WO2015195671A1 - Fonctionnalités de plate-forme mobile dynamique employant des variantes proximales et des procédés de personnalisation avancés en termes de structure, navigation, thème, contenu, et fonctionnalité - Google Patents

Fonctionnalités de plate-forme mobile dynamique employant des variantes proximales et des procédés de personnalisation avancés en termes de structure, navigation, thème, contenu, et fonctionnalité Download PDF

Info

Publication number
WO2015195671A1
WO2015195671A1 PCT/US2015/036039 US2015036039W WO2015195671A1 WO 2015195671 A1 WO2015195671 A1 WO 2015195671A1 US 2015036039 W US2015036039 W US 2015036039W WO 2015195671 A1 WO2015195671 A1 WO 2015195671A1
Authority
WO
WIPO (PCT)
Prior art keywords
computing device
mobile computing
mobile
content
relevant
Prior art date
Application number
PCT/US2015/036039
Other languages
English (en)
Inventor
Richard L. Baker
Original Assignee
Baker Richard L
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Baker Richard L filed Critical Baker Richard L
Publication of WO2015195671A1 publication Critical patent/WO2015195671A1/fr

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication

Definitions

  • the present invention relates to computing devices and environments involving mobile computing devices. Particularly, although not exclusively, it relates to computer-implemented methods for controlling information display on a mobile computing device display screen using measures of user proximity and content relevance. Other embodiments contemplate computing systems, to name a few.
  • app is a shorthand description for a software application.
  • Many websites dynamically customize the home screen of a particular user based on user location and on the characteristics (interests, social category, context, etc.) of an individual user. This type of personalization is founded upon the premise that these changes are based on implicit data, such as items purchased or pages viewed. The term customization is used instead when the site only uses explicit data such as ratings or preferences.
  • Bayesian inference is a method of inference in which Bayes Rule is used to update the probability estimate for a hypothesis as additional evidence is acquired.
  • Bayesian updating is an important technique throughout statistics, and especially in mathematical statistics. For some cases, exhibiting a Bayesian derivation for a statistical method automatically ensures that the method works as well as any competing method.
  • Dynamic Content is a term for the aspects of a website, mobile screen, ad, or email body that change based on the interests or past behavior of the viewer. It creates an experience that's customized specifically for the visitor or reader at that moment.
  • smart content is the recommendation engines utilized by various online retailers. Other forms, however, range from personalization fields in emails to entire images or offers on a webpage that change based on who is looking at them.
  • Centralized Database information repository on a network or cloud based
  • Centralized Application Layer that holds the content and application rules
  • Mobile, network or cloud service including location services
  • Viewing device computer, mobile device, etc.
  • Application and / or client that resides on the viewing device.
  • Portable communication devices which as is known are mobile computing devices, are typically capable of supporting wireless communication.
  • portable communication devices include, although are not limited to, mobile telephones, cellular phones, wireless-enabled tablet computers, "smart" phones, laptop computing devices, personal digital assistants ("PDA's") and other such similar devices.
  • PDA's personal digital assistants
  • portable communication devices including smart phones utilize a wide variety of different operating systems depending on the manufacturer to execute different functions. Most of these devices have the ability to determine proximity, either via GPS, or the more recently released BLUETOOTH Low Energy (BLE) technologies.
  • BLE BLUETOOTH Low Energy
  • a major predominant problem on all mobile devices is the explosion in the number of apps available on various operating systems and platforms, and conversely, the very limited space available on the home screens of small and medium sized mobile devices.
  • apps there are millions of apps and only a few inches of viable screen real estate upon which to display them. Even if the real estate were infinite, the user would encounter great difficulties locating apps visually on this extended screen real estate.
  • the more apps installed on a particular device the more difficult it is to find the app a user is looking for.
  • the current solutions are, to display sets of scrolling home screens with a large number of apps on each screen, or to place apps related to a particular heading into a folder. But this is not a long term solution to the problem.
  • location and personalization tools are used not only to determine content displayed on a mobile computing device, but also content display (app) structure, theme, functionality, and others.
  • a method for selecting most relevant information for display on a display screen of a mobile computing device includes providing a user mobile computing device having at least one processor and at least one memory, and also providing a remote computing system comprising one or more computing devices and also non- transitory storage including at least a stored database comprising a listing of past user activities and preferences and a stored database comprising informational content relevant to a plurality of geographical locations.
  • the remote computing system may include one or more of a server hosted in a cloud computing environment, one or more computing devices hosting an app database; one or more administrative computing devices hosting an administrative database, a router or network wi-fi transmitter, and combinations thereof.
  • the remote computing system is also configured at least to determine a proximity of the mobile computing device to a geographical location of the plurality of geographical locations.
  • a stored unique identifier of the mobile computing device may be communicated to the remote computing system.
  • the method in accordance with the present disclosure is implementable on, and is compatible with, any portable communication device that supports wireless communication such as BLUETOOTH or WLAN technology, and is in operable connection with wireless communication networks, or BLUETOOTH stations, WLAN stations etc.
  • the disclosure is not limited merely to only smart phones, but works equally well with other portable communication devices/mobile computing devices as summarized above.
  • the remote computing system is configured to compare the listing of past user activities and preferences to the informational content according to the unique identifier and detected proximity, and to select a most relevant content of the informational content according to the comparing.
