Patent Application
20190236470
Wireless communication networks are widely deployed to provide various types of communication content such as, voice, data, and so on. Typical wireless communication networks may be multiple-access systems capable of supporting communication with multiple users by sharing available system resources (e.g., bandwidth, transmission power, etc.). Earlier examples of such multiple-access systems may include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, and more recent examples include orthogonal frequency division multiple access (OFDMA) systems, and the like. Additionally, the systems can conform to specifications such as third generation partnership project (3GPP), 3GPP long-term evolution (LTE), ultra mobile broadband (UMB), evolution data optimized (EV-DO), etc.
Generally, wireless multiple-access communication systems may simultaneously support communication for multiple wireless devices. Each wireless device may communicate with one or more base stations via transmissions on forward and reverse links. The forward link (or downlink) refers to the communication link from base stations to wireless devices, and the reverse link (or uplink) refers to the communication link from wireless devices to base stations.
The wireless communication network is controlled and operated by a mobile network operator (MNO). In some scenarios, access to the wireless communication network is granted to all users associated with an active account with the MNO. For example, users comprising the family or group for an account may be granted access to the wireless communication network.
Parents and/or guardians are generally responsible for their children and their wards. However, some children may conceal their behavior via their mobile devices. Thus, a parent may feel compelled to break into the mobile device to determine what accounts and/or activities the child has been engaged in. This may be especially true in cases of emergencies (e.g., child missing, harmed, etc.).
However, security on mobile devices has continued to improve making access to the mobile device increasingly difficult, if not reasonably possible. Furthermore, the breaking into a child's mobile device may damage the parent/child relationship. Furthermore, even in the case of emergencies, the information a parent may need does not require full access to a child's mobile device.
The detailed description is described with reference to the accompanying figures, in which the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical items.
Aspects of the present disclosure are directed to computing platforms (i.e., user equipment, server, etc.), computer-readable media, and processes for determining user behavior with respect to a wireless communication network.
As mentioned above, some parents may desire access to information on a child's mobile device in order to ascertain the child's behavior. However, as mentioned above, access to the mobile device itself, may be difficult to obtain. In further examples, a user may utilize multiple devices, including different device types when accessing various services. Accordingly, aspects of the present disclosure include a server-side user behavior determination module to enable parents and/or other guardian/supervisor to ascertain a user's (e.g., child's) behavior based on the user's usage of one or more user devices.
A client device, referred to herein as a user equipment (UE), may be mobile or stationary, and may communicate with a radio access network (RAN). As used herein, the term “UE” may be referred to interchangeably as an “access terminal” or “AT”, a “wireless device”, a “subscriber device”, a “subscriber terminal”, a “subscriber station”, a “user terminal” or UT, a “mobile terminal”, a “mobile station” and variations thereof. Generally, UEs can communicate with a core network via the RAN, and through the core network the UEs can be connected with external networks such as the Internet. Of course, other mechanisms of connecting to the core network and/or the Internet are also possible for the UEs, such as over wired access networks, WiFi networks (e.g., based on IEEE 802.11, etc.) and so on. UEs can be embodied by any of a number of types of devices including but not limited to PC cards, compact flash devices, external or internal modems, wireless or wireline phones, and so on. A communication link through which UEs can send signals to the RAN is called an uplink channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). A communication link through which the RAN can send signals to UEs is called a downlink or forward link channel (e.g., a paging channel, a control channel, a broadcast channel, a forward traffic channel, etc.). As used herein the term traffic channel (TCH) can refer to either an uplink/reverse or downlink/forward traffic channel
Referring to
The core network 140 is configured to support one or more communication services (e.g., Voice-over-Internet Protocol (VoIP) sessions, Push-to-Talk (PTT) sessions, group communication sessions, social networking services, etc.) for UEs that can connect to the core network 140 via the RANs 120 and/or via the Internet 175, and/or to provide content (e.g., web page downloads) to the UEs.
Referring to
The Information generated by a UE may be quite comprehensive. Accordingly, aspects of the present disclosure may enable a parent to ascertain much of a user's (e.g., child's) behavior by cross referencing the entire mobile experience. A child's geolocation, voice, chat, application use, as well as internet use and generally anything on a UE, may be monitored and collected, and forensic techniques applied. The information can be accessed under controlled cases ranging from determining whether a child is about to bring a gun to school and perform violence, whether a child is buying illegal drugs, or whether a child is sneaking out of the house. Accordingly, the application server 170 includes a user behavior determination module 176 that is configured to collect and store user telemetry data for enabling on demand queries from a primary account holder (PAH), or parent, or delegate, or by law enforcement in the case of a valid CALEA request. As will be described in more detail below, the stored telemetry data may be analyzed by a machine learning service module to determine one or more behavior patterns.
