Patent Application
20200160384
The present invention relates to a system, method, and non-transitory computer-readable medium for leveraging changes in a user's mobility behavior to enhance the user's experience by automatically providing enhanced service information to the user based on the changes in mobility.
In conventional behavior pattern systems, a user's behavior patterns are determined by monitoring a user's data from a device, such as a vehicle. In the case of a vehicle that is used by the user, conventional behavior pattern systems collect data in order to train a predictive model for the user's regular mobility pattern that can be used, for example, to predict the user's next trip based on the user's commute pattern.
Conventional systems, however, do not automatically determine and evaluate changes in the user's behavior patterns in order to provide the user with enhanced service offerings to more closely meet the needs and capabilities of the user as they change over time. Thus, the present invention provides improvements over conventional behavior pattern systems by automatically determining changes in the user's behavior patterns and providing enhanced service offerings to the user based on the type of changes in the user's behavior patterns.
Other objects, advantages and novel features of the present invention will become apparent from the following detailed description of one or more preferred embodiments when considered in conjunction with the accompanying drawings.
In accordance with the present application, it is assumed that a user's mobility activity data is driven by the user's behavior patterns. The user's mobility activity data is observable, but the behavior patterns are generally hidden. In the present invention, a behavior model is defined that generates the mobility activity data, from which the behavior model is reversely estimated using observable activity data.
A user's daily mobility pattern is described by states, such as a workday pattern (e.g., Monday through Friday) and a weekend pattern (e.g., Saturday through Sunday). A workday pattern is described by a sequence of mobility including, for example, home in the morning→commute to work→stay in the office→optional short travel to meeting during workday→commute to home→optional shopping, social, or exercise→sleep overnight at home. According to the reference implementation in the present invention, behavior patterns such as this can be modeled by a hierarchical hidden Markov model (HMM) or other model.
A user's devices, e.g., vehicle, mobile phone, etc., can collect real-time sensor data regarding the mobility of the user via the real-time sensor 104. This data can be collected, for example, from a telemetry data system in the vehicle, such as a GPS system. Thus, the days and times during which the user's vehicle is located at particular locations and movements of the vehicle between locations can be monitored. A typical user is likely to have a home location, a work location, and one or more travel locations. One of the user's behavior patterns involves a regular commute pattern, i.e., from home to work and from work to home. The user's initial behavior patterns can be based on the historical data, but the initial behavior patterns are continuously or periodically updated based on the real-time sensor data to refine the behavior patterns.
The real-time sensor data is output to the context engine 105, which also receives behavior pattern data of the user based on the historical data from the machine learning processor 102. The context engine 105 compares the behavior pattern data to the real-time data to determine when changes in the behavior patterns occur. The context engine 105 publishes (P) behaviors and changes thereto 107 as topics in a publish/subscribe (pub/sub) messaging pattern. The API Interface can be used as the interface for the user's regular behavior, while the pub/sub messaging platform can be used as the event-oriented interface for the user's behavioral changes including abnormal behavior. Also, the context engine 105 outputs context (C) information 108 as a pub/sub condition. A pub/sub match processor 106 performs a pub/sub match between a published behavior and a subscriber (S) for a particular context. Subscriber information 110 can come from a variety of services, e.g., a financial service provider 111, a connected vehicle 112 (vehicle service provider), and the like.
As an example, a behavior pattern can be used as a context in order to predict that the user will leave his or her current location at a particular time (e.g., in 20 minutes). The system can determine whether the user is traveling on his or her regular commute. A sensor can be used as a context to determine, for example, whether the vehicle is parked or is low on fuel.
According to an alternative embodiment, the real-time sensor data is collected from a mobile device, such as a smart phone, of the user. Further, the real-time sensor data can be concurrently collected from a plurality of devices, such as a vehicle and a smart phone. In order to identify changes in the user's behavior patterns, the real-time sensor data can be compared to the user's stored behavior pattern data.
