Patent Publication Number: US-2022237646-A1

Title: Context-aware systems and methods for selecting smartphone applications/services and awarding reward tokens

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part (CIP) of and claims priority from now-allowed U.S. patent application Ser. No. 16/874,360 filed on May 14, 2020, and which is incorporated herein by reference for all purposes in its entirety. 
    
    
     FIELD OF THE INVENTION 
     This invention is related to the field of contextually selecting most relevant applications on a mobile device for a user and for awarding reward points to the user based on context. 
     BACKGROUND OF THE INVENTION 
     The tagline of the modern world of smartphone-mediated services is “there&#39;s an app for that.” Applications have proliferated to the extent where smartphone users are overwhelmed with the choices of potential applications and services from a greater variety of vendors, and these applications are never integrated in a cohesive manner and/or curated manner. As most merchants and service providers prompt users at every opportunity to engage through their own proprietary applications, the fragmentation continues to increase. As vendors target users with their advertising and promotion in the interest of selling or engaging in their applications while harvesting user data through their proprietary interfaces, users often wonder who is looking out for their interests. Therefore, there is a strong desire to personalize the experience of a user on the mobile device and put the user in control of the entire experience. 
     There is plenty of prior art that attempts to address the above desire. U.S. Pat. No. 9,769,634 issued on 19 Sep. 2017 to Marti et al. describes methods and computer products that provide personalized content based on historical interaction with a mobile device. A computing device can receive information about a user interaction with an application running on the mobile device at a first time and location. A type of the application can be identified by parsing a description of the application (e.g., using a natural language processing algorithm). An affinity model can be generated that associates the type of the application with the first time and/or location. At a second time and location, it can be determined that the second time corresponds to the first time and/or that the second location corresponds to the first location. Using the affinity model, the second time and/or location can be associated with the type of the application, and the mobile device may then display content related to the type of the application. 
     U.S. Pat. No. 9,003,327 issued on 7 Apr. 2015 to Moshrefi et al. describes a system that may include a computing device configured to provide a proactive user interface, the proactive user interface configured to selectively propose suggested actions when a user of the computing device is determined to be in a passive mode. The computing device may include a proactive user interface module configured to: wait for at least one event, determine whether trigger criteria are met based on the at least one event, and when the trigger criteria are met, propose a suggested action to the user based on the at least one event. 
     U.S. Pat. No. 10,332,184 issued on 25 Jun. 2019 to Glover describes a system that includes a recommendation module. The recommendation module may receive a recommendation request including a set of installed application identifiers and identify a set of candidate application groups including at least one matching candidate application identifier. For each candidate application group, the recommendation module may determine a recommendation score based on a number of matching candidate application identifiers included in the candidate application group that match at least one installed application identifier. The recommendation module may select a first candidate application group based on the recommendation scores and select at least one non-matching candidate application identifier from the first candidate application group that does not match any of the installed application identifiers, resulting in a set of recommended application identifiers. The recommendation module may transmit to the user device, recommendation data for at least one recommended application identified by the set of recommended application identifiers. 
     U.S. Pat. No. 9,405,848 issued on 2 Aug. 2016 to Liang et. al describes techniques for recommending mobile device activities, such as accessing mobile applications and/or mobile Web pages. Some embodiments provide an Activity Recommendation System (“ARS”) configured to recommend relevant activities for a user to perform with a mobile device, based on context of the mobile device. In one embodiment, the ARS recommends mobile applications-based content items (e.g., Web pages, images, videos) that are being currently accessed via the mobile device. The ARS may process information about mobile applications and content items to determine semantic information, such as entities and/or categories referenced or associated therewith. The ARS may then use the semantic information to determine mobile applications that have semantic information that is at least similar to that of a content item accessed via a mobile device. 
     U.S. Pat. No. 10,325,277 issued on 18 Jun. 2019 to Ballepu describes systems and methods for providing rewards to a user. Providing rewards to the user may include receiving transaction data associated with a user&#39;s purchase, determining a merchant and a category associated with the transaction data, and assigning the transaction data to a merchant icon or a badge icon in a graphical user interface. The merchant icon or the badge icon may comprise a progress bar that indicates the user&#39;s progress in reaching a milestone associated with the icon. Based on the transaction data, a number of loyalty points of a number of transaction points may be assigned to the merchant icon or the badge icon, respectively. Further, based on whether a total number of loyalty points exceeds a first threshold or a total number of transaction points exceeds a second threshold, a reward may be generated to the user when the first or second threshold is exceeded. 
     U.S. Patent Publication No. 2009/0163183 A1 to O&#39;Donoghue et al. describes a method for generating recommendations for a user of a mobile device. The user is associated with a service provider. A request for a recommendation is obtained. Data associated with the user and data on the content available to the user is retrieved from the service provider. A list of recommendations is generated based on an analysis of the retrieved user data. The recommendations are generated by a plurality of different recommendation techniques. 
