Apparatus, method and computer program for controlling a device

Abstract

Examples of the present disclosure provide apparatus, methods and computer programs for assisting in controlling actions performed by user equipment and network equipment. In some examples, an apparatus may be provided that includes components for detecting network traffic activity. The network traffic activity indicates one or more actions performed by at least one network device. The apparatus may also include means for identifying a user input made by a user of the user device. The at least one network device is different from the user equipment. The user input is made within a time interval prior to the detected network traffic activity. The apparatus may also include means for determining a user intent of one or more actions performed by the at least one network device by comparing the identified user input and the detected network traffic activity with data stored in the database relating to the user input and the associated expected network traffic activity. The apparatus may also include means for controlling an output of the user device based on the determined user intent.

Description

Technical Field

[0001] Examples of the present disclosure relate to apparatus, methods, and computer programs for controlling a device. Some embodiments relate to apparatus, methods, and computer programs for controlling a device based on detected network traffic activity. Background Art

[0002] A user device (such as an augmented reality device or a smart device) can be controlled using gesture input. The gesture input can be a user moving a part of their body (such as a hand or arm) or making a voice input. The gesture input does not need to involve the user making physical contact with the user device. Therefore, the gesture input may be detected by other devices near the user device. If such gesture input is detected and / or misinterpreted by an unintentional device, this may cause the wrong function to be performed by the user device and / or any other device near the user device. Summary of the invention

[0003] According to various but not necessarily all examples of the present disclosure, there is provided an apparatus comprising means for:

[0004] detecting network traffic activity, wherein the network traffic activity is indicative of one or more actions performed by at least one network device;

[0005] identifying a user input made by a user of a user device, wherein at least one network device is different than the user device, and wherein the user input was made within a time interval preceding the detected network traffic activity;

[0006] determining user intent for one or more actions performed by at least one network device by comparing the identified user input and the detected network traffic activity to data stored in a database relating to the user input and the associated expected network traffic activity; and

[0007] Based on the determined user intent, the output of the user device is controlled.

[0008] Determining user intent may include comparing the identified user input with a first user input for controlling a user device and a second user input for controlling at least at least one network device to generate the detected network traffic activity to determine whether the identified user input more closely matches the first user input or the second user input.

[0009] The outputting may include enabling a user input used to control an action of the user device to be changed to a different user input.

[0010] The output may be configured to provide one or more suggested user inputs having a similarity level below a threshold compared to the identified user input.

[0011] The outputting may include providing an alert to a user of the user device indicating that the action was determined to be unintentional.

[0012] The user input may be identified by comparing the user input made within a time interval preceding the detected network traffic activity to data associated with the user input stored in the database.

[0013] The user input may include at least one of the following: voice input and gesture input.

[0014] The detected network traffic activity may include one or more of the following: number of data packets, length of data packets, timing of data packets, rise time of data packets, fall time of data packets.

[0015] The detected network traffic activity may include one or more of the following: 5G signals, WiFi signals, Bluetooth signals, and Bluetooth low energy signals.

[0016] The component may be used to identify one or more actions corresponding to the detected network traffic activity by comparing the detected network traffic activity to one or more network traffic activity profiles.

[0017] This component can be used to identify network devices based on detected network traffic activity.

[0018] The component may be used to identify one or more actions corresponding to detected network traffic activity using signals from one or more sensors.

[0019] The component may be used to generate a database including user inputs and corresponding network traffic activity.

[0020] Machine learning models can be used to determine user intent.

[0021] The user device may include a mediated reality device.

[0022] According to various but not necessarily all examples of the present disclosure, a method may be provided, including:

[0023] detecting network traffic activity, wherein the network traffic activity is indicative of one or more actions performed by at least one network device;

[0024] identifying a user input made by a user of a user device, wherein at least one network device is different than the user device, and wherein the user input was made within a time interval preceding the detected network traffic activity;

[0025] determining user intent for one or more actions performed by at least one network device by comparing the identified user input and the detected network traffic activity to data stored in a database relating to the user input and the associated expected network traffic activity; and

[0026] Based on the determined user intent, the output of the user device is controlled.

[0027] According to various but not necessarily all examples of the present disclosure, there may be provided a computer program comprising computer program instructions which, when executed by a processing circuit system, cause:

[0028] detecting network traffic activity, wherein the network traffic activity is indicative of one or more actions performed by at least one network device;

[0029] identifying a user input made by a user of a user device, wherein at least one network device is different than the user device, and wherein the user input was made within a time interval preceding the detected network traffic activity;

[0030] determining user intent for one or more actions performed by at least one network device by comparing the identified user input and the detected network traffic activity to data stored in a database relating to the user input and the associated expected network traffic activity; and

[0031] Based on the determined user intent, the output of the user device is controlled.

[0032] According to various but not necessarily all examples of the present disclosure, a user device may be provided, including components for:

[0033] Collect data related to detected user input;

[0034] providing collected data associated with the detected user input to at least one processing device so that the collected data can be analyzed to determine user intent; and

[0035] Based on the determined user intent, input is received.

[0036] The component can be used to obtain data related to network traffic activity, where the network traffic activity indicates one or more actions performed by at least one network device, and provide data related to the detected network traffic activity to one or more processing devices so that the data related to the detected network traffic activity can be analyzed to determine user intent.

[0037] The component may be used to enable one or more user inputs used to control a user device to be adjusted to increase distinguishability between the one or more user inputs used to control the user device and one or more inputs used to control at least one network device.

[0038] According to various but not necessarily all examples of the present disclosure, a method may be provided, including:

[0039] Collect data related to detected user input;

[0040] providing collected data associated with the detected user input to at least one processing device so that the collected data can be analyzed to determine user intent; and

[0041] Based on the determined user intent, input is received.

[0042] According to various but not necessarily all examples of the present disclosure, there may be provided a computer program comprising computer program instructions which, when executed by a processing circuit system, cause:

[0043] Collect data related to detected user input;

[0044] providing collected data associated with the detected user input to at least one processing device so that the collected data can be analyzed to determine user intent; and

[0045] Based on the determined user intent, input is received.

[0046] Although the above examples and optional features of the present disclosure are described separately, it should be understood that they are included in the present disclosure in all possible combinations and arrangements. It should be understood that various examples of the present disclosure may include any or all of the features described for other examples of the present disclosure, and vice versa. In addition, it should be understood that any one or more or all of the features, in any combination, can be implemented / included in / executable by devices, methods and / or computer program instructions as needed and appropriately. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Some examples will now be described with reference to the accompanying drawings, in which

[0048] Figure 1 An example network is shown;

[0049] Figure 2 An example method is shown;

[0050] Figure 3 Another example method is shown;

[0051] Figure 4 Another example method is shown;

[0052] Figure 5 Another example method is shown;

[0053] Figure 6 shows an example user device in use; and

[0054] Figure 7 An example apparatus is shown.

