Mobile device position determination

Abstract

Methods and apparatuses associated with determining a position of a mobile device are described. Examples can include receiving, at a mobile device, signaling indicating that the mobile device is in an unauthorized position, has an unauthorized user, or both, in response to a triggering event. Examples can include prompting for input representative of authorized user verification, and enabling one or more circuits or power sources of the mobile device based at least in part on determining that a value of the input satisfies a threshold. In response and based at least in part on determining that the value of the input fails to satisfy the threshold, examples can include capturing environmental data and position data associated with the mobile device, and transmitting the environmental data and position data and a position determination to an authorized user. In some examples, a mobile device can be deactivated in response to an unconfirmed authorized verification.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to devices and methods associated with determining a location of a mobile device. BACKGROUND

[0002] Computing devices are mechanical or electrical devices that emit or modify energy to perform or assist in the performance of human tasks. Examples include thin clients, personal computers, printing devices, notebook computers, mobile devices, Internet of Things (IoT)-enabled devices, and game consoles, among others. IoT-enabled devices can refer to devices that are embedded with electronics, software, sensors, actuators, and / or network connectivity that enables such devices to connect to a network and / or exchange data. Examples of IoT-enabled devices include mobile phones, smartphones, tablet computers, phablets, computing devices, implantable devices, vehicles, home appliances, smart home devices, monitoring devices, wearable devices, devices that implement a smart shopping system, and other cyber-physical systems.

[0003] As used herein, a mobile device can include a portable computing device, such as a smartphone, a tablet computer, an e-reader, a smartwatch or other wearable device, a laptop computer, a camera, etc. A mobile device can include one or more cameras, sensors, and security capability features, such as a biometric scanner (e.g., an eye scanner, facial recognition, a fingerprint scanner), and password requirements, among others. SUMMARY

[0004] According to embodiments of the present disclosure, a method is provided and includes the steps of: receiving, at a mobile device (220, 340), signaling indicating that the mobile device is in an unauthorized location, is in possession of an unauthorized user, or both, in response to a triggering event; prompting, via a display of the mobile device, for an input indicative of an authorized user verification (104, 474); in response to the input indicative of the authorized user verification, enabling one or more circuits or power sources of the mobile device (106) based at least in part on determining that a value of the input satisfies a threshold; or in response to the input indicative of the authorized user verification and based at least in part on determining that the value of the input fails to satisfy the threshold: capturing, at the mobile device via a sensor, environmental data associated with the mobile device (108); capturing, at the mobile device, location data associated with a location of the mobile device (110); and communicating the captured environmental data and location data to an authorized user of the mobile device (112).

[0005] According to embodiments of the present disclosure, a mobile device is provided and includes a processing resource (222, 322) and a memory resource (224, 324) in communication with the processing resource and having instructions executable to receive a triggering event (226) indicating that the mobile device is in an unauthorized location, has an unauthorized user, or both; prompt an authorized user for verification (228, 348) via a display of the mobile device; in response to confirmed authorized verification, allow use of the mobile device (230); in response to unconfirmed authorized verification: capture image data, temperature data, sound data, or a combination thereof associated with an environment of the mobile device via a sensor at the mobile device (232, 352); capture location data associated with the mobile device at the mobile device (232, 354); determine a location of the mobile device based on the captured image data, temperature data, sound data, or a combination thereof and the captured location data utilizing a first machine learning model (234, 356); and communicate the location of the mobile device to the authorized user of the mobile device (236, 358). BRIEF DESCRIPTION OF DRAWINGS

[0006] Figure 1 is a flow diagram representative of an example method for determining a location of a mobile device according to embodiments of the present disclosure.

[0007] Figure 2 is a diagram of an example device having a processor and a memory resource with executable instructions thereon according to embodiments of the present disclosure.

[0008] Figure 3 is a diagram of an example device having a processor, a memory resource, and a plurality of sensors thereon according to embodiments of the present disclosure.

[0009] Figure 4 is another flow diagram representative of an example method for determining a location of a mobile device according to embodiments of the present disclosure. DETAILED DESCRIPTION

[0010] Examples of the disclosure include apparatuses and methods for determining a location of a mobile device. Several examples of the disclosure can include receiving, at a mobile device, a triggering event indicating that the mobile device is in an unauthorized location, possesses an unauthorized user, or both, and prompting, via a display of the mobile device, an authorized user for verification. Such examples can include allowing use of the mobile device in response to confirmed authorized verification. In response to unconfirmed authorized verification, examples can include capturing, at the mobile device via a sensor, environmental data associated with the mobile device, capturing, at the mobile device, location data associated with the mobile device, and transmitting the captured environmental data and location data to an authorized user of the mobile device.

[0011] Other examples of the disclosure can include a mobile device including a processing resource and a memory resource in communication with the processing resource and having instructions executable to receive a triggering event indicating that the mobile device is in an unauthorized location, possesses an unauthorized user, or both, and prompt, via a display of the mobile device, an authorized user for verification. Use of the mobile device can be allowed in response to confirmed authorized verification. In such examples, in response to unconfirmed authorized verification, image data, temperature data, sound data, or a combination thereof associated with an environment of the mobile device can be captured at the mobile device via a sensor, and location data associated with the mobile device.

