System and method for providing an improved payment process over a wireless link

Description

BACKGROUND
1. Technical Field

The present disclosure relates to an improved payment processes between a first device and a second device using an application programming interface or similar interface such that the user is presented on the second device in a state preconditioned to be in a one-click state or a state ready to make a payment without the need of other user interactions.

2. Introduction

The current use of input fields, such as the search field on Google™, is simple. The user inputs text related to a search and hits enter or clicks on the search button. Then Google processes the search and returns a list of results. Consider, however, if the user then desires to search Amazon® to purchase an item. The user then must enter the address www.Amazon.com into the URL field of the web browser and hit enter or click “go” or provide some similar input, at which point the web browser contacts Amazon, retrieves the page data, and presents the Amazon web page user interface with a separate search field. The user then enters a search term in that input field and hits enter or clicks on the search button. This is the typical approach where each website has an input field for use in searching under the umbrella of that particular website.

Thus, if a user transitions between doing a Google search, and then making a purchase on Amazon or on eBay, the user must navigate to multiple websites separately, and use multiple, separate input search fields to ultimately make a desired purchase or execute a desired search. The existing paradigm involves first going to a website and then inputting data into the search field of that website.

In some desktop versions of the Opera™ web browser, for example, a user can type into a search field an indicator of what type of search to do. For example, the user can set shortcut codes for various search engines so that a search of “g Olympics” will result in a Google search on the word “Olympics.” Similarly, a search of “b Olympics” results in a Bing® search on the Olympics, while a search of “z Olympics” results in a search of Amazon.com™ on the Olympics. However, these require extra typing, and thus can take longer, and are also only limited to a single search source, and still require the user to navigate through the search results at the requested site. Thus, the user still must perform multiple additional steps to accomplish a desired goal.

Some versions of the Mozilla Firefox™ web browser provide a search field to the side of the URL field. The user can select from different search engines or websites for that search field, such as changing the field to search Google, Wikipedia, Yahoo, Bing, or Amazon. However, this is still cumbersome, as the user must change the field manually between different sites if the user desires to switch search sources.

An additional issue relates to the long-standing problem of requiring users to enter payment data such as credit card information and a user address when making a purchase. Some sites like Amazon.com provide a “one-click” purchasing option but those simplifications are only available in the controlled Amazon.com environment. For all other merchants, users must enter payment data which is cumbersome. Mobile users outside of Amazon.com still need to include and enter in much information which reduces the number of conversions when making purchases on-line either on the desktop or mobile devices. Thus, although the Internet and use of applications downloaded on user devices have been around for many years, there were still many instances of friction particularly in the context of a user being at one site or in one app and wanting to make a purchase or perform a task which was ultimately handled by a separate site or a separate application. In general, when payment data needs to flow from one compute device to another compute device, there still remains friction, rooted in how computing technology is configured, in the user experience when using compute devices to make payments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system architecture;

FIG. 2 illustrates an example search field;

FIG. 3 illustrates a drop down and drop up menu according to an aspect of this disclosure;

FIG. 4A illustrates a first example resulting interface according to an aspect of this disclosure;

FIG. 4B illustrates a second example resulting interface according to an aspect of this disclosure;

FIG. 5 illustrates a method embodiment;

FIG. 6 illustrates another graphical resulting interface in response to received user input;

FIG. 7 illustrates an example system embodiment;

FIG. 8 illustrates an example method embodiment;

FIG. 9 illustrates an example architecture for pre-populating a merchant shopping cart; and

FIG. 10 illustrates example user interfaces for a pre-populated merchant shopping cart.

DETAILED DESCRIPTION

Disclosed herein is an improved payment processes between a first device and a second device using an application programming interface or similar interface such that the user can be placed in a state preconditioned to be in a one-click state or a state ready to make a payment with the need of other user interactions. The application programming interface enables the exchange of data between the two devices to cause the user to be placed in a state ready to make a purchase and thus addresses the problems rooted in computer-based payment processes outlined above.

A system, method and computer-readable storage devices are disclosed which unify access to multiple websites or other information sources such that the user only needs to visit one location, and utilize one input search field to achieve a number of different potential results such as doing a search or purchasing a product. That one location can be a website, a search bar in a web browser, an application on a desktop, laptop, smartphone, tablet, or other mobile device, etc. Rather than navigating to a website to perform a search in the context of that website, a user can instead navigate to or open a generalized search field. Via the generalized search field, the system can implicitly or explicitly process and analyze the input from the user and the resulting context. The system can also analyze based on a corpus of existing context for the user, such as recently viewed or opened web pages, recent actions the user has performed on the computing device, calendar information for the user, location data, recent purchases or other transactions, social networking data including posts, messages sent to friends, birthdays of friends, and so forth. The system can incorporate, as a data source, any information that can provide direct or indirect context for understanding or processing the input. For example, previous search history or purchasing history can provide direct context, while social media posts of friends of the user can provide indirect context.

Thus, the user goes to the website second, after the search is entered. This approach reduces the number of interactions, starting when the user opens a browser or application, to get to a webpage to make a purchase or a webpage of search results. In another aspect, drop down or drop “up” menus provide a much more rich opportunity for processing options such as one-click purchases or searching particular websites such as eBay® using the text input as search data. These drop down or drop “up” menus can be based on the location of the search input box, a search button, or some other element in the user interface. In yet another aspect, the generalized search field can still provide ‘traditional’ search results from one or multiple search sources, but can present, in addition to the traditional search results, one-click actions that the user can use, for example, to make a purchase directly from the listing of search results.

Furthermore, as shown in FIG. 9, this disclosure introduces the concept of an application programming interface which enables the transmission or exchange of payment data and related data which can remove some of the previous manual interactions that were necessary to make payments and/or to pass payment data between compute devices such as between a browser and a merchant site or make a payment from an app configured on a user device to another compute device.

Various embodiments of the disclosure are described in detail below. While specific implementations are described, it should be understood that this is done for illustration purposes only. Other components and configurations may be used without parting from the spirit and scope of the disclosure. When specific method embodiment examples are discussed, the various steps of the method embodiments can be implemented in different orders, combinations, or permutations, including additional steps, or excluding specific steps.

The present disclosure overcomes the above-indicated deficiencies in current search implementations by providing a unified search field that enables a user to provide user input and achieve, in very few steps, one of a set of goals, such as completing a purchase, executing a search, executing a program, or interacting with an online service. The user can provide the user input as text, or in any other suitable form including multimodal input, gesture input, voice input, etc. When the disclosure refers to “input text” or “text” from the user, it is understood that the input can be provided as text or via some other input modality. The system can process the user input using traditional options such as a web search, but additionally, the system can process the user input to identify, present, and/or execute purchasing options or more focused searching options on other websites. The system can present these options in a tag cloud or drop down or drop up or drop sideways menus as the flexibility of the processing of the user input expands.

The basic concept according to a first embodiment is illustrated below. Assume that an example website www.one-search.com includes a user interface with an input field or search field. The input field can be a text input field, or can be a voice input field that utilizes speech recognition to populate the field with text from recognized speech, for example. The field is not just a search field but is a more generic input field from which multiple functions can be performed based on a determined intent of the input provided by the user. The search field is different from other search fields in how the www.one-search.com search field processes input. Usually, a person goes to the webpage, then searches, or chooses a search website, then the search field is conditioned with a particular website context for searching. In this disclosure, the search context is open when the user enters data into the generalized input field. There is no presumption or setting that it will be a Google search, or an Amazon search. The resulting context will be dependent on an analysis of the input. The user interface can include a number of different search or processing buttons, each of which can expand the types of processing to perform on the input text. Different types of the buttons can include a Google search button, an Amazon search button, an Amazon one-click purchasing button, and an Apple.com purchasing button. The system can establish and provide the various button types in advance. Alternatively, a user can set up a collection of personalized buttons for tasks that the user desires or expects to perform with some regularity. The system can generate and present these buttons based on general search and activity trends of users, current promotions, advertisers paying for placement, and so forth. In place of or in addition to buttons, as the user types input into the field, the system can present “peeks” into various webpages which can be destinations for the users whether it is a search result, a purchase, an auction, or any other website destination. In this regard, rather than go to the website first, and then enter a search into a search field, this disclosure focuses on entering data in a general input field and then going to the website, or making the purchase, and different ways of processing that more improved input.

