Patent Application Titled “Context-aware systems and methods for selecting smartphone applications/services and awarding reward tokens” Published Online (USPTO 20220237646): Patent Application
Insurance Daily News
2022 AUG 15 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Brown, Stephen J. (Malibu, CA, US); Michel, Samuel P. (Coconut Creek, FL, US); Patel, Alpesh (Old Basing, GB), filed on April 14, 2022, was made available online on July 28, 2022.
No assignee for this patent application has been made.
Reporters obtained the following quote from the background information supplied by the inventors: “The tagline of the modern world of smartphone-mediated services is “there’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’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’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’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.”
In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “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.”
“2. The method of claim 1 with said context attributes of said user comprising one or more of a spending history, a usage, a location and a profile of said user.
“3. The method of claim 1 assigning weights to said context attributes of said plurality of applications and said context attributes of said user.
“4. The method of claim 3 representing said attributes with said weights assigned, as a vector.
“5. The method of claim 3 performing said assigning by one or both of a human curator and a machine learning algorithm.
“6. The method of claim 3 basing said weights on one or more of a user preference, a usage, a value of said reward points and a sponsor promotion for one or more of said plurality of applications.
“7. The method of claim 1 wherein said presenting of said icons in said step (b) is based on said rank-order in one of a sequential manner, an alternating manner and around a circle with a movable indicator.
“8. The method of claim 1 wherein said presenting of said icons in said step (b) is done on a 3D holographic sphere.
“9. The method of claim 1 with said first application as a fitness application and said second application as a fast-food restaurant application.
“10. The method of claim 1 with said plurality of applications comprising a messaging application, a shopping application, an entertainment application, a restaurant application, a weather application, a transportation application, a social networking application, a banking application, an education application, a healthcare application and a health insurance application.
“13. The context-aware mobile device system of claim 12 wherein said first application is a fitness application and said second application is a fast-food restaurant application.
“14. The context-aware mobile device system of claim 12 wherein said plurality of applications comprise a messaging application, a shopping application, an entertainment application, a restaurant application, a weather application, a transportation application, a social networking application, a banking application, an education application, a healthcare application and a health insurance application.
“15. The context-aware mobile device system of claim 12 wherein said context attributes of said user comprise one or more of a spending history, a usage, a location information and a biometrically generated profile of said user.
“16. The context-aware mobile device system of claim 12 wherein weights are assigned to said context attributes of said plurality of applications and said context attributes of said user.
“17. The context-aware mobile device system of claim 16 wherein said weights are assigned by one or both of a human curator and a machine learning algorithm.
“18. The context-aware mobile device system of claim 16 wherein said weights are based on one or more of a user preference, a usage, value of said reward points and a sponsor promotion for one or more of said plurality of applications.