As mentioned above, the wireless communication network 100 may provide for multi-user to multi-device capabilities. That is, the same user may utilize multiple different devices to access the wireless communication network 100 and multiple different users may utilize the same device to access the wireless communication network 100. For example, as shown in
A mobile phone device type, such as UEs 200A and 200B, may also be referred to as a cellular phone and includes portable telephones that can make and receive calls over a radio frequency link while the user is moving within a telephone service area.
A game console device type, such as UE 200C may include an electronic, digital, or computer device that outputs a video signal or visual images to display a video game that one or more users can play. In some aspects, a game console device type may use proprietary formats unlike other consumer electronics (e.g., music players, movie players, etc.) which utilize industry-wide standard formats.
A television receiver device type, such as UE 200D, may include a television set, a television tuner, a digital video recorder, and/or a video streaming device. In some aspects, a television receiver device type may include a display as well as speakers for the purpose of viewing video content.
A voice-activated virtual assistant device type, such as UE 200E, may be configured to perform tasks or services for a user based on voice commands.
A network device type, such as UE 200F, may include networking hardware and/or software, which are configured to facilitate communication and interaction between devices on a computer network. Network device types may include gateways, routers, network bridges, modems, wireless access points, networking cables, line drivers, switches, hubs, and repeaters; and may also include hybrid network devices such as multilayer switches, protocol converters, bridge routers, proxy servers, firewalls, network address translators, multiplexers, network interface controllers, wireless network interface controllers, ISDN terminal adapters and other related hardware.
A personal computer (PC) device type, such as UE 200G, may include a multi-purpose computer whose size, capabilities, and price make it feasible for individual use. In some aspects, PCs are intended to be operated directly by an end user, rather than by a computer expert or technician.
While internal components of UEs such as the UEs 200A-G can be embodied with different hardware configurations, a basic high-level UE configuration for internal hardware components is shown as platform 202 in
Accordingly, an embodiment of the invention can include a UE (e.g., UE 200A-G, etc.) including the ability to perform the functions described herein. As will be appreciated by those skilled in the art, the various logic elements can be embodied in discrete elements, software modules executed on a processor or any combination of software and hardware to achieve the functionality disclosed herein. For example, the platform 202 is illustrated as including a monitoring module 216. In one aspect, monitoring module 216 is a client-side application that interacts with an operating system of the platform 202 to intercept client-side application and device use. The device/application use may then be incorporated into telemetry data that is then provided to the application server 170 for analysis. In some aspects, the telemetry data may include information regarding which applications are being used, a location of the UE (e.g., GPS location coordinates and/or Wi-Fi location), internet use, chat, voice, and so on.
Thus, in some aspects, the ASIC 208, memory 212, API 209, local database 214, and monitoring module 216 may all be used cooperatively to load, store and execute the various functions disclosed herein and thus the logic to perform these functions may be distributed over various elements. Alternatively, the functionality could be incorporated into one discrete component. Therefore, the features of the UEs 200A-G in
The wireless communication between the UEs 200A and/or 200B and the RAN 120 can be based on different technologies, such as CDMA, W-CDMA, time division multiple access (TDMA), frequency division multiple access (FDMA), Orthogonal Frequency Division Multiplexing (OFDM), GSM, or other protocols that may be used in a wireless communications network or a data communications network. Voice transmission and/or data can be transmitted to the UEs from the RAN using a variety of networks and configurations. Accordingly, the illustrations provided herein are not intended to limit the embodiments of the invention and are merely to aid in the description of aspects of embodiments of the invention.
The server 302 may include at least one communication device (represented by the communication device 304) for communicating with other nodes. For example, the communication device 304 may comprise a network interface that is configured to communicate with one or more network entities via a wire-based or wireless links. In some aspects, the communication device 304 may be implemented as a transceiver configured to support wire-based or wireless signal communication. This communication may involve, for example, sending and receiving: messages, parameters, or other types of information. Accordingly, in the example of
The server 302 may also include other components that may be used in conjunction with the operations as taught herein. For example, the server 302 may include hardware 310, one or more processors 312, memory 314, and a user interface 326.
The hardware 310 may include additional hardware interfaces, data communications, and/or data storage hardware. For example, the hardware interfaces may include a data output device (e.g., visual display, audio speakers), and one or more data input devices. The data input devices may include, but are not limited to, combinations of one or more of keypads, keyboards, mouse devices, touch screens that accept gestures, microphones, voice or speech recognition devices, and any other suitable devices.
