Abstract:
Method of determining user&#39;s life change based on behavioral abnormality starts with processor receiving first location data and first proximity information from first mobile device. First proximity information includes identification of mobile devices within proximity sensitivity radius of first mobile device. Processor determines whether first location data and first proximity information are included in historical location data and historical proximity information, respectively, associated with first mobile device. When first location data and first proximity information is not included, processor determines whether subsequent location data and subsequent proximity information received from first mobile device over predetermined time period is included. Processor signals to monitor life change of user of first mobile device when subsequent location data and subsequent proximity information received from first mobile device over predetermined time period is not included in historical location data and historical proximity information, respectively, associated with first mobile device. Other embodiments are described.

Description:
FIELD 
       [0001]    The invention relates to system and method for use in identifying life changes of users of mobile devices using identified behavior changes of the mobile devices; and more specifically use of changes of behavior of a mobile device, from the normal behavior identified using stored data of group associations, proximity identification, and location identification of the mobile device, to determine and recognize possible life changes of the user of the mobile device. 
       BACKGROUND 
       [0002]    The prolific growth of cell phones and other mobile devices like iPads and other mobile communication devices, in recent years, have increased the use of these devices in daily lives of the individual users. These devices find use mainly in entertainment, commerce and financial transaction areas. In practice it has been shown that the mobile devices are mostly associated with an individual and have characteristics, properties and preferences that are unique to the individual owner of the mobile device. This linking of the individual user with specific mobile devices has created a number of opportunities to understand the individual&#39;s preferences characteristics. This preference characteristic has been used for identifying the behavior and choices of the individuals. This has also been used by advertisers to tailor ads etc. to fit an individual&#39;s preferences and influence the purchase decisions. 
         [0003]    A user&#39;s preferences typically depend on the users behavior patterns, which are based on the users circumstances, life constraints as well as group involvements. Any changes in these characteristics will impact the preferences and activities of the user. Hence it will be advantageous to be able to understand life changes that impact the user at an early stage by changes in the identified and historically consistent behavior patterns. 
         [0004]    It will hence be useful to have a method and system that can provide the capability to assess the change in behavior of a mobile device in use, with a reasonable probability of success through identification of changes in locations visited and group affiliation changes. It will be further useful to have a system and method capable of correlating these changes in behavior to change in life situations of the user of the mobile device. This ability for checking and verification of the changes in life situations of a user of mobile device will be very useful in predicting the preference characteristics of a user. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0005]    The embodiments of the invention are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings in which like references indicate similar elements. It should be noted that references to “an” or “one” embodiment of the invention in this disclosure are not necessarily to the same embodiment, and they mean at least one. In the drawings: 
           [0006]      FIG. 1  is an exemplary block representation  100  of a system according to one embodiment of the invention. 
           [0007]      FIG. 2  is an exemplary view of the display  200  at a tracking and monitoring server of a mobile device according to an embodiment of the invention. 
           [0008]      FIG. 3  is an exemplary block diagram  300  of a normal routine behavior pattern of locations and associations of a mobile device, supported by historic behavior according to an embodiment of the invention. 
           [0009]      FIG. 4  is an exemplary block diagram  400  showing an example of an identifiable change in normal routine behavior pattern that indicates a possible life changes for the user of the mobile device according to an embodiment of the invention. 
           [0010]      FIG. 5  is an exemplary flow chart  500  showing a method of assessing behavioral and change to determine life changes according to an embodiment of the invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0011]    In the following description, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known circuits, structures, and techniques have not been shown to avoid obscuring the understanding of this description. 
         [0012]    In the description, certain terminology is used to describe features of the invention. For example, in certain situations, the terms “component,” “unit,” “module,” and “logic” are representative of hardware and/or software configured to perform one or more functions. For instance, examples of “hardware” include, but are not limited or restricted to an integrated circuit such as a processor (e.g., a digital signal processor, microprocessor, application specific integrated circuit, a micro-controller, etc.). Of course, the hardware may be alternatively implemented as a finite state machine or even combinatorial logic. An example of “software” includes executable code in the form of an application, an applet, a routine or even a series of instructions. The software may be stored in any type of machine-readable medium. 