  • the remote computing system is also configured to make available the selected most relevant content to the mobile computing device as a configured mobile computing device-readable application.
  • the configured application is made available in a format compatible with a determined operating system of the mobile computing device.
  • the step of making available the selected most relevant content to the mobile computing device comprises displaying an icon operatively connected to the configured application on a display screen of the mobile computing device.
  • the method may further include steps of selecting, by the remote computing system, most relevant content by factoring other parameters.
  • the other parameters may include determining or refining a most relevant content according to a stored predetermined activity category, to a stored geographical location of a creator of the informational content, and/or to a determined date and/or time at which the mobile computing device is determined to be proximal to the geographical location.
  • a computing network for performing the above- described method for displaying most relevant information on a display screen of a mobile computing device.
  • the network includes at least a mobile computing device having at least one processor and one memory and a display screen and configured for communicating a unique identifier of the mobile computing device to a remote computing system and a remote computing system comprising one or more computing devices each having at least one processor and at least one memory, the remote computing system further comprising non-transitory storage including at least a stored database comprising a listing of past user activities and preferences and a stored database comprising informational content relevant to a plurality of geographical locations.
  • the remote computing system is configured at least to determine a proximity of the mobile computing device to a geographical location of the plurality of geographical locations, to compare the listing of past user activities and preferences to the plurality of applications according to said unique identifier and said detected proximity, to select a most relevant content of the informational content according to the comparing, and to make available the selected most relevant content of the informational content to the mobile computing device as a configured mobile computing device-readable application.
  • these icons generally correspond to mobile application programs, Web sites (aka "web apps"), and others.
  • the icons displayed by OMVERIA can be retrieved from a cloud database and service that identifies a particular app, and its corresponding icon, as relating to a particular location but also to certain user behaviors/preferences, and still further to particular temporal metrics. So this method is focused on the art of using sensors to confirm location of a user, then automatically searching to identify apps and their icons providing a predicted most relevant content to the user, for example apps registered in a cloud service. In the cloud service database these icons will have been associated with the most relevant content.
  • the described OMVERIA method dynamically retrieves and presents the associated app icon, making it easier for the user to access and use a more relevant app to their location without having to search for that app on their various home screens or an "app store" provided by their particular device OS.
  • Figure 1 schematically illustrates a representative programming structure for implementing a dynamic mobile computing framework according to the present disclosure
  • Figure 2 illustrates a determination of distance detection for a mobile computing device using BLUETOOTH Low Energy (BLE) beacons
  • Figure 3 illustrates a system for providing a dynamic icon on a mobile computing device according to proximity to a content provider according to the present disclosure
  • Figure 4 illustrates the system for providing a dynamic icon on a mobile computing device by a push notification according to proximity to a specific content provider such as a retail store as shown in Figure 2;
  • Figure 5 illustrates a representative content/informational flow through the dynamic mobile computing framework according to the present disclosure
  • Figure 6 illustrates a representative computing architecture for a dynamic mobile application framework to provide the content/informational flow according to Figure 5;
  • Figure 7 illustrates a representative hive structure defined by the dynamic mobile application framework.
  • situational awareness is a field of research within cognitive science defined as "the perception of elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future.” Stated differently, situational awareness is knowing what is going on around a user and what is most important to the user's current goals.
  • a particular class of user can draw from significant quantities of content and data in order to quickly comprehend the information and arrive at a suitable decision.
  • GDTA can help us display the right content at the right time for the right situational awareness. GDTA methods combine content and data to help users comprehend quickly-and then make a decision.
  • a goal-directed framework such as the presently described dynamic mobile framework, including location, recent crime statistics, and closeness in time to other trip-related events to determine an answer to the question "is this hotel in a good location.”
  • the ultimate question is how to turn this simple data flow into awareness and generate predictive analytics to anticipate and provide the right functionality and content on a mobile device at the right time for a particular user.
  • the presently described dynamic mobile framework provides a different approach to data, analytics and the application of Bayesian inference on behalf of the mobile user, utilizing protocol-oriented programming and first class value semantics for benefits in predictability, performance, and productivity.
  • Classes and structures are general-purpose, flexible constructs that become the building blocks of OMVERIA and its sense of app awareness and app inference.
  • Classes and Structures can both: 1) define properties to store values; 2) define methods to provide functionality; 3) define initializers to set up an initial state; and 4) be extended to expand functionality beyond a default implementation.
  • These methods are a model of Object Oriented (OO) Programming Language. We can model problems using objects which can encapsulate data and functionality, and build complex relationships between these objects in a modular fashion.
