a second embedding based on the parameters; and automatically executing the function if the first embedding has a predetermined similarity to the second embedding.

[0011] The LLM is a large language model designed to process natural language. Various LLMs have been published that can be used to implement the technique proposed here. By converting the linguistically formulated condition into an embedding, the actual intention of a person setting the condition can be more accurately determined. In particular, any vagueness of the linguistic description can be represented. For example, if the description includes the condition "if the weather is good," many different situations are conceivable that could lead to a determination of "good" weather. By converting the parameters into the second embedding, different information sources can be used to deduce whether the predetermined condition is met or not.In the given example, the quality of prevailing weather can be determined based on temperature, humidity, wind speed, precipitation, or air pressure. Based on completely different parameters, it can also be determined that good weather prevails if a vehicle's top is open while the vehicle's heater is deactivated. The LLM can determine the embeddings in such a way that their similarities can be correctly determined despite the blurriness. The vehicle's function can thus be automatically executed with improved accuracy when the described condition is met.

[0012] The method can be used to perform virtually any function of the motor vehicle. Examples include opening or closing a window, operating an entertainment system, operating an air conditioning system (HVAC), or operating a comfort function such as seat adjustment. Optionally, a function relevant to the movement of the motor vehicle can also be controlled, such as activating exterior lighting. The control and operation can theoretically be as complex as desired. In practice, the complexity is limited by the linguistic complexity of the description.

[0013] The situation is preferably determined depending on a driving state of the motor vehicle. For example, the situation can be determined periodically or continuously while the motor vehicle is actively being used. Example driving states include an unlocked state, a state with a person on board, a ready-to-drive state, and a state during which the motor vehicle is moving. In different driving states, the situation can be determined with different frequencies or based on different parameters. The frequency or a selection of parameters can be assigned to a driving state.

[0014] It is preferred that the description be captured and processed in the form of an utterance from the person. In one embodiment, the person's utterance can be converted into text. A technique known as speech-to-text (STT) can be used for this purpose. Alternatively, a multimodal LLM configured to process utterances can be used.

[0015] The possibility of an acoustic utterance makes it easier for the person to provide a verbal description, especially while on board the motor vehicle. If the person is a driver of the motor vehicle, the driver can provide the verbal description while driving the motor vehicle without being unduly distracted from their driving task.

[0016] An embedding typically comprises a vector in a vector space with a large number of dimensions. The vector cannot contain a defined value for all of its dimensions. Similarities between two embeddings can, in principle, be determined in the same way as similarities between two vectors. In particular, a distance between the ends of the vectors or embeddings can be used as a measure of dissimilarity. The smaller the distance, the smaller the dissimilarity and the more similar the vectors or embeddings are. The similarity of the embeddings can be determined, for example, based on a Euclidean distance, a dot product, a cosine similarity, or a Manhattan metric. Different similarity measures can be used for differently fuzzy definitions of a condition or situation.

[0017] The situation may include at least one of a driving state of the motor vehicle, the weather conditions prevailing in the area of ​​the motor vehicle, a person on board the motor vehicle, a planned route, and a destination of the motor vehicle. It should be noted that the situation of the motor vehicle may also have other characteristics.

[0018] The driving state can include, for example, a geographical position or a driving speed of the motor vehicle. Furthermore, the driving state can include traffic volume in the area of ​​the motor vehicle, an activated driving mode, an activated control function of the motor vehicle, a date, a time of day, or a day of the week. The weather can include, in particular, precipitation, air temperature, air humidity, air pressure, wind, and / or solar radiation. Example destinations include a commute, a vacation, a school trip, and a leisure trip.

[0019] In a further embodiment, environmental information of the motor vehicle is determined, which in particular cannot be detected by a sensor on board the motor vehicle. The second embedding can be determined based on the additional environmental information.

[0020] The environmental information can, in particular, be obtained from an information source external to the motor vehicle. Such an information source can, for example, comprise a server or a service, for example, in a computer cloud. For example, information regarding traffic flow on a planned route of the motor vehicle can be obtained from such an external information source. Another exemplary external information source includes a weather service that can provide the prevailing weather at a predetermined location.