         [0013]    In one embodiment, a method determines the normal use pattern of each registered mobile device versus other mobile devices within an explicit or implicit group of mobile devices using collected historic data. Life change identification occurs when a repetitive abnormal use pattern, or a change in a use pattern, is found. Location based changes as well as changes in clustering are used to determine change in use patterns of mobile devices. For example, a change in concentration of multiple mobile devices indicating a school, college, or work place change can indicate graduation from school and admission in college, or start of work. This clustering change is further supported by location identification to substantiate the change. As an example: a change of location of a family group of mobile devices indicating a move to a new home, etc. Confidence levels and thresholds may be further added. 
         [0014]    In one embodiment, a method determines the normal behavior of mobile devices by analyzing the routine locations visited and the association the mobile device keeps in terms of explicit and implicit group members. The locations and group associations are analyzed and help determine the use pattern that dictates the behavior of the user. For example during week days a mobile device (associated to an individual user) may go from home, where the user device is in contact and association with member devices that are explicit group members (e.g., devices used by members of a family) to office or factory, at an identified location, where the user device will be in contact with and associate with explicit group member devices (e.g., members who form the individual&#39;s work group) and implicit group member devices (e.g., members of the office but not members of the individual&#39;s work group). Further, at the end of the day, the user may visit a club location where the associations may include explicit group member devices, implicit group member devices and unknown or unregistered devices. The information of routine movements and associations are stored in a historic database that is updated on a regular basis. When a change in behavior pattern occurs either in location or association, it can be a temporary change (e.g., which will revert back within a short time), in which case no behavioral and life change is indicated, or it can be a change in routine which indicates a life style change. As an example, the user, who has been working, goes back to school for studies. In this case, the location identification is different and the associations are different as well. The location identification changes to a school which may be identified from the location identification of the user device. New implicit associations (e.g., student body) will be made part of which may change to explicit associations (e.g., classmates and friends) over time. When a change in behavior occurs, such as a routine change, it can be an indication of life change for the user. Checking such behavior patterns can provide an early indication of the changes taking place in the life of the user. Moreover, clustering, that is having a number of registered mobile users, i.e. implicit group members, with similar interests in the same new location, can take place to determine behavioral patterns and changes in the preferences of mobile device user. Confidence levels and thresholds may be further added to verify the user preferences and further determine the impact of the life changes. 
         [0015]    In some embodiments, the system and/or method uses the capability established for a group of pre-registered mobile devices registered with a tracking and monitoring server system (TMSS) to be tracked and monitored for location and associations. The location fixing is handled by any of the refined and available methods (e.g., GPS, triangulation, etc.). The normal locations and typical associations at these normal locations are collected for each of the registered mobile devices and saved in a historic location-association database (HLA-DB) included in the TMSS. In the HLA-DB, the associations and the locations may be linked. This HLA-DB is used to establish the normal and typical behavioral pattern of each of the mobile devices. Deviations from the normal behavioral pattern of a mobile device are considered abnormal behavior and an indication to the TMSS to monitor the activity of the mobile device more closely. If the behavior is recognized as a recurring change in pattern then the locations and associations at the new locations are evaluated to identify any possible change in life patterns. 