[0012] In some examples, a location of the mobile device can be determined based on the captured image data, temperature data, sound data, or a combination thereof, and the captured location data using a machine learning model, the location of the mobile device can be transmitted to an authorized user of the mobile device, the mobile device can be deactivated.

[0013] Still other examples of the disclosure can include a mobile device including a plurality of sensors, a processing resource, and a memory resource communicatively coupled to the plurality of sensors, in communication with the processing resource, and having instructions executable to determine, using a first machine learning model, based on received biometric data, password data, location pattern data, or a combination thereof, that a triggering event indicating that the mobile device is in an unauthorized location, possesses an unauthorized user, or both, has occurred. The instructions can be executable to prompt, via a display of the mobile device, an authorized user for verification, and in response to confirmed authorized verification within a threshold period of time, allow use of the mobile device. As used herein, "communicatively coupled" can include coupled via various wired and / or wireless connections between devices such that data can be transmitted between the devices in various directions. The coupling can not be a direct connection, and in some examples can be an indirect connection.

[0014] In such instances, the instructions can be executable to capture, at the mobile device, environmental data associated with the mobile device via a first sensor of the plurality of sensors and to capture, at the mobile device, orientation data associated with the mobile device via a second sensor of the plurality of sensors in response to an unconfirmed authorized authentication or failing to respond within a threshold period of time. The instructions can be further executable to determine, with a second machine learning model, an orientation of the mobile device based on the captured environmental data and the captured orientation data, transmit the orientation of the mobile device to an authorized user of the mobile device, and deactivate the mobile device.

[0015] In the following detailed description of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration embodiments of the disclosure in which one or more embodiments of the disclosure can be practiced. The embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments can be utilized and that process, electrical, and structural changes can be made without departing from the scope of the present disclosure.

[0016] As used herein, the singular forms "a," "an," and "the" can include both singular and plural referents unless the context clearly dictates otherwise. Further, "a number of," "at least one," and "one or more" (e.g., a number of memory devices) can refer to one or more memory devices, while "a plurality of" is intended to refer to more than one of such things. Additionally, the words "may" and "might" are used by the Applicant in a permissive sense (i.e., having the potential to, being able to), rather than in a mandatory sense (i.e., must). The term "include" and derivations thereof mean "including, but not limited to." The terms "coupled" and "coupling" mean to be directly or indirectly connected or accessed, and to move (transmit) commands and / or data, as appropriate, depending on the context. The terms "data" and "data values" are used interchangeably herein and can have the same meaning, depending on the context.

[0017] The drawings herein are not necessarily drawn to scale of one another. The drawings herein follow a numbering convention in which the first digit or digits correspond to the figure number and the remaining digits identify an element or component in the drawing. Similar elements or components between different figures can be identified by the use of similar digits. For example, element "24" in Figure 2 may be indicated as "224" in Figure 2 and as "324" in Figure 3 Multiple similar elements within a figure can be indicated by a figure number followed by a hyphen and another number or letter. For example, 362-1 can indicate a first of two similar elements in Figure 3Elements 62-1 and 362-N can represent element 62-N, which can be similar to element 62-1. Such similar elements can be generally referred to without the hyphen and extra number or letter. For example, elements 362-1 and 362-N can generally be referred to as 362. As used herein, designators such as "N," with respect to reference numerals in the drawings, indicate that multiple similar elements can be included (i.e., a plurality of elements). It will be further understood that the terms used herein are for the purpose of describing specific embodiments and are not intended to be limiting.

[0018] As should be appreciated, elements shown in various embodiments herein can be added, exchanged, and / or removed so as to provide a number of additional embodiments of the present disclosure. Additionally, the proportions and / or relative scales of the various elements provided in the figures are intended to illustrate certain embodiments of the present disclosure and should not be taken in a limiting sense.

[0019] A user can need to locate their mobile device due to it being lost, stolen, misplaced, or carried incorrectly, among other possibilities. While some mobile devices include applications that locate or track the mobile device, such applications can be based solely on global positioning system (GPS) data and do not allow for increased localization granularity. Examples of the present disclosure can utilize a machine learning model (e.g., artificial intelligence (AI)) to determine a trigger (e.g., via an application) for a position determination of a mobile device a triggering event. For example, a machine learning model can be used to determine an abnormality in a position pattern associated with an authorized user and, along with other triggering events (e.g., unauthorized biometric data or password data, among others), can trigger a localization of the mobile device. In some examples, a machine learning model can be used to determine a position of a mobile device, e.g., using environmental and position data captured at the mobile device, when the mobile device is lost, stolen, misplaced, among other possibilities. The position can be communicated to an authorized user or other authorized party.

[0020] Figure 1 is a flow diagram representative of an example method 100 for determining a position of a mobile device in accordance with embodiments of the present disclosure. Method 100 can be performed by a device, e.g., devices 220 and 340 described with respect to FIGS. 2 and 3, respectively. Figure 2 and 3 described with respect to FIGS. 2 and 3, respectively.