     U.S. Patent Publication No. 2017/0168653 A1 to Spiess et al. describes a system in which a situation description is received from a context engine, the situation description describing a context of a user. The user is associated with a graphical user interface, and the graphical user interface is associated with a screen area. A user interface adaptation rule is identified based on the received situation description. A logical layout is determined based on the identified user interface adaptation rule. A physical layout is determined based on the logical layout. Display of the graphical user interface on the screen area is initiated based on the determined physical layout. 
     U.S. Patent Publication No. 2013/0339345 to Matamala et al. describes mobile devices that can provide app recommendations that are relevant to a location of interest. A localized app recommendation can be triggered (e.g., by a mobile device coming within a threshold distance of an application hotspot or some other user action). A location of interest can be determined. The location of interest can be the current location of the mobile device or another location (e.g., the destination in a mapping app). Using the location of interest, a localized application ranking database with app hotspot data can be queried with location data representing the location of interest. App recommendations can be received and displayed on the mobile device. Icons for apps that are relevant to the location of interest can be visually distinguished from other apps. 
     U.S. Patent Publication No. 2013/0238413 to Carlson et al. describes a mobile device that is configured with a mobile application to direct a user to a location of more interest to the user based on benefits of offers provided to the user. The mobile application may be configured to capture an image of an offer presented in an advertisement via a digital camera, identify the offer from the image, and store data associating the offer with one or more accounts of the user. The mobile application is configured to present information to suggest a direction of travel to the user for improved opportunities to take advantage of the offers. 
     U.S. Patent Publication No. 2014/0074569 to Francis et al. describes systems and methods for facilitating loyalty and reward functionality in mobile commerce. In one embodiment, a method for providing a loyalty/reward program can include receiving an indication that a consumer has entered a predefined merchant location; transmitting a notification to a mobile device associated with the consumer; providing one or more payment tools on a mobile device for the consumer to facilitate a purchase from a merchant associated with the predefined merchant location; generating a loyalty/reward credit for an account associated with the consumer; and receiving an instruction from the consumer to redeem loyalty/reward credit for goods and/or service from the merchant. 
     What is missing from the prior art are techniques for curating and contextually selecting from the multitude of available applications and presenting only the most relevant applications to the user. The applications may be preloaded on the device or added by the user. What is also missing are techniques of contextually rewarding the user for his or her usage or non-usage of applications and services, not merely in a simplistic manner, but in a wholistic, reconciled or integrated manner regardless of the user activity being performed, including searching, shopping, messaging, capturing content, sharing, or any other activity. What is also missing from the prior art is the ability to award the reward points as cryptocurrency tokens. 
     OBJECTS AND ADVANTAGES 
     In view of the shortcomings of the prior art, it is an object of the present invention to provide techniques for recommending or selecting the most relevant applications on a mobile device for the user, whether the applications are preloaded or later added by the user. 
     It is also an object of the invention to determine the relevance of an application for the user based on a user context and the application context. 
     It is further an object of the invention to determine a rank-order of the applications for the user based on context and present the most relevant applications to the user first. 
     It is also an object of the invention to provide a digital wallet and award reward points to the user based on his/her usage and non-usage of the applications in a wholistic and integrated manner. 
     It is also an object of the invention to award these reward points as reward tokens that are cryptocurrency/virtual currency tokens. 
     These and other objects and advantages of the invention will become apparent upon reading the detailed specification and reviewing the accompanying drawing figures. 
     SUMMARY OF THE INVENTION 
     A number of objects and advantages of the invention are achieved by apparatus and methods designed for context-aware techniques of selecting smartphone applications/services and providing rewards to the user. The user as well as the applications or services resident/installed on the mobile device are characterized by a context. A user context is based on one or more of spending history, usage/behavior history, location of the user, profile of the user, etc. Conversely, an application context is based on the importance of various user activities and behavior while using that application. User and application contexts are represented by respective context attributes. 
     In the preferred embodiment, appropriate weights are assigned to various user and application context attributes. The above task is done by a context assignor and weighting module. Preferably, the context attributes which may be weighted are represented as vectors. A comparison engine determines the similarity between the user context attribute vector and application context attributes vectors using techniques known in the art. The comparison engine is preferably a part of a recommendation module or at least communicatively coupled with it. 
     Based on the above similarity computation performed by the comparison engine, the recommendation module produces a rank-order that is provided to an interface module. The interface module uses the rank-order to determine the order of presenting/presentation of the application icons on the display of the mobile device for the user. The preferred/selected icons may be presented sequentially or alternatively in a toggled or alternating manner. Thus, the applications are presented to the user in the order of their relevance/context for the user and not in an arbitrary and disorganized manner. This is one of the main contributions of the present technology over the prior art. The relevance of the applications thus presented may be amongst all applications on the device or within categories of applications, such as shopping, dining, health, messaging, social media, etc. 
     There is also a digital/rewards wallet/module that is in charge of tracking the reward points awarded by the instant system to the user. The reward points are awarded based on the usage of various applications and the spending/purchase behavior/habits of the user while using the applications. In this manner, the instant digital wallet maintains the instant reward points for the user based on the reward points awarded to him/her by the underlying constituent applications/services. 
