Abstract:
An apparatus may include a computing device including a display, a memory coupled to at least one processor, the at least one processor being configured to: execute a method to anticipate user spending activity based on a plurality of inputs including at least one of a historical purchase history and a location of the user; and providing an alert to be displayed to the user via the computing device to warn the user about an effect of a possible upcoming spending transaction to predefined wealth goals of the user.

Description:
BACKGROUND 
     Technical Field 
       [0001]    The present disclosure generally relates to electronic devices and more particularly to a method and apparatus for alerting or notifying a user in real-time to events affecting his current and future wealth projections. 
       Description of the Related Art 
       [0002]    Categorizing and tracking spending transaction and classifying income source would be beneficial so that users could understand how they spend their money and prepare budgets accordingly. 
         [0003]    While a variety of personal finance and wealth management services and applications exist, these services and applications do not provide personalized and real-time suggestions in response to location, context, transaction history, etc. Thus, the systems are incapable of proactively guiding a user toward smart spending habits by anticipating user behavior. Rather, the conventionally available applications are only able to provide alerts once a spending activity has already occurred. 
         [0004]    Thus, there exists a need for new and improved applications and systems. 
       SUMMARY 
       [0005]    In an embodiment of the present disclosure, an apparatus may include a computing device including a display, a memory coupled to at least one processor, the at least one processor being configured to: execute a method to anticipate user spending activity based on a plurality of inputs including historical purchase history and a location of the user; and providing an alert to the user via the computing device (e.g., display of the alert, an audible sound, vibration, etc.) to alert or warn the user about an effect of a possible upcoming spending transaction to predefined wealth goals of the user. The alert may be generated based on the location if the user is steadily located within reasonably contained radius for over reasonably short period of time, the system will assume that a user is visiting a specific location and analyzes whether current location is either a store, shopping mall, restaurant or any other service location where that user may incur expenditure. 
         [0006]    In an aspect of the present disclosure, a near real-time alert system may be provided where a user continuously benefits from the real-time alerts and notifications from the system which in turn communicates with User&#39;s Financial Technology Stack and Personal Financial Data where sophisticated streaming analysis of real-time financial data is being conducted. Alerts and/or suggestions may be generated by a continuous communication with a context analyzer component which performs complex logical operations to predict future or current events that may affect one&#39;s wealth and/or financial health. It is to be understood that the term “real-time” more generally mean, “just-in-time”—that is the ability to predict an action based on data analyzed in a particular context as opposed to merely reacting after an action has already been undertaken by a user. 
         [0007]    The system may include a server computer to host Alerts/Suggestions Generation Engine and/or a mobile device to collect personal financial data and user&#39;s location to transmit them to Alerts/Suggestions Generation Engine. 
         [0008]    In another aspect of the present invention, the system further includes a mobile application to get and display alerts and notifications received from Alerts/Suggestions Generation Engine. 
         [0009]    The alert may include at least one a current financial balance, end-of-month projected balance, recent transactions, and month-to-date spending. Other events for which the alert may be generated includes, for example, upcoming financial bills, checking account overdraft, a personal credit account showing a transaction that is greater or lesser than a predetermined percentage of the maximum available credit amount (e.g., greater than 30% or less than 10%.) Alerts may also or alternatively be generated in response to an ATM (automated teller machine) fee, a late fee, an international transaction fee, detection of a suspicious transaction (e.g., a transaction that is at least three standard deviations from an average transaction amount over a three-month period of time), a change in the user&#39;s credit score or other new credit events from a credit bureau, an inability to update financial information, spending above a predetermined amount or greater than peer (e.g., individuals of similar financial status or capability) spending, a projected negative end of month balance, a calendar event (e.g., taxes), investment events occurrences (e.g., dividend payments), and/or other financial events or payments or bills. 
         [0010]    The above and other aspects, features, and advantages of the present disclosure will become apparent from the following description read in conjunction with the accompanying drawings, in which like reference numerals designate the same elements. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]      FIG. 1  is a diagram of an example of a system according to aspects of the present disclosure; 
           [0012]      FIG. 2  is a diagram of an example of a client device according to aspects of the present disclosure; 
           [0013]      FIG. 3  is a block diagram illustrating a real-time debt avoidance alert system in accordance with the present disclosure; 
           [0014]      FIG. 4  is a block diagram illustrating an embodiment of a real-time debt avoidance alert system in accordance with the present disclosure; 
           [0015]      FIG. 5  is a block diagram of a further embodiment of a real-time debt avoidance alert system in accordance with the present disclosure; 
           [0016]      FIG. 6  is a block diagram illustrating a structural representation of contextual information sources utilized by a real-time debt avoidance alert system in accordance with the present disclosure; 
           [0017]      FIG. 7  is a block diagram of components of a real-time debt avoidance alert system in accordance with the present disclosure; and 
           [0018]      FIG. 8  is a flowchart of a method for generating alerts and suggestions in accordance with the present disclosure. 