[0021] At 102, the method 100 can include receiving, at the mobile device, signaling indicating that the mobile device is in an unauthorized location, is in possession of an unauthorized user, or both, in response to a triggering event. As used herein, a triggering event can include an event that triggers the launch of an application that locates the mobile device, prompts a user for authorization, or a combination thereof. For example, if an authorized user loses their mobile device, the triggering event can include the mobile device (e.g., via an application) remotely receiving a notification from the authorized user that the mobile device is in an unauthorized location, is in possession of an unauthorized user, or both. For example, the authorized user can log into an account linked to the mobile device via a computing device to request that the mobile device be located.

[0022] As used herein, an authorized user includes a user that has permission to access a mobile device. An authorized user can include a user that has a password and / or biometric authorization to access a mobile device. For example, the owner of a smartphone can be an authorized user and can access their smartphone using their fingerprint or facial recognition and / or password. The owner’s spouse or other family member can also be an authorized user of the smartphone via biometric data or password knowledge. In some examples, an authorized user can appoint other authorized users to receive location information but not access the mobile device (e.g., a sibling can receive text messages about the location of the mobile device).

[0023] In some examples, the triggering event can include, for example, unauthorized biometric data (e.g., fingerprint, facial pattern / recognition, retinal scan, voice pattern / recognition, typing pattern, etc.), unauthorized password data (e.g., password data), anomalous location pattern data, or a combination thereof. In other words, receiving signaling in response to a triggering event can include receiving signaling representative of the foregoing triggering event. For example, if an unauthorized user attempts to access the mobile device using their fingerprint or attempted password and fails, the application can be triggered to locate the mobile device.

[0024] In another example, the application can be triggered to locate the mobile device if the mobile device is experiencing an anomalous location pattern. As used herein, a location pattern can include a learned location pattern of an authorized user learned using a machine learning model on the mobile device. For example, a machine learning model can determine a location pattern of the mobile device using data collected during the daily routine of an authorized user. For example, based on GPS data, Bluetooth data, mobile device tower data, Wi-Fi data, etc., a location pattern of an authorized user can be learned, including work locations and times, grocery stores, gas stations, normal travel (e.g., travel to a relative’s house), etc.

[0025] The machine learning model can be trained and updated with the received new data. For example, if the authorized user is frequently on business trips or on vacation, among other reasons, the machine learning model can be turned off or suspended. The anomalous orientation pattern (and associated anomalous orientation pattern data) can include an orientation pattern that is different from the learned orientation pattern (e.g., different time, different orientation, significant temperature difference, etc.).

[0026] In some examples, the trigger event can be determined by the authorized user. For example, the authorized user can want two trigger events to occur before the mobile device is located. This can prevent the location of the mobile device from being triggered in response to a child playing with a parent's phone or entering an incorrect password (e.g., passcode). For example, the authorized user can select to trigger the location of the mobile device when unauthorized biometric data (e.g., fingerprint, facial recognition, etc.) and anomalous orientation pattern data, among other possible combinations, are received. In another example, the authorized user can request that the wearable device or other computing device prompt for permission to locate the mobile device. This prompt can occur in response to a trigger event such as receiving anomalous orientation pattern data. For example, the permission granting can be the trigger event. However, the authorized user can be on vacation and carrying the mobile device with them, and can deny permission to locate the mobile device.

[0027] At 104, the method 100 can include prompting, via a display of the mobile device, for input representative of authorized user authentication and at 106, the method 100 can include enabling one or more circuits or power sources of the mobile device in response to the input representative of authorized user authentication based at least in part on determining that a value of the input satisfies a threshold. For example, in response to a trigger event such as received anomalous orientation pattern data, the mobile device (e.g., via a touchscreen display) can prompt the current user to provide a fingerprint or password as authorized user authentication. If the user is able to successfully provide the requested data, confirming authorized authentication (e.g., meeting a required threshold), the user is allowed to use the mobile device (e.g., circuits or power sources are enabled).

[0028] At 108, in response to the input indicative of authorized user verification and based at least in part on determining that the value of the input fails to satisfy the threshold, the method 100 can include capturing, at the mobile device via a sensor, environmental data associated with the mobile device and, at 110, capturing orientation data associated with an orientation of the mobile device. Unconfirmed authorized verification (e.g., a failed threshold) can include an incorrect password or pin, unconfirmed received biometric data, or other incorrect responses to a prompt. In some cases, unconfirmed authorized verification can include a failure to respond to a prompt within a threshold period of time. For example, if the mobile device prompts the user to verify that he or she is authorized to use the device, but does not receive a response within five minutes (e.g., or other desired threshold period of time set by an authorized user or application maker), then it can be determined that the authorized verification is unconfirmed.