[0055] These figures are not necessarily drawn to scale. For clarity and brevity, certain features and views in the figures may be shown schematically or scaled up. For example, the size of certain elements in the figures may be enlarged relative to other elements to aid in explanation. Corresponding reference numbers are used in the figures to indicate corresponding features. For clarity, all reference numbers are not necessarily shown in all figures. DETAILED DESCRIPTION

[0056] Figure 1 An example network 101 is shown that may be used to implement examples of the present disclosure. The network 101 includes a processing device 103, a user device 105, and one or more network devices 107. Figure 1 The example network 101 includes one processing device 103, one user device 105, and two network devices 107. In some examples of the present disclosure, other numbers of corresponding devices may be used.

[0057] The network 101 may be any suitable type of network. In some examples, the network 101 may be a wireless network. The respective devices 103, 105, 107 within the network 101 may be configured to communicate with each other using a wireless protocol, such as Bluetooth, Bluetooth Low Energy, Bluetooth Smart, 6LoWPan (IPv6 over Low Power Personal Area Network), ZigBee, ANT+, Near Field Communication (NFC), Radio Frequency Identification, Wireless Local Area Network (Wireless LAN), or any other suitable protocol. The network 101 may be part of an Internet of Things (IoT) network or any other suitable type of network.

[0058] Devices in network 101 may be located in close proximity to one another. For example, the devices may be co-located in the same room. The devices may be located close enough to enable low-energy communication between the devices.

[0059] Processing device 103 may be configured to control at least a portion of network 101. Processing device 103 may be configured to receive input from user device 105 and control one or more actions performed by one or more network devices 107 based on the user input from user device 105. Processing device 103 may include a smartphone, a personal computer, a smart speaker, or any other suitable type of device.

[0060] exist Figure 1 In the example of FIG. 1 , the processing device 103 includes a device 109 and a transmitter / receiver 111. Only the components mentioned in the following description are included in the Figure 1 Additional components may be provided in some examples of the present disclosure. For example, processing device 103 may include one or more sensors for detecting gesture input.

[0061] The device 109 may include a controller including a processor and a memory. An example of the device 109 is Figure 7 The apparatus 109 may be configured to enable control of the processing device 103. For example, the apparatus 109 may be configured to control messages sent by the processing device 103. The apparatus 109 may also be configured to determine a user's intent for an action performed by the network device 107.

[0062] Transmitter / receiver 111 may include any components that enable processing device 103 to communicate with other devices within network 101. Transmitter / receiver 111 may enable wireless communication or any other suitable type of communication between processing device 103 and user equipment 105 and / or network device 107.

[0063] User device 105 may include a user device that belongs to a user or is otherwise associated with a user. For example, user device 105 may be a personal electronic device, such as a mobile device that is typically used by only one user at a time. In some examples, user device 105 may be a mediated reality device, such as an augmented reality device. In some examples, user device 105 may be a device worn by a user. For example, user device 105 may include augmented reality glasses or a headset or any other suitable component.

[0064] exist Figure 1 In the example of FIG. 1 , the user device 105 includes a device 113 and a transmitter / receiver 115, an output module 117 and one or more sensors 119. Only the components mentioned in the following description are included in the description. Figure 1 Additional components may be provided in some examples of the present disclosure.

[0065] The device 113 may include a controller including a processor and a memory. The device 113 may be similar to the device 109 of the processing device 103 or may be different. An example of the device 113 is Figure 7 The device 113 may be configured to enable control of the user device 105. For example, the device 113 may be configured to control messages sent by the user device 105, and / or functions performed by the output module 117 and / or any other functions of the user device 105.

[0066] Transmitter / receiver 115 may include any components that enable user device 105 to communicate with other devices within network 101. Transmitter / receiver 115 may enable wireless communication between user device 105 and processing device 103 and / or network device 107, or any other suitable type of communication.

[0067] Output module 117 may include any component that can be configured to provide an output of user device 105. In some examples, output module 117 may be configured to provide information to a user of user device 105. For example, output module 117 may include a display that may be configured to display information such as text or images. In some examples, output module 117 may include a speaker or other component that may be configured to provide an audible output for a user. In some examples, other types of output modules 117 may be provided.

[0068] exist Figure 1 In the example of FIG. 1 , user device 105 is shown to include only one output module 117. In some examples, user device 105 may include more than one output module 117. Different output modules may be configured to provide different outputs and / or functions.

[0069] Sensor 119 may include any component that may be configured to detect gesture user input made by a user of user device 105. The type of sensor 119 used may depend on the type of gesture user input to be detected.

[0070] In some examples, gesture user input may include movement of a user or a part of the user's body. For example, gesture user input may include a user making a series of defined movements with his arm or hand or head or one or more other suitable parts of his body. In some examples, user device 105 may be a wearable device or a device held by a user. In such examples, sensor 119 for detecting gesture input may include one or more accelerometers, which may be configured to detect the movement of the user. In some examples, user device 105 may be configured so that the movement of the user can be detected by electromagnetic signals. In such examples, sensor 119 may include one or more suitable detectors, which may detect signals reflected by appropriate parts of the user's body. Examples of sensors 119 that may be used to detect such gestures include imaging sensors, LiDAR sensors, mm-wave sensors, Wi-Fi sensors, inertial measurement units, electromyography (EMG) sensors, or any other suitable type of sensor.

[0071] In some examples, gesture user input may include voice input. For example, gesture input may include a user saying a wake-up word followed by appropriate instructions. In such examples, sensor 119 may include one or more microphones or any other suitable components for detecting acoustic signals.

[0072] Gesture user input may be detected without the user making physical contact with the user device 105. This may mean that the same gesture input may be detected by more than one device in the network 101, or may mean that a device that is not the intended device for the gesture input registers the input.

[0073] exist Figure 1 In the example of FIG. 1 , the network 101 includes two network devices 107A, 107B. In other examples, other numbers of network devices 107 may be included in the network 101 .

[0074] The network device 107 is a device different from the user device 105 .

[0075] exist Figure 1 In the example of FIG. 1 , the corresponding network devices 107A, 107B include transmitters / receivers 121A, 121B and output modules 123A, 123B. Only the components mentioned in the following description are in Figure 1 Additional components may be provided in some examples of the present disclosure. For example, network devices 107A, 107B may include a sensor for detecting gesture input or any other suitable component.

[0076] Transmitters / receivers 121A, 121B may include any components that enable network devices 107A, 107B to communicate with other devices within network 101. Transmitters / receivers 121A, 121B may enable wireless communication or any other suitable type of communication between network devices 107A, 107B and processing device 103 and / or user device 105.

[0077] Output modules 123A, 123B may include any components that can be configured to provide output. Different types of network devices 107A, 107B may include different types of output modules 123A, 123B so that different types of outputs can be provided. For example, in some examples, network device 107 may be configured to provide information to a user. In this case, the output module includes a display and / or a speaker and / or any other components for presenting information. In some examples, the network device may be configured to control one or more household appliances or other devices or systems in the area of ​​network 101. In such an example, output module 123 may be configured to enable control of one or more household appliances or devices or systems. For example, network devices 107A, 107B may be a part of a lighting system, a heating system, a kettle, an entertainment system, or any other suitable device or system. In this case, output modules 123A, 123B may be configured to enable a device or system or a part of a device or system to be turned on or off, or to perform any other suitable function. As an illustrative example, network device 107 may be a kettle, which may be turned on or off by output module 123. In some examples, other types of network devices 107 and output modules 123 may be used.