It is presumed, such as in the case of Amazon or an auction website, that when the user navigates to one-search.com, that user information, debit/credit card information, address information, etc., is stored in a user profile and available, as in the case of a registered user at Amazon.com. For example, as part of a registration or enrollment process, the user can establish an account with one-search.com, and authenticate or provide credentials to link the one-search.com account with accounts at other websites. So, as part of creating an account with one-search.com, the user can provide credentials for Google.com, Amazon.com, ebay.com, newegg.com, thinkgeek.com, and cheaperthandirt.com. Alternatively, the user can ‘link’ the accounts without providing credentials. For example, the user can authorize Amazon to share all or part of the user's information associated with his or her Amazon profile without providing the Amazon credentials to one-search.com.

Then, when the user performs searches at one-search.com, the system can use the existing linked accounts to generate one-click actions, or one-function (speech, gesture, multimodal input, etc.) actions. The user can then manage linked accounts via a user portal or user management interface, to link additional accounts, update credentials, remove linked accounts, or manage which portions of the linked accounts are shared with one-search.com. Some websites may not require a linked account, but can still be incorporated into the one-search.com search field. For example, some e-commerce sites allow purchases with a guest account, in which case a one-search.com action can include navigating to the e-commerce site, adding a desired item to the cart, providing sufficient information about the user, such as payment information, a delivery address, etc., to complete the purchase. In another example, some websites, such as a search engine, can be enhanced when linked to an account, but do not require a linked account. In these situations, the user can decide whether to link an existing account with the search engine, or whether to use the search engine without a linked account.

The one-search.com website can inspect and use browser cookies from other sites to glean user data, glean search history, or any other information stored in or made available via cookies. The system can, for example, use a session cookie to determine that a user has or had an active session with a particular website, and can use information in the session cookie to construct a URL for a one-click page to execute a purchase in response to user provided input. Alternatively, the system can use the live session to negotiate the website, add a desired item to a shopping cart, populate payment and shipping information on behalf of the user, and present to the user the final stage in the checkout process so the user can simply click once on a “submit order” button, or hit “enter” in the one-search.com unified input field to complete the purchase. In this way, the number of steps from search to purchase (or from search to performing some other action), is drastically reduced. While many of the examples provided herein discuss making a purchase, the principles disclosed herein can be applied to other, non-purchase transactions as well. For instance, in much the same way that the system can navigate to a website, populate a shopping cart with an item, and fill in shipping and payment information on behalf of the user, the system can also navigate to some other website for a result that requires a set of information to be provided. If the user enters the text “Why did my credit score just drop?” in the input field, the system can identify one of the major credit reporting bureaus, a third-party credit report aggregation service, or a free credit report site. The system can automatically provide the necessary information, on behalf of the user, to get to the credit score information, and present that page as a potential result or as an option in response to the user input. Many similar tasks on the world wide web require navigation from one page to the next to the next, and input in response to various questions. The one-search.com system can shorten or automate the input required from the user to navigate through these series of web pages to obtain a desired piece of information, a desired action, or a desired outcome.

FIG. 2 depicts an example search or input field. In this initial embodiment, the user enters a term in the input field of one-search.com, such as “iPhone 5S 32 GB silver.” At this point, the user can click on any number of options for processing the input, such as a Google search 204, an Amazon.com one-click purchase button 206, or an Amazon.com search 208 button. In this example, the user clicks on the Amazon.com one-click purchasing button 206. Thus, from this field, the system receives that input, processes the input, and can execute a purchase, just as though the user had navigated through Amazon.com to an iPhone 5S, having 32 GB of storage, and a silver color, and had just clicked on the one-click purchase button. However, in this first embodiment, the user did not need to navigate to Amazon.com but rather was able to make a one-click purchase from a separate website, namely the one-search.com website. In one aspect, the user does not even need to click a particular button, and can instead simply hit “enter” as the user would to execute a normal search request. From that, the system can analyze the text input to determine if a probability of the user desiring to make a one-click purchase is above a certainty threshold, and the system can then process an “enter” input as a request to execute a purchase.

The system can process the input according to the button clicked, as though the user entered the text into an input directly at Amazon.com or Google.com and simply clicked search. If the user clicked a Google search 204, then the system would return search results from Google, but could similarly provide search results from Bing, Yahoo, or some other search engine. In one aspect, the system can transfer the user to Google.com, cause a search to be performed using the user's search input, and present the results as though the user had initially done the search at Google.com. In another aspect, the system can generate a URL at google.com as if the user had performed the search using the user's search input, and open that URL at google.com for the user. If the user selects a one-click purchase 206, then the system processes a purchase and delivery of the item through Amazon.com as though the user had navigated via Amazon.com to the item and made the purchase. In other words, the functionality of the “enter” button can be modified (dynamically, and several times) based on an analysis of the user input. Based on a variety of factors, the initial default might be a purchase context, but then the user starts to enter data and the context may change to a web search, and then finally when the user is done entering input, the “enter” button may cause processing associated with mapping, or back to a purchase context.

If the user selects an Amazon.com search 208, then the system returns a view of the search results on Amazon.com for that phrase. In other words, the user could be transferred to Amazon.com, logged into their account or joined into an existing session for the account, and presented with a screen which is the equivalent (or essentially or functionally equivalent) of the state as though the user had searched Amazon.com for “iPhone 5S 32 GB silver.” From that state, the user could peruse the returned list of items and then perhaps choose an item, at which point the user could “one-click” purchase an iPhone.

Indeed, in one example, the system can redirect the user to Amazon.com (or navigate to Amazon.com on behalf of the user) in the same manner as if the user had started at Amazon.com and entered the search terms. In this case, the algorithm of one-search.com would receive the search input, receive the desired instruction from the user (by clicking on the Amazon.com search button) and transition the user to Amazon.com. User registration information or web browsing state information stored in a cookie or elsewhere or sent via XML can also be read or transferred such that the user is logged into their Amazon.com account in the transition. Data can be stored with one-search.com or with a browser or app. The result of this process is that when the user opens a browser to start browsing the Internet, the system enables the user to initiate any number of searches, purchases, or other actions via a single, unified input field that requires fewer clicks or user input to get to search results, or to make a purchase.

Another embodiment simplifies the process even further. Typically, as described above, a website such as Google or Amazon has a single-purpose entry so that the user can click “enter” and the received meaning of that is to process the text in the input field as a Google web search or as an Amazon product search. In this second embodiment, the search field has multiple possible ways of processing the text in the input field. An algorithm analyzes and processes the input to determine or predict the meaning or user intent of the text input. Via such an analysis, the system determines what type of search or action the user wants. Thus, if the user types “Olympics” into a search field at one-search.com, the system can determine via the algorithm that user is unlikely to want to search Amazon.com or eBay for “Olympics” because the Olympics is not something available for purchase. However, if the user enters additional information, such as “Olympics windbreaker Sochi 2014,” the system can revise the determination of intent, because the additional information input by the user is now directed to a specific item or category. Thus, the system can continuously evaluate or determine intent of the user based on the text or data provided. The system can reevaluate intent as each character or word is input, for example. The system can anticipate intent and cache or pre-load results or actions for a number of anticipated intent scenarios based on context information for the user and the data provided so far. Thus, if the anticipated intent (i.e., Google search versus an Amazon purchase versus an Amazon search) turns out to be correct, the system already has the components in place or the pages fetched to service that intent.

The system may utilize any type of data such as user profile data, social media data, historical data, time of year (holidays are coming, summer time, a friend or family member has a birthday in one week, etc.), to make this determination. In this example, the system may determine when the user clicks on “enter” that the user intended a Google search for that input. For example, if the user types “Paul Revere American revolution,” the system can detect that the semantic content and the structure of the text is more closely aligned with an informational search instead of a product search, and can route the search text through a search engine. In that case, the primary results as though the user had entered a Google search are presented. The one-search.com results screen could also provide alternates in case the user actually desired a Bing search or did want an Amazon.com search. If the user enters that information into a search field at one-search.com, the system can cause the browser to navigate to google.com, upon the user pressing enter, as if the user had searched at Google originally for the search string. Alternatively, the system can load the corresponding Google search page in an iframe or other embedded mechanism in a webpage, or as a new tab or window. The system can utilize any of a number of various transitions to present the Google search page to the user, even though the user initiated the search at the one-search.com page.