[0029] The environmental data can include, for example, weather data of the environment of the mobile device, sound data of the environment of the mobile device, or image data such as images of an unauthorized user and / or images of the environment of the mobile device, among other environmental data. For example, the environmental data 102 can be collected via sensors such as sensor devices that can include a relative humidity (RH) sensor, a temperature sensor, a weight sensor, a light sensor, a pressure sensor, a chemical sensor, a biological sensor, an image sensor (e.g., a camera), a velocity sensor, a biometric sensor, a weather sensor, a pollution sensor, a light sensor, a security sensor, a gas sensor, or a combination thereof. Other sensor devices can also be present or coupled to the mobile device for capturing environmental data. The orientation data can include, for example, GPS orientation data, Wi-Fi signal orientation data, mobile device tower data, Bluetooth data, or a combination thereof. In some examples, the environmental data and the orientation data can be captured without the unauthorized user being aware. For example, a photo of the unauthorized user or the environment can be taken secretly, and the GPS or other orientation capabilities can be turned on without requesting permission from the unauthorized user. In some cases, some or all of the sensors can be automatically turned on and capture data secretly in response to unconfirmed authorized verification.

[0030] In some examples, based on the captured environmental data and the captured orientation data, the method 100 can include determining, with a machine learning model, an orientation of the mobile device. For example, the mobile device or an application thereon can include a machine learning algorithm that has been trained with environmental and orientation data. Additionally, as the authorized user carries the mobile device, the mobile device can collect environmental data and orientation data using its sensors, and can update the machine learning model (and associated database) with the learned data. Using this trained and learned data, a determination can be made as to the orientation of the mobile device.

[0031] For example, the mobile device can capture temperature data, image data, GPS data, and Wi-Fi signal data. The captured data can be compared to a database generated using a machine learning model (e.g., stored through a cloud service, stored within a memory resource, etc.) and a determination that the mobile device is on the third floor of a particular building of a particular business can be made. For example, temperature data can indicate an indoor location, image data can indicate a particular logo, GPS can provide general coordinates or a physical address, and Wi-Fi signal data can indicate a business or floor that the mobile device is receiving wireless signals from. In some cases, the location determination can be a suggestion or estimate, for example, if the match in the database is not sufficient to determine an exact location.

[0032] Method 100 can include, at 112, transmitting the captured environmental data and location data to an authorized user of the mobile device. For example, the location of the mobile device determined using the machine learning model can be transmitted to the authorized user. The transmission can include a text message to a list of authorized users, a notification to a different mobile device (e.g., a connected tablet computer, smart watch, etc.), an email address to a notification to the authorized user, or an automatic call to a secondary phone number of the authorized user, among other methods of communication. In some examples, the mobile device can be deactivated in response to an input indicative of authorized user verification and based at least in part on determining that a value of the input fails to satisfy a threshold value. In other words, the mobile device can be deactivated in response to unconfirmed authorized verification. In some examples, the authorized user can select to deactivate the mobile device in response to the transmission regarding unconfirmed authorized verification or the transmission regarding the location of the mobile device. For example, if the authorized user learns that their mobile device is in their apartment building likely to be in the laundry room, the authorized user can select to check if he or she left the mobile device there before deactivating the mobile device. Deactivation can include locking, powering off, being in a power saving mode (e.g., a high-efficiency power saving mode that conserves battery, appearing to power off, etc.), in some examples, the deactivated mobile device can continue to collect undetected data.

[0033] Figure 2 is a diagram of an example device having a processing resource 222 and a memory resource 224 having executable instructions 226, 228, 230, 232, 234, 236 thereon in accordance with a number of embodiments of the present disclosure. Figure 2The device illustrated in the middle can be a mobile device 220 and can include a processing resource 222. The device can additionally include a memory resource 224 (e.g., a non-transitory MRM) on which instructions, such as 226, 228, 230, 232, 234, 236, can be stored. While the following description refers to a processing resource and a memory resource, the description can also apply to a system having multiple processing resources and multiple memory resources. In such instances, instructions can be distributed (e.g., stored) across multiple memory resources and instructions can be distributed (e.g., executed by) across multiple processing resources.

[0034] The memory resource 224 can be an electronic, magnetic, optical, or other physical storage device that stores executable instructions. Thus, the memory resource 224 can be, for example, a non-volatile or volatile memory. For example, a non-volatile memory can provide persistent data by retaining written data when not powered, and non-volatile memory types can include NAND flash memory, NOR flash memory, read-only memory (ROM), electrically-erasable programmable ROM (EEPROM), erasable programmable ROM (EPROM), and storage class memory (SCM) that can include resistive variable memory such as phase change random access memory (PCRAM), three-dimensional cross point memory, resistive random access memory (RRAM), ferroelectric random access memory (FeRAM), magnetoresistive random access memory (MRAM), and programmable conductive memory, among other types of memory. A volatile memory can require power to maintain its data, and can include random access memory (RAM), dynamic random access memory (DRAM), and static random access memory (SRAM), among others.

[0035] In some instances, the memory resource 224 is a non-transitory MRM, including random access memory (RAM), electrically erasable programmable ROM (EEPROM), a storage drive, an optical disc, etc. The memory resource 446 can be disposed within a controller and / or computing device. In this instance, the executable instructions 226, 228, 230, 232, 234, 236 can be “installed” on the device. Additionally and / or alternatively, the memory resource 224 can be a portable external or remote storage medium, such as allowing the system to download the instructions 226, 228, 230, 232, 234, 236 from the portable / external / remote storage medium. In this case, the executable instructions can be part of an “installation package.” As described herein, the memory resource 224 can be encoded with executable instructions for determining a position of the mobile device 220.