[0078] exist Figure 1 In the example of FIG. 1 , the network 101 is configured so that the user device 105 communicates with the processing device 103. The processing device 103 may also communicate with the corresponding network devices 107A, 107B. In this example, the user device 105 may communicate with the network devices 107A, 107B via the processing device 103. In some examples, the network may be configured so that the user device 105 may communicate directly with one or more of the network devices 107A, 107B.

[0079] In the example of the present disclosure, a user may use a user device 105. The user may make gesture inputs to control the user device 105. However, there may be other devices in the environment, such as network devices 107 or processing devices 103, which may also detect gestures and trigger actions. The action may be performed by one or more of the network devices 107.

[0080] For example, if a user is playing an augmented reality game using user device 105, they may make a gesture user input. This may be detected by one or more other devices in the vicinity of the user. This may cause an unintentional action to be performed by one or more network devices 107. For example, it may cause a light to be turned on or off, or may turn on a kettle, or may cause any other unintentional action to be performed.

[0081] Examples of the present disclosure provide apparatus, methods, and computer programs for assisting in controlling operations performed by user devices and network devices. In examples of the present disclosure, network traffic in network 101 following a gesture user input may be monitored. This may be used to determine whether the action being performed is intentional or unintentional.

[0082] Figure 2 An example method is shown. The method can be performed as follows Figure 1 The processing device 103 and / or the user equipment 105 shown in FIG. 1 may be implemented by another other suitable apparatus or device.

[0083] The method includes detecting network traffic activity at block 201. The network traffic activity indicates one or more actions performed by at least one network device 107.

[0084] The network activity may be detected by any suitable device within the network 101. For example, the network traffic activity may be detected by the processing device 103, the user device 105, or one or more network devices 107, or a combination of these devices.

[0085] Network activity may include packets or communications that may be sent between respective devices within network 101. For example, wireless communications including one or more packets may be exchanged between processing device 103 and network device 107, between respective network devices 107, or between any suitable devices within network 101.

[0086] The detected network traffic activity may include one or more of the following: the number of data packets, the length of the data packets, the timing of the data packets, the rise time of the data packets, the fall time of the data packets, or any other suitable characteristics. In some examples, the network activity may include no traffic being sent, or the traffic indicating that no action is being performed by the network device 107.

[0087] In some examples, the detected network traffic activity includes one or more of: a 5G signal, a WiFi signal, a Bluetooth signal, a Bluetooth low energy signal, or any other suitable type of signal.

[0088] The network traffic activity may provide a unique radio frequency fingerprint for a network device 107 performing a particular action. By monitoring the network traffic activity, the action performed by one or more of the network devices 107 may be identified. In some examples, the network device 107 performing the action may be identified based on the network traffic activity.

[0089] In some examples, actions of the network device corresponding to the detected network traffic activity can be identified by comparing the detected network traffic activity to one or more network traffic activity profiles. The network traffic activity profiles can be stored in a database or any other suitable component.

[0090] At block 203, the method includes identifying a user input made by a user of the user device 105. At least one network device 107 is different from the user device 105. The user input may be detected by one or more sensors 119 of the user device 105.

[0091] In some examples, the user input may include gesture user input. The gesture user input may include movement of the user device 105, movement of the user of the user device, voice input, or any other suitable type of input. In some examples, the user input may be made without the user directly contacting the user device 105 or the user input device of the user device 105.

[0092] Gesture user input can potentially be detected by devices other than the device for which the input is expected. For example, movement of the user's arm of the user's device can be detected by the user device 105 and can also be detected by one or more network devices in the area.

[0093] The user input is made in a time interval preceding the detected network traffic activity.

[0094] The user input may be identified using any suitable method or process. In some examples, the user input is identified by comparing the user input made in a time interval prior to the detected network traffic activity with data associated with the user input. The data associated with the user input may be stored in a database or any other suitable location.

[0095] At block 205, the method includes determining a user intent for one or more actions performed by at least one network device 107. The user intent may be determined by comparing the identified user input and the detected network traffic activity to data stored in a database relating to the user input and the associated expected network traffic activity.

[0096] In some examples, determining user intent may include comparing the identified user input to a first user input for controlling user device 105 and a second user input for controlling at least at least one network device 107 to generate the detected network traffic activity to determine whether the identified user input more closely matches the first user input or the second user input. If the user input closely matches the second user input, this will be deemed intentional, and the action performed by network device 107 may be allowed to proceed. If the user input does not closely match the second user input, the action performed by the network device may be deemed unintentional, and an alert or other output may be provided, which may cause the action performed by network device 107 to be stopped or otherwise limited.

[0097] The determination of user input can be made using any suitable method.In some examples, a machine learning model can be used to determine user intent.

[0098] At block 207 , the method includes controlling an output of the user device 105 based on the determined user intent.

[0099] In some examples, the output of user device 105 may include providing an alert to a user of user device 105. The alert may indicate to the user that an unintentional action may have been activated for one or more network devices 107. In some examples, the alert may indicate the action that has been activated and / or the network device 107 that has activated the action. This may enable the user to choose whether to stop the action of network device 107.

[0100] In some examples, the output of user device 105 may include enabling a user to change how user device 105 is controlled to reduce the likelihood of unintended actions being performed by network device 107 .

[0101] For example, the output may include enabling a user input used to control an action of user device 105 to be changed to a different user input. The different user input may have a higher distinguishability than the user input used to control network device 107. This may make it less likely that the user input used to control user device 105 will be mistaken for the user input used to control network device 107.

[0102] To enable the change of user input, the output of the user device 105 may be configured to provide one or more suggested user inputs, which may be selected by the user as replacement user inputs.The potential replacement user inputs may have a similarity below a threshold compared to the identified user input.

[0103] In some examples, network traffic activity may indicate that no action is being performed by network device 107. This may indicate that an expected action has not yet been performed. In such an example, an alert or opportunity to change user input may also be provided to the user of user device 105.

[0104] exist Figure 2 In some examples, actions being performed or not performed by network device 107 can be identified from network traffic data. In some examples, additional information can be used to help identify actions being performed by network device 107. For example, signals detected by one or more sensors can also be used. The sensor can be part of user device 105 or part of any other suitable device within the network. For example, one or more light sensors can be used to detect whether a light has been turned on or off, one or more microphones can be used to detect whether a device providing an acoustic output has been adjusted, or other signals can be detected.

[0105] Figure 3 Another example method is shown. This method can be performed as follows Figure 1 The user equipment 105 shown in FIG. 1 may be implemented by other suitable devices or apparatuses.

[0106] At block 301, the method includes collecting data related to detected user input. The data may be detected by a sensor 119 of a user device 105 or by any other suitable component.