  • Classes provide functionality for two important pieces of OO functionality: 1) Inheritance, where one class can inherit the characteristics of another; and 2) Type casting, where you can check and interpret the type of a class instance at runtime of an app. On a mobile platform this kind of thinking is useful because the method helps manage application memory which optimizes performance, an important criteria when creating complex predictive behaviors. Different memory semantics is not the only advantage of having these distinct types. Because static structures are simpler than classes, and cannot be as heavily modified after declaration, they provide an opportunity to create value objects which represent pieces of data independent from their behavior.
  • Protocols are a valuable addition to classes and structures. They provide a way to define behavior separately from the classes which implement them.
  • a protocol defines a blueprint of methods, properties, and other requirements that suit a particular task or piece of functionality. Protocols embrace flexible design by encapsulating the necessary data and behavior for a domain idea outside the scope of a Class or Structure definition. This means that a concept can be represented separately from its implementation, allowing for more creative reuse, composition, and inclusion of dynamic data from an internal or external source.
  • instantiation is the creation of a real instance or particular realization of an abstraction or template such as a class of objects.
  • To instantiate is to create such an instance by, for example, defining one particular variation of object within a class, giving it a name, and locating it in some physical place within the application code.
  • Eventization is a variant of location that can be related to a particular User's behavior patterns to create Inference. For example, the location of an entertainment venue is known and very likely will not change. On one night, the venue holds a sporting event. On another night a music concert is held. Expressed in Superclass and Subclass terminology, Eventization applies time-contextual Subclasses (types of events which may vary) to a Superclass (Location which will remain fixed). Stated differently, Location alone may not determine relevance. Eventization represents a time variable layered on a location, i.e.
  • the location parameter further refined by the time parameter, or the eventization of the Superclass provides relevant information of interest to a user - a restaurant location (Superclass) by itself may be relevant information to a decisionmaking process from Monday to Saturday, but on Sunday the restaurant is closed and so the restaurant location is not relevant to the query "where is a good place to eat," and/or information about the restaurant location should not be pushed to a user on Sunday.
  • information regarding the Subclass may be relevant in the future (for example, "the restaurant is having a special next Thursday"), and so that information could be pushed to the user.
  • Eventization is a time-contextual variable used to determine an event's potential relevance at a particular location based on applying practical limits to the User's available time to create a "true” or "false” related to Inference or Suggestion. For example, if a User's available time while visiting a particular city is limited, Eventization would infer relevance of a particular event occurring during the time period that the user is visiting the city ("true"). The information of the event would be determined to be relevant to decisionmaking and would be pushed to the user. On the other hand, a time available Subclass would change this Inference to "false” if the event was occurring outside of the time period that the user was visiting, the information of the event would be determined not to be relevant, and the suggestion would not be delivered to the user.
  • the described dynamic mobile framework combines protocol programming to Instantiate stored properties using a Superclass and Subclass structure within these Object Oriented programming principles.
  • Each Superclass is modified with a Subclass of dynamic variants, i.e. Superclass Location, Subclass Eventization, etc.
  • Each Superclass and Subclass can be cross-referenced using time forward and time regressive data for a particular user to build personalization models.
  • Another advantage of the described dynamic mobile framework is information sharing between apps.
  • Conventional mobile operating systems do not allow one app to access data from another app, and the mobile OS shares very little, if any, of the data about a particular user on the OS.
  • the present dynamic mobile framework acts like a colony of apps that are presented dynamically under certain conditions, certain behavioral data is received, stored and analyzed by the OMVERIA app framework and can be used to build awareness of the user's preferences and use predictive methods to present the right app or function at the right moment for a particular user.
  • Bayesian Inference yields the following: If a Venue calendar indicates Artist A will be playing at the same venue or a nearby at a future date, we could provide the following notice to the User: "Would you like add this event to your calendar, or purchase tickets?" These types of notices can likewise be used to present the most relevant application for a given User's current location, or can suggest the most relevant OMNIA app based on these conditions. Expressed in Bayesian math the formula for adding probability to a particular Subclass variable of a Superclass could be expressed as follows:
  • E a variable representing an eventization string
  • CT a variable representing the current moment in time
  • TR stored data from a past moment in time
  • L a variable representing a location
  • P a calculated probability of relevancy of information.
  • the algorithms are based on Bayesian Networks, and can include: 1) Visual analysis of Bayesian Networks to find initially interesting patterns, variables and their relationships, 2) user segmentation analysis, 3) node force analysis and 4) a combination of expert-based service clustering and machine learning for usage diversity vs. intensity analysis. All the analyses will involve handset-based data collected from the OMVERIA app.
  • Handset-based measurements are a data collection method utilizing smartphones' ability to respond to the OMVERIA application software. These measurements are implemented by installing a data collection application to the mobile phones of opt-in participants and by collecting data in the cloud. With these measurements rich contextual user level data on business locations visited can be collected. Handset-based measurements have increasingly been used for a number of purposes in the recent years, applications ranging from sociology to consumer behavior. Business locations visited in particular will be an important data set.
  • a Bayesian Network (BN) also called Bayes Belief Network (BBN)
  • BBN Bayes Belief Network
  • This method may use BN to find business types and to cluster locations, using handset-based measurement data and GPS/Bluetooth low energy sensors or other location services.