[0036] Instructions 226, when executed by a processing resource such as processing resource 222, can receive a triggering event indicating that mobile device 220 is in an unauthorized location, has an unauthorized user, or both. For example, the triggering event can include determining that mobile device 220 is following an unusual location pattern. Based on captured GPS and other location data, it can be determined that mobile device 220 is traveling in an adjacent state, and it can be determined using a machine learning model that this location data pattern is unusual because mobile device 220 rarely travels in an out-of-state. Similarly, the triggering event can include receiving an unauthorized password or unauthorized biometric data. In some instances, the triggering event can be an authorized user requesting a mobile device location determination because the authorized user has lost, misplaced, or is unable to find his or her mobile device.

[0037] Instructions 228, when executed by a processing resource such as processing resource 222, can prompt an authorized user for verification via a display of mobile device 220. The user can be prompted for a password, biometric data, or other identifying information to verify that he or she is an authorized user. Instructions 230, when executed by a processing resource such as processing resource 222, can allow use of mobile device 220 in response to confirmed authorized verification. For example, if the user provides a correct password or authorized biometric data, use of mobile device 220 can be allowed.

[0038] Instructions 232, when executed by a processing resource such as processing resource 222, can capture, at mobile device 220 via sensors, image data, temperature data, sound data, or a combination thereof associated with an environment of mobile device 220 in response to unconfirmed authorized verification. Instructions 232, when executed by a processing resource such as processing resource 222, can also capture, at mobile device 220 in response to unconfirmed authorized verification, location data (e.g., GPS location data, Wi-Fi signal location data, mobile device tower data, Bluetooth data, or a combination thereof) associated with mobile device 220.

[0039] For example, if it is determined that the user is not an authorized user due to an incorrect password, unauthorized biometric data, etc., or no response after a threshold period of time, multiple environmental data and location data can be captured. In a non-limiting example, an authorized user can misplace his or her smartphone. In this example, the triggering event can include the smartphone detecting unauthorized biometric data (e.g., an unauthorized fingerprint) and the authorized user (e.g., via a different mobile device or computing device) requesting a determination of the location of his or her smartphone.

[0040] The smartphone can respond to capturing environmental data by activating sensors including image sensors, temperature sensors, and noise sensors, among others. The image sensors can capture a photo of a picture on the wall and a person's face, while the temperature sensor captures a consistent 72 degree temperature, and the noise sensor captures classical music. The orientation data can include GPS data, such as a physical address, and Wi-Fi signal data.

[0041] The instructions 234, when executed by a processing resource such as the processing resource 222, can determine an orientation of the mobile device 220 based on the captured image data, temperature data, sound data, or a combination thereof, and the captured orientation data using a machine learning model. For example, the captured image data, temperature data, sound data, or a combination thereof, and the captured orientation data can be compared to a database of trained environmental and orientation data (e.g., stored by a cloud service, stored in the memory resource 224, etc.). In the former non-limiting example, the machine learning model can take the captured environmental data and orientation data to determine that the smartphone is in office building A, floor B, office C. For example, an authorized user can have previously left their smartphone in a colleague's office, and the model determines a likely orientation using previous environmental data including a person's face and a picture. Together with the orientation data including Wi-Fi signal data, the accuracy of the orientation data can be improved. In some examples, the machine learning model can be updated using the captured image data, temperature data, sound data, or a combination thereof, and the orientation data.

[0042] The instructions 236, when executed by a processing resource such as the processing resource 222, can communicate the orientation of the mobile device 220 to an authorized user of the mobile device 220. For example, the authorized user can receive an email indicating the orientation of the smartphone. While in some examples the instructions can be executable to automatically deactivate the mobile device 220 in response to unconfirmed authorized authentication, in the former non-limiting example, the instructions can be executable to request an indication from the authorized user of the mobile device 220 whether to deactivate the mobile device 220 in response to unconfirmed authorized authentication, deactivate the mobile device 220 in response to receiving an indication from the authorized user to deactivate the mobile device 220 or in response to no indication from the authorized user after a threshold period of time, and allow use of the mobile device 220 in response to receiving an indication from the authorized user to allow use of the mobile device 220. For example, the authorized user can recognize the orientation as well as the likely possessor of the smartphone and can choose not to deactivate the smartphone. In the same example, if the authorized user recognizes the building and / or office, but not the unauthorized user, an image of the user's face can be sent to a security officer of the building to confirm identification or alert them that their mobile device was stolen.

[0043] As used herein, "automatically" can include actions performed with limited or no user input and / or limited or no prompting. For example, deactivation of a cell phone can occur with limited or no user input in response to unconfirmed authorized authentication if selected by an authorized user as a desired action in a settings menu.