[0107] At block 303, the method includes providing the collected data related to the detected user input to at least one processing device 103. The provision of the data related to the detected user input to the at least one processing device 103 enables the collected data to be analyzed to determine user intent.

[0108] Processing equipment can be used Figure 2 or any other suitable method to determine user intent. For example, processing device 103 can compare user input with network traffic activity to determine whether an unintentional action of network device 107 has been activated, or whether an intended action of network device 107 has not yet been activated.

[0109] At block 305 , the method includes receiving input based on the determined user intent.

[0110] In some examples, user device 105 may be configured to send additional data or information to processing device 103. Additional data may be any data provided in addition to user input data. The additional data may be used by processing device 103 to perform a method or a portion of a method, such as Figure 2For example, in some examples, user device 105 may be configured to obtain data related to network traffic activity and enable the data to be sent as additional data to processing device 103. Network traffic activity may be a fingerprint of communications within network 101, which may be used to indicate actions being performed by network device 107. Information related to network traffic activity includes additional data that may be sent to processing device 103 to enable user intent to be determined.

[0111] In some examples, user device 105 may be configured so that one or more user inputs used to control the user device can be adjusted. The adjustment of the user input for user device 105 may be intended to increase the distinguishability between the user input used to control user device 105 and one or more inputs used to control at least network device 107. For example, one or more new user inputs may be provided to the user of the user device as selectable options. The selected new user input may be selected to have a distinguishability level above a threshold so as to reduce the possibility that they are misunderstood by network 101. The user of user device 105 may then select a preferred new user input from the available options.

[0112] Figure 4 Another example system 401 is schematically illustrated that may be used to implement examples of the present disclosure and create a database that associates network traffic activity with gesture user input. Figure 4 An example system 401 may include Figure 1 1. The processing device 103 and the user device 105 are shown in FIG. 1. In other examples, other types of devices and arrangements of devices may be used. Corresponding reference numerals are used for corresponding features.

[0113] Figure 4 The example system of can be configured to create a database that connects actions performed by network devices 107 with gesture user input. The connection between actions performed by network devices 107 and gesture user input is generated by analyzing network traffic activity.

[0114] exist Figure 4 In the example of FIG. 1 , the user device 105 includes a sensor 119 configured to detect a gesture input or a potential gesture input and one or more network traffic sensors 403. The user device 105 may also include Figure 4 For example, the user equipment 105 may include a transmitter / receiver 115, a device 113 and / or any other suitable components.

[0115] User device 105 can be any suitable type of user device 105. In some examples, user device 105 can be a mediated reality or augmented reality device. In some examples, the user device can include an augmented reality headset or any other suitable type of device. User device 105 can be configured so that the user can interact with user device 105 using gesture input.

[0116] Sensor 119 may be configured to detect one or more inputs. The input may be a gesture input made by a user of user device 105. The gesture input may be a movement of the user, movement of user device 105, voice input, or any other suitable type of input.

[0117] Sensor 119 may be configured to provide gesture data 405 as output. Gesture data 405 indicates a gesture or potential gesture that has been detected by sensor 119. User device 105 may be configured to send gesture data 405 to processing device 103.

[0118] Network traffic sensor 403 may be configured to detect network traffic. Network traffic may include communication activity within network 101. Network traffic activity may be caused by communications involving one or more network devices 107. Network device 107 may communicate with processing device 103 and / or any other suitable device within network 101.

[0119] The network flow sensor 403 may include an antenna or any other suitable component for sensing network flow. The antenna may be a 5G antenna, a WiFi antenna, or any other suitable type of antenna. The network flow sensor 403 may be configured to detect a 5G signal, a WiFi signal, a Bluetooth signal, a Bluetooth low energy signal, or any other suitable type of signal.

[0120] Network flow sensor 403 may be configured to detect characteristics of the signal, such as the number of data packets, the length of the data packets, the timing of the data packets, the rise time of the data packets, the fall time of the data packets, or any other suitable characteristic.

[0121] The network traffic sensor 403 may be configured to provide as output network traffic activity data 407. The network traffic activity data 407 indicates network traffic activity detected by the network traffic sensor 403. The user device 105 may be configured to send the network traffic activity data 407 to the processing device 103.

[0122] The processing device 103 may be Figure 1 The processing device 103 shown may also be any other suitable type of processing device 103. Figure 4In some examples, the processing device is a separate device from the user device 105. The gesture data 405 and the network traffic activity data 407 can be sent from the user device 105 to the processing device 103. In some examples, the processing device 103 can be a part of the user device 105. In some examples, the processing device 103 can be multiple distributed devices, and the gesture data 405 and the network traffic activity data 407 can be sent to the appropriate location to enable processing of the data.

[0123] exist Figure 4 In the example of FIG. 4 , the processing device 103 includes a gesture data collection module 409 and a network traffic event detection module 411. The modules 409 and 411 may be as follows: Figure 1 The device 109 may be a portion of the device 109 shown, or may be implemented using any other suitable components.

[0124] Gesture data 405 is provided as input to gesture data collection module 409. Gesture data collection module 409 provides means for collecting data related to gesture user input that may have been detected by user device 105.

[0125] The network traffic event detection module 411 provides means for analyzing the network traffic activity data 407 and looking for patterns or fingerprints indicative of network traffic events.

[0126] A network traffic event may be a sequence of network traffic activity that indicates an action being performed by a network device 107. For example, a network traffic event may be a sequence of packets of a particular size or other characteristics that are sent at a particular time. The timing and / or other characteristics of the packets may be unique to a particular network device 107 that is performing a particular function. Thus, the timing and / or other characteristics of the packets may provide a network traffic event that may be used to identify an action being performed by a network device 107.

[0127] The network traffic event detection module 411 may be configured to access a network traffic event database 413. The network traffic event database 413 may be stored in the processing device 103, or may be stored in any other suitable location accessible by the processing device 103.

[0128] The network traffic event database 413 may be configured to store data associated with network traffic events. The network traffic event database 413 may store data associated with network traffic patterns or sequences corresponding to corresponding network events. The data associated with network traffic patterns or sequences corresponding to corresponding network events may be specific to a particular location or network. The data associated with network traffic patterns or sequences corresponding to corresponding network events may be generated by a processing device by monitoring network traffic activity data and may then be transmitted to the network traffic event database 413 for storage. The data associated with network traffic patterns or sequences corresponding to corresponding network events may be generated by the processing device 103 during the configuration phase or at any other suitable time.

[0129] In some examples, data related to network traffic patterns or sequences corresponding to respective network events may be associated with one or more actions performed by one or more network devices 107. The actions performed by network devices 107 corresponding to the network events may be determined through information obtained by sensors that may detect outputs of network devices 107 or through any other suitable means.

[0130] The network traffic event detection module 411 is configured such that if a match is detected between the network traffic activity data 407 and the network traffic event data 415, a trigger input 417 is provided from the network traffic event detection module 411 to the gesture data collection module 409. The trigger input 417 may indicate that a match has been detected between the network traffic activity data 407 and the network traffic event data 415. The trigger input 417 may include an indication of the timing of the network event that has occurred.