     As another contribution of the present technology over the prior art, the instant award points in the digital wallet are not merely awarded to the user based on the sum or accumulation of the reward points from the underlying applications. Instead, they are awarded in a reconciled or integrated manner. What this means is that the instant reward points are awarded after reconciling sometimes conflicting behavior or habits of the user. As one example, the user may be awarded reward points redeemable at a healthcare provider, if the user has refrained from dining excessively at unhealthy fast-food chains, and vice-versa. The reward points in the digital wallet can also be converted to a cash value. 
     The cash value may be applied to a debit card, a credit card or any other financial services application that may be linked to the digital wallet. The cash value on a debit card may then be withdrawn from the user at an automatic teller machine (ATM). In a highly preferred set of embodiments, the cash value is represented by digital or cryptocurrency or virtual currency tokens that are stored in the digital or e-wallet. These embodiments thus provide a mechanism for awarding the user in crypto tokens that may be stored in the e-wallet and later spent by the user as desired. 
     Preferably, the weights of the user and application context attributes discussed above are assigned by a human curator. This assignment may done in a supervised, semi-supervised and unsupervised machine learning manner. Preferably, the weights are further based on user preferences, existing value of reward points in the digital wallet and the sponsorship or promotion of a certain application/service by its vendor/provider. 
     There is a large number and types of applications/services for mobile devices that may be installed on the mobile device and that may benefit from the present teachings. These include messaging applications, shopping applications, entertainment/games applications, restaurant applications, weather applications, transportation applications, social networking applications, banking applications, education applications, healthcare applications, insurance applications, etc. 
     The present techniques may be implemented by an appropriate apportionment of functionality in the frontend or device-based, and backend portions. Clearly, the system and methods of the invention find many advantageous embodiments. The details of the invention, including its preferred embodiments, are presented in the below detailed description with reference to the appended drawing figures. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
         FIG. 1  illustrates at a high-level a mobile device on which various applications/services are installed by/for the user that can benefit from the instant design. 
         FIG. 2  represents a user and selected exemplary applications on the mobile device based on the user and application contexts of the present principles. 
         FIG. 3  represents in detail the key component modules of the present design working together to produce a rank-order for the applications/services on the mobile device. 
         FIG. 4  illustrates exemplary usage habits of a user at some familiar establishments and associated applications/services. 
         FIG. 5  provides a block diagram view of the frontend components/portions of the key modules of an exemplary implementation of the present teachings. 
         FIG. 6  provides a block diagram view of the backend components/portions of the key modules of an exemplary implementation of the present teachings. 
         FIG. 7  shows in a flowchart form the steps performed by a preferred embodiment for assigning and updating the weights of context attributes vectors for presenting selected applications to the user. 
         FIG. 8  shows an embodiment of the present technology with the selected/preferred application categories around a circle with an indicator and the highest-ranking selected/preferred application at the center of the circle. 
     
    
    
     DETAILED DESCRIPTION 
     The figures and the following description relate to preferred embodiments of the present invention by way of illustration only. It should be noted that from the following discussion, alternative embodiments of the structures and methods disclosed herein will be readily recognized as viable alternatives that may be employed without departing from the principles of the claimed invention. 
     Reference will now be made in detail to several embodiments of the present invention(s), examples of which are illustrated in the accompanying figures. It is noted that wherever practicable, similar or like reference numbers may be used in the figures and may indicate similar or like functionality. The figures depict embodiments of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein. 
     The techniques described herein may employ computer code that may be implemented purely in software, hardware, firmware or a combination thereof as required for a given implementation. The present invention will be best understood by first reviewing the systems and methods for providing a context-based or context-aware or contextual access to applications on a mobile device for a user as illustrated in  FIG. 1 .  FIG. 1  shows an embodiment  100  of the present technology with a mobile device  102 , such as a cellular phone, a smart-phone, a personal digital assistant (PDA), a tablet device (such as an iPad), a laptop, or any other suitable personal computing device used by a user  104  as shown. 
     There are a number of applications, such as applications,  102 A,  102 B,  102 C, . . . installed on mobile device  102 . These applications or services may have been preinstalled on device  102  by the device vendor or installed by user  104  over the lifetime of the device for various purposes and benefits, perceived or real, for the user. As noted in the Background section above, the number of such applications/services on the device rises dramatically over the lifetime of the device. Very commonly, the applications have confusingly overlapping functionalities making it extremely hard for the user to derive an optimal experience from the device and from the various applications/services installed on it. The present design solves this problem based on techniques provided herein. 
     To understand the principles behind the present design in greater detail, let us now turn our attention to the embodiment shown in  FIG. 2 . Embodiment  200  of  FIG. 2  shows a smartphone  202  and its user  204 . Smartphone  202  is of a slightly different style as compared to smartphone  102  shown in  FIG. 1 . Nevertheless, just as device  102 , there are various applications presented to user  204  by various icons on display  203  of smartphone  202 . Specifically, there is an application  202 A that is used to make dining reservations at various restaurants. Examples of application  202 A include Opentable™, Bookalable™, Yelp™, etc. There is a shopping or an e-commerce application  202 B, examples of which include Amazon.com™, Walmart.com™, etc. There is a music application  202 C, examples of which include iTunes™, Spotify™, Pandora™, etc. 