  • BN is an analytical method that we can use for inferential analysis, e.g., to make "what if simulations, to predict behavior patterns and future trends, to understand why something most probably happened, and to understand which data correlate with other data. It is challenging to analyze the relationships between business locations visited patterns as the number of possible places to visit is very high in the used dataset. Although a BN procedure will offer easier methods to study this data the results could be further qualified against other methods like Regression analysis or Neural Networks.
  • a BN can be created in three ways, namely manually by using expert knowledge, by using machine learning, or a combination of them.
  • a BN is used for inferential analysis (often called predictive analytics), e.g., to predict behavior patterns and future trends, to make "what if simulations, to understand why something most probably happened and to understand which data correlate with other data.
  • predictive analytics often called predictive analytics
  • the communication devices including mobile devices, generally have a Radio Frequency Blue-tooth Transceiver that lies at their physical layer, and an adapter which may be in-built, or can be in the form of a card that connects to the device.
  • BLUETOOTH is a wireless technology standard for exchanging data over short distances (using short-wavelength radio waves in the ISM (Industrial, Scientific, Medical) band from 2.4 to 2.485 GHz) from fixed and mobile devices, building personal area networks (PANs).
  • BLUETOOTH is managed by the BLUETOOTH Special Interest Group aka, "the SIG"), which has more than 19,000 member companies in the areas of telecommunication, computing, networking, and consumer electronics.
  • BLUETOOTH was standardized as IEEE 802.15.1, but the standard is no longer maintained.
  • the SIG oversees the development of the spec for BLUETOOTH and BLE (BLUETOOTH Low Energy), manages the qualification program, and protects the trademarks. To be marketed as a BLUETOOTH device, it must be qualified to standards defined by the SIG.
  • BLUETOOTH low energy is a wireless area network technology designed and marketed by the nonprofit, nonstock corporation BLUETOOTH Special Interest Group aimed at novel applications in the healthcare, fitness, security, and home entertainment industries. Compared to "Classic" BLUETOOTH, BLE is intended to provide considerably reduced power consumption and cost while maintaining a similar broadcast range of about 20 meters. BLE was merged into the main BLUETOOTH standard in 2010 with the adoption of the BLUETOOTH Core Specification Version 4.0. Many modern mobile operating systems natively support BLE. The BLUETOOTH SIG predicts more than 90 percent of BLUETOOTH-enabled smartphones will support the low energy standard by 2018.
  • RSSI Received Signal Strength Indication
  • All BLE beacons are omnidirectional (broadcasting in a 360 degree pattern) in nature and the primary method used to make BLE beacons commercially useful is the RSSI method. In general, the greater the distance between the device and the beacon, the lesser the strength of the received signal. This is illustrated in Figure 2, showing a BLE beacon 10 determining various proximity indices ("immediate,” “near,” “far,” “unknown") according to a detected distance from a BLUETOOTH-equipped smartphone 12.
  • Measured Power is a factory-calibrated, read-only constant which indicates the expected RSSI at a distance of 1 meter to the beacon. Combined with RSSI, this allows a method of estimating the actual distance between the device and the beacon.
  • Broadcasting Power is the power with which the beacon broadcasts its signal, i.e. the power with which the signal leaves the beacon's antenna. These Broadcast Power settings can be varied. The value ranges between -30 dBm and +4 dBm, lowest to highest power settings respectively. The higher the power, the bigger the beacon's range and the more stable the signal, but if the beacon is battery powered, high power may shorten the battery life.
  • the mobile device 10 is situated between and in range of 2 different BLE beacons 10, 10'.
  • the presently described methods and systems apply rules based on movement, signal strength and other factors to determine which icon 20, 20' is most relevant to the user and so which to display.
  • the selected icon is operatively connected to informational content such as an application, web page, service, etc. of relevance to the user based on the applied rules.
  • the application, web page, service, etc. may be hosted on a remote computing system which may be maintained in a cloud computing environment, depicted nebulously in Figure 3 as cloud service 22.
  • Figure 4 depicts schematically the various determinations of proximity for mobile computing devices (not shown in this figure, but see Figure 2), a service infrastructure such as an application server hosted in a cloud computing environment (nebulously, ref. num. 30) and/or a third party app store 32 from which apps and/or web pages may be retrieved according to measures of relevance including proximity ("immediate,” "near,” etc.) and user-based criteria.
  • the icons may be displayed on a map graphic 34 indicating locations proximal to the mobile computing device position.
  • the presently described methods/systems do not rely exclusively on location services, but rather also incorporate determining most relevant content of a database of informational content according to certain user behavior based metrics, including without intending any limitation user past preferences and activities, user context, and others. While it is possible to individually search a particular mobile device OS for apps created by third party application developers, as described at a high level above OMVERIA is an integrated, context- aware mobile framework. Therefore, in embodiments the presently described methods and systems are powered by a cloud-based publishing system that allows a virtually infinite number of users to publish apps stored within and retrieved from a framework referred to variously as the OMVERIA framework and a dynamic mobile application framework.
  • the cloud based publishing system allows users to create and style content online.
  • API's Application Program Interfaces
  • OMVERIA mobile apps on a mobile device OS respond to the system API' s to call functionality within the app and to push new content updates to the app.
  • API's Application Program Interfaces
  • the system learns user behaviors and presents apps when they need them. Rather than asking mobile users to download more apps— users such as businesses can seamlessly detect, connect and communicate with mobile users in context through the described dynamic mobile app framework.