[0021] In one embodiment, an information source for the environmental information is determined based on the condition. For this purpose, a capability of the LLM known as reasoning can be exploited. When processing the description, the LLM can determine an external information source that can be consulted to determine the content of a linguistic input or the subject of an output in response to an input. For example, if the condition includes a reference to prevailing weather, a weather service can be identified as an external information source. The situation of the motor vehicle can then be determined taking into account the weather provided by the weather service. In this case, a current geographical position of the motor vehicle can be passed to the weather service as a parameter for the weather to be determined.The LLM can also determine a possible parameter for the external information source.

[0022] In a further development of the invention, an anticipated situation of the motor vehicle can be predicted. A predetermined future can be assumed as the prediction horizon. This can be exploited to control a function in anticipation of the occurrence of a predetermined situation. For example, a motor vehicle's convertible top can be closed if rain is expected within a predetermined forecast horizon of, for example, approximately 1 hour, and not only when it has already started to rain. A motor vehicle's auxiliary heater can be activated if the motor vehicle is expected to be used within a predetermined forecast horizon.

[0023] Whether the use of the motor vehicle is likely within the forecast horizon can be determined, for example, based on past observations of regular use. For example, commutes by motor vehicle may be subject to a predetermined rhythm that can be easily determined based on past usage. The motor vehicle can be prepared in advance for a likely upcoming use of the motor vehicle. In this example, a function of the motor vehicle is controlled without a person on board. Functions that can be executed in this driving state may be limited.

[0024] According to a further aspect of the present invention, a control system for controlling a function of a motor vehicle comprises an input device for capturing a linguistic description of a conditional function execution; wherein the description comprises a condition and a function of the motor vehicle to be executed if the condition is fulfilled; at least one sensor for determining parameters of a situation of the motor vehicle; an interface to an LLM that is configured to determine a first embedding based on the condition and a second embedding based on the parameters; and a processing device that is configured to execute the function if the first embedding has a predetermined similarity to the second embedding.

[0025] The processing device is preferably configured to partially or completely execute a method described herein. For this purpose, the processing device can be implemented electronically and, for example, comprise an integrated circuit, a programmable logic module, or a programmable microcomputer. The method can be implemented in the form of a configuration or as a computer program product with program code means for the processing device. The configuration or the computer program product can be stored on a computer-readable data carrier. Features or advantages of the method can be transferred to the device, or vice versa.

[0026] In one embodiment, the LLM is implemented outside the motor vehicle, and the interface can enable wireless communication between the motor vehicle and the LLM. The LLM can in particular be implemented on a server or as a service, in particular in a cloud. In another embodiment, the LLM is included in the control system. In this case, the control system and the LLM can be located on board the motor vehicle. In one embodiment, an LLM is used that is specifically trained to process conditions, functions, and situations of the motor vehicle. Such an LLM can be executed on board the motor vehicle using available resources in terms of processing capacity and storage space, for example by means of the aforementioned processing device.

[0027] In a further embodiment, a vector-oriented database for storing embeddings is included in the control system. The database is designed to determine a similarity between stored embeddings. The database, like the LLM, can alternatively be provided on board the motor vehicle or on a device external to the motor vehicle. Preferably, the LLM and the vector-oriented database are integrated with one another and are in particular executed on the same processing system. For example, Chroma can be used as the database, which is freely available as an open source product. The determination of similarities between stored embeddings can be carried out inherently by the vector-oriented database. For example, an embedding can be searched for that is similar to another embedding. The determination of a degree of similarity orThe determination of whether an embedding has a predetermined similarity to another embedding can be performed by the database.

[0028] The control system may include an interface to an information source remote from the motor vehicle. The information source is configured to provide environmental information for the motor vehicle. A number of predetermined remote information sources may be defined, which may provide, for example, traffic information, weather information, etc. An external information source may provide information regarding a geographical position and / or a point in time. An information source may, in particular, provide information regarding a current or planned geographical position of the motor vehicle or regarding a current, historical, or future point in time.

[0029] According to yet another aspect of the present invention, a device for a motor vehicle comprises a device for determining that a situation of the motor vehicle has a predetermined similarity to a predetermined condition; wherein a linguistic description of the condition is assigned a first embedding and parameters of the situation are assigned a second embedding; and a control device for controlling a predetermined function of the motor vehicle assigned to the condition if the first embedding has a predetermined similarity to the second embedding.

[0030] The device can be considered a local part of a control system described herein. In various embodiments, a further element of the control system can be provided alternatively on board the motor vehicle or outside the motor vehicle, in particular at a location external to it.