     There is also a video or movies application  202 D, such as Netflix™, Youtube™, Hulu™, etc. There is further a transportation application  202 E, such as Uber™, Lyft™, etc. There is a weather application  202 F, such as Apple&#39;s stock weather application, The Weather Channel™, etc. There is a banking application  202 G, that is provided by the banking institution that user  204  banks with. There is a social networking application  202 H, such as Facebook™, Twitter™, LinkedIn™, etc. There is a healthcare provider application  202 I such as BlueCross and Blue Shield™, Kaiser Permanente™, etc. There is an education or learning application  202 J, such as Lumosity™, Khan Academy™, DuoLingo™, etc. Furthermore, there may be other applications below or above or right or left of the icons shown on display  203  of  FIG. 2 , that user  204  may have to scroll or swipe to view. 
     According to the chief aspects, applications  202 A,  202 B, . . . are presented to user  204  in a ranked-order manner. In other words, as opposed to the application icons appearing in any order as chosen by the operating system of device  204 , such as in an alphabetical order, or in the order of their installation, the icons of applications on device  202  are presented based on their relevance to the behavior of user  204 . This is one of the key innovative aspects of the present technology. Explained further, a subset of overall applications on device  202  is recommended to be presented first or on the top page or home screen of display  203  of device  202  for user  204 . The recommendation of which application icons should be presented first is made by a recommendation module of the instant design. 
     The recommended application may be from amongst all the applications on the mobile device or from within a category of applications. For example, the recommendation module may select the top-ranking shopping application, the top-ranking dining application, the top-ranking health application, and the-top ranking financial/money application based on the user context. These top-ranking applications may further be selected based on the context. For instance, exemplary money applications may include a rewards application, a loan application, a credit score application, etc. They may further include a savings or points conversion application that converts points to rewards that can be redeemed in another shopping application or to cash that can be redeemed at an ATM with a debit card or another card linked to the rewards wallet. 
     The recommendation module accomplishes its objects by matching a “context” of user  204  to the “contexts” of applications  202 A,  202 B, . . . on device  202 . Such a user context of the present context-aware technology is shown by star  206 , while the application contexts for applications  202 A,  202 B, . . . are shown by respective stars  208 A,  208 B, . . . in  FIG. 2 . This matching of contexts between the user and the applications is in turn governed by a matching of the various attributes of the user context to the attributes of the application contexts. This is illustrated in  FIG. 3 . 
       FIG. 3  shows a recommendation module  220  of the present design that compares contexts  206  of user  204  with contexts  208 A,  208 B, . . .  208 N of applications  202 A,  202 B, . . .  202 N respectively. As discussed above, the result is the derivation of a rank-order  224  for applications  202 A,  202 B, . . .  202 N as shown. Note that in  FIG. 3  applications  202 A-N represent any number and types of applications installed on device  204  of  FIG. 2 . These include messaging applications, shopping applications, entertainment/games applications, restaurant applications, weather applications, transportation applications, social networking applications, banking applications, education applications, healthcare applications, insurance applications, etc. 
     Device  204  is not explicitly shown in  FIG. 3  to avoid clutter. Furthermore, applications or services  202 A-N may be just virtual or also have a physical or brick-and-mortar component. In other words, while a restaurant application may have a corresponding physical establishment or restaurant, a social networking application may purely be virtual. 
     Rank-order  224  derived by recommendation module  220  is then used by an interface module  240  of the present design to present application icons on display  203  of device  202  in a sequential or prioritized manner for user  204 . Because of interface module  240  the user is able to choose (such as by highlighting a given icon via touching or arrow keys) and subsequently use a given application. Rank-order  224  may dictate that banking application  202 G should be presented first to user  204  while music application  202 D last on the home page display  203 , while for another user healthcare application  202 I may be displayed first. 
     Of course, the rank-order may determine the order of presentation of all applications on device  202 , even those that come on the second page above/below (or to the side) of the home page. Rank-order  224  preferably has a cutoff value that marks the end of the selected or preferred applications for user  204 , and after which the applications icons are not presented to the user by interface module  240 . To access those applications beyond the rank-order cutoff, user  204  may have to exit the present system and resort to the traditional interface of device  202 . 
     In another embodiment, instead of showing application icons sequentially, interface module  240  displays the icons alternatingly or in a “toggled” or an alternating manner. In such an embodiment, one or more icons of the recommended or preferred subset of applications as determined by rank-order  224  above are presented to the user in a time-based loop of the icons of the selected applications. 
     Let us now further understand the workings of recommendation module  220  shown in  FIG. 3 . Context  206  of user  204  is represented as a vector/set of attributes  210  as shown. The context attributes include spending habits/history of user  204 , usage habits/history i.e. how much time the user dwells (user attention) in a given physical or virtual space of a given service/application, the location information of the user and an account profile or simply profile of the user. The location information may include his/her present location as well as location history as appropriate. The profile may include his/her date of birth, place of residence, name/alias, present status, etc. 