[0044] Figure 3 is a diagram of an example device having processing resources 322, memory resources 324, and a plurality of sensors 362-1,..., 362-N thereon in accordance with a number of embodiments of the present disclosure. Figure 2 The device illustrated in FIG. 1 can be a mobile device 340 and can be similar to the device (e.g., mobile device) 220 described with respect to Figure 2 The device (e.g., mobile device) 220 described with respect to Figure 2 The processing resources 322 and memory resources 324 can be similar to the processing resources 222 and memory resources 224, respectively, as described with respect to

[0045] The instructions 346, when executed by a processing resource such as the processing resources 322, can determine, based on received biometric data, password data, location pattern data, or a combination thereof, using a first machine learning model, that a trigger event has occurred indicating that the mobile device 340 is in an unauthorized location, in possession of an unauthorized user, or both. For example, the received biometric data, password data, location pattern data, or a combination thereof can be compared to a database of trained identifying and location data, and a mismatch to authorized data or a match to previously learned unauthorized data can be a trigger event. In some examples, the determination that a trigger event has occurred can be made in response to receiving a request from an authorized user to determine a location of the mobile device. For example, if an authorized user determines that he or she misplaced his or her tablet, he or she can request a location determination.

[0046] The instructions 348, when executed by a processing resource such as the processing resources 322, can prompt an authorized user for authentication via a display of the mobile device 340, and the instructions 350, when executed by a processing resource such as the processing resources 322, can allow use of the mobile device 340 as a function of confirmed authorized authentication within a threshold period of time. For example, if an authorized user (e.g., a spouse of an authorized user) occasionally takes the authorized user's tablet to work, causing a trigger event (e.g., an anomalous location data pattern), the spouse can enter a password when prompted and can access the tablet. In such an example, a communication (e.g., email, text message, etc.) indicating confirmed authorized user authentication can be sent to the authorized user.

[0047] Instructions 352, when executed by a processing resource such as processing resource 322, can capture, at mobile device 340, via a first sensor 362-1 of the plurality of sensors 362, environmental data associated with mobile device 340 in response to an unconfirmed authorized authentication or no response within a threshold period of time. For example, if in the previous example, the tablet is stolen and the thief does not respond to the prompt or responds incorrectly, the first sensor 362-1 can be activated. In some examples, the plurality of sensors 362 can be activated.

[0048] The first sensor 362-1 can be, for example, an image sensor and can capture a photograph of the environment (e.g., vehicle, face, symbol, etc.) of mobile device 340 or the first sensor 362-1 can be an RH sensor or the like to capture environmental data suitable for making a determination as to the environment in which mobile device 340 is located and / or the user (e.g., thief) carrying mobile device 340. In some examples, the captured environmental data includes captured image data, temperature data, sound data, or combinations thereof.

[0049] Instructions 354, when executed by a processing resource such as processing resource 322, can capture, at mobile device 340, via a second sensor 362-N of the plurality of sensors 362, location data associated with mobile device 340 in response to an unconfirmed authorized authentication or no response within a threshold period of time. For example, if in the previous example, the tablet is stolen and the thief does not respond to the prompt or responds incorrectly, the second sensor 362-N can be activated. In some examples, the plurality of sensors 362 can be activated.

[0050] The second sensor 362-N can be a GPS sensor, a Wi-Fi signal sensor, or other location data sensor or the like to capture location data (e.g., physical address, location of Wi-Fi network, etc.) associated with mobile device 340 that can be suitable for making a determination as to the location in which mobile device 340 is located and / or the user (e.g., thief) carrying mobile device 340.

[0051] In some examples, the environmental data and location data can be captured secretly. For example, the sensors 362 can be activated (e.g., turned on) without indicating activation to the unauthorized user. In the previous example, the front-facing camera of the tablet can capture a photograph of the thief without sound or flash, while the rear-facing camera captures a photograph of the surrounding environment. The GPS of the device can be activated without prompting the unauthorized user for permission. Other sensors can be activated in a similar manner.

[0052] Instructions 356, when executed by a processing resource such as processing resource 322, can determine a location of mobile device 340 based on captured environmental data and captured location data using a second machine learning model. For example, captured image data, temperature data, sound data, other environmental data, or combinations thereof, and captured location data can be compared to a database of trained environmental and location data. In the thief example, environmental data can include a photo of a hotel logo, high RH levels and temperature levels, and a photo of a car. Location data can include GPS data including a physical address, and Wi-Fi data including a particular wireless network. This data can be compared to the database, and a determination can be made that the tablet is in a hot, humid location at a particular hotel with a physical address and connected to a particular wireless network. The wireless network can allow the location of the tablet to be narrowed, and the car can be used for identification purposes.

[0053] Instructions 358, when executed by a processing resource such as processing resource 322, can transmit a location of mobile device 340 to an authorized user of mobile device 340. In some examples, environmental and location data captured by sensors 362 can be transmitted to an authorized user. In the thief example, data can be transmitted to law enforcement or the hotel.

[0054] Instructions 360, when executed by a processing resource such as processing resource 322, can deactivate mobile device 340. For example, once a determination is made that a user is not authorized, mobile device 340 can be deactivated. In some examples, sensors 362 can still be activated (e.g., secretly) even though mobile device 340 is deactivated.