On the other hand, if the user enters “Revere tea kettle,” the system can analyze the input text to determine that the user likely desires to make a purchase. Thus, when the user hits “enter,” the system can route the search to Amazon or another suitable e-commerce site, or can immediately execute a one-click purchase from Amazon based on the search. Upon determining that the user intent is a purchase, the system can perform an analysis of or rely on a previously performed analysis of the user's purchasing habits or other purchase related information such as lowest price, lowest price plus shipping, availability, shipping time or method, user membership in a shopping club, whether the user has an account with an online merchant, and so forth. Based on this analysis, the system can determine which retailers are above an intent threshold, and provide the user with ways to easily access those retailers. The system can sort the retailers in an order of likelihood to be what the user desires, and can restrict the list of retailers presented to the user. For example, the list can be restricted based on a price spread, available screen space to present options to the user, or other factors.

In an example of these principles, the user enters the text “large supreme pizza” into the one-search.com input field. The system can analyze the user's browser history, previous queries at one-search.com, user accounts at various pizza delivery places, a location of the user and nearby pizza delivery places, credit card transaction data of pizza purchases, and so forth. Based on this information, one-search.com can, before the user presses enter and/or mid-query, determine that Dominos, Papa Johns, and Pizza Hut are nearby, are open, and that the user has made purchases with them in the past 6 months. Then, the system can present a preview of each of these merchants so that the user can simply click once to place an order for a large supreme pizza. The one-search.com system can display the logo of each pizza merchant, with a summary of the order that would be placed and the associated cost if the user clicks on the logo. For example, the system can display, below the Dominos logo, “16” large supreme pizza, $16.24, delivered to 123 Fake Street, Springfield, OH. Delivery by 6:15 pm.” Then, the user can click on the Dominos logo to place the order, or the user can interact with the one-search.com page or Dominos webpage directly to modify various aspects of the order before placing the order. The one-search.com system can dynamically update the previews as the user types additional information in the search field. The one-search.com system can further provide an indication of a ‘default’ action that will be executed if the user presses “enter” on the keyboard. In this way, when the user is satisfied with the default result, or only one result remains after the user inputs the text, the user can simply press “enter” and the system can execute the action, such as placing an order for pizza.

In another example, the user enters the term “iPhone 5S 32 GB silver” into one-search.com. The system can analyze the text, to determine that this search is clearly directed to a product based on the specific amount of detail to identify one or a few items that could be purchased. Further, if the search is executed on December 8^th, then the system can be especially tuned to be more sensitive to recognize purchase requests due to the gift giving atmosphere surrounding Christmas or other holidays. The algorithm can analyze previous searches for various iPhones to determine which, based on running the algorithm, would result in a threshold value being passed that there is a high likelihood that the user desires to purchase this product rather than just search for it. When the user hits “enter,” the system processes that input as though the user was viewing the iPhone 5S 32 GB silver on Amazon.com with the option to make a “one-click” purchase. Here, by entering that data into the one-search.com field, and clicking “enter”, the system can, on behalf of the user, implement the steps at Amazon.com as if the user had completed a purchase of the item. The system can perform these actions via HTTP requests, as if the user had navigated to the website and entered the information herself, or the system can communicate with the various web services via their established APIs. The system can notify the user that the order has been placed, and provide any shipping or order details to the user. Alternatively, the system can transition the user directly to an Amazon.com environment or present a user interface notifying them that the purchase is being processed by a website that processes via user profile data a purchase and delivery of the product as can be done at Amazon.com or by Apple.com, etc.

In one embodiment, the user can confirm the order before the system places the order on behalf of the user. In another embodiment, the system places the order automatically for the user, and the user can choose to accept the order by doing nothing or choose to reject or modify the order by providing some input, such as clicking a button or opening an order page in a new tab or new window. In one example, the system may have placed an order for a silver iPhone 5S, but the user changes his or her mind and wants to order a gold iPhone 5S. The user can modify the order directly at one-search.com, or one-search.com can redirect the user to Amazon.com to modify the order. Sellers can compete for the business of processing this input, and the system could report on who bid for the lowest price. The system can provide the user with an ‘out’ by cancelling the purchase within a certain amount of time. In a similar manner, the system can detect that a user has just placed an order for an iPhone 5S, and implement a ‘cool-down’ period, during which the system will not automatically order an additional iPhone on behalf of the user without some additional or explicit approval from the user.

The system can cap or confirm orders that appear to be erroneous or unintentional. For example, if a new user does not realize how the system works, he or she may search for an iPhone 5S 32 GB silver multiple times, and inadvertently order multiple telephones. The system can have a built-in mechanism to detect such potentially unintentional purchase patterns, and incorporate some heightened level of user approval or confirmation before proceeding to make purchases on behalf of the user when such patterns are detected. The user can establish security measures or purchase limits on the account, so that a child or unauthorized person is unable to make purchases above a specific spending limit, or so that purchases above a threshold require authentication via email or text message or some other mechanism. If the system detects an unauthorized purchase, the system can temporarily stop or prevent purchase transactions altogether for the entire one-search.com account, or for specific log-in locations.

Using the “enter” button and processing the input based on a predicted intent can result in ambiguities. When a user searches via Amazon.com for a product, the user navigates to the right model with the desired size, color, carrier, and so forth. Then when the user makes an Amazon.com one-click purchase, the user knows all of the data about the product before making the purchase. In the model disclosed herein, the system can also deal with product ambiguity. Assume the user enters “iPhone 5S 32 GB” at one-search.com, and that the available colors are black, silver, and gold. The algorithm determines, based on the input text, that the user likely desires to make a purchase and processes the input text accordingly. The system can select the most popular color and fill in that unknown parameter accordingly. The system can select not only the most popular model based on popular size and color, but the system can incorporate demographics data to determine the most popular model for people similar to the user. For example, if the user enters “iPhone 5,” the system can select a yellow 16 GB iPhone 5C for a teenage girl, or a black 64 GB iPhone 5S for her father. The system can further analyze past purchases of similar or related devices to determine likely user preferences for this purchase. If the user is already registered, and via the browser, application or website, the system knows who is doing the search, then user preferences, history, classification model based on previous searches across multiple websites, etc. can be applied to analyze the one-search input field. If the user has made electronics purchases in the past that are all silver, the system can assume that the user is likely to want a silver iPhone 5S, and populate the cart accordingly. Similarly, if the user has consistently purchased the largest storage capacity model in previous purchases of mobile devices, the system can automatically populate the cart with the iPhone 5S with the largest storage.

Returning to the above example, the user clicks “enter” and the system presents a user interface screen that states “You have purchased the black iPhone 5S 32 GB-if you want silver instead, hit enter.” In other words, the system can choose the most popular color, and present an option to change a parameter such as the color via hitting “enter” again. This second hitting of “enter” cancels the previous order of the black iPhone and replaces it with a silver one, or the system can simply update the purchase request. At that point of time in the process, it is as though the user had been viewing a silver iPhone, with the right features, and hit the one-click purchase button such that no other action needs to be taken to have it charged and delivered. The system can integrate with the merchant via an API to place a hold on a particular item, such as the black iPhone 5S 32 GB, while waiting for a period of time to allow the user to modify the order before committing or completing the purchase.

The process can be repeated as well. The system can present to the user “You have now purchased the silver iPhone 5S 32 GB—if you want the gold one, hit enter.” Hitting enter this time will cancel the order of the silver iPhone, and replace it with the gold iPhone. If the user does nothing else at this stage, the system commits the order for the gold iPhone, and the merchant will execute the order so the user will receive the gold iPhone, and the merchant will charge the user for the order in the normal fashion. Of course, button clicks can be provided for the user to change the various parameters and change the order. The interface can say “you have purchased the iPhone 5S 32 GB black—to change any of these parameters click here.” The system can present various options to change the storage size, model, carrier, color, shipping options, etc. However, if the user does nothing, the system arranges for and places the order with the merchant on behalf of the user using the predicted parameters. As can be appreciated, the process enables the user from the time a browser or an application is opened up, to successfully make a purchase of the desired product in less interactions or fewer steps than was previously required.

In another embodiment, the system can include an autocorrect or autocomplete feature with one-click purchasing ability in the context of a single search or at Amazon.com, one-search.com, or any other website where a purchaser has registered data such as credit card, address, etc. A website search field can include an “autocomplete” where when the user types in a search term the autocomplete feature can either automatically complete the concept that user may desire, or present a list of suggested or recommended options based on the text input up to that point. The user can review the various autocomplete options and select one, thus alleviating the need to continue typing out the rest of the query. In this embodiment, the system receives a partial user input (or full input) via an input field, and, when analyzing the input for producing autocomplete options, the system can include a “one-click” purchasing option in the listing of autocomplete options. In other words, if the user enters the text “iPhone” as the partial user input, at that stage the system can identify and present “iPhone 5S 16 GB <one-click purchase>” as one of the “autocomplete” options. In that case, this modified listing of the autocomplete features reduces the number of clicks and the amount of text from the user in order to purchase the item. In other words, drop down or drop up features are not limited to the concept of seeking a standard autocomplete feature but rather blends autocomplete with purchasing options or other options such as jumps to other websites. Normally, the user would choose one of the autocomplete options, which would take the user to either an item or a listing of items, then the user has to click again to narrow down to one particular item, and then at that point the user is in position to “one-click” purchase the item. However, if the user clicks on the “one-click purchase” variation in the autocomplete listing, the system can place the order immediately.