     While the physical location discussed above may be derived based on a location sensor on the device, a virtual location may also be ascribed to the user based on his/her activities, whether current or scheduled in the future. Examples of such activities include video conferencing or viewing the streaming of an event, or visits to locations within a game environment or a virtual reality environment. A user may not be physically present at a given physical location such as a store, live entertainment event, or health clinic, but may be participating in such a store or event or clinic virtually through teleconferencing, virtual reality or telehealth. Such a virtual location may then be weighted just as a physical location would be amongst the context attributes of the user per present teachings. Exemplary meetings, streaming and events platforms that may provide such a virtual location context include Zoom™, Google Meet™, Microsoft Teams™, WhatsApp™, Verizon BlueJeans™, Facebook™ Messenger, etc. 
     In yet another embodiment, the user profile is based on the authentication of the user of the device by a biometric identification and authentication, including face recognition, fingerprint, or acoustic or chemical breath print. These biometrics contribute to the user profile as the biometrics used for identification may also contain other data that can classify the user. For example, facial recognition technology may confirm the identity of the user, but may also be used to classify mood. Breath recognition may be used to confirm user identity, but may also contain classifiable signals to indicate stress, exercise or other health parameters. Such a biometrically generated profile may then be used to define user context attributes and their weights per present teachings. 
     Applications  202 A-N also have respective context attributes  208 A-N represented by respective vectors/sets of attributes  214 A-N as shown. To accomplish its objective of producing rank-order  224 , recommendation module  220  utilizes a comparison engine  222 . Comparison engine  222  compares context attributes vector  210  with context attributes vectors  214 A-N to determine a similarity. For this purpose, comparison engine  222  may employ one of the many techniques of vector similarity computation known in the art, such as Cosine similarity or distance-based similarity. Regardless, the end result is that a rank-order  224  is produced, whose first entry identifies the application that is to be displayed first for user  204 , and second entry identifies the second application, and so on. 
     In a preferred embodiment, user context attributes and application context attributes are weighted. As such, user attributes vector  210  and application attributes vectors  214 A-N contain weighted attributes. The weights may be assigned by a human curator. In such, or a related embodiment, the human curator may provide these inputs to the system in a supervised machine learning manner. Alternatively, the weights may be assigned completely automatically in an unsupervised machine learning manner. The task of determining relevant contexts for the user and the applications as well as assigning weights to the contexts is performed by a context assignor or a context assignor and weighting module  260  a shown in  FIG. 3 . Module  260  may be either be integrated with recommendation module  220  or be external to and communicatively coupled with it. Similarly, comparison engine  222  may be apart of recommendation module  220  or communicatively coupled with it. Moreover, there may be a separate machine learning engine/module for learning the weights to be assigned to the context attributes via context assignor module  260 . 
     In other related variations, the user attributes and their weights may also be based on a promotion from a vendor and/or on the existing value of reward points in the digital wallet of the user further discussed below. In other words, a specific application vendor or service provider may promote its application to rank higher than others. Similarly, if the user has a very high value of reward points from a specific application/service then that application/service may be ranked higher than others. Existing value of reward points thus provide a way of contextualizing the immediate or next actions of the user for ranking the relevance of the selected/preferred applications and services. 
     The weights may also be based on the choosing (or selection) of the preferred/selected applications presented by interface module  240  for subsequent usage by user  204 . Explained further, the weights are so assigned that if user  204  repeatedly ignores a high-ranked application, then the weights for the context attributes for that application are reduced. This results in a lowering of its rank in rank-order  224  in the future iterations of presentation by interface module  240 . On the other hand, if the user consistently chooses a lower-ranking application for usage, then its weights are adjusted so it is ranked higher in the future iterations. 
     As noted, the context attributes capture the behavior, habits and preferences of user  204  while she/he is using a given application  202 A-N. These attributes may also include the user profile, including the name, data of birth and a specific status message associated with the user and existing value of reward points from various applications/services associated with the user. These may include shopping, dining and other preferences, such as whether the user prefers shopping at Walmart™, and if so what departments and products he/she typically shops, his/her shopping history and patterns, or whether he/she prefers eating at a McDonald&#39;s, and if so what typically he/she orders, how he/she typically pays, and so on. 
     The application context against which the user context is matched, can be thought of as the converse of the user context. As such, an application context determines the relative importance of various user activities and behavior while using that specific application. In other words, for a dining application, the application context attributes may include the specific restaurant location the user prefers to dine in, the menu items he/she typically orders, forms of payment etc.  FIG. 4  explicitly illustrates the usage behavior of the user  204  at some familiar establishments, namely a Walmart™ store  252 , an Apple™ store  254 , a McDonald&#39;s™ restaurant  256  and a Nike™ store  258 .  FIG. 4  further shows that user  204  is using the McDonald&#39;s service/application  259  on display  203  from a cash value  232  assigned to his rewards points, as further discussed below. 