  • the dynamic mobile app framework is built on the following integrated technologies: 1) A cloud-based CMS that businesses can use to design and deliver HTML and multimedia content to consumer's mobile devices; 2) A rich reporting system with time, location and other reports; 3) Classic Bluetooth Low Emission and iBeacon micro-location sensors or other location services. Placed in a client's physical space, iBeacons or other locator services allow businesses to detect, connect and track mobile devices in their physical space and to deliver contextual content; and 4) a dynamic app (the OMVERIA app) that delivers app content and functionality for millions of businesses from a single mobile application icon.
  • Figure 5 shows a representative content/informational flow through the dynamic mobile computing framework.
  • a user such as a business or a venue hosting a number of businesses designs an app using a suitable editor, for example a WYSIWYG editor.
  • the app may include as much or as little and as varied information as desired, for example a "bare bones" app providing only the business' address, phone number, and web site.
  • the app may be content and information rich, including the above information plus sale offers, sale information, coupons, loyalty reward programs, etc.
  • the user may update, alter, or replace the app content and/or layout and/or design and/or appearance at will using the editor.
  • a parsing editor parses the designed app in a suitable programming language.
  • the parsed app layout may be stored in a database 54, and updated or replaced as needed as the app evolves to include new and/or updated content.
  • the parsing editor 52 also provides the newly formatted (parsed) app layout to an app publishing module (step 56).
  • the app publishing system pushes the formatted app(s) at to user mobile computing devices 60. As described, this occurs when the mobile device is determined to be in proximity (by one or more of the location service technologies described above) to a geographical location such as the location of the business or venue.
  • the app layout likewise evolves and the mobile device user sees a different layout on the device, potentially each time she passes a particular geographical location.
  • a representative computing architecture 62 for providing the information flow shown in Figure 5 and described above is presented in Figure 6.
  • an online "dashboard" 64 comprising a user interface such as the WYSIWYG interface described above, whereby a user may create and manage the design (styles, themes), layout (navigation, functions), and informational content of an app as described above.
  • the editor may be similar in form and function to known editors, for example a web-based CMS system using a conventional programming language such as javascript.
  • the dashboard 64 may communicate via API interfaces with a parsing engine 66 which authenticates and validates submitted code and parses the app into a suitable computing language such as Ruby or other language.
  • the app is parsed in Ruby, in order to provide compatibility with an iOS system as manufactured by APPLE.
  • parsing for compatibility with other OS for different types of mobile computing device is contemplated.
  • the dashboard 64 includes a service layer 66, a business layer 68, and a data layer 70.
  • the parsing engine service layer 66 pushes the parsed app layout to an app publishing module 72, which in turn pushes the formatted app to a compatible mobile computing device 74, or alternatively may communicate directly with the mobile computing device 74.
  • this step of pushing a formatted app to a compatible mobile computing device 74 is conditional on one or more of a mobile device location/proximity to a geographical location, stored user behaviors, and stored user preferences such as visiting a particular venue multiple times in a short time frame, attending a function featuring a particular entertainer with a frequency that exceeds a particular threshold, or the like.
  • the parsing engine data layer 70 communicates with one or more databases 76 comprising, for example, business information such as business listings provided by a data puller module 78 which in turn collects such business information from multiple data providers (generically designated as 80).
  • the business information may be formatted in an appropriate computing language for storage, such as Postgresql.
  • Other information stored in databases 76 may include metrics of user preferences, user behavior, etc.
  • hives a hybrid business/social application function referred to herein as "hives" for its collective nature.
  • hives of informational content allow users such as businesses to form groups and present themselves both individually and in group fashion to users of the described mobile framework.
  • a number of metrics can be employed to define particular hives.
  • Hives may be created/defined as to membership using the online dashboard described above.
  • a representative hive structure is shown in Figure 7, with different hives being represented by differently shaded points.
  • a hive can be based on a number of metrics, such as geographical location. For example, businesses in a shopping center or mall can join together and act as a hive and cross promote to users. Similarly, businesses in a particular city can form a hive. Still more, a zip code can be a hive (i.e., "show all the locations or deals in my home zip code," or "show all the locations or deals in my current zip code.”
  • hives can be defined as "affinity groups," i.e. by shared common interests. For example, if a user enjoys the sport of surfing she can join a surfing hive and get content, coupons or other things related to that topic
  • categories of hives may be established, for example relating to broad topics such as entertainment, food, travel, shopping, cooking, etc. This is similar in principle to how conventional news feeds are selected.
  • apps participating in the above-described dynamic mobile framework and grouped hives provide informational content to a user as a collective, rather than as individual websites, apps, or pages of information that must be separately searched.
  • a particular hive (or even group of hives) may be governed by rules established by a particularly influential member of the hive (for example, a large venue such as a shopping mall or an entertainment venue hosting numerous smaller businesses), or may be governed by rules established by all or substantially all members of the hive participating as equals.
  • rules may be created that affect how deals, newsletters, ads and other content are presented within their individual member-created mobile apps stored within the mobile application framework.
  • users move about a city or geographical area, they are in a mode of exploration — not checked into a particular business. In this mode of exploring, tiles or other icons may be presented to users showing businesses nearby. Also navigational tabs may appear showing deals, offers, loyalty and other incentives. These offers and content types can be published by individual businesses or hive partners, co-promoting business traffic and increasing points of brand activation. Because the described dynamic mobile framework is a framework allowing mobile applications to be published and displayed from a single icon based on dynamic variables, these additional relationship constructs are possible based on the creation of rules by the businesses that chose to associate themselves together.