The system can present various one-click options via the input field listing. For example, if, at the stage of typing “iPhone” the most popular iPhone is the 5S, with 32 GB and a silver color, the system can place that option, with a one-click purchase option, high or first on the list of autocomplete options for purchase. The next most popular model, might be the 16 GB iPhone in black, which the system can display next in the autocomplete listing. Competitors can also provide offers in the autocomplete listing for a one-click purchase. A competitor can purchase the right to present an autocomplete one-click purchase option that is related, but does not include the searched-for text. For example, when a user is searching for “iPhone,” the system can present an autocomplete entry to one-click purchase a “Samsung Galaxy S4.” The system can further present promotional material in these autocomplete listings. However, because space is limited, the promotional material may be limited. One example of such a promotion is an autocomplete listing advertising “Samsung Galaxy S4-20% off <one-click purchase>” at Amazon.com. Companies can purchase advertising space under the autocomplete listing, or can pay a premium to elevate their products in autocomplete listings for a specific keyword, specific product, brand, and so forth. However, the system can also use business intelligence or feedback from various merchants to include, in the autocomplete options, results based on what people searching for item X eventually end up purchasing, even if the autocomplete option does not include the searched-for text.

Similarly, the system can track users' behavior, and can price certain users' attention at a premium for advertisers. For example, if the user has been researching smartphones daily for several weeks, advertisers of flagship smartphones may pay a higher price premium to target an interested, engaged buyer with advertising in the form of autocomplete options.

The system can provide a “one-click” purchasing option right in a drop down list of autocomplete options. Additionally, the autocomplete can include a listing that, if selected by the user, places the user in the context of one step prior to a one-click purchase at the merchant site. In other words, if a user enters “iPhone 5S” on a website like Amazon.com, Amazon.com presents to the user a number of listings of items. The user has to click on one of those items to narrow the results down to a single item, at which point the counting of clicks begins in the context of a “one-click” purchase. While viewing that single item, the user is then presented with a “one-click” purchasing option. Such a context, including the user's successful login with Amazon.com, would be characterized as a “pre-one-click” web page where the user has navigated to a point where the item is identified and the context is such that the user can make a one-click purchase. The problem is that getting to the pre-one-click page takes too many clicks and interactions.

Thus, the autocomplete listing can provide a simple way for the user to jump immediately to the “pre-one-click” stage in the merchant's web site. The autocomplete listing can not only include a “one-click” purchasing option at that stage, but could also include an option to take the user to a “pre-one-click” purchasing page, at which point, typically, there is more information about the item, a larger picture, reviews, a rating, product details, and so forth, such that the user can make a more informed purchasing decision. For well-known products, the user can make a one-click decision to purchase directly from an autocomplete listing, but for other products, the user may want to verify that the product is suitable for an intended purpose or compatible with some other user needs. The previous result of clicking on an autocomplete option is to process that option as though it was a search entered into the input field. However, that returns a listing of search results and not a “pre-one-click” page with one item ready to purchase. Accordingly, this alternate feature reduces the number of interactions necessary to get to a pre-one-click purchase page.

The purchasing autocomplete type options could be presented on a drop “up” listing and the searching or traditional autocomplete options could be presented on a traditional drop “down” menu. In other words, the directionality of the listing can be indicative of the functionality of the items listed. The directionality can be side to side, or in some other direction or angle. For example, the various one-click purchase and pre-one-click autocomplete listings can all be drop “down” menus, but at opposing 45 degree angles. The system can also present options in a tag field or tag cloud arrangement, where most likely options are presented closest to the input field (where they would be the quickest and easiest to access from a mouse perspective) and with the largest icon, text, graphic or other visual cues for selection.

FIG. 3 illustrates an example one search field and drop down menu feature 300. In this example, the single field 302 enables the user to provide input that the system analyzes to identify other options besides a search that are available. In this example, the user inputs “iPhone 5S” in the field 302. The algorithm analyzes that input to recognize that the search is directed to a product. The system can access a database of current products, purchasing patterns, product popularity, purchasing history of the user or of other users, and so forth. The system can access the database via an API call to one or more merchant databases. The algorithm can use this data to make a more accurate determination of whether the user desires a search or a specific product to purchase. In this case, the input “iPhone 5S” is clearly a product, thus this knowledge will help to drive and control the construction of the drop down menu options.

Because the user input in field 302 is a product, the example drop down menu options can include a standard Google search 304. Although this is the first option, the system can arrange the drop down menus to place this option lower if the algorithm determines that the user is less likely to desire a Google search. The system can present more likely options closer to the input field 302, or closer to the mouse cursor, for example. If a user selects that option, then the result that is returned would be as though the user had entered “iPhone 5S” as a Google search. The drop down menu can include an Amazon.com one-click purchasing option 306. If the user selects this option, the system can process the input as though the user were on Amazon.com, having searched for an iPhone 5S, and at a screen in which the user can select to “one-click”, execute the purchase of the product for the user. In another variation, the system can present a one-click option at the one-search page, directly from the drop-down or drop-up menu. So, the user could click a button, an image, or a link to place the order with Amazon.com as if the user had navigated to the one-click point at Amazon.com and clicked the “order now” button. In this case, FIG. 4A illustrates the resulting screen 400 presented to the user from choosing option 306. Screen 400 includes data 402 informing the user that the iPhone 5S had been purchased via Amazon.com. When a color was not provided, the system can chose the most likely color for the user or for similar users. In this case, the system selected silver. A storage size of 32 GB is also shown as part of the purchase data.

In the case of unwanted or unintended purchases as people perhaps hit a wrong key or chose the wrong drop down menu option, the system allows users to cancel the purchase 404 or modify the purchase 406. The user can modify any number of different options depending on the product. Options shown by way of example include changing the color from silver to gold or black. Similarly, the system can display an option to change the storage size to 16 GB. An option such as “add accessories” can bring the user to another interactive screen to choose accessories. The system can determine which modification options to present and the order in which to present them based on a confidence score for each option. For example, the system may have a confidence score of 95% that the user wants a silver iPhone 5S, and can either not display the option to modify the color, or can display the option in a less prominent place or manner, or can provide the option to change the color through a menu or other ‘hidden’ location. This approach can allow the system to present purchase or item options to the user so that the user is only concerned with and can easily modify options about which the system is less sure. The system can present options to modify not only details about the actual item itself, but also about details surrounding the order, such as delivery address, billing address, payment method, or delivery method. The system can even allow the user to switch the order from one merchant to another merchant, if the user inadvertently clicked the wrong menu item in the pull-down menu, for example.

In that the entity which is processing the purchase is Amazon.com, as is noted in field 402, the system could also present an option to process the purchase through Apple.com. If any of these options are chosen, then the user selects the modify button 408 and the order is modified and automatically continues to be processed. Of course the system has the user profile, purchasing (credit/debit/PayPal, etc. account), address and any other information and can move seamlessly between purchasing/processing entities with case. When the user sets up a profile and account on the website, all of these permissions and accessibility capability is established and approved.

Returning to FIG. 3, feature 308 represents an Apple search. If the user selects this option, then the next field that is returned would be as though the user searched for iPhone 5S on Apple.com. The information presented by Apple on that product would be presented to the user. Optionally, the system can prompt the user to provide or confirm credentials for logging in to Apple.com. In that the transition from one-search.com to Apple.com occurred from one-search.com, the system can present an option in the new Apple.com web page to enable the user to return to one-search.com for further searches. For example, the system can provide a frame, in the browser, for returning to the one-search.com search while presenting the Apple.com web site. The frame can allow the user to modify the original input text, which can dynamically change aspects of the presented Apple.com web site presented in conjunction with the frame.