In addition, the server 302 may include a user interface 326 for providing indications (e.g., audible and/or visual indications) to a user and/or for receiving user input (e.g., upon user actuation of a sensing device such a keypad, a touch screen, a microphone, and so on).
The memory 314 may be implemented using computer-readable media, such as computer storage media. Computer-readable media includes, at least, two types of computer-readable media, namely computer storage media and communications media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD), high-definition multimedia/data storage disks, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device. In contrast, communication media may embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transmission mechanism.
The processor 312 of server 302 may execute instructions and perform tasks under the direction of software components that are stored in memory 314. For example, the memory 314 may store various software components that are executable or accessible by the one or more processors 312 of the server 302. The various components may include software 316, a usage and behavior collection module 318, a machine learning service module 320, a correlation module 322, and a parental control admin module 324. The software 316, usage and behavior collection module 318, machine learning service module 320, correlation module 322, and parental control admin module 324, collectively, may be one possible implementation of user behavior determination module 176 of
The software 316, usage and behavior collection module 318, machine learning service module 320, correlation module 322, and parental control admin module 324 may include routines, program instructions, objects, and/or data structures that perform particular tasks or implement particular abstract data types. For example, the usage and behavior collection module 318 may include one or more instructions, which when executed by the one or more processors 312 direct the server 302 to perform operations related to the collection of telemetry data. That is, the usage and behavior collection module 318 may be configured to receive telemetry data from one or more UEs and store the telemetry data into one or more databases 328. In some aspects, the usage and behavior collection module 318 may be configured to receive (and store in one or more databases 328) telemetry data from one or more other servers included in core network 140 related to the usage of the wireless communication network 100 by a UE and/or by particular user. In one example, the one or more databases 328 are included in memory 314 of server 302. Furthermore, in some aspects, access to the telemetry data stored in the one or more databases 328 may be subject to access controls consistent with current privacy laws. In yet another aspect, the usage and behavior collection module 318 may store received telemetry data according to a statistical model. Specifically, the usage and behavior collection module 318 may first identify whether the received data is cumulative and, if so, may either not store the received telemetry or may store and mark the newly received telemetry as repetitive or redundant when a machine learning algorithm is applied to the data.
The machine learning service module 320 may include one or more instructions, which when executed by the one or more processors 312 direct the server 302 to perform operations related to the analysis of telemetry data stored in the one or more databases 328 to determine user behavior patterns. In one example, the determined behavior patterns are clustered by identity and/or persona. For example, a cluster may be a behavior pattern associate with “John Smith”, or alternatively, “John Smith Personal,” or “John Smith Work,” etc.
In some aspects, server 302 may be configured to maintain a profile database 330. The profile database 330 may be included in memory 314 of server 302. In operation, the machine learning service module 320 may store one or more of the identified behavior patterns to the profile database 330.
In some examples, the machine learning service module 320 may implement a machine learning technique that is a supervised, unsupervised, or a reinforcement learning technique. Examples of supervised learning techniques include K-nearest neighbor (KNN), Naive Bayes, logistic regression, support vector machine (SVM), and others. Other supervised learning analysis techniques include linear or polynomial regression analysis, decision tress analysis, and random forests analysis. Examples of unsupervised learning analysis techniques include association analysis, clustering analysis, dimensionality reduction analysis, hidden Markov model analysis techniques, and others. Examples of clustering analysis techniques include K-means, principal component analysis (PCA), singular value decomposition (SVD), incremental clustering, and probability-based clustering techniques. The reinforcement learning technique may be, for example, a Q-learning analysis technique. The techniques described above are some examples of machine learning techniques that may be utilized by the machine learning service module 320 to generate clustered behavior patterns. These are not intended to be limiting.
In some aspects, the machine learning service module 320 may also be configured to determine (e.g., calculate) a confidence level mapping the data stored in the one or more databases 328 to a particular behavior.
Still referring to
In other aspects, the correlation module 322 may be configured to query the one or more databases 328 to identify user behavior in response to a valid request from the primary account holder (PAH), or by a parent of the child accessing the wireless communication network 100.
In some aspects, the correlation module 322 may interface with the machine learning service module 320 to apply one or more machine learning techniques to one or more behavior patterns stored in the profile database 330.
The parental control admin module 324 may include one or more instructions, which when executed by the one or more processors 312 direct the server 302 to perform operations related to providing an administrator (e.g., a parent or PAH) with the ability to control the degree of monitoring performed. For example, the parental control admin module 324 may provide an interface (e.g., via a secure website) to allow a parent to create a user profile for their child in the profile database 330. In operation, the parental control admin module 324 may receive a request to update at least one parameter included in the profile database 330. The request to update a parameter may include a request to change the frequency with which telemetry data is to be received from a UE associated with a user. The request may also include a change to the amount of telemetry data that is to be received from the UE, as well as what types of telemetry data are to be collected by the UE, itself. For example, one parameter included in the profile database may specify that the UE is to collect text messaging telemetry data, but to exclude GPS or other location information.