     It should be noted that the applications/services  202 A-N as shown in  FIG. 2-3 , from various application vendors and service providers incentivize user  204  for the usage of their applications/services as per the prevailing art. As the user uses their applications/services he/she is awarded reward points that the user may then redeem at the same establishments or partner establishments and in some cases convert to cash. However, based on its context-aware design, the present technology integrates/reconciles these reward points and awards its own reward points to the user based on his/her behavior. In other words, the present design awards its own rewards points to user  204  in a digital wallet  230  shown in  FIG. 3 . The digital wallet may be linked to a physical and/or virtual debit card, credit card or another financial service. An exemplary debit/credit card  231  is shown in  FIG. 3  that with cash value  232  loaded on it. Such a card may then be used at a point-of-sale (POS) terminal for shopping and/or for withdrawing cash from an ATM. 
     These rewards points of the present technology are in turn based on the reward points accumulated by the user from his/her usage of the various application/services installed on device  202 . However, the reward points in digital wallet  230  are not merely a simplistic accumulation or sum of all other underlying reward points. Instead, they are awarded in a wholistic or integrated manner after reconciling the shopping/spending and usage behavior (context) of the user, which in some cases may be based on contradictory or conflicting or mutually exclusive choices. 
     As an example, if a healthcare application is ranked high in the selected/preferred subset of applications/services for the user, then the present technology may award reward points to the user for not dining at an unhealthy fast-food establishment. In other words, user  204  of  FIG. 3  may be awarded reward points in his/her digital wallet  230  for having a low/no value of reward points from a fast-food restaurant application/service. In other words, the user is awarded reward point for refraining from a purchase or service. This is indeed one of the unique aspects of the present design. As provided herein, the present design thus provides a curating capability for the plethora of mobile device applications by selecting only the most relevant applications for the user. They further provide this curating capability by awarding instant reward points wholistically across the many applications with sometimes conflicting user behavior. 
     The instant approach of awarding reward points in this manner is wholistic because the eating habits of the user are relevant to his/her healthcare costs and relatedly insurance premium. In embodiments assigning weights to context attributes, the eating and lifestyle preferences of the user are thus weighted high/more for healthcare and health insurance applications/services. As a result, based on the comparison of weighted attributes by comparison engine  222  of  FIG. 3 , a user that has an unhealthy dining and lifestyle habits, may have his/her healthcare and insurance applications rank higher than other applications in rank-order  224 . 
     As already noted, user  204  is assigned a digital or rewards wallet  230  as shown in  FIG. 3 . Rewards wallet  230  is a module that may be implemented separately from recommendation module  220  or it may be integrated with it. As user  204  uses/shops various applications/services  202 A-N on device  202 , or refrains therefrom per above, he/she is awarded reward points in digital wallet  230  as discussed above. The reward points in digital wallet  230  are designated a suitable cash value  232  based on appropriate business rules. The cash value may be in any suitable currency, such as United States dollars. Subsequently, user  204  may then apply this cash value towards the purchase of more products and services from applications  202 A-N on device  202 . 
     Most typically, the more user  204  uses an application, such as for shopping, downloading music/movies, dining, etc. the more reward points he/she is awarded from these underlying constituent applications. In yet other embodiments, the reward points provided by these underlying applications and services are themselves awarded based on the context-aware principles of the present design. In other words, as user  204  uses applications  202 A-N based on his/her lifestyle choices, the accrual of reward points from one application may result in a dwindling or decrease of reward points from another. This concept was introduced in an exemplary scenario above where the instant reward points were being awarded to the user by reconciling the reward points from the underlying applications/services. However, the same principles can be extended to the underlying applications/services themselves. 
     Explained further, if a user accumulates many reward points from fast-food chains, then this knowledge may indicate to an insurance carrier that the user has a higher risk of disease and medical bills. This provision of the knowledge of reward points between constituent underlying applications of the present design may be afforded by a suitable integration layer based on techniques known in the art. Consequently, the carrier may decide to increase the premium for user  204  on its insurance application on device  202  or from other retail channels of the insurance provider. Alternatively or in addition, the carrier may also effect its punitive action on user  204  by reducing the number of reward points awarded to the user from his/her use of the insurance application. 
     The present technology and its various modules may be implemented in a number of variations. Recommendation module  220 , comparison engine  222  and context assignor and weighting module  260  will have frontend portions/components that are installed on mobile device  202  and bankend portions/components that are implemented in an application server behind the cloud. To facilitate the understanding of the implementation design of the preferred embodiment of the present technology better,  FIG. 5  shows the frontend or device-specific components while  FIG. 6  shows the backend components. 
     More specifically,  FIG. 5  shows in a block diagram view  270  mobile device  202  of  FIG. 3  with the various frontend portions/components of a preferred implementation. Brace labeled with reference numeral  272  on the left shows the typical modules and components of mobile communication device  202 , these are well known and not discussed further for brevity. Components specific to or related to the above teachings of the present design are shown on the right. The righthand side shows a memory unit  280  with an operating system  282 , such as an Android™ OS or and Apple™ iOS, and various exemplary applications  202 A-N discussed above. Other operating systems may include Sailfish, KaiOS, Xiaomi, Samsung Tizen. In addition, the mobile device may be a smart phone, tablet, smart watch, computer, wearable device or an embedded device. 