[0131] The gesture data collection module 409 may be configured to cause gesture data collected during a time period prior to a network traffic event to be collected in response to receiving a trigger input 417. The gesture data obtained during this time period may include user inputs made during a time interval prior to a detected network traffic activity corresponding to the network traffic event. The gesture data obtained during a defined time period prior to the network event may be provided as an output of the gesture data collection module 409. The time period may be a predetermined time period, such as ninety seconds prior to the network traffic event, or may be any other suitable duration.

[0132] The output of gesture data collection module 409 may include trigger gesture data 419. In some examples, trigger gesture data 419 may include all gesture data collected during the relevant time interval. In some examples, trigger gesture data 419 may include a subset of gesture data collected during the relevant time interval. Because trigger gesture data includes data 419 related to user input detected during the time interval prior to the detected network traffic event, trigger gesture data 419 may include information indicating gesture input that caused an action to be performed by one or more network devices 107.

[0133] Figure 4 The system is configured such that trigger gesture data 419 is provided as input to a gesture database 421. Gesture database 421 is configured to store trigger gesture data 419. Gesture database 421 is configured to be accessible by processing device 103 and any other suitable components of system 401.

[0134] Figure 4 The system also includes a gesture classification module 423. The gesture classification module 423 may include any component that can be configured to implement a gesture classification algorithm. The gesture classification module 423 may be part of the processing device 103 or any other suitable device. In some examples, the gesture classification module 423 may be implemented by one or more distributed devices.

[0135] In some examples, gesture classification module 423 can be configured to implement a classification algorithm to process trigger gesture data 419 to detect patterns and define gesture inputs that trigger network traffic events. The classification algorithm can be a machine learning algorithm or any other suitable type of algorithm. In the case where a machine learning algorithm is used, the algorithm can be pre-trained using labeled gesture data for known gesture user inputs or using any other suitable data.

[0136] The gesture classification module 423 provides pairing data 425 as an output. The pairing data may associate a gesture input with a network traffic event. The pairing data may indicate a gesture input entered by a user of the user device 105, and the gesture input is known to trigger an action performed by one or more network devices 107 to cause a corresponding network traffic event.

[0137] Pairing data 425 may be provided as input to a pairing database 427. Pairing database 427 is accessible by gesture classification module 423. Database 427 may be stored in processing device 103 or in any other suitable location. Figure 4 The illustrated system-created pairing database 427 may be used in examples of the present disclosure to help determine user intent for actions performed by the network device 107 .

[0138] Figure 5 Another example system 501 is shown that can be used to implement examples of the present disclosure and determine user intent for an action performed by a network device 107 . Figure 5 The example system 501 includes a plurality of different modules. These modules may be part of the user device 105, part of the processing device 103, or part of any other suitable device. In some examples, one or more modules may be distributed across a plurality of different devices. Corresponding reference numerals are used for corresponding features that have been previously described.

[0139] Figure 5 The system includes a pairing database 427. The pairing database 427 can be used Figure 4 The pairing database 427 includes information that associates corresponding gesture inputs with corresponding network traffic events. The pairing database 427 is stored in a location so that it can be accessed by appropriate modules within the system 501.

[0140] Figure 5 The system 501 further includes one or more sensors 119. The sensor 119 may be a part of the user equipment 105, or may be provided in any other suitable part of the network 101.

[0141] Sensor 119 may be configured to detect one or more inputs. The input may be a gesture input made by a user of user device 105. The gesture input may be a movement of the user, movement of user device 105, voice input, or any other suitable type of input.

[0142] Sensor 119 may be configured such that when gesture user input is detected, gesture data 405 is provided as output. Gesture data 405 indicates a gesture input that has been detected by sensor 119. User device 105 may be configured to send gesture data 405 related to the detected user input to processing device 103.

[0143] The gesture data 405 is provided as an input to the gesture classification module 423. The gesture classification module 423 is also configured to obtain trigger gesture data 419 from the pairing database 427. The trigger gesture data 419 may include the gesture data that has been detected and associated with the gesture. Figure 4 or using any other suitable process to associate gesture data with network traffic events.

[0144] Gesture classification module 423 may include any component that may be configured to implement a gesture classification algorithm. Gesture classification module 423 may be part of processing device 103 or any other suitable device. In some examples, gesture classification module 423 may be implemented by one or more distributed devices.

[0145] In some examples, gesture classification module 423 can be configured to implement a classification algorithm to process trigger gesture data 419 to detect patterns and define gesture inputs that trigger network traffic events. The classification algorithm can be a machine learning algorithm or any other suitable type of algorithm. In the case where a machine learning algorithm is used, the algorithm can be pre-trained using labeled gesture data for known gesture user inputs or using any other suitable data.

[0146] Gesture classification module 423 may compare gesture data 405 to trigger gesture data 419 to determine the level of correlation between the corresponding data. This may indicate the level of match between gesture data 405 and trigger gesture data 419. This effectively compares the detected gesture user input to gesture user inputs that are known to be intended to cause network device 107 to perform an action.

[0147] Gesture classification module 423 is configured to provide as output gesture match data 503. Gesture match data 503 may include a value indicating the strength of the match between gesture data 405 and trigger gesture data 419. This provides an indication of how closely gesture data 405 matches trigger gesture data 419.

[0148] System 501 also includes one or more network traffic sensors 403. Network traffic sensors 403 may be part of user device 105 or any other suitable part of network 101.

[0149] Network traffic sensor 403 may be configured to detect network traffic 403. Network traffic may include communication activity within network 101. Network traffic activity may result from communications involving one or more network devices 107. Network device 107 may communicate with processing device 103 and / or any other suitable device within network 101.

[0150] The network traffic sensor 403 may be configured to provide as output network traffic activity data 407. The network traffic activity data 407 indicates network traffic activity that has been detected by the network traffic sensor 403. The user device 105 may be configured to send the network traffic activity data 407 to the processing device 103.

[0151] The network traffic activity data 407 is provided as input to the network traffic event detection module 411. The network traffic event detection module 411 is also configured to obtain network traffic event data 415 from the pairing database 427. The network traffic event data 415 may include information related to network traffic activity and information that has been detected and associated with the network traffic event. Figure 4 or using any other suitable process to associate event-related data with the gesture input.

[0152] The network traffic event detection module 411 may include any component that may be configured to evaluate similarities between detected network traffic activity and network traffic activity corresponding to a network traffic event. This may be used to infer whether the network device 107 is performing an action.

[0153] The network traffic event detection module 411 may be part of the processing device 103 or any other suitable device. In some examples, the network traffic event detection module 411 may be implemented by one or more distributed devices.

[0154] In some examples, the network traffic event detection module 411 can be configured to implement a classification algorithm to process the network traffic activity data 407 to detect patterns and define patterns corresponding to network traffic events. The classification algorithm can be a machine learning algorithm or any other suitable type of algorithm. In the case where a machine learning algorithm is used, the algorithm can be pre-trained using any suitable data set.