         [0016]      FIG. 1  is an exemplary block representation  100  of a system according to one embodiment of the invention. Specifically,  FIG. 1  is an exemplary block representation,  100 , of the typical proximity detection and associations of a registered mobile device,  101 , at a location. In  FIG. 1 , the mobile devices associated with explicit group members  102 - 1  to  102 -m (m&gt;1) in combination with the mobile device  101  forms an explicit (association) group  102 , and the group members  103 - 1  to  103 -p (p&gt;1) may be an implicit group  103  that comprise all the registered members, registered with a server system, and are not included in the explicit groups associated with device  101  (such as group  102 ). A third group that is shown in  FIG. 1  is the group comprising devices  104 - 1  to  104 -n (n&gt;1) that are un registered devices that do not fall into any of the explicit or implicit groups associated with device  101 . In one embodiment, the explicit group  102  may include the mobile device  101  and mobile devices associated with explicit group members  102 - 1  to  102 -m that are members of a social networking group that are being monitored and tracked by their location. For instance, the membership to these social networks include registration with one or more central servers as part of explicit groups, such as family group, friends group, extended family and friends group, sport and club groups etc. Each of these explicit groups comprises different set of members that confirm to differing group characteristics. Combinations of these explicit groups also make up an implicit group associated with device  101 . Moreover, the combinations of these explicit and implicit groups, explicit group  102 , implicit group  103  together also form an implicit groups of mobile devices that has common properties, such as registration with a common server, but are not linked by an explicit registration for monitoring and tracking. For instance, implicit groups can be as indicated all registered devices, all members of a club, etc. that have common properties but are not explicitly linked for monitoring and tracking. 
         [0017]    The mobile device  101  and the mobile devices associated with groups including explicit group  102  and implicit group  103  are registered devices with a tracking and monitoring server that uses the available sensors on the registered mobile devices to fix their locations and monitor their associations with proximity sensing capability, using proximity sensors included in the mobile devices, and monitor other activities that are allowed/approved by the devices. According to one embodiment, the mobile device  101  has a proximity sensitivity radius such that the proximity information received by the TMSS from the mobile device  101  may include an identification of the proximate mobile devices. The typical location fixing capabilities used by the mobile devices include the GPS satellite  110 , the cell towers  105 - 1 ,  105 - 2 , and any Wi-Fi hotspots  106  whose location is known and that allow connections. The location and proximity information generated by the mobile device  101  is collected by the TMSS  120  over an Internet  115  or other available connection means for tracking and monitoring to the mobile device  101 . Further this information is stored in a part of the memory  119  in the HLA-DB of the TMSS  120 . The TMSS  120  typically comprise at least a server  116  with sufficient processing power to handle the processing of the collected data to track and monitor the registered group of devices  101 ,  102 ,  103  at least a memory  119  that comprise program storage memory and database memory, at least a display device  117  having a display screen  108 , and at least an input output device  118 . 
         [0018]    The explicit group of devices  102 - 1  to  102 - 6  that are shown as being in the proximity of the mobile device  101  are part of an explicit group  102  of devices with the mobile device  101 . The implicit group of devices  103 - 1  to  103 - 6  shown as being in the proximity of the mobile device  101  are part of implicit group  103  of registered devices due to the fact that they are part of the registered group of devices, but not part of the explicit group of devices. Each of these groups, although shown as a single group can be divided into multiple sub-groups, each having its own characteristics. The group of devices  104 - 1  to  104 - 11  forms an un-registered and non-trackable group (e.g., cannot be tracked by the system) that is in the proximity of the mobile device  101  and sensed by the proximity sensor of mobile device  101 . Typically, these three sets of device association groups  102 ,  103 ,  104  form the proximity group of association-group members that is sensed by the mobile device  101  in  FIG. 1 . 
         [0019]      FIG. 2  is an exemplary view of the display  200  at a tracking and monitoring server of a mobile device according to an embodiment of the invention. Specifically, in  FIG. 2 , the exemplary view  200  on the display  108  of the TMSS  120  of a mobile device  101  at a typically frequented location  203  shows the explicit group members  102 , and implicit group members  103 , and non-registered members  104 , that the specific communication device  101  is associating with or is in proximity of. The view  200  shows the location of the mobile device  101  on a map that shows the location of distinguishing items such as major roads  201  and locations of structures  202 ,  203 , etc. The mobile device&#39;s  101  proximity sensor has a sensing radius  212  enabling it to sense any devices in the proximity falling within the sense limit  212  of the proximity sensors. This capability is used to define an association-group  211  for the mobile device  101 . In the view  200 , the association-group  211  of mobile device  101  comprise four explicit group members  102 , two implicit group members  103  and one non-registered mobile device  104 . In this embodiment of the invention, the TMSS  120  is able to collect the association-group  211  member information from the registered group of mobile devices using proximity sensing, at various frequented locations, and store the data in the in the HLA-DB in the memory  119  of the TMSS  120 . 