[0031] According to yet another aspect of the present invention, a motor vehicle comprises a device described herein. The motor vehicle may, in particular, comprise a passenger car or a motorcycle.

[0032] The invention will now be described in more detail with reference to the accompanying drawings, in which: Fig. 1 a tax system; and Fig. 2 illustrates a flow chart of a process.

[0033] Fig. 1 shows a control system 100 for controlling a predetermined function of a motor vehicle 105. The control system 100 is configured to automatically control the predetermined function when a condition associated with the function is met. The condition and the function can be specified by a person 110 assigned to the motor vehicle 105. In one embodiment, the person 110 can use a user interface on board the motor vehicle 105 to store the definition. In another embodiment, the person 110 uses another device for this purpose, in particular a mobile device 115, particularly preferably a smartphone. For this purpose, the mobile device 115 can exchange information with the motor vehicle 105.

[0034] On board the motor vehicle 105 is a device 120 with several components, some of which can also be arranged outside the motor vehicle 105 as part of the control system 100. First, a variant of the control system 100 is described, which can be used entirely in the form of the device 120 on board the motor vehicle 105.

[0035] In this embodiment, the device 120 comprises a processing device 125, an LLM 130, a vector-oriented database 135, an interface 140 for connection to another device on board the motor vehicle 105, at least one sensor 142 and a user interface 145.

[0036] The sensor 142 is configured to provide one or more parameters that can provide an indication of a situation of the motor vehicle 105. The situation can be defined by a number of parameters, one of which can originate from a sensor 142. Optionally, the sensor 142 is part of another device on board the motor vehicle 105 and connected via the interface 140. The other device can also function as a sensor. Example sensors include a position sensor, a speed sensor, and a sensor for determining a driving state. An example device that can function as a sensor 142 includes a control unit for a drive motor of the motor vehicle 105. The parameters provided here can include, for example, a provided torque, an engaged gear, or a remaining amount of energy in an on-board energy storage device.

[0037] The user interface 145 is configured to be used by the person 110. The user interface 145 can be implemented acoustically, so that the person 110 can make an input using spoken language. Optionally, an acoustic output to the person 110 can be provided using spoken language. In another embodiment, the user interface 145 is configured to process text entered by the person 110. Feedback can also be provided in text form.

[0038] If the LLM 130 processes text data while speech data was provided via the user interface 145, the processing device 125 can convert the speech into text. Conversely, a response provided by the LLM 130 in text form can be converted into speech by the processing device 125. If the user interface 145 is acoustic, it is preferred that the LLM 130 be able to operate directly on acoustic data. A multimodal LLM 130 can be used for this purpose.

[0039] The database 135 is configured to store an embedding, which is determined in particular by the LLM 130, and optionally to determine a similarity between the stored embedding and another embedding. The similarity can be specified on a predetermined scale, for example, between zero (dissimilar) and one (very similar or identical).

[0040] Via the interface 140, a further device on board the motor vehicle 105 can be controlled to control or trigger a predetermined function of the motor vehicle 105. The further device can include, for example, an entertainment system, a seat adjustment system, a lighting device, or a ventilation system.

[0041] In some embodiments, a communication device 150 is provided, by means of which the device 120 can communicate with another device. The communication device 150 is preferably wireless and can in particular support mobile radio or Wi-Fi. In one embodiment, a mobile device 115 can be connected via the communication device 150 in order to operate as a user interface 145. In another embodiment, an external information source 155 can be contactable by means of the communication device 150. The external information source 155 can in particular provide parameters relating to a situation in the surroundings of the motor vehicle 105. As parameters, the information source 155 can process a geographical position of the motor vehicle 105 and / or a point in time to which the desired information relates.A current position of the motor vehicle 105 can be determined by means of a positioning device (not shown), which can in particular comprise a receiver for a satellite-based global navigation system (GNSS).

[0042] In a further embodiment, one or more of the described elements of the device 120 can also be provided at an external location 160, with which the device 120 can communicate via the communication device 150. For example, the external location 160 can host the LLM 130. The database 135 is preferably provided where the LLM 130 is provided, in this embodiment, on the side of the external location 160.

[0043] Fig. 2 shows a flowchart of a method 200, which can be carried out in particular by means of a control system 100.

[0044] In a step 205, an utterance ("utterance") of person 110 can be captured. The utterance can include a linguistic description of a conditional functional execution to be defined for a motor vehicle 105. Instead of an utterance using spoken language, text-based processing can also be provided, as described herein.