[0155] The network traffic event detection module 411 may compare the network traffic activity data 407 with the network traffic event data 415 to identify a level of correlation between the corresponding data. This may indicate a level of match between the network traffic activity data 407 and the network traffic event data 415. This effectively compares the detected network traffic activity with network traffic activity known to be caused by the network device 107 performing the action.

[0156] The network traffic event detection module 411 is configured to provide as output network traffic match data 505. The network traffic match data 505 may include a value indicating the strength of the match between the network traffic activity data 407 and the network traffic event data 415. This provides an indication of how closely the network traffic activity data 407 matches the network traffic event data 415, which indicates the likelihood that the action being performed by the network device 107 can be correctly identified.

[0157] Figure 5 The system 501 further includes a user device gesture classification module 507. The user device gesture classification module 507 may be a part of the user device 105, or a part of any other suitable device within the network 101 or connected to the network.

[0158] User device gesture classification module 507 may include any component that may be configured to evaluate similarities between gesture user input that has been detected by user device 105 and gesture user input that is used to control one or more functions of user device 105. For example, gesture data may be compared to gesture input that is used to control an application currently being used by a user or to enable any other appropriate control of user device 105.

[0159] Any suitable component or process may be used to evaluate the similarity between gesture user input that has been detected by user device 105 and gesture user input that is used to control one or more functions of user device 105. In some examples, a classification algorithm may be used to evaluate the similarity between gesture user input that has been detected by user device 105 and gesture user input that is used to control one or more functions of user device 105.

[0160] The user device gesture classification module 507 may be configured to provide user device matching data 509 as an output.

[0161] The user device matching data 509 is provided as input to the intent classification module 511. The intent classification module 511 may be part of the user device 105, part of the processing device 103, and / or part of any other suitable device within or connected to the network 101.

[0162] The intent classification module 511 receives as input the user device match data 509, the gesture match data 503, and the network traffic match data 505. The intent classification module 511 can be configured to determine the user intent of the action that has been performed by the network device 107 and which corresponds to the detected network traffic activity. That is, the intent classification module 511 can be configured to determine whether the inputs made by the user are intended to control the functions of the network device 107, or whether they are intended to control one or more functions of the user device 105. The intent classification module 511 can be configured to compare the identified gesture inputs with the data related to gesture user inputs and network traffic activity stored in the pairing database 427.

[0163] exist Figure 5 In the example of , intent classification module 511 can be configured to compare the value of user device matching data 509 with the values ​​of gesture matching data 503 and network traffic matching data 505 .

[0164] If user device match data 509 is below a threshold or below gesture match data 503 and / or network traffic match data 505, it may be determined that the gesture input is not intended to control a function of user device 105, but is instead intended to control a function of network device 107. In such a case, the gesture input may be assumed to have been correctly interpreted, and no further action or intervention from system 501 is required.

[0165] If user device match data 509 is above a threshold or above gesture match data 503 and / or network traffic match data 505, it may be determined that the gesture input is intended to control a function of user device 105 rather than a function of network device 107. In such a case, the gesture input may be assumed to have been incorrectly interpreted as causing an action of network device 107. In such a case, an incorrect input detection notification 513 may be provided as an output of intent classification module 511.

[0166] Incorrect input detection notification 513 may be provided to any suitable module or portion of network 101. Figure 5 In the example system 501 of FIG. 5 , an incorrect input detection notification 513 is provided to a user alert module 515. The user alert can be configured to provide an output to a user of the user device 105 to indicate that an action 107 of the network device has been unintentionally initiated. The user alert module 515 can be configured to provide any suitable type of user alert 517 as an output. For example, the user alert can be provided as a visual alert on a display or an audible alert via a speaker.

[0167] In some examples, if it is determined that the detected gesture input has caused an unintentional action, the user device 105 can be configured to enable the user to change the user input used to control the user device and / or network device 107 so as to reduce the similarity between the corresponding gesture inputs. This can reduce the likelihood that an unintentional action will be performed in future usage scenarios of the user device 105.

[0168] Figure 6 An example user device 105 in use according to an example of the present disclosure is shown. The user device 105 in this example is a pair of augmented reality glasses. User 601 is wearing the augmented reality glasses.

[0169] The user makes a gesture user input by moving their hand 603, as indicated by arrow 605, in a figure eight motion. In other examples, other user input and gesture types may be used. The gesture user input may be detected using any suitable sensor 119 of the user device 105. The sensor 119 is not in Figure 6 Sensor 119 may include a LiDAR sensor or any other suitable type of sensor.

[0170] The user device 105 may be configured to provide gesture data 405 indicative of the detected gesture input to the processing device 103 .

[0171] The processing device 103 may also obtain network traffic activity data 407. The processing device may use the obtained network traffic activity data to determine whether a network action has been activated.

[0172] If a network action has been activated, processing device 103 may use examples of the present disclosure to determine whether this was intentional. The processing device may compare the gesture data to a gesture used to control the network device and a gesture used to control user device 105. If the gesture is closer to a gesture used to control user device 105, it may be assumed that the action of network device 107 was initiated in error because the gesture input was not intended to control network device 107.

[0173] If it is determined that the action of the network device is not intended to be activated by user 601, then an incorrect input notification is sent from processing device 103 to user device 105. This may enable user device 105 to provide an alert to user 601 indicating that network device 107 has been inadvertently activated.

[0174] In some examples, user device 105 or other devices in the network may be configured to enable a user to change the gesture user input that is used so there is a reduced chance of network device 107 being inadvertently activated.

[0175] In some examples, the interactive locations for other devices in the network can be changed. For example, the interactive location for network device 107 can be the location where network device 107 detects user input. These can be moved to different locations to avoid overlapping with the area around user device 105.

[0176] exist Figure 6 In the example of FIG. 1 , processing device 103 is configured to perform processing to determine whether network device 107 has been inadvertently activated. In other examples, other devices or groups of devices may be used to perform the processing or role of the processing device.

[0177] Figure 7 Schematically illustrates an apparatus 109 / 113 that can be used to implement an example of the present disclosure. In this example, the apparatus 109 / 113 includes a controller 701. The controller 701 can be a chip or a chipset. In some examples, the controller 701 can be provided in a communication device or any other suitable type of device.

[0178] exist Figure 7 In some examples, the controller 701 may be implemented as a controller circuit system. In some examples, the controller 701 may be implemented in hardware alone, with certain aspects in software (including firmware), or may be a combination of hardware and software (including firmware).

[0179] like Figure 7As shown, the controller 701 can be implemented using instructions that enable hardware functions, for example, by using executable instructions of a computer program 707 in a general or special purpose processor 703, which executable instructions can be stored on a computer-readable storage medium (disk, memory, etc.) for execution by such a processor 703.

[0180] The processor 703 is configured to read from and write to the memory 705. The processor 703 may also include an output interface via which the processor 703 outputs data and / or commands and an input interface via which data and / or commands are input to the processor 703.