         [0020]      FIG. 3  is an exemplary block diagram  300  of a normal routine behavior pattern of locations and associations of a mobile device, supported by historic behavior according to an embodiment of the invention. Specifically,  FIG. 3  is a block diagram representation  300  of a normal routine behavior pattern of locations and associations of a mobile device  101  at the locations, supported by historic behavior. Association-group  211  indicate members association identified at a location from the proximity sensing capability built into the mobile device  101 . Using the data collected using the proximity sensor of the mobile device  101  of each of the groups  102 ,  103   a,    103   b  the association-group  211  members for the mobile device  101  may be formed. Over a period of time, this data collected is used to generate the historic data of associations at each location frequented by the user of mobile device  101 . This data is again saved in the HLA-DB in the memory  119  of the TMSS  120 . The typical behavioral pattern may vary with days of the week etc., but an average or regular pattern can be established from the historic data of activity of the mobile device  101  saved in the HLA-DB. This behavior pattern will include the locations  211  frequented by the mobile device  101  and associations at these locations. 
         [0021]    For instance, an exemplary list (or specified set of locations) for one day in consideration from HLA-DB is shown in  FIG. 3  to include: Home  211 - 1 , Office  211 - 2 , and Club  211 - 3 . If on one day, this specified set of locations, or a selection from a specified set of locations from HLA-DB stored in the memory  119  of the TMSS  120  are frequented by the mobile device  101 , the frequented locations are considered normal. Similarly, if each of the groups  102 ,  103 ,  104  associating with the mobile device  101 , at each of the above locations  211 - 1 ,  211 - 2 ,  211 - 3 , remain same or within the set of parameters established by the historic data (e.g., the parameters may be defining the proportions and range for each type of association identified by the proximity sensor), the associations are considered typical. 
         [0022]      FIG. 4  is an exemplary block diagram  400  showing an example of an identifiable change in normal routine behavior pattern that indicates a possible life changes for the user of the mobile device according to an embodiment of the invention. 
         [0023]    Specifically,  FIG. 4  is a block diagram representation  400  of an example of identifiable change in behavior pattern leading to possible life change indication for the user of the mobile device  101 . As can be seen in  FIG. 4 , the mobile device  101  is identified as changing the routine from an office location with its associations  211 - 2  ( 102 : 103   a : 103   b : 104 =12:5:0:0) to an identifiable school location where the association  211 - 4  ( 102 : 103   a : 103   b : 104 =2:11:13:2) are also different and changes to include a large number of new implicit group members and possible unregistered members. The block diagram  400  further shows another new location (e.g., a sports field) where the associations are again different ( 102 : 103   a : 103   b : 104 =3:7:14:6). The club  211 - 3  and home  211 - 1  locations remain the same for the mobile device  101 . 
         [0024]    The deviation from historic data in terms of the locations and the associations are an indication for the TMSS to monitor the mobile device  101  further. The TMSS may signal to start monitoring the mobile device  101 &#39;s behavior patterns further. If the behavioral pattern returns to the earlier pattern stored in the HLA-DB in the memory  119  of the TMSS  120 , a decision is made by the processor included in the TMSS  120  that the change in behavior was an instance of aberration and not a change in life pattern. If, on the other hand, the behavior pattern is repeated continuously, that is indicative of a life change. The detail of the operation for identifying the life change from the behavior pattern recognition is described below with the help of the flow chart of  FIG. 5 . 
         [0025]    The embodiments of the invention may be described as a process, which is usually depicted as a flowchart, a flow diagram, a structure diagram, or a block diagram. 
         [0026]    Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed. A process may correspond to a method, a procedure, etc. 
         [0027]      FIG. 5  is an exemplary flow chart  500  showing a method of assessing behavioral and change to determine life changes according to an embodiment of the invention. 