       
    
    
     DETAILED DESCRIPTION 
       [0019]    Reference will now be made in detail to embodiments of the invention. Wherever possible, same or similar reference numerals are used in the drawings and the description to refer to the same or like parts or steps. The drawings are in simplified form and are provided only for illustrative purposes. 
         [0020]      FIG. 1  is a diagram of an example of a system  100  according to aspects of the disclosure. As illustrated, the system  100  may include a commercial service system (CSS)  101 , client devices  102 - 105 , and a communications network  106 . 
         [0021]    According to aspects of the disclosure, the CSS  101  may include one or more servers (e.g., a single server, a server farm, etc.) that are used to provide a commercial service (e.g., a stock portfolio management service). In operation, CSS  101  may maintain a consumer or user profile database (CDB) that includes records for the consumer or users who have joined the commercial service (e.g., the members of the service). Additionally, or alternatively, the CSS may include hardware and/or software for generating alerts such as those generated by the system  300  described herein. 
         [0022]    The client devices  102 - 105  may include any suitable type of computing device. For example, any of the client devices  102 - 105  may include a smartphone, a desktop computer, a laptop, a gaming console, a digital media player, etc. The communications network  106  may include one or more of a local area network (LAN), a wide area network (WAN), a wireless network (e.g., 802.11, 4G, etc.), and or any other suitable type of network. Each of the client devices  102 - 105  may be associated with a respective user  102   a - 105   a  who is a member of the service. 
         [0023]      FIG. 2  is a diagram of an example of the commercial service system (CSS)  101 , according to aspects of the disclosure. As illustrated, the CSS  101  includes a processor  201 , a communications interface  203 , and a memory  205 . According to aspects of the disclosure, the processor  201  may include any suitable type of processing circuitry, such as a general-purpose processor (e.g., an ARM-based processor), an application-specific integrated circuit (ASIC), or a Field-Programmable Gate Array (FPGA). The communications interface  203  may include any suitable type of communications interface, such as a WiFi interface, an Ethernet interface, a Long-Term Evolution (LTE) interface, a Bluetooth Interface, an Infrared interface, etc. The memory  205  may include any suitable type of volatile and non-volatile memory, such as random-access memory (RAM), read-only memory (ROM), a hard disk (HD), a solid state drive (SSD), a CD-ROM, flash memory, cloud storage, or network accessible storage (NAS). In some implementations, the memory  205  may store a consumer or user profile database (CDB)  207  and service logic  209 . 
         [0024]    The CDB  207  may include any suitable type of data structure that is arranged to store one or more consumer or user profile records. By way of example, the CDB  207  may include one or more of a file system folder, a relational database, an SQL database, etc. Each profile record may include a data structure containing consumer or user profile information. 
         [0025]    The service logic  209  may include one or more of a load balancer, a service frontend that is arranged to interface with instances of a mobile application (e.g. an App) that are executed on the client devices  102 - 105 , and a service backend arranged to provide the particular service. For example, the service backend may be arranged to provide various portfolio management functions, such as buying stock, selling of stock, generating of portfolio reports. Additionally or alternatively, as another example, the service backend may implement an online store, a shopping cart, a billing system, etc. 
         [0026]    Although in this example the CSS  101  is presented as an integral device, in some implementations the CSS  101  may be implemented as a network of computers, and/or a server farm. For instance, the CDB  207  and the service logic  209  may be hosted on different computers. Moreover, any of the CDB  207  and the service logic  209  may be hosted on multiple computers. 
         [0027]    The CSS  101  may host a real-time debt avoidance alert system  300 , which communicates both with client devices, e.g., client device  102 . The system  300  may access personal financial data  302  pertaining to the user, as well as external financial data sources  304 . The system may receive data from the user&#39;s device (e.g., client device  102 ) regarding the user&#39;s use of his or client device. For example, the system may receive indication(s) of what websites the user is visiting, indication(s) of what content the user is viewing on the websites(s), indication(s) of transactions in which the user is currently engaging or has engaged. Additionally or alternatively, the system  300  may receive an indication of the user&#39;s current location from the user&#39;s client device  102  and/or another source (e.g., a mobile carrier). 
         [0028]    The system  300  may determine or predict based on the context of transactions, e.g., times, locations, and types of purchases, the types of purchases to be made. For example, a user may purchase luggage and sunblock in the winter and may be driving in the direction of an airport, and the system  300  may intuit that the user is traveling somewhere warm. This is an example of an artificial intelligence that utilizes contextual data in near real-time. Similarly, referring now to  FIG. 4 , a user  102  with a mobile terminal device  102   a  (e.g., a smartphone) may be at or near a location or store S (e.g., a store) for a predetermined amount of time. The system  300  having received information that the user  102  is at a co-location (i.e., the same location or at a proximate location) as the store S may send a notification to the mobile terminal  102   a  about the financial situation  102  of the user. For example, the system  300  may notify the user  102  that a given amount of money is available to be spent while remaining on track with a particular financial goal. 