[0181] The memory 705 stores a computer program 707, which includes computer program instructions (computer program code) that control the operation of the controller 701 when loaded into the processor 703. The computer program instructions of the computer program 707 provide logic and routines so that the controller 701 can perform the methods shown in the accompanying drawings. By reading the memory 705, the processor 703 can load and execute the computer program 707.

[0182] The device 109 / 113 comprises:

[0183] at least one processor 703; and

[0184] At least one memory 705 stores instructions, which when executed by at least one processor cause the apparatus to at least perform:

[0185] detecting 201 network traffic activity, wherein the network traffic activity indicates one or more actions performed by at least one network device;

[0186] identifying 203 a user input made by a user of a user device, wherein at least one network device is different than the user device, and wherein the user input was made within a time interval preceding the detected network traffic activity;

[0187] determining 205 a user intent for one or more actions performed by at least one network device by comparing the identified user input and the detected network traffic activity with data stored in a database relating to the user input and the associated expected network traffic activity; and

[0188] Based on the determined user intent, an output of the user device is controlled 207 .

[0189] In some examples, the apparatus 113 may be configured for use in the user equipment 105. In such an example, the apparatus 109 includes:

[0190] at least one processor 703; and

[0191] At least one memory 705 stores instructions, which when executed by at least one processor cause the apparatus to at least perform:

[0192] Collecting 301 data related to the detected user input;

[0193] The collected data related to the detected user input is provided 303 to at least one processing device so that the collected data can be analyzed to determine the user intent; and based on the determined user intent, an input is received 305.

[0194] like Figure 7 As shown, the computer program 707 can reach the controller 701 via any suitable transmission mechanism 711. The transmission mechanism 711 can be, for example, a machine-readable medium, a computer-readable medium, a non-transitory computer-readable storage medium, a computer program product, a memory device, a recording medium (such as a compact disc read-only memory (CD-ROM) or a digital versatile disc (DVD) or a solid-state memory), an article of manufacture that includes or tangibly embodies the computer program 707. The transmission mechanism can be a signal configured to reliably transmit the computer program 707. The controller 701 can propagate or send the computer program 707 as a computer data signal. In some examples, the computer program 707 can be sent to the controller 701 using a wireless protocol (such as Bluetooth, Bluetooth low energy, Bluetooth smart, 6LoWPan (IPv6 on low power personal area network), ZigBee, ANT+, near field communication (NFC), radio frequency identification, wireless local area network (wireless LAN) or any other suitable protocol).

[0195] The computer program 707 includes computer program instructions for causing the device 109 / 113 to perform at least the following items or for performing at least the following items:

[0196] detecting 201 network traffic activity, wherein the network traffic activity indicates one or more actions performed by at least one network device;

[0197] identifying 203 a user input made by a user of a user device, wherein at least one network device is different than the user device, and wherein the user input was made within a time interval preceding the detected network traffic activity;

[0198] determining 205 a user intent for one or more actions performed by at least one network device by comparing the identified user input and the detected network traffic activity with data stored in a database relating to user input and associated expected network traffic activity; and

[0199] Based on the determined user intent, an output of the user device is controlled 207 .

[0200] If the computer program 707 is for the user equipment 105, the computer program 707 may include computer program instructions for causing the apparatus 113 to perform at least the following items or for performing at least the following items:

[0201] Collecting 301 data related to the detected user input;

[0202] providing collected data associated with the detected user input to at least one processing device so that the collected data is analyzed to determine user intent; and

[0203] Based on the determined user intent, input is received 305 .

[0204] The computer program instructions may be included in a computer program 707 , a non-transitory computer readable medium, a computer program product, a machine readable medium. In some but not necessarily all examples, the computer program instructions may be distributed over more than one computer program 707 .

[0205] Although memory 705 is shown as a single component / circuitry, it may be implemented as one or more separate components / circuitry, some or all of which may be integrated / removable and / or may provide permanent / semi-permanent / dynamic / cache storage.

[0206] Although processor 703 is shown as a single component / circuitry, it may be implemented as one or more separate components / circuitry, some or all of which may be integrated / removable.Processor 703 may be a single-core or multi-core processor.

[0207] References to "computer-readable storage medium," "computer program product," "tangibly embodied computer program," etc. or "controller," "computer," "processor," etc. should be understood to include not only computers having different architectures (such as single / multi-processor architectures and sequential (von Neumann) / parallel architectures), but also special-purpose circuits (such as field-programmable gate arrays (FPGAs), application-specific circuits (ASICs), signal processing devices, and other processing circuit systems. References to computer programs, instructions, code, etc. should be understood to cover software for programmable processors or firmware, such as, for example, the programmable content of a hardware device, whether instructions for a processor, or configuration settings for a fixed-function device, gate array, or programmable logic device, etc.

[0208] As used in this application, the term "circuitry" may refer to one or more or all of the following:

[0209] (a) hardware circuitry implementation only (such as implementation only in analog and / or digital circuitry) and

[0210] (b) a combination of hardware circuitry and software such as (where applicable):

[0211] (i) a combination of analog and / or digital hardware circuits and software / firmware and

[0212] (ii) any portion of hardware processor(s) with software (including digital signal processor(s), software and one or more memories that work together to enable a device (such as a mobile phone or server) to perform various functions and (c) hardware circuit(s) and / or processor(s), such as microprocessor(s) or portion(s) of microprocessor(s), that require software (e.g., firmware) for operation, but where the software is not required for operation, the software may be absent.

[0213] This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of only hardware circuits or processors and their (or their) accompanying software and / or firmware. The term circuitry also covers, for example, a baseband integrated circuit for a mobile device or a similar integrated circuit in a server, cellular network device, or other computing or network device, if applicable to a particular claim element.

[0214] Figure 2 The blocks shown in the figure may represent steps in the method and / or code sections in the computer program 707. The description of a particular order of blocks does not necessarily mean that the blocks have a necessary or preferred order, and the order and arrangement of the blocks may be changed. In addition, some blocks may be omitted.

[0215] According to examples of the present disclosure, device 109 / 113 may be provided in an electronic device, such as a mobile terminal. However, it should be understood that the mobile terminal is only an example of an electronic device that will benefit from the implementation examples of the present disclosure, and therefore should not be considered to limit the scope of the present disclosure to this. Although in some implementation examples, device 109 / 113 may be provided in a mobile terminal, other types of electronic devices, such as but not limited to: mobile communication devices, handheld portable electronic devices, wearable computing devices, portable digital assistants (PDAs), pagers, mobile computers, desktop computers, televisions, gaming devices, laptop computers, cameras, video recorders, GPS devices, and other types of electronic systems, can easily adopt examples of the present disclosure. In addition, whether or not the device is intended to provide mobility, the device can easily adopt examples of the present disclosure.

[0216] The term "comprising" is used in this document in an inclusive rather than exclusive sense. That is, any reference to X including Y indicates that X may include only one Y or may include more than one Y. If the exclusive meaning of "comprising" is intended, it will be made clear in the context by referring to "including only one..." or using "consisting of..."