       FIG. 5  also shows the frontend portion of digital wallet  230  of  FIG. 3 . Also shown is the frontend portion of context assignor and/or weighting module  260  in charge of maintaining/assigning the context attributes of the user and applications on device  202  as discussed above. Frontend application data module  284  is in charge of maintaining and storing all data related the applications on the backend as discussed below. Recommendation module  220  of  FIG. 3  is also shown in  FIG. 5  that works in conjunction with the digital/rewards wallet  230  and context assignor  260  as discussed above. 
     Finally, also shown is our interface module  240  that works in concert with comparison engine  220  (not shown in  FIG. 5  to avoid clutter) and recommendation module  220  to display/present the icons of applications  202 A-N on device  202  for the user in a rank-ordered manner and for subsequent choosing/usage of the applications. There may be other frontend components added or removed from  FIG. 5  based on the requirements of a given implementation but within the scope of the present teachings. 
     Now, let us pay or attention to the backend components of the preferred implementation as illustrated in  FIG. 6 .  FIG. 6  shows the backend components in a block diagram view  300 . It also shows our mobile device  202  from earlier drawing figures and explanation connected to backend  300  via network or cloud or internet  350 . Specifically, backend  300  consists of an application program/programming interface (API) server that presents the functionality of the backend as API&#39;s over the cloud to client mobile devices, using techniques known in the art. In turn API Server  302  is communicatively coupled with an application server  320  that houses the key functionality of the backend. Of course, backend  300  executes on one or more hardware systems and platforms or servers with their own microprocessors and memory. The architectural techniques for hosting backend  300  are well known in the art and not explicitly discussed in this disclosure. 
     The backend functionality housed or implemented by application server  320  consists of a user profile application  322  in charge of maintaining the user profile and related context information in database(s)  336  and database server(s)  334 . There is an application store  324  from which the user may purchase/install additional applications/services on device  202 . There is a user messaging application  326  in charge of providing alerts and messages to the user via the frontend display of device  202 . Note that the frontend messaging/alert application on display  203  of device  202  for user  204  in  FIG. 3  was not explicitly discussed but is presumed to exist. 
     Application server  320  also houses the backend portion of context assignor and/or weighting module  260  of  FIG. 3  and  FIG. 5 . The backend portion is responsible for storing and retrieving user and application context information and any associated weights from database(s)/server(s)  336 / 334  of backend  300 . There is further the backend portion of digital wallet  230  of  FIG. 3  and  FIG. 5  that is in charge of storing and retrieving reward points for the user from the backend database(s) and database server(s)  336  and  334  respectively. 
     Finally, there is also shown a machine learning engine  332  that is responsible for learning weights for context attributes maintained by context assignor  260  based using a learning process. The learning process involves training the engine based on the user and application contexts, which are in turn based on the usage behavior of the user and the corresponding capabilities of the applications respectively per above teachings. As noted above, the learning and assignment of weights to the contexts by learning engine  332  may be performed in a supervised, semi-supervised or completely unsupervised manner. Note that any corresponding frontend portion of machine learning engine  332  was not explicitly discussed in our discussion of the frontend but is presumed to exist. 
     Based on the above principles, the present technology thus provides a context-aware design for recommending/selecting a preferred/selected set of applications of a mobile device user, and to award reward points that reconcile conflicting and mutually exclusive behavior of the user on the mobile device. Based on user and application contexts, the present principles thus provide techniques for curating the multitude of available products/applications/services that have proliferated on users&#39; mobile devices. 
       FIG. 7  presents the process of comparison of user context attributes and application context attributes for a preferred embodiment in a flowchart form. While still referring back to  FIG. 3 ,  FIG. 5  and  FIG. 6 , flowchart  400  shows that our context assignor and weighting module  260  first retrieves weighted user context attributes  210  from database(s)/server(s)  336 / 334  per above. This is shown by step/box  402 . Next, module  260  retrieves weighted application context attributes  214 A-N as shown by step/box  404 . The order of steps  402  and  404  may be switched or they may be performed in parallel. 
     Next our comparison engine  222  performs similarity computation between the weighted user context attributes vector and weighted application context attributes vectors. This is shown by box/step  406 . This results in the creation of rank-order  224  by recommendation module  220 , as indicated in flowchart  400  by step/box  408 . At this stage, the rank-order is fed to interface module  240  that uses it to present the selected/preferred applications to the user per above. This is shown by step/box  410 . 
     Next, as shown by step/box  412 , the weights are continually updated and learned based on the behavior of the user with respect to the various applications. This includes basing the weights on user preferences, usage patterns, existing value of reward points, sponsor promotions, etc. per above teachings as well as the use of machine learning engine  332  also per above teachings. 