The parental control admin module 324 may communicate with the monitoring module 216 on the child's UE to update the monitoring module 216 with the updated parameters (e.g., to control how much, how often, and or what types of information are collected by the UE). In some embodiments, the monitoring module 216 may determine that the user of the respective UE is of the age of majority (e.g. 18 in most states), in which case the monitoring module 216 may expose a user setting on the UE to enable the user to opt out of parental settings. Otherwise, the UE will either not expose an opt out user setting or may disable the setting.
In process blocks 404 and 406, the usage and behavior collection module 318 collects (e.g., receives) telemetry data. As mentioned above, the usage and behavior collection module 318 may receive the telemetry data from one or more UEs by way of the monitoring module 216 (e.g., see
The usage and behavior collection module 318 may then store the collected telemetry data to the one or more databases 328 (i.e., process block 408).
The telemetry data stored in the one or more databases 328 may also include an indication of services utilized by a particular account (e.g., text messaging, phone calling, etc.). The telemetry data may further include, for example, which applications have been launched, and the like. In addition, the telemetry data may include associated timing information, such as the duration of a usage (e.g., how long was the web browser open) and/or a respective time that the usage occurred (e.g., web browsing occurred immediately after text message was sent).
In a process block 410, the machine learning service module 320 may then analyze the telemetry data stored in the one or more databases 328 to determine behavior patterns clustered by an identity of a user. For example, machine learning service module 320 may apply one or more machine learning techniques to the data stored in the databases 328 to associate the device usages (e.g., telemetry data) with one or more behavior patterns of the identified user. In one example, the machine learning service module 320 may maintain a plurality of clusters and an associated behavior pattern of each in profile database 330 (e.g., process block 412).
Next, in a process block 414, the correlation module 322 may query the one or more databases 328 based on a request from a parent and/or a valid request from law enforcement. As mentioned above, the correlation module 322 may interface with the machine learning service module 320 to apply one or more machine learning techniques to the one or more behavior patterns of the user and the predetermined behavior models to identify a particular behavior.
In one example, the correlation module 322 may be configured to periodically query the one or more databases to automatically generate a notification in response to identifying one or more predefined behaviors (e.g., process block 416). For example, a parent, by way of the parental controls admin module 324 may configure the correlation module 322 to notify the parent if a dangerous behavior (e.g., child taking drugs) is identified. In one example, the notification may be sent to a UE associated with the parent via a text message.
For example, User1 may interact with UE3 to generate a usage event 502. Usage event 502 could possibly be User1 using UE3 to access a web site at a particular URL. User1 may also make some purchases during the usage event 502. Data collected during usage event 502 and subsequent usage events may be sent from the monitoring module 216 to usage and behavior collection module 318 as telemetry data 504. The usage and behavior collection module 318 then stores records of usage event 502 to the one or more databases 328.
As User1 progresses over time, telemetry data (e.g., 508, 512, and 516) of subsequent usage events (e.g., 506, 510, and 514) are also collected by the usage and behavior collection module 318. For example, as shown via usage event 506, User1 may later interact with a different information system (e.g., different website) using the same UE3. For example, usage event 506 may be User1 using UE3 to update the user's social network records at another URL. Usage and behavior collection module 318 may receive the telemetry data 508 associated with usage event 506 and store the telemetry data 508 to the one or more databases 328.
Accordingly, the telemetry data collected with respect to a particular user need not be specific to a particular site or to a particular type of interaction. Any definable and observable user event whose parameters may be captured is a candidate for storing as one or more telemetry data for a user.
Furthermore, telemetry data for a user need not be specific to a particular client device. As shown via usage event 510, which may be after a number of other usage events, User1 may use a different client device, here client UE2 to interact with an information system. Usage event 510 could potentially be User1 further updating the user's social network records, perhaps to upload a picture just taken with UE2. Again, usage and behavior collection module 318 may receive the telemetry data 512 and store the telemetry data 512 to the one or more databases 328.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as exemplary forms of implementing the claims.
This application claims the benefit of U.S. Provisional Application No. 62/624,664, entitled “USER BEHAVIOR DETERMINATION FROM CORROBORATING CONTROL DATA,” filed Jan. 31, 2018 and expressly incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 62624664 | Jan 2018 | US |