[0055] Figure 4 is another flowchart representative of an example method for determining a location of a mobile device in accordance with a number of embodiments of the present disclosure. At 470, a determination is made that a mobile device has been lost, misplaced, stolen, etc. This determination can come from a triggering event. For example, a triggering event can occur at 472 when a communication is received from an authorized user that their phone is lost (e.g., via an application on a different mobile device, via a vendor's website, etc.), when unauthorized data is received (e.g., biometric, password / codes, patterns of location data), or a combination thereof.

[0056] At 474, authorization of a user of the mobile device is requested. For example, upon receiving a triggering event, the user of the mobile device can be prompted to provide biometric data or a password. If at 476, it is determined that the user is authorized because the correct authentication was provided, then at 478, the user is allowed to use the mobile device.

[0057] If at 480, the user is determined to be unauthorized due to incorrect biometric, password, or other authorization data or because the user did not respond to the authorization prompt within a threshold period of time, then location and environmental data associated with the mobile device can be captured at 482. For example, using sensors of the mobile device, data that can be helpful in determining the location of the mobile device can be captured (e.g., image data of an unauthorized user, images of a vehicle, GPS data, Wi-Fi data, temperature data, etc.). This location and environmental data can be provided to the authorized user at 484.

[0058] At 486, the location of the mobile phone can be determined using a machine learning model. For example, based on the captured environmental and location data, the machine learning model can make comparisons to a database and estimate the location of the mobile device. For example, the machine learning model can use previously trained data, as well as any updates to the location and environmental data and any hardware changes to estimate the location of the mobile device. For example, the machine learning model can have data associated with certain Wi-Fi networks, GPS locations, image data, etc. that can be used to make a location determination. The location determination can be communicated to the authorized user at 484. In some examples, the location determination can be estimated based on the data available to the machine learning model. For example, if a match is not available for all captured data, an estimate of the location can be provided.

[0059] In a non-limiting example, at block 470, the authorized user misplaces their smartwatch. The triggering event at block 472 includes the authorized user requesting a location determination of the smartwatch. At 474, the smartwatch prompts a password via its display. At 480, no authorization is received within a threshold period of time, thus a determination is made that the smartwatch is in an unauthorized location, in possession of an unauthorized user, or both. At 482, sensors of the smartwatch capture environmental data (e.g., photos, temperature data, etc.) and location data (e.g., GPS data, Wi-Fi data), and at 484, the environmental and location data is communicated to the authorized user.

[0060] At 486, using the captured location and environmental data, a machine learning model is used to determine the location of the smartwatch. For example, the captured data can include an image of a trash can, a temperature of 72 degrees, a physical address of the authorized user’s office building, and a Wi-Fi signal associated with a conference room on a particular floor of the authorized user’s office building. The machine learning model can compare this captured location and environmental data to determine that the smartwatch is in the trash can of the conference room. This determined location can be communicated to the authorized user at 484.

[0061] While specific embodiments have been shown and described in the present disclosure, it will be understood by those skilled in the art that other arrangements can be constructed to implement the same results. The present disclosure is intended to cover modifications or variations in one or more embodiments of the present disclosure. It should be appreciated that the preceding description is by way of illustration and not limitation. Combinations of the above embodiments and other embodiments not specifically described herein will be apparent to those of skill in the art upon reviewing the above description. The scope of the one or more embodiments of the present disclosure includes other applications that use the above structures and processes. Therefore, the scope of the one or more embodiments of the present disclosure should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0062] In the foregoing detailed description, some features were grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the disclosed embodiments of the present disclosure require more features than are explicitly recited in each claim. Rather, inventive subject matter can lie in fewer than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment.

Claims

1. A method for a mobile device, the method comprising: determining, using a machine learning model of the mobile device, a location pattern of the mobile device based on data collected during routine transactions of an authorized user; receiving, at the mobile device in response to a first trigger event, signaling indicating that the mobile device is in an unauthorized location, possesses an unauthorized user, or both; receiving, at the mobile device in response to a second trigger event, signaling indicating that the mobile device has experienced an anomalous location pattern different from the determined location pattern; in response to receiving the first trigger event and the second trigger event and via a display of the mobile device, prompting an input representative of an authorized user verification; in response to the input representative of the authorized user verification, enabling one or more circuits or power sources of the mobile device based at least in part on determining that a value of the input satisfies a threshold; or in response to the input representative of the authorized user verification and based at least in part on determining that the value of the input fails to satisfy the threshold: capturing, at the mobile device via a sensor, environmental data associated with the mobile device; capturing, at the mobile device, location data associated with a location of the mobile device; and communicating the captured environmental data and location data to the authorized user of the mobile device.

2. The method of claim 1, further comprising deactivating the mobile device in response to the input representative of the authorized user verification and based at least in part on determining that the value of the input fails to satisfy the threshold.

3. The method of claim 1, further comprising the mobile device: determining, with the machine learning model, the location of the mobile device based on the captured environmental data and the captured location data; and communicating the determined location of the mobile device to the authorized user of the mobile device.