[0217] In this specification, the terms "connected", "coupled" and "in communication" and their derivatives refer to being operationally connected / coupled / in communication. It should be understood that any number or combination of intermediate components (including no intermediate components) may be present, i.e., to provide direct or indirect connection / coupling / in communication. Any such intermediate components may include hardware and / or software components.

[0218] As used herein, the term "determine / determining" (and grammatical variations thereof) may include (but are not limited to): calculate, compute, process, derive, measure, investigate, identify, search (e.g., search in a table, database or other data structure), ascertain, etc. In addition, "determine" may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory), obtaining, etc. In addition, "determine / determining" may include resolving, selecting, choosing, establishing, etc.

[0219] In this specification, reference has been made to various examples. The description of features or functions associated with an example indicates that those features or functions are present in that example. The use of the term "example" or "for example" or "may" or "might" in the text indicates that, whether explicitly stated or not, these features or functions are present in at least the example, whether described as an example or not, and they may, but not necessarily, be present in some or all other examples. Therefore, "example", "for example", "may" or "might" refers to a specific instance in an example class. The attributes of an instance may be attributes of only that instance, or attributes of a class, or attributes of a subclass of a class that includes some but not all instances in the class. Therefore, features described with reference to one example but not with reference to another example are implicitly disclosed, and where possible, may be used as part of a working combination in that other example, but do not necessarily have to be used in that other example.

[0220] Although the examples have been described in the preceding paragraphs with reference to various examples, it should be appreciated that modifications to the examples given can be made without departing from the scope of the claims.

[0221] Features described in the preceding description may be used in combinations other than the combinations explicitly described above.

[0222] Although functions have been described with reference to certain features, those functions may be performable by other features whether described or not.

[0223] Although features have been described with reference to certain examples, those features may also be present in other examples whether described or not.

[0224] As used herein, the terms "a", "an", or "the" have an inclusive, rather than exclusive, meaning. That is, any reference to X including a / an / the Y indicates that X may include only one Y or may include more than one Y, unless the context clearly indicates otherwise. If "a", "an", or "the" is intended to be used in an exclusive sense, this will be made clear from the context. In some cases, "at least one" or "one or more" may be used to emphasize an inclusive meaning, but the absence of these terms should not be understood to infer any exclusive meaning.

[0225] The presence of a feature (or combination of features) in a claim refers to the feature or (combination of features) itself, and also to features that achieve substantially the same technical effect (equivalent features). Equivalent features include, for example, features that are variants and achieve substantially the same result in substantially the same way. Equivalent features include, for example, features that perform substantially the same function in substantially the same way to achieve substantially the same result.

[0226] In this specification, reference has been made to various examples using adjectives or adjective phrases to describe characteristics of examples. Such description of characteristics associated with examples indicates that the feature exists in some examples exactly as described, and in other examples substantially as described.

[0227] The above description describes some examples of the present disclosure, however, those of ordinary skill in the art will be aware of possible alternative structures and method features that provide equivalent functions to the specific examples of the above structures and features, and have been omitted from the above description for the sake of brevity and clarity. Nevertheless, the above description should be understood to implicitly include references to such alternative structures and method features that provide equivalent functions, unless such alternative structures or method features are explicitly excluded in the above description of the examples of the present disclosure.

[0228] While an effort has been made in the foregoing description to call attention to those features regarded as important, it should be understood that the applicant may seek protection by way of the claims for any patentable feature or combination of features mentioned above and / or shown in the drawings, whether or not emphasis has been placed thereon.

Claims

1. An apparatus comprising components for: detecting network traffic activity, wherein the network traffic activity is indicative of one or more actions performed by at least one network device; identifying a user input made by a user of a user device, wherein the at least one network device is different than the user device, and wherein the user input was made within a time interval preceding the detected network traffic activity; determining user intent for the one or more actions performed by the at least one network device by comparing the identified user input and the detected network traffic activity to data stored in a database relating to user input and associated expected network traffic activity; as well as Based on the determined user intent, an output of the user device is controlled.

2. The apparatus of claim 1 , wherein determining the user intent comprises: The identified user input is compared to a first user input for controlling the user device and a second user input for controlling at least the at least one network device to generate the detected network traffic activity to determine whether the identified user input more closely matches the first user input or the second user input.

3. An apparatus according to any preceding claim, wherein the output comprises at least one of: enabling a user input used to control an action of the user device to be changed to a different user input, providing an alert to the user of the user device, the alert indicating that the action is determined to be unintentional. 4 . The apparatus of claim 3 , wherein the output is configured to provide one or more suggested user inputs having a similarity level below a threshold compared to the identified user input.

5. An apparatus according to any preceding claim, wherein the user input is identified by comparing user input made within the time interval preceding the detected network traffic activity with the data associated with user input stored in a database.

6. The apparatus according to any preceding claim, wherein the user input comprises at least one of the following: voice input, gesture input.

7. An apparatus according to any preceding claim, wherein the detected network traffic activity comprises one or more of: number of data packets, length of data packets, timing of data packets, rise time of data packets, fall time of data packets.

8. The apparatus according to any preceding claim, wherein the detected network traffic activity comprises one or more of the following: a 5G signal, a WiFi signal, a Bluetooth signal, a Bluetooth low energy signal.

9. An apparatus according to any preceding claim, wherein the component is used to identify the one or more actions corresponding to the detected network traffic activity by comparing the detected network traffic activity to one or more network traffic activity profiles.

10. An apparatus as claimed in any preceding claim, wherein said means is configured to identify said network device based on said detected network traffic activity.

11. An apparatus according to any preceding claim, wherein a machine learning model is used to determine the user intent.

12. An apparatus as claimed in any preceding claim, wherein the user device comprises a mediated reality device.

13. A method comprising: detecting network traffic activity, wherein the network traffic activity is indicative of one or more actions performed by at least one network device; identifying a user input made by a user of a user device, wherein the at least one network device is different than the user device, and wherein the user input was made within a time interval preceding the detected network traffic activity; determining user intent for the one or more actions performed by the at least one network device by comparing the identified user input and the detected network traffic activity to data stored in a database relating to user input and associated expected network traffic activity; as well as Based on the determined user intent, an output of the user device is controlled.

14. A computer program comprising computer program instructions which, when executed by processing circuitry, cause: detecting network traffic activity, wherein the network traffic activity is indicative of one or more actions performed by at least one network device; identifying a user input made by a user of a user device, wherein the at least one network device is different than the user device, and wherein the user input was made within a time interval preceding the detected network traffic activity; determining user intent for the one or more actions performed by the at least one network device by comparing the identified user input and the detected network traffic activity to data stored in a database relating to user input and associated expected network traffic activity; as well as Based on the determined user intent, an output of the user device is controlled.

15. A user device comprising means for: Collect data related to detected user input; providing collected data associated with the detected user input to at least one processing device so that the collected data can be analyzed to determine user intent; as well as Based on the determined user intent, input is received.

Images