  • the system functions as a collective: when a master application created by the particularly influential member of the hive is connected to partner locations, visitors get branding from the master location via the dynamic app, while also extending fully customizable app functionality to an unlimited number of partner locations that are members of the particular hive.
  • the foregoing scheme ensures that the user always receives the most relevant content according to a mobile computing device location, i.e. proximity to a particular content provider, and also according to predetermined user metrics. Furthermore, the protocol ensures that the most relevant content of a database of informational content is displayed within a display screen of a mobile computing device, but evolves as the user alters the geographical location of the mobile computing device (and so alters what is the most relevant content to be made accessible via the icon).
  • the described methods and systems do not require users to install and launch third party apps.
  • the displayed apps are configured from a colony of apps stored within the framework. Each app is called up based on a determined relevance to the user.
  • the system allows the user to download once, but use the mobile framework everywhere and anywhere an OMVERIA app exists that is relevant to that location. Particularly in the context of business owners, this solves a significant problem: the average user only downloads a few apps from the millions of available apps. So small businesses miss out.
  • the disclosed methods and systems use machine learning algorithms to dynamically present users with choices nearby or based on behavior, logic or other conditions.
  • the described mobile framework in an embodiment presents a "colony” or “hive” of published apps with full content and function, solving a significant business problem not addressed by current technologies.
  • methods and apparatus of the invention further contemplate computer executable instructions, e.g., code or software, as part of computer program products on readable media, e.g., disks for insertion in a drive of computing device, or available as downloads or direct use from an upstream computing device.
  • computer executable instructions e.g., code or software
  • readable media e.g., disks for insertion in a drive of computing device, or available as downloads or direct use from an upstream computing device.
  • items thereof such as modules, routines, programs, objects, components, data structures, etc., perform particular tasks or implement particular abstract data types within various structures of the computing system which cause a certain function or group of function, and such are well known in the art.
  • the disclosed embodiments may also include software and computer programs embodying the process steps and instructions described above.
  • the programs incorporating the process described herein can be stored as part of a computer program product and executed in one or more computers in one or more of the devices or systems.
  • the computers can each include computer readable program code means stored on a non-transitory computer readable storage medium for carrying out and executing the process steps described herein.
  • the computer readable program code is stored in a memory.
  • one or more of the devices and systems include or are comprised of machine- readable instructions that are executable by a processor of a computing device.
  • the systems and devices shown in the embodiments disclosed herein are configured to utilize program storage devices embodying machine-readable program source code that is adapted to cause the devices to perform the method steps and processes disclosed herein.
  • the program storage devices incorporating aspects of the disclosed embodiments may be devised, made and used as a component of a machine utilizing optics, magnetic properties and/or electronics to perform the procedures and methods disclosed herein.
  • the program storage devices may include magnetic media, such as a diskette, disk, memory stick or computer hard drive, which is readable and executable by a computer.
  • the program storage devices could include optical disks, read only-memory ("ROM”) floppy disks and semiconductor materials and chips.
  • the systems and devices may also include one or more processors or processor devices for executing stored programs, and may include a data storage or memory device on its program storage device for the storage of information and data.
  • the computer program or software incorporating the processes and method steps incorporating aspects of the disclosed embodiments may be stored in one or more computer systems or on an otherwise conventional program storage device.
  • the devices and systems can include one or more controllers that are comprised of, or include, machine-readable instructions that are executable by a processing device.
  • the method and the system of the present disclosure can be used for various purposes, including, though not limited to, plain device discovery, facilitating multiplayer online gaming between users of different communication devices operating through different incompatible operating systems which are generally incompatible, or to exchange data or enable short range communication between such devices.
  • cloud platform refers to an application platform that may provide services that may be used by mobile application developers to simplify the development of their mobile applications.
  • the cloud platform may be accessible over a network by mobile applications operating on mobile devices.
  • the services provided by a cloud platform may vary, but they may provide, online storage, user databases, support for payment transactions, or other significant functions delivered dynamically using our methods. While a cloud platform may be operative in a cloud-based environment, it is not so limited; other well-known operational architectures may be employed.
  • Cloud code refers to selection of software configurations that may be provided to the cloud platform by an OMVERIA developer or user.
  • Cloud code is a set of software that may be customized and configured by the OMVERIA mobile application user in one or more mobile application.
  • the cloud code may be deployed to a cloud platform and may be integrated into the cloud platform services and made available one or more mobile applications.
  • Cloud code may be developed using a variety of computer programming languages including but not limited to the ones mentioned herein.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