4. The method of any one of claims 1-3, wherein receiving the signaling in response to the first trigger event comprises receiving signaling representative of unauthorized biometric data, unauthorized password data, or a combination thereof.

5. The method of any one of claims 1-3, wherein receiving the signaling in response to the first trigger event comprises remotely receiving signaling representative of a notification from an authorized user that the mobile device is in the unauthorized location, possesses the unauthorized user, or both.

6. The method of any one of claims 1-3, wherein capturing the environmental data comprises capturing weather data of an environment of the mobile device.

7. The method of any one of claims 1-3, wherein capturing the environmental data comprises capturing sound data of an environment of the mobile device.

8. The method of any one of claims 1-3, wherein capturing the environmental data comprises capturing an image of the unauthorized user, an image of the environment of the mobile device, or both. ​ 9. The method of any one of claims 1-3, wherein capturing the location data comprises capturing global positioning system (GPS) location data, Wi-Fi signal location data, mobile device tower data, Bluetooth data, or a combination thereof.

10. A mobile device comprising: a processing resource; and a memory resource in communication with the processing resource and having instructions executable to: determine, using a machine learning model of the mobile device, a location pattern of the mobile device based on data collected during routine activities of an authorized user; receive a first trigger event indicating that the mobile device is in an unauthorized location, possesses an unauthorized user, or both; receive a second trigger event indicating that the mobile device has experienced an anomalous location pattern different from the determined location pattern; prompt, in response to receiving the first trigger event and the second trigger event and via a display of the mobile device, an authorized user for verification; in response to confirmed authorized verification, allow use of the mobile device; in response to unconfirmed authorized verification: capture, at the mobile device via a sensor, image data, temperature data, sound data, or a combination thereof associated with an environment of the mobile device; capture, at the mobile device, location data associated with the mobile device; determine, with the machine learning model, a location of the mobile device based on the captured image data, temperature data, sound data, or a combination thereof and the captured location data; and communicate the location of the mobile device to the authorized user of the mobile device.

11. The mobile device of claim 10, further comprising the instructions executable to update the machine learning model using the captured image data, temperature data, sound data, or a combination thereof and the location data.

12. The mobile device of any one of claims 10-11, further comprising the instructions executable to automatically deactivate the mobile device in response to unconfirmed authorized verification.

13. The mobile device of any one of claims 10-11, wherein the location data comprises global positioning system location data, Wi-Fi signal location data, mobile device tower data, Bluetooth data, or a combination thereof.

14. The mobile device of any one of claims 10-11, further comprising the instructions executable to: request, from the authorized user of the mobile device, an indication of whether to deactivate the mobile device in response to unconfirmed authorized verification; deactivate the mobile device in response to receiving an indication from the authorized user to deactivate the mobile device or in response to no indication from the authorized user after a threshold period of time; and allow use of the mobile device in response to receiving an indication from the authorized user to allow use of the mobile device. ​ 15. The mobile device of claim 10, wherein the instructions executable to determine the location of the mobile device utilizing the machine learning model further comprise instructions executable to compare the captured image data, temperature data, sound data, or a combination thereof, and the captured location data to a database of trained environmental and location data.

16. A mobile device comprising: a plurality of sensors; a processing resource coupled to the plurality of sensors; and a memory resource in communication with the processing resource and having instructions executable to: determine a location pattern of the mobile device based on data collected during routine transactions of an authorized user using a machine learning model of the mobile device; determine, utilizing a first machine learning model, that a first trigger event indicative that the mobile device is in an unauthorized location, in possession of an unauthorized user, or both has occurred based on received biometric data, password data, or a combination thereof; determine a second trigger event indicative that the mobile device has experienced an abnormal location pattern different from the determined location pattern; prompt, responsive to receiving the first trigger event and the second trigger event and via a display of the mobile device, an authorized user for verification; permit use of the mobile device responsive to confirmed authorized verification within a threshold period of time; responsive to unconfirmed authorized verification or no response within the threshold period of time: capture, at the mobile device via a first sensor of the plurality of sensors, environmental data associated with the mobile device; capture, at the mobile device via a second sensor of the plurality of sensors, location data associated with the mobile device; determine, utilizing a second machine learning model, a location of the mobile device based on the captured environmental data and the captured location data; communicate the location of the mobile device to the authorized user of the mobile device; and deactivate the mobile device.

17. The mobile device of claim 16, further comprising the instructions executable to capture the environmental data and the location data secretly.

18. The mobile device of claim 16, further comprising the instructions executable to determine that the first trigger event has occurred responsive to receiving a request from the authorized user to determine a location of the mobile device.

19. The mobile device of claim 16, wherein the instructions executable to determine the trigger event has occurred utilizing the first machine learning model further comprise instructions executable to compare the received biometric data, password data, or a combination thereof to a database of trained identification and location data.

20. The mobile device of claim 16, wherein the instructions executable to determine the location of the mobile device utilizing the second machine learning model further comprise instructions executable to compare captured image data, temperature data, sound data, or a combination thereof, and the captured location data to a database of trained environmental and location data. ​

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