The operation of the system 100 is described below in relation to
As described above, the user's mobility behavior change or abnormal behavior are recognized by the sequence modeling in the system as part of the user's context and event discovery. In general, the pub/sub messages are used to offer enhanced services to the user based on the user's mobility behavior change, i.e., IF (context) PUBLISH (abnormal or regular event) THEN SUBSCRIBER (action). As a specific example, IF (vehicle maps to two user IDs and vehicle data is generated from user 1 and then user 2 AND user 1 and user 2 have different home and work locations) PUBLISH (user 1 likely sold the vehicle to user 2) THEN SUBSCRIBER (vehicle service provider (e.g., BMW Connected) asks user 1 to delete their app and/or account). As another example, IF (user keeps same home AND no longer visits work AND no vacation) PUBLISH (user lost their job) to warn a financial service provider that the user might have a financial challenge THEN SUBSCRIBER (financial service provider (e.g., BMW Financial Services) to take an action). Here the action could be to offer a reduced service package to the user that is less expensive or to ask the user if they would like to cancel their services in order to reduce their financial burden.
User observable mobility data is represented as X(t) in
Periodically, such as every hour, the system obtains the most significant stay location in which the user has stayed. If there are no significant stay locations, the symbol is mapped as “moving.” When the stay location belongs to home, work, or learned frequent destination, the corresponding symbol is used. In a case in which the stay location is new and the distance to the home-work region is less than a predefined threshold (e.g., 40 km), the mapped symbol could be “new location near d” where “d” means the dth new nearby location on the current date. In a case in which the stay location is new and the distance to the home-work region is greater than the threshold, the mapped symbol could be “new location outside d” where d means the dth new outside location on the current date. Other features can also be introduced for different tasks.
According to the present invention, the user's mobility data in one day is mapped into a fixed length symbol sequence. The daily home-work information can be encoded with a home and work confidence score and an indication of whether the home location changed or whether the work location changed. In a case in which one or both of the home location and the work location have changed, a consequence of other changes are likely to occur, such as the user taking vacation, moving to another place to work, etc.
The system according to the present invention proactively reaches out to the user to offer services tailored to the user's life as changes occur. For example, in a case in which the user has sold the vehicle, then the service provider is notified so that it reminds the user to delete personal data and the account if it has not been done yet. For another example, in a case in which the user lost their job, the vehicle service provider is notified so that a reduced (less expensive) level of services can be offered to the user with a corresponding lower monthly fee. On the other hand, when it is determined that the user has a new home that has a higher value than the user's old home or the user has a new job that pays more than the user's old job, financial services can be correlated with these changes to inform the user of options for a higher level of service or purchasing a more expensive vehicle. In one embodiment of the invention, a determination of a financial change in a user's life based on the determined mobility changes results in a notification being sent to financial services provider and/or the vehicle service provider so that an analyst can further analyze the changes in the user's life.
The two level hierarchical hidden Markov model (HMM) shown in
According to the present invention, the model takes advantage of offline training and an online algorithm. The system collects a user's historical mobility data labels such as Workday, Weekend, Travel, etc. The offline training provides first level hidden states and final observation sequences to estimate the transition probability between second level hidden states and the probability of emitting observations. The offline training also provides for estimating the transition probability of first level hidden states and emission probability between first and second level hidden states.
As illustrated, for example, in
Based on the determined type of change in the user's behavior pattern, in step S605, updated service information from the vehicle service provider 203 is outputted to the user and/or a third party such as a financial service provider 204. Further, the system can output other data, such as mobility graphs of the user's mobility, recommendations for new places to go near a new home or work location, and the like. In order to continuously refine the user's behavior pattern, in step S606, the historical behavior pattern data is adapted based on newly acquired mobility data. Moreover, the process continuously repeats the steps of the method in order to continue to automatically provide updated and enhanced service information to the user.
In another exemplary embodiment of the present invention, a non-transitory computer-readable medium is encoded with a computer program that performs the above-described method. Common forms of non-transitory computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read.
The foregoing disclosure has been set forth merely to illustrate the invention and is not intended to be limiting. Since modifications of the disclosed embodiments incorporating the spirit and substance of the invention may occur to persons skilled in the art, the invention should be construed to include everything within the scope of the appended claims and equivalents thereof.