[0045] In a step 210, a condition and a function can be determined based on the description. If the condition is met, the function of the motor vehicle 105 is to be executed. The condition is met if a situation of the motor vehicle 105 corresponds to the condition. Typically, the conditions and functions are linguistically included in the description in the form of an if-then construction. The description can include one or more natural language sentences. Optionally, input using different, predetermined human languages ​​can be permitted.

[0046] Based on the condition, an external information source 155 can be determined in a step 215. This external information source can provide a parameter that can more precisely characterize a situation of the motor vehicle 105 with respect to the condition. In particular, the external information source 155 can provide a parameter of the situation that is relevant for checking whether the condition is met. If the condition relates to the weather, for example, a weather service can be determined as the external information source 155, which can provide a weather parameter, such as a temperature in the area of ​​the motor vehicle 105.

[0047] In a step 220, a first embedding can be determined based on the condition. The embedding typically comprises a vector with a large number of dimensions, whereby the embedding can be viewed as an internal representation of the condition. A separate embedding can be determined for the function. The function or its embedding can be assigned to the first embedding. The function can also be converted into a machine-related description of the function and assigned to the first embedding. The machine-related function can be directly implemented by the motor vehicle 105 or a device comprised by it and connected to the interface 140 in order to control or execute the function. It is preferred that the first embedding be stored in the vector-oriented database 135.

[0048] Steps 205 to 220 may be performed only once with respect to a technique described herein.

[0049] In a step 225, a driving state of the motor vehicle 105 can be determined. The driving state can, for example, distinguish whether the motor vehicle 105 is moving, stationary, or parked. Further gradations can also be determined.

[0050] In a step 230, a parameter can be determined that indicates a situation of the motor vehicle 105. In one embodiment, as many parameters as possible are determined that indicate a situation of the motor vehicle 105. In another embodiment, parameters can be specifically determined that are relevant to determining the situation of the motor vehicle 105 with respect to a stored condition from step 210.

[0051] In a step 235, a parameter may additionally be determined from an external information source 155. The parameter and / or the external information source 155 may be determined based on determinations from step 215.

[0052] In a step 240, a second embedding can be determined based on the determined parameters, which represents the situation of the motor vehicle 105. The second embedding can also be stored in the database 135.

[0053] Steps 225 to 240 can be executed multiple times in succession. In particular, steps 225 to 240 can be executed periodically or continuously. Optionally, the frequency or speed of execution of these steps depends on a driving state of the motor vehicle 105 determined in step 225.

[0054] In a step 245, it can be determined whether a similarity between the first and second embeddings exceeds a predetermined value. In other words, it can be determined whether the embeddings differ from each other by less than a predetermined amount. If this is the case, the function of the motor vehicle 105 determined in step 210 can be controlled or triggered in a step 250. Optionally, the function can be controlled depending on a parameter that is part of the situation of the motor vehicle 105. A type of control or a parameter of the controlled function can be determined based on the function description from step 210.

[0055] In an exemplary use case, a person 110 could establish a rule for the automatic control of a function of the motor vehicle 105 by entering as input (see step 205): “If the weather is nice, please open my window and activate the expressive mode.”

[0056] In step 210, it can be recognized that two functions of the motor vehicle 105 are to be controlled: opening a window and activating a predetermined driving mode, namely the "Expressive" mode. Furthermore, it can be determined that the condition requires good weather. Both of these statements cannot be readily resolved because it is initially unclear which window should be opened, what is meant by good weather, and where the weather is to be determined.

[0057] In step 215, it can be determined how the missing information can be obtained. To determine which window should be opened, speaker recognition can be performed on board the motor vehicle 105, which determines from which location an acoustic input was made. The weather at the location of the motor vehicle 105 must be determined, for which purpose a geographical position of the motor vehicle 105 must be determined, for example, using a positioning device of the motor vehicle 105. The prevailing weather in the area of ​​the motor vehicle can be obtained from a weather service with reference to the geographical position.

[0058] If no further criteria for "nice" weather are defined, a corresponding statement from the weather service can be evaluated. Alternatively, a common definition can be used, for example, based on the world knowledge of the executing LLM. In step 220, the first embedding can be determined. A description of "nice" weather can be part of the first embedding.