Un procédé de sélection d'informations les plus pertinentes pour l'affichage sur un écran d'affichage d'un dispositif informatique mobile consiste à : comparer la liste d'activités et de préférences d'un utilisateur à un contenu informationnel enregistré, d'après une proximité détectée par rapport à une position géographique ; et à sélectionner un contenu le plus pertinent du contenu informationnel d'après le résultat de la comparaison. Le contenu le plus pertinent est rendu disponible sous la forme d'une application configurée. Le contenu le plus pertinent peut être sélectionné d'après des paramètres supplémentaires, notamment une catégorie d'activité prédéterminée enregistrée, une position géographique enregistrée d'un créateur du contenu informationnel, et une période de temps à laquelle le dispositif mobile est déterminé comme étant proximal de la position géographique. Le contenu le plus pertinent peut être affiché sur le dispositif mobile en tant qu'une icône configurée d'après la position géographique d'un dispositif informatique mobile et/ou le contenu de l'application configurée.
PCT/US2015/036039 2014-06-16 2015-06-16 Fonctionnalités de plate-forme mobile dynamique employant des variantes proximales et des procédés de personnalisation avancés en termes de structure, navigation, thème, contenu, et fonctionnalité WO2015195671A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201462012641P 2014-06-16 2014-06-16
US62/012,641 2014-06-16