         [0029]    Further, as shown in  FIG. 5 , a co-location event in conjunction with a search indicating an intention may trigger the system  300  to send a notification to the client device  102   a . In particular, the user  102  may receive notification based on the recent online activity, e.g., a search for specific product via build-in web browser of the personal mobile device  102   a , and collocation event (i.e., the user  102  being at the store location L). The notification may be transmitted in any suitable manner. For example, the notification may be transmitted as a pop-up window to the user, a Short Messaging Service (SMS) message, and/or any other suitable type of message. In some implementations, the notification may be actionable. For example, it may provide an insight leads to an action (e.g., transfer of money into an account or avoidance or spending or using a credit card with a better rewards structure for a particular transaction) that reduces risk of over expenditure or additional debt and therefore improves wealth. 
         [0030]    For example, user  102  may be at an electronics search and may have performed a search  302  on an online store for a television. The system  300  may therefore analyze the context of the search and the colocation event to conclude that the user  102  intends to purchase a television set. This event may trigger the system  300  to send the user  102  a notification as to a discretionary budget for the user  102 , thereby preventing the user from purchasing a model television outside of greater than the budget allocated to such discretionary purchases. 
         [0031]    Contextual based analysis may utilize a variety of data. As shown in  FIG. 6 , the user  102  has a user context  400  that is determined based upon public data sources  402  (e.g., particular locations being associated with particular merchants), personal financial data  403  (e.g., payment transactions for the user  102 ), and personal sensors data  404  (e.g., GPS sensors, accelerometers, etc.) so that the activity of the user  102  may be determined (e.g., location, movement, etc.). The data may be received from the user&#39;s client device and/or any other source that is associated with the user. For example, the context data may be received from the server/system of a bank or another financial institution that services the user&#39;s debt and/or credit card. 
         [0032]    Turning now to  FIG. 7 , which reflects substantial logical parts of the joint system, it should be noted that financial entities (e.g. debt) optimization logic is represented as a standalone logical component which is leveraged by the core part of the system—Alerts/Suggestions Generation Engine  500 . The engine  500  continuously communicates with Context Analyzer Component  501  which perform a sort of Complex Event Processing logic operations. A Context Analyzer Component  501  may have constant communication interface with Financial Streams Optimizer  502 . The Investment Approach/Portfolio component  503  which incorporates the generic model of the user&#39;s investment approach and portfolio composition. This model generally represents mathematical entity which serves as an input for Alerts/Suggestions Generation Engine  500 . 
         [0033]      FIG. 8  depicts a data flow diagram for substantial data input parts for the alerts/suggestion generation mechanism a user  102 .  FIG. 8  illustrates an example of data, e.g., location of the user  102 , which may be analyzed based on context. For example, location data  601  may trigger a contextual analysis of the location of the user  102 . Such an analysis may include Location Data Event Filtering  602  (e.g., a determination of whether there is a colocation event with a known merchant). This includes Location Data Filtering step  602 , Location Data Accumulation  604  and Location Data Clustering  605 . Investment Approach/Portfolio component  606  may provide Investment Model Data Structure  607  which may be processed as a main data input for the Investment. Model Data Normalization Step  608 . As a third major stream Financial Context Data  609  information is being continuously collected across the system, such as existing credit card debt, existing checking and savings amounts available, due dates to pay credit, etc. and streamed through the Context Financial Data Processing Step  610 . All above steps form the data inputs for the Alert/Suggestion Generation Engine  611 . Such data may be stored so that future such events may more immediately trigger particular notifications assuming there is a pattern of behavior. 
         [0034]    The above-described embodiments are merely exemplary. At least some of the steps discussed with respect to these figures can be performed concurrently, performed in a different order, and/or altogether omitted. It will be understood that the provision of the examples described herein, as well as clauses phrased as “such as,” “e.g.”, “including”, “in some aspects,” “in some implementations,” and the like should not be interpreted as limiting the claimed subject matter to the specific examples. Any of the functions and steps provided in the Figures may be implemented in hardware, software or a combination of both and may be performed in whole or in part within the programmed instructions of a computer. No claim element herein is to be construed under the provisions of 35 U.S.C. 112, sixth paragraph, unless the element is expressly recited using the phrase “means for”. 
         [0035]    While the present disclosure has been particularly shown and described with reference to the examples provided therein, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims. 
         [0036]    Having described at least one of the preferred embodiments of the present invention with reference to the accompanying drawings, it will be apparent to those of skill in the art that the invention is not limited to those precise embodiments, and that various modifications and variations can be made in the presently disclosed system without departing from the scope or spirit of the invention. Thus, it is intended that the present disclosure cover modifications and variations of this disclosure provided they come within the scope of the appended claims and their equivalents.