     Moreover, the weights are also updated based on the choice/choosing (selection) or usage of the applications by the user. As the user chooses (selects) or ignores the various applications from the display, the weights of the context attributes vectors of the applications are adjusted, as further captured by box/step  412 . If the user shows a preference for a lower-ranking application, then its context attributes weights are increased. Conversely, if the user consistently ignores a high-ranking application, then its context attributes weights are decreased. These steps are respectively shown by boxes  414 A and  414 B. As discussed above, this adjustment of weights based on usage will result in the improvement of the rank of the chosen application and in the lowering of the rank of the ignored and application. Ultimately, the updated weights along with the context attributes vectors are saved in database(s)/server(s)  336 / 334  of  FIG. 6  per above. 
     As noted, and while referring back to  FIG. 3 , interface module  240  is in charge of displaying the icons of the selected/preferred applications on display  203 . In a prior embodiment, the icons were displayed either sequentially or in an alternating or a toggled fashion. However, in a variation, interface module  240  displays the icons in a circle. More specifically, the highest-ranking recommended/preferred application icon is shown in the center of the circle, while preferred categories of the applications are shown around the periphery of an outer circle that has an indicator movable around the circumference. The user may accelerate the cycling through the applications by touching a different part of the circle or by sliding a finger around the circle. 
     Different parts of the outer circle may be designated to represent different categories of applications within which the applications are selected per above.  FIG. 8  shows such an embodiment. Specifically,  FIG. 8  shows a device  502  with a display  503  displaying an outer circle  504  and indicator  506  discussed above. 12:00 position on circle  504  is reserved for selected/preferred messaging applications  510 , 3:00 is reserved for selected/preferred shopping applications  512 , 6:00 for selected/preferred money/financial applications  514  and 9:00 for recommended or selected or preferred healthcare applications  516 . Display  503  also shows the overall applications  518 A,  518 B, . . .  518 N installed on device  502 , whether preloaded or added by the user. 
     In a variation, circle  504  is represented by a three-dimensional (3D) sphere and the user is able to spin or rotate the sphere with a swipe on the touchscreen. This representation allows for more categories of applications to be represented and navigated graphically. In a further variation, while employing appropriate viewing environments, such a circle or sphere may be projected holographically as coming out of the screen in three dimensions (3D). 
     In a highly preferred set of embodiments, the reward points of the above teachings are awarded as reward tokens. These tokens are cryptocurrency or virtual currency tokens that represent an asset owned by their holder, and as such may also be referred to as crypt-assets. The tokens have specific denomination and reside on a blockchain address, allowing their holder to use them for investment or economic purposes. 
     In the present crypto embodiments of the instant technology, and while referring to  FIG. 3 , the reward points in digital wallet  230  are designated a suitable cash value  232  based on appropriate business rules. The cash value may be in any suitable currency, such as United States dollars as in the prior embodiments, or alternatively in cryptocurrency or virtual currency or as digital reward tokens. As such, cash value  232  shown in  FIG. 3  now refers to or is represented by a certain number of crypto/digital tokens of certain denomination. 
     The number and type/denomination of the tokens is determined by business rules governing reward points/tokens for various applications  202 A,  202 B, . . .  202 N. Depending on the variation, these reward tokens may be Bitcoins, Ethereum, Altcoins, Dodgecoins, etc. or any other specific or newly minted tokens. Subsequently, user  204  can apply this cash value in tokens towards the purchase of more products and services from applications  202 A-N on device  202  per prior teachings. 
     In the present crypto embodiments, our digital wallet  230  can now generate and access blockchain addresses for the transactions of the instant reward tokens. Those familiar with the blockchain architecture will understand that a blockchain address is a unique address. Bitcoin addresses for example start with either a ‘1’ or a ‘3’ or a ‘bc1’ and is 26-35 alphanumeric characters in length. This address is generated from the private key of the user. A private key is cryptographic code that functions as a secret password that allows the user to sign a cryptocurrency transaction and transfer funds to another cryptocurrency address. 
     The private key of the user, such as user  204  of  FIG. 3 , is stored in digital or e-wallet  230  and which may be a software wallet, a hardware wallet, a web wallet or a paper wallet known in the art. Wallet  230  accesses the private key of user  204  to send or receive the reward tokens representing cash value  232 . When a new reward token is awarded to user  204 , it receives the token and when the user spends the token, it sends the token to the requested address. The user may for example, spend the token to buy more products/services per above teachings. A unique blockchain address is used for each such transaction. 
     Consequently, digital wallet  230  is also able to report on the balance of the tokens to the user. The present embodiments provide a convenient and useful way to apply cryptocurrency assets to the context-aware technology of the prior teachings. With the recent popularity of cryptocurrencies, a user can thus receive as well as spend his/her virtual currency or tokens by utilizing the present context-aware reward design for a number of economic or other benefits. 
     The above teachings are provided as reference to those skilled in the art in order to explain the salient aspects of the invention. It will be appreciated from the above disclosure that a range of variations on the above-described examples and embodiments may be practiced by the skilled artisan without departing from the scope of the invention(s) herein described. The scope of the invention should therefore be judged by the appended claims and their equivalents.