Feature 310 in FIG. 3 represents an eBay bid option. In this case, if the user selects this option, the system sends the user to eBay and presents a screen 410, as shown in the example user interface of FIG. 4B, as though the user had gone to eBay.com and entered in “iPhone 5S” into the eBay search field 412. Feature 414 represent a selectable returned item for an iPhone 5S 32 GB with a current bid at $199. Feature 416 is an iPhone 5S 16 GB for $175 and feature 418 represents an iPhone 5 16 GB at $150. All of these are examples of the kind of processing that can occur. As noted above, a “return to one-search” button 420 can also be included in the screen for easy access back to the one-search field. The system can transition to the indicated destination page, such as the Apple.com, eBay.com, or Amazon.com purchase page for an iPhone 5S as an overlay, such that returning to the one-search field involves removing the overlay instead of a back navigation command to a previous page.

FIG. 3 also shows an Amazon search 312. When the user chooses this option, the system can present a screen as though the user had searched on Amazon.com for an iPhone 5S. From there, the user could continue shopping and searching as though the user had begun browsing on Amazon.com. The drop down menu can include an option to purchase the product directly via Apple.com 314. If the user selects that option, and assuming that there is not a “one-click” purchase option at Apple.com, the user is brought to the point where they can, in very few interactions, complete the purchase. For instance, the system can bring the user to a shopping cart showing the product ready to be purchased. In one option, the system brings the user to the point of seeing the product and being able to place the product (iPhone 5S) into a shopping cart. In another aspect, the system could navigate the shopping cart model on behalf of the user and complete the purchase, thereby making the transaction a one-click purchase.

FIG. 3 also shows another embodiment of this disclosure. In this case, because the “drop down” menus include different types of data, the options can include a “drop down” menu as well as a “drop up” menu. The purchase options could be dropped “up” as shown in features 316 and 318, while all of the search options or more traditional options can be dropped “down.” The system can present menus to the left, right, diagonal, or in any direction, orientation, or angle as desired. Separating the purchasing options from search-type options can also reduce the number of inadvertent purchases. In this example, the drop down menus of FIG. 3 could only include features 304, 308, 310 and 312 as these involve further searching. The system can position items 306 and 314 in “drop up” menus 318 and 316, respectively. The algorithm can predict the most likely search if the user desired a search and the most likely purchase if the user were to desire to purchase the item and position those as the first option down and the first option up in the menus. The user could use the arrow buttons on a keyboard or a touch screen to select the desired options. Alternatively, the drop down or drop up menus can indicate shortcut keys which the user can press to select the options without using the mouse. For example, the menu can indicate that the user can press alt-1, alt-2, or alt-3 to select the various drop up menu options, or ctrl-1, ctrl-2, or ctrl-3, or some other single key or key combination to select the various drop down menu options. The system can present auto-complete options which the user can activate using similar keyboard shortcuts. For example, if the user has typed “iPhone,” the system can indicate that pressing “S64” after “iPhone” would autocomplete to “iPhone 5S 64 GB.” The types and quantities of such autocomplete keyboard shortcuts can vary widely depending on the determined intent of the user, as well as attributes of the product as the system understands it up to that point. Voice activity or gesture input or any other type of input can enable the user to select a desired option.

In some cases, the system can determine that the data in the search field is not intended for a purchase. For example, if the user enters the text “South Dakota,” the system can identify that the user does not desire to make a purchase. The “drop down” menu in that case could simply list the traditional search options, or could list options to one-click purchase items related to South Dakota, such as a South Dakota t-shirt or a souvenir of Mount Rushmore.

The user can also add hints or shorthand instructions in the search field to guide purchase options presented in a one-search.com field. For example, the user can provide the text “buy amaz iPhone 5S.” These hints tell the algorithm that the user desires a purchase function, and that the desired merchant is Amazon. Based on these type of hints, the system can eliminate features 304, 308, 310, 312 and 314 from the drop down menus shown in FIG. 3. In that case, the user could just hit “return” and the most likely desired product will be automatically purchased and processed for shipment. Options to cancel or modify of course can be presented, such as the cancel purchase button 404 and modify purchase button 408 shown in FIG. 4.

In one embodiment, the unified input field is part of an application downloadable or installable on a smartphone, tablet, or other mobile computing device. The functionality could also apply to a unified search field on a website. The application can be customizable as can any website disclosed herein. The application includes a single input field that is generic to multiple different types of processing. For example, the application can present an input field with a number of different options, such as a Skype or telephone call. The field therefore can be used to input a search for a contact. The user could type in the field “mom” and then select the Skype® video conference option, or the FaceTime® option. The system processes the input field according to the appropriate context by extending a video conferencing request or making a phone call. It is important to note that the unified field concept disclosed herein is not limited to the processing of the user input being related to web searches or purchases. Other functionality can be implemented from the unified field. Phone calls, video conferencing, triggering of any sensor on a smartphone, taking a picture, sending a text, etc. Several examples if these features follow. In the unified field, the user may input the text: “Mark S., are we getting together for lunch?” The user may then select the processing option of “texting,” chatting in an online chat room, or posting the comment on a social media website, and so forth.

Having disclosed some basic system components and concepts, the disclosure now turns to the exemplary method embodiment shown in FIG. 5. For the sake of clarity, the method is described in terms of an exemplary system 700 as shown in FIG. 7 configured to practice the method. The steps outlined herein are exemplary and can be implemented in any combination thereof, including combinations that exclude, add, or modify certain steps.

FIG. 5 illustrates a general method embodiment. The system receives user input (502). The system can access a product database in processing the user input (504). For example, if a new product just came out and is available for purchase on-line, the system can access that information so that when a user enters “iPhone 5S” that the system can match that input with a product. The system analyzes the input (506) for a determination of the user intent. For example, if the user enters “Rhode Island” the system can calculate a very low likelihood that the user desires to purchase Rhode Island. User profile, user search and purchasing history, and any other data can be used by the algorithm to determine how to structure an extendible menu to enable the user to quickly make a choice of what they desire. However, as the user enters additional text, the system can update autocomplete options accordingly. For example, if the user enters “Rhode Island cookbook,” the system can, at some point, determine that the user is not likely interested in the state, but in a cookbook, which is a purchasable item. The system can then adapt the autocomplete options automatically as the user continues to enter additional text.

FIG. 5 next shows analyzing the input (506) using all this data and information and constructing a menu (508) or a presentation of various options. This construction can also include a marketing aspect as companies may pay for how the option is presented. Amazon.com, or a product manufacturer, can pay a small fee to present their product with graphics or multimedia content, if it appears that the user may desire to buy that product, in order to encourage the user to select that option to purchase the product. The system presents the menu or other structured presentation of options for the user to choose (510). The options include one or more purchasing options (512) when the user input indicates via the algorithm that a purchase may be desired.

In another aspect, a classifier can process the user input in the general unified search field. The classifier can be trained to determine the intent of the user and to select which websites or applications to provide in response to the input. Classification algorithms are often used in processing speech or phone calls. For example, some classification features can process and classify calls in various call types like local, international, voicemail, conference, etc. In some cases, as a user calls an interactive voice response system, a classifier can be trained using previous calls to process the user input to conclude that the user wants to talk to accounting or pay a bill. For example, the user might say in the call “I want to pay a bill” or “I need help with my account.” By classifying that input, the system can route the call to the right person, destination, or entity.

Technologies that are used for classification include statistics, data mining, pattern recognition, machine learning, and in some cases neural computation and artificial intelligence. A general classification system approach involves receiving input, pre-processing the input, segmenting and labeling the input, extracting features from the input, post-processing, and ultimately classifying the input to arrive at a decision. While these principles have been applied in many fields, these technologies can be applied to a new classification domain. The new classification domain is the context or intent of a unified input field such as on a browser in which the user provides input, and that input can be applied to many different websites, applications, or actions. Right now, when a person goes to google.com, the assumption is that the user wants to search the internet. When a user goes to Amazon.com, the user wants to buy something. The user must go to different websites for these different functions, requiring additional, unnecessary mouse clicks. This disclosure provides, in one respect, the introduction of a classifier that processes user input in a field on a website where there is no assumption that the user wants to search or buy a product. The classifier will determine via a classification decision what the intent of the user is.

In order to train the classifier, which is called herein an ‘intent classifier’, the system can monitor the web usage of a user for a period of time. The classifier can utilize data of one user or multiple users. For example, one could generate training data of input at a google input field compared to input in an amazon.com input field for people of a particular demographic, such as 20-30 year old men, or women or a particular minority or religious group. The training data preferably would be particular in some respects to the individual user. If a user is logged into a browser such that it can connect to that user's training data or relevant training data, then the system can more efficiently process the user input in the unified input field. The system can use supervised learning (or unsupervised or semi-supervised learning) to label the training set so that the training data can provide which class (i.e., search, purchase, Wikipedia, etc.) the input belongs to. The training data involves the input provided via a search field as opposed to a purchase field. Other fields can apply as well such as auctions, medical advice, twitter input text, and so forth. The general concept is that there is no assumption when the user inputs data into the field regarding what the desired function is.