[0059] The determination of the current position and the determination of the weather typically do not occur at the time the condition is set, and these parameters are preferably not included in the first embedding. Rather, in step 215, it can be specified that a geographical position must be determined to determine the second embedding in order to query the weather prevailing at that location. This determination can be performed later, for example, in step 230, to determine the second embedding based on current parameters.

[0060] If the motor vehicle 105 is in use, the second embedding can be continuously determined in steps 225 to 240, which reflects the situation of the motor vehicle 105. This situation can include the weather prevailing at the geographical location of the motor vehicle 105 or its quality in terms of its "beauty." If a sufficient match between the embeddings is determined in step 245, the stored functions can be controlled automatically. Optionally, confirmation from a person 110 on board the motor vehicle 105 can be obtained before the specific function is actually executed. Reference symbol 100 Tax system 105 Motor vehicle 110 people 115 Mobile device 120 device 125 processing facility 130 Large Language Model, LLM 135 vector-oriented database 140 Interface 142 Sensor 145 User interface 150 communication device 155 external information source 160 external positions 200 procedures 205 Record utterance 210 Determine condition and function 215 Determine external information source 220 Determine first embedding 225 Determine driving condition Determine 230 parameters of a motor vehicle situation 235 Determine parameters from external information source 240 determine second embedding 245 Similarity > predetermined value? 250 Control the function of the motor vehicle

Claims

[1] Method (200) for controlling a function of a motor vehicle (105), the method (200) comprising the following steps: - capturing (205) a linguistic description of a conditional function execution; - wherein the description comprises a condition and a function of the motor vehicle (105) to be carried out when the condition is fulfilled; - determining (220) a first embedding based on the condition by means of an LLM (130); - determining (230) parameters of a situation of the motor vehicle (105); - determining (240) a second embedding based on the parameters; and - automatically executing (250) the function if the first embedding has a predetermined similarity to the second embedding (245). [2] Method (200) according to claim 1, wherein the situation is determined as a function of a driving state of the motor vehicle (105). [3] Method (200) according to claim 1 or 2, wherein the description is captured and processed in the form of an acoustic utterance of a person (110). [4] The method (200) of any preceding claim, wherein the similarity is determined based on a Euclidean distance, a dot product, a cosine similarity, or a Manhattan metric. [5] Method (200) according to one of the preceding claims, wherein the situation comprises at least one of a driving state of the motor vehicle (105), a weather condition prevailing in the area of ​​the motor vehicle (105), a person (110) on board the motor vehicle (105), a planned route and a destination of the motor vehicle (105). [6] Method (200) according to one of the preceding claims, further comprising determining environmental information of the motor vehicle (105); wherein the second embedding is determined on the additional basis of the environmental information. [7] The method (200) of claim 6, wherein an information source (155) for the environmental information is determined (215) based on the condition. [8] Method (200) according to one of the preceding claims, wherein an expected situation of the motor vehicle (105) is predicted. [9] Control system (100) for controlling a function of a motor vehicle (105), the control system (100) comprising the following elements: - an input device (145) for capturing a linguistic description of a conditional function execution; - wherein the description comprises a condition and a function of the motor vehicle (105) to be carried out when the condition is fulfilled; - at least one sensor (142) for determining parameters of a situation of the motor vehicle (105); - an interface to an LLM (130) configured to determine a first embedding based on the condition and a second embedding based on the parameters; and - a processing device (125) configured to execute the function if the first embedding has a predetermined similarity to the second embedding. [10] The control system (100) of claim 9, wherein the LLM (130) is included in the control system (100). [11] The control system (100) according to claim 9 or 10, further comprising a vector-oriented database (135) for storing embeddings; wherein the database (135) is configured to determine a similarity between stored embeddings. [12] Control system (100) according to one of claims 9 to 11, further comprising an interface to an information source (155) remote from the motor vehicle (105) and configured to provide environmental information for the motor vehicle (105). [13] Device (120) for a motor vehicle (105), the device (120) comprising: - means for determining that a situation of the motor vehicle (105) has a predetermined similarity to a predetermined condition; - where a linguistic description of the condition is assigned a first embedding; - where parameters of the situation are assigned a second embedding; - a control device (125) for controlling a predetermined function of the motor vehicle (105) associated with the condition if the first embedding has a predetermined similarity to the second embedding. [14] Motor vehicle (105) comprising a device (120) according to claim 13.

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