         [0028]    A TMSS server acts as a group registration server system (“server”) to register the mobile devices as part of a multiplicity of explicit and implicit groups of mobile devices. This server system may comprise at least one of: local servers, servers implemented as distributed servers, and servers in the cloud. (Block S 501 ). 
         [0029]    The server instructs the mobile device to use the available multi-sensor and other information to find the location of the mobile device. The sensors can be any or all of GPS, triangulation using cell towers, known Wi-Fi connections etc. (Block S 502 ). 
         [0030]    The location information is sent to the TMSS. The information it is collected by the server, compiled, and used for tracking the device and monitoring its activities and behavior. (Block S 503 ). 
         [0031]    The server instructs the mobile device to check for other mobile devices that form part of groups, both explicit and implicit, as well as non-registered devices, at the locations using the proximity checker (e.g., proximity sensors). (Block S 504 ). 
         [0032]    The server collects and stores in a history database, HLA-DB, locations frequented by the mobile device and implicit and explicit members of groups, and the non-registered devices that the mobile device identifies to be in close proximity at each frequented location. (Block S 505 ). 
         [0033]    The server keeps a check of the preferred locations and group member associations of the mobile device, as identified by the proximity sensors of the mobile device. This information is used to generate an association-group of devices whose composition is recorded in the history database. The stored information is used to generate a behavioral and association pattern comprising routine of locations and associations for the mobile device with times, locations and association-group data. (Block S 506 ). 
         [0034]    When a change in the routine of the mobile device is recognized by the server, in terms of locations (e.g., new locations) and associations (e.g., change of association details at the newly visited locations), the server initiates increased tracking and monitoring of the activity of the mobile device, in a continuous fashion, to identify the change as either a persistent change or a short term change in the behavior routine of the mobile device. (Block S 507 ). 
         [0035]    The server continually checks for a predetermined period of time if the mobile device has returned to normal association and normal routine or continues with the modified routine and associations. (Block S 508 ). 
         [0036]    If a return to normal routine is recognized, the server discontinues the extended monitoring activity with respect to the mobile device and returns to the standard monitoring process. (Block S 509 ). 
         [0037]    Since the activity of the mobile device is recognized as normal (e.g., even with the changes), the collected information on location and association-groups are used to update the history database on the server for future use. (Block S 510 ). 
         [0038]    If the activity and association of the mobile device does not return to normal within reasonable time period (e.g., a predetermined period of time), the mobile device is considered as undergoing a life change. The TMSS then identifies the collected location and association data as a new routine relating to life change. (Block S 511 ). 
         [0039]    Once the life change is identified from the location and associations, the TMSS uses the collected new location information and new associations at the locations to identify and characterize the features and characteristics of the life change. The collected data is further used to update the historic database and identify the changes as the new behavioral routine for the mobile device for future behavioral change comparisons. In one embodiment, the life change of the user include changes in the user&#39;s the historical location data and historical proximity information (Block S 512 ). 
         [0040]    An embodiment of the invention may be a machine-readable medium having stored thereon instructions which program a processor to perform some or all of the operations described above. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), such as Compact Disc Read-Only Memory (CD-ROMs), Read-Only Memory (ROMs), Random Access Memory (RAM), and Erasable Programmable Read-Only Memory (EPROM). In other embodiments, some of these operations might be performed by specific hardware components that contain hardwired logic. Those operations might alternatively be performed by any combination of programmable computer components and fixed hardware circuit components. 
         [0041]    While the invention has been described in terms of several embodiments, those of ordinary skill in the art will recognize that the invention is not limited to the embodiments described, but can be practiced with modification and alteration known to practitioners of the art. These modifications and alternate practices, though not explicitly described, are covered under the current application. The practice of the invention is further covered within the spirit and scope of the appended claims. The description is thus to be regarded as illustrative instead of limiting. There are numerous other variations to different aspects of the invention described above, which in the interest of conciseness have not been provided in detail. Accordingly, other embodiments are within the scope of the claims.