Other data that can be useful for the training model is personal user information. For example, if the user is registered for making one-click purchases at amazon.com, then when the user types in “Android 4.4 KitKat”, the system will know that the shortest number of clicks and computer interactions possible for the person to complete a purchase of the most popular smartphone with Android 4.4 is through Amazon.com. Otherwise, the user might be sent to another website and have to go through the shopping cart model, enter their credit card, and take a lot of extra time and effort to complete the purchase. Thus, knowing that the user is registered at one or two purchasing websites (thus enabling a quick “one-click” purchase) can drive the result of the classification.

In another example, the classifier in this case can have classification types of search, browse, purchase now, play game, update software, send email, send tweet, check Facebook, make call via Skype, and so forth. Some of the classification data for a trained model can be drawn from the different types of input that are used in input fields between google.com, amazon.com, Wikipedia.com, and so forth. The system for training the classifier can look at the different types of input in connection with which website is being used and develops training data. In other cases, if a user types in “call mom”, such input would not be provided in an input field because normally the user would go to the Skype App or other calling app and choose his mother to make the call. Thus, in other cases, training may occur in a different way to capture that “command” type of input. However, since there is no assumption of the desired intent of the input at the start, such training can be used to enable the user to perform a host of functions starting at one input field.

The user can in this case also provide hints as to the desired input. It might be quicker for the user to type “buy” before “iPhone 5S” so that the entire input is “buy iPhone 5S” than moving one's hand to the mouse and moving the mouse to click on an amazon.com tab in a browser or on an icon, or typing “www.amazon.com” in an input field for URL's. Clearly, the suggestion to buy tells the classifier that a purchase is the intent. Currently, typing in “buy iPhone 5S” is still considered via a searching algorithm in which search results are provided. There are advertisements that are sponsored that come up which enable the user to go to an advertisement for the iPhone 5S, but those still take additional clicks to get to the point of actually being able to make a purchase. Further, the system can still present the results on a webpage (google.com) that does not enable the user to complete a purchase/delivery within one click.

Therefore, the classification algorithm according to this disclosure can utilize training data which includes different kinds of input that the user or similar users have provided via input fields on various web pages to determine an intent describing which web page the user desires to open, or which action the user desires to perform on the web page. The system can user other data to make the determination, such as time of day, time of the year, social media data like birthdays of friends, holidays, weather information, which websites the user had made purchases on and with which website the has previously registered, etc.

For example, the classifier might determine that the user wants to make a purchase. The classifier can make a basic determination of an intent to search or an intent to make a purchase. Then the classifier can take a secondary step to determine based on history, user profile, registrations, best price, closest outlet to the user's address, etc., at which merchant the user likely wants to make the purchase. If the user has an account set up at amazon.com, then the system may choose amazon as the primary likely destination and take the appropriate step. For example, the system can create a new tab with the input term pre-entered at amazon.com, or where the user can open the tab and be at the state in the amazon.com website where the user can just click the “one-click purchase” button to complete the purchase. The system can present the new page in a new tab, within the same page as the unified input field, or in some other fashion.

The system can, instead of a more traditional menu, present the options in a completely different form, such as a tag field. FIG. 6 illustrates options in which parameters associated with each selectable option are chosen based on relevance. For example, the system can select and modify positioning, size, shape, color, detail, of the items in the tag field for various options. Feature 602 is an Amazon one-click purchase. Feature 604 is a Google search and feature 606 is an eBay search. In a tag cloud or word cloud, the size, shape, color, and other details of the items can provide information about the items. In this example, the Amazon one-click purchase 602 is listed in a large font, in bold, and in close proximity to the search field 302. The large font can indicate that the system has determined that it is highly relevant to the text entered in the search field. The bold font can indicate that clicking the item will trigger a purchase. The eBay search 606 is similarly large, potentially indicating that it is also highly relevant, but not bold because there is no one-click purchase associated with that item 606. The Google search 604 is presented to the side, in a smaller font, indicating that is may be of lesser relevance or importance. The various details of these items can vary in a smooth, animated fashion as the user enters additional information in the search field 302. For example, as the user enters more information about the specific desired iPhone, the system can adjust the Amazon 602 option on the user interface to gradually increase in size, move closer to the search field, be drawn with thicker lines, and so forth. The system can provide these details as an animation for the user so that increasingly relevant items are presented in increasingly discoverable places or increasingly prominently.

FIG. 6 illustrates various options of how to structure and present the selectable options from a one-search input field. In one aspect, traditional drop down options can be presented in a normal fashion with purchasing options presented like feature 602 in FIG. 6 thus providing a further differentiation of which items are standard drop down menu, auto-complete type options and which ones are one-click purchasing type options. The system can, in the tag field or in other embodiments, present targeted advertising. For example, in FIG. 6, the user could have entered “buy Amazon iPhone 5S.” This would result in a high likelihood or probability that the user wants to buy that product via Amazon, thus causing the system to present feature 602 showing that option in a large font and close to the input field 302. In other words, like tag clouds which make larger words of higher usage or interest in a story or from cloud input. Feature 604 could represent a paid-for advertisement from a competitor who may offer a cheaper price for the same product. Such information could be presented as part of an icon or advertisement represented as feature 604.

A description of a basic general-purpose system or computing device in FIG. 7 which can be employed to practice the concepts, methods, and techniques disclosed is illustrated. With reference to FIG. 7, an exemplary system and/or computing device 700 includes a processing unit (CPU or processor) 720 and a system bus 710 that couples various system components including the system memory 730 such as read only memory (ROM) 740 and random access memory (RAM) 750 to the processor 720. The system 700 can include a cache 722 of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 720. The system 700 copies data from the memory 730 and/or the storage device 760 to the cache 722 for quick access by the processor 720. In this way, the cache provides a performance boost that avoids processor 720 delays while waiting for data. These and other modules can control or be configured to control the processor 720 to perform various operations or actions. Other system memory 730 may be available for use as well. The memory 730 can include multiple different types of memory with different performance characteristics. It can be appreciated that the disclosure may operate on a computing device 700 with more than one processor 720 or on a group or cluster of computing devices networked together to provide greater processing capability. The processor 720 can include any general purpose processor and a hardware module or software module, such as module 1 762, module 2 764, and module 3 766 stored in storage device 760, configured to control the processor 720 as well as a special-purpose processor where software instructions are incorporated into the processor. The processor 720 may be a self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. The processor 720 can include multiple processors, such as a system having multiple, physically separate processors in different sockets, or a system having multiple processor cores on a single physical chip. Similarly, the processor 720 can include multiple distributed processors located in multiple separate computing devices, but working together such as via a communications network. Multiple processors or processor cores can share resources such as memory 730 or the cache 722, or can operate using independent resources. The processor 720 can include one or more of a state machine, an application specific integrated circuit (ASIC), or a programmable gate array (PGA) including a field PGA.

The system bus 710 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input/output (BIOS) stored in ROM 740 or the like, may provide the basic routine that helps to transfer information between elements within the computing device 700, such as during start-up. The computing device 700 further includes storage devices 760 or computer-readable storage media such as a hard disk drive, a magnetic disk drive, an optical disk drive, tape drive, solid-state drive, RAM drive, removable storage devices, a redundant array of inexpensive disks (RAID), hybrid storage device, or the like. The storage device 760 can include software modules 762, 764, 766 for controlling the processor 720. The system 700 can include other hardware or software modules. The storage device 760 is connected to the system bus 710 by a drive interface. The drives and the associated computer-readable storage devices provide nonvolatile storage of computer-readable instructions, data structures, program modules and other data for the computing device 700. In one aspect, a hardware module that performs a particular function includes the software component stored in a tangible computer-readable storage device in connection with the necessary hardware components, such as the processor 720, bus 710, display 770, and so forth, to carry out a particular function. In another aspect, the system can use a processor and computer-readable storage device to store instructions which, when executed by the processor, cause the processor to perform operations, a method or other specific actions. The basic components and appropriate variations can be modified depending on the type of device, such as whether the device 700 is a small, handheld computing device, a desktop computer, or a computer server. When the processor 720 executes instructions to perform “operations”, the processor 720 can perform the operations directly and/or facilitate, direct, or cooperate with another device or component to perform the operations.