Publications (1)

Publication Number Publication Date
WO2015195671A1 true WO2015195671A1 (fr) 2015-12-23

Family

ID=54936044

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2015/036039 WO2015195671A1 (fr) 2014-06-16 2015-06-16 Fonctionnalités de plate-forme mobile dynamique employant des variantes proximales et des procédés de personnalisation avancés en termes de structure, navigation, thème, contenu, et fonctionnalité

Country Status (1)

Country Link
WO (1) WO2015195671A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3082053A2 (fr) 2016-08-04 2016-10-19 Clickky Group Ltd. Système de distribution de trafic et de validation de clic
US20180047092A1 (en) * 2016-08-12 2018-02-15 Venuenext, Inc. Communicating configuration information for an application from an online system to the application based on contextual information from a client device executing the application
CN113490142A (zh) * 2021-07-15 2021-10-08 重庆庄周科技有限责任公司 一种蓝牙定位终端、定位方法及跟随移动装置

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030069693A1 (en) * 2001-01-16 2003-04-10 Snapp Douglas N. Geographic pointing device
US20090197582A1 (en) * 2008-02-01 2009-08-06 Lewis Robert C Platform for mobile advertising and microtargeting of promotions
US20100138416A1 (en) * 2008-12-02 2010-06-03 Palo Alto Research Center Incorporated Context and activity-driven content delivery and interaction
US20110029362A1 (en) * 2009-07-29 2011-02-03 Cyriac Roeding Method and system for adaptive offer determination
US20110258049A1 (en) * 2005-09-14 2011-10-20 Jorey Ramer Integrated Advertising System
US20130210461A1 (en) * 2011-08-15 2013-08-15 Connectquest Close proximity notification system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030069693A1 (en) * 2001-01-16 2003-04-10 Snapp Douglas N. Geographic pointing device
US20110258049A1 (en) * 2005-09-14 2011-10-20 Jorey Ramer Integrated Advertising System
US20090197582A1 (en) * 2008-02-01 2009-08-06 Lewis Robert C Platform for mobile advertising and microtargeting of promotions
US20100138416A1 (en) * 2008-12-02 2010-06-03 Palo Alto Research Center Incorporated Context and activity-driven content delivery and interaction
US20110029362A1 (en) * 2009-07-29 2011-02-03 Cyriac Roeding Method and system for adaptive offer determination
US20130210461A1 (en) * 2011-08-15 2013-08-15 Connectquest Close proximity notification system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3082053A2 (fr) 2016-08-04 2016-10-19 Clickky Group Ltd. Système de distribution de trafic et de validation de clic
US20180047092A1 (en) * 2016-08-12 2018-02-15 Venuenext, Inc. Communicating configuration information for an application from an online system to the application based on contextual information from a client device executing the application
CN113490142A (zh) * 2021-07-15 2021-10-08 重庆庄周科技有限责任公司 一种蓝牙定位终端、定位方法及跟随移动装置

Similar Documents

Publication Publication Date Title
US20150189070A1 (en) Mobile platform functionalities employing proximal variants and advanced personalization methods to control dynamic icon display on a mobile computing device display screen
Colombo-Mendoza et al. RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes
US8341196B2 (en) Method and apparatus for creating a contextual model based on offline user context data
EP3213534B1 (fr) Classification d'intention d'utilisateur en fonction d'informations d'emplacement communiquées électroniquement à partir d'un dispositif mobile
Amoretti et al. UTravel: Smart mobility with a novel user profiling and recommendation approach
KR102067278B1 (ko) 친구 추천 방법 및 이를 위한 서버 및 단말
Ojagh et al. A location-based orientation-aware recommender system using IoT smart devices and Social Networks
CN107851231A (zh) 基于活动模型的活动检测
US20110125743A1 (en) Method and apparatus for providing a contextual model based upon user context data
EP3803726A1 (fr) Prédiction et présentation de motif d'événement d'utilisateur
Orciuoli et al. An ontology-driven context-aware recommender system for indoor shopping based on cellular automata
CN103620595A (zh) 用于情境感知角色建模和推荐的方法和装置
KR20150103721A (ko) 클라이언트 디바이스 상의 애플리케이션들 관리
EP3912123A1 (fr) Caractérisation d'un emplacement par des caractéristiques d'une visite d'utilisateur
US20170249325A1 (en) Proactive favorite leisure interest identification for personalized experiences
US20150095281A1 (en) Method and apparatus for adjusting the frequency of content updates
WO2015195671A1 (fr) Fonctionnalités de plate-forme mobile dynamique employant des variantes proximales et des procédés de personnalisation avancés en termes de structure, navigation, thème, contenu, et fonctionnalité
US11276078B2 (en) Personalized identification of visit start
US20200389438A1 (en) Method and system of privacy enablement in a family networking computing platform
US9992647B2 (en) Tag based filtering on geographic regions, digital assets, messages, and anonymous user profiles
US20190090197A1 (en) Saving battery life with inferred location
US20220201426A1 (en) Assisted micro-environment interaction
Rosi et al. Integrating social sensors and pervasive services: approaches and perspectives
EP3610697B1 (fr) Partage de signaux entre des groupes de dispositifs de confiance
Otani et al. Practical Challenges in Indoor Mobile Recommendation

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15809904

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 15809904

Country of ref document: EP

Kind code of ref document: A1