Although the exemplary embodiment(s) described herein employs the hard disk 760, other types of computer-readable storage devices which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, digital versatile disks (DVDs), cartridges, random access memories (RAMs) 750, read only memory (ROM) 740, a cable containing a bit stream and the like, may also be used in the exemplary operating environment. Tangible computer-readable storage media, computer-readable storage devices, or computer-readable memory devices, expressly exclude media such as transitory waves, energy, carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with the computing device 700, an input device 790 represents any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 770 can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems enable a user to provide multiple types of input to communicate with the computing device 700. The communications interface 780 generally governs and manages the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic hardware depicted may easily be substituted for improved hardware or firmware arrangements as they are developed.

For clarity of explanation, the illustrative system embodiment is presented as including individual functional blocks including functional blocks labeled as a “processor” or processor 720. The functions these blocks represent may be provided through the use of either shared or dedicated hardware, including, but not limited to, hardware capable of executing software and hardware, such as a processor 720, that is purpose-built to operate as an equivalent to software executing on a general purpose processor. For example the functions of one or more processors presented in FIG. 7 may be provided by a single shared processor or multiple processors. (Use of the term “processor” should not be construed to refer exclusively to hardware capable of executing software.) Illustrative embodiments may include microprocessor and/or digital signal processor (DSP) hardware, read-only memory (ROM) 740 for storing software performing the operations described below, and random access memory (RAM) 750 for storing results. Very large scale integration (VLSI) hardware embodiments, as well as custom VLSI circuitry in combination with a general purpose DSP circuit, may also be provided.

The logical operations of the various embodiments are implemented as: (1) a sequence of computer implemented steps, operations, or procedures running on a programmable circuit within a general use computer, (2) a sequence of computer implemented steps, operations, or procedures running on a specific-use programmable circuit; and/or (3) interconnected machine modules or program engines within the programmable circuits. The system 700 shown in FIG. 7 can practice all or part of the recited methods, can be a part of the recited systems, and/or can operate according to instructions in the recited tangible computer-readable storage devices. Such logical operations can be implemented as modules configured to control the processor 720 to perform particular functions according to the programming of the module. For example, FIG. 7 illustrates three modules Mod1 762, Mod2 764 and Mod3 766 which are modules configured to control the processor 720. These modules may be stored on the storage device 760 and loaded into RAM 750 or memory 730 at runtime or may be stored in other computer-readable memory locations.

One or more parts of the example computing device 700, up to and including the entire computing device 700, can be virtualized. For example, a virtual processor can be a software object that executes according to a particular instruction set, even when a physical processor of the same type as the virtual processor is unavailable. A virtualization layer or a virtual “host” can enable virtualized components of one or more different computing devices or device types by translating virtualized operations to actual operations. Ultimately however, virtualized hardware of every type is implemented or executed by some underlying physical hardware. Thus, a virtualization compute layer can operate on top of a physical compute layer. The virtualization compute layer can include one or more of a virtual machine, an overlay network, a hypervisor, virtual switching, and any other virtualization application.

The processor 720 can include all types of processors disclosed herein, including a virtual processor. However, when referring to a virtual processor, the processor 720 includes the software components associated with executing the virtual processor in a virtualization layer and underlying hardware necessary to execute the virtualization layer. The system 700 can include a physical or virtual processor 720 that receive instructions stored in a computer-readable storage device, which cause the processor 720 to perform certain operations. When referring to a virtual processor 720, the system also includes the underlying physical hardware executing the virtual processor 720.

In each case within this disclosure, reference to “Amazon” or Amazon.com” is broad enough to encompass any purchasing/delivery or ecommerce website, as well as websites for traditional, brick-and-mortar businesses that provide goods or services. References to a “Google” site or search refer to any generalized search engine. In many instances, the principles set forth herein may be applicable to other, non-search and non-commerce sites, which the system can manipulate or traverse in order to accomplish a specific, intended action on behalf of the user.

In a typical web search, a user clicks to select a search field, enters text, waits for the results page to load, clicks a desired link in the results page, and finally arrives at the desired link after waiting for it to load. If the user desires to continue and make a purchase, then the user must either log in with an existing account or create a new account with the website, place the item in a virtual shopping cart, check out, provide shipping information, and so forth. This process requires many steps. A unified input field that prepopulates a shopping cart for the user can greatly simplify this process.

A system, method and computer-readable storage devices are disclosed which prepopulate a shopping cart in an online vendor's website based on intent determined by a classifier that processes input provided to the unified input field. A user can be registered with and/or logged in with his or her browser, such as Chrome, Firefox, Internet Explorer, or Opera, such that user's purchasing credit/debit card or other accounts and delivery addresses are stored in a user profile. Then, if the user desires to make a purchase through a destination website that may have a product not found on their registered website, the browser (or system processing in a network) can handle negotiation between the browser and the website such that the system can convert a shopping-cart based model website with which the user is not registered into a “one-click” purchasing experience for the user.

Assuming that appropriate permissions are in place, such as in a browser setting authorizing the browser to navigate and provide input in a website's purchasing, registration, or shopping cart process to automatically input name, address, credit card information, etc., the system can automatically populate a shopping cart, including even registering on behalf of the user at a website with which the user has not registered. The user provides input in a unified input field, such as “buy acme toaster 4.5” which the system and the classifier categorize as a desire to make a purchase of a particular toaster model. The system determines, based on various criteria such as pricing, shipping, country, and so forth, a merchant website that offers that toaster model. The user has never registered or made a purchase at the merchant website offering the toaster for sale. Thus, if the user were to buy the toaster from that merchant website, the user would have to place the toaster in a virtual shopping cart, then enter personal information such as address, credit/debit card information, user name, password, and so forth to have the item shipped.

In this model, the system can identify the merchant website based on the input provided via the unified input field. Then the system can navigate to the state in the merchant website where the user would normally click to place the item in the shopping cart. In this case, however, the system will navigate the merchant website where the item can be placed in the shopping cart, and determine that the user is not registered with this merchant website. Then the system can automatically communicate with the merchant website, such as via a new user registration page, to create a new account on behalf of the user using the user data available to the system and/or to the browser. Thus, the system can present to the user just the confirmation in a button to purchase via one click. The confirmation can be a double confirmation of authorization to create a new account and authorization to then place the order using the new account. The user can automatically authorize new registrations and can establish settings or preferences governing new profile registrations. The system can then enter all the necessary data, retrieved from the browser or some other location, to complete the registration and cause the item to be purchased and shipped without the need of further user input. This approach can turn non-one-click purchasing websites into one-click purchasing websites, via input through the unified input field.

The system can process the new registration pages silently, such as in a background tab or new window in the browser, or can negotiate the new registration process with the merchant website without presenting the registration page to the user at all. The system can present a progress bar on the same page containing the unified input field after the user requests a purchase, such as by pressing enter after providing input via the unified input field. The progress bar can indicate to the user the progress of creating a new account, entering shipping data, adding the item to the cart of the merchant website, and placing the order.

The user can establish default preferences for which personal information to use for new registrations. The user can establish these default preferences in advance of encountering the merchant website, or even in advance of encountering the unified input field. Alternatively, at the first instance where a registration is required, the system can prompt the user to establish such settings and/or provide additional information.

The system can prefer merchant websites with which the user already has an account or with which the user is already registered. For example, if Merchant X and Merchant Y both offer the same item for sale, the system can choose to use the one of these two merchants where the user already has an account. The system can determine this by examining a browser history, profile data available to the browser, or cookies stored by the browser or by other browsers associated with the user. The system can intelligently determine when to use a merchant with which the user is not registered, such as based on a difference in shipping time, or a difference in price. For example, if the merchant with which the user is not registered offers the desired item at a price that is at least 25% lower than the merchant with which the user is registered, then the system can create a new account. If the user does not desire to have the system create user accounts on his or her behalf, the user can instruct the system to make purchases where he or she already has accounts, or at merchant websites which allow guest accounts.

However, the system can identify, using the classifier and user profile data, the merchants the user is most likely to select, and present them to the user for disambiguation. The system can present to the user a table, such as by a JavaScript popup, showing merchants, shipping times, and total price including shipping and tax. The user can simply click on a desired merchant from the table, and the system can automatically register the user with that merchant if necessary, and process the order with that merchant. The user only has to make one click to identify the merchant, and the system handles the rest of the steps, thereby providing a one-click interface into a non-one-click website via the unified input field.

FIG. 8 illustrates a method embodiment. The steps in the method embodiment can be performed in any order, can be performed in other combinations or permutations that include additional steps or exclude all or part of some of the described steps. The system can present in a first user interface a first input field associated with a first website processed via a browser (802), and analyze the input from the user into the first input field to determine whether the user desires to perform a search or to make a purchase to yield a determination (804). If the determination indicates that the user desires to make a purchase, and without any other input from the user other than the input, the system can transition the user from the first website to a second website that can process the purchase of a product via a shopping cart model (806). The system can prepopulate a second input field associated with a second website with the input (808).

The system can preprocess the second website using the input in the second input field such that the user is in a state after the automatic transitioning where a product associated with the input can be processed for purchase and delivery via a one-click action from the user (810). Preprocessing the second website can include transmitting user data from one of the first website and a browser to the second website, or automatically navigating through the shopping cart model of the second website to yield the state where the product can be processed for purchase and delivery via the one-click action. The system can further preprocess a third website using the input in a third input field such that the user is in a state after the automatic transitioning where a product associated with the input can be processed for purchase and delivery via a one-click action from the user, and present the third website as an option for the user to choose in the browser.

The preprocessing can be according to a user profile with various preferences. For example, in a user profile associated with the first website, or with a browser. The user profile can utilize information such as payment account data, delivery data, preferences such as not to click on receiving any email notifications, and any other possible choices that can be made in processing a shopping cart model. The application, browser, or website, via an API, can then communicate in the background can perform all of those navigations and input necessary so that such input can be automatically handled to the extent that the user is presented with the second website in a state where they are ready to “one-click” and make the final purchase and delivery. The API can include all of the information necessary for the second website that is necessary the fill in and enter all of the necessary data to finalize the purchase. The first website or browser or application can hold all possible information that various shopping cart models can and do request in processing the purchase. As a particular second website is chosen as the one to which or through which the user is likely to make a purchase from. The first website or browser establishes via an API a connection to the second website with the various information necessary for preprocessing the input. The data such as the user input identifying the product that they want to buy and any other generated data that helps to narrow down and disambiguate which product to present for purchase via a one-click input. For example, the user may input “iPhone 5S 32 GB” but not a color. The system can choose a most popular color and provide that data to the second website to enable a narrowing down of which model to present. The system can also present secondary options such that alternate choices can be easily made accessible via the second website for potential selection if the primary choice is the wrong color or is not the desired product to buy.

FIG. 9 illustrates an example architecture 900 for pre-populating a merchant shopping cart. In this example, a user 902 of a computing device 904 running a web browser 906 loads a web page with a unified input field. The unified input field communicates with a server 910 via an API 912. As the user 902 enters data in the unified input field, the browser 906 sends the data, via the API 912, to the server 910. The server 910 can analyze the data to identify that the intent of the user 902 is to make a purchase, based on the data entered in the unified input field. The server 910 can identify a merchant website 916 that sells the desired item, and communicate with that merchant website 916 via its associated API 918, if available. If an API is not available, the server 910 can communicate with the merchant website 916 via HTTP and can navigate through the website in an automated fashion as if a user were clicking or entering data on the merchant website 916. The server 910 can use a network-based database 914 of payment and delivery data or other personal data about the user 902 to populate data fields at the merchant website 916. However, if a network-based database 914 is incomplete or does not exist, the server 910 can, via the API 912, request data from a database of payment and delivery data 908 local to the browser 906 or to the computing device 904. The server 910 can continuously receive additional data entered by the user in the unified input field via, and update or modify data entered at the merchant website 916, products selected for purchase at the merchant website 916, or even switch to a different merchant website altogether. The server 910 can transmit a response to the browser 906, via the API 912, so the browser 906 can present actions or destinations to the user 902 based on the data entered in the unified input field. Then, if the user selects one of the actions or destinations, the browser 906 can navigate to that page and communicate with the merchant website 916 directly, or without the server 910, although the server 910 may continue to communicate with the browser 906 to track behavior of the user 902 at the merchant website 916. For instance, the server 910 may track eventual purchase details referred through the unified input field. The server 910 can update the network-based payment and delivery data 914 from time to time based on information processed from the local payment and delivery data 908, or based on user input.

FIG. 10 illustrates example user interfaces for a pre-populated merchant shopping cart. In the first user interface 1000, the user has entered the text “Buy ACME toaster 4.5” in the unified input field 1002 of a web page in a browser. After hitting enter or clicking a search button, the browser communicates with a server that has already navigated to (or is currently in the process of navigating to) a merchant website to populate the shopping cart at the merchant website with the desired toaster. The server can hand off that browsing session to the browser to continue at a specific point in the checkout or shopping cart process, or can return a URL to the browser that is formed to go directly to a shopping cart that is populated based on the text entered in the unified input field. In this case, after hitting enter or clicking a search button, the browser navigates directly and automatically to a merchant website 1004 listing the item already placed in the shopping cart, order details, and a one-click purchase button 1008. Alternatively, the browser can navigate to a stage where the order has already been placed, such as the page that would load after the user clicks the purchase button 1008. The system can populate other details of the shopping cart automatically on behalf of the user, as well. The system can even create a new account at the merchant on behalf of the user, if the user does not have an account with that merchant. In this way, the system enables a user to access websites, through a unified search field, as if they were one-click purchase merchant websites, even if the user has not previously registered with that merchant or if the merchant does not offer an “Amazon style” one-click purchase interface.

Embodiments within the scope of the present disclosure may also include tangible and/or non-transitory computer-readable storage devices for carrying or having computer-executable instructions or data structures stored thereon. Such tangible computer-readable storage devices can be any available device that can be accessed by a general purpose or special purpose computer, including the functional design of any special purpose processor as described above. By way of example, and not limitation, such tangible computer-readable devices can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other device which can be used to carry or store desired program code in the form of computer-executable instructions, data structures, or processor chip design. When information or instructions are provided via a network or another communications connection (either hardwired, wireless, or combination thereof) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of the computer-readable storage devices.

Computer-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, components, data structures, objects, and the functions inherent in the design of special-purpose processors, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.

Other embodiments of the disclosure may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

The various embodiments described above are provided by way of illustration only and should not be construed to limit the scope of the disclosure. Various modifications and changes may be made to the principles described herein without following the example embodiments and applications illustrated and described herein, and without departing from the spirit and scope of the disclosure. Claim language reciting “at least one of” a set indicates that one member of the set or multiple members of the set satisfy the claim.

Claims

1. A wireless communication device used to process payments for purchases, the wireless communication device comprising: a processor; anda computer-readable storage device storing instructions which, when executed by the processor, cause the processor to be configured to: establish, via a wireless link, a communication between the wireless communication device and a user device, the communication being associated with a purchase for a product by a user;transmit, via the wireless link and via a protocol used to exchange user payment data and data for navigation or input useful for making purchases, a signal to the user device to initiate a payment process on the user device, wherein the signal is configured to cause the user device, automatically and based on the data for navigation or input, to be placed in a state of being configured next to transmit the user payment data to the wireless communication device after a confirmation from the user to finalize the purchase;receive, via the wireless link, from the user device and at the wireless communication device, the user payment data to make the purchase, wherein the user payment data is obtained based on the user device: (1) receiving the confirmation from the user to finalize the purchase; (2) retrieving, based on the confirmation, the user payment data from a memory of the user device; (3) providing, as part of the payment process, an indication on a display of the user device regarding a status of the payment process; and (4) transmitting, according to the protocol used to exchange user payment data for purchases and data for navigation or input useful for making purchases, the user payment data to the wireless communication device; andtransmit, to the user device and via the wireless link, a payment confirmation upon completion of the payment process.
2. The wireless communication device of claim 1, wherein the indication further instructs the user to click twice on a physical button on the user device.
3. The wireless communication device of claim 1, wherein the user payment data is obtained further based on the user device receiving motion input from the user and performing pattern recognition on the motion input and independent of any user physical interaction with the display.
4. The wireless communication device of claim 1, wherein the state of being configured next to transmit the user payment data after the confirmation from the user to finalize the purchase comprises the user device being configured to interpret two clicks on a physical button on the user device as an indication of a user intent to continue with the purchase as part of the confirmation.
5. The wireless communication device of claim 1, wherein the user device further receives motion input from the user as part of the confirmation and as a security measure to prevent unauthorized purchases.
6. The wireless communication device of claim 5, wherein the user device further receives at least one button click on a physical button from the user and receives the motion input from the user as part of the confirmation and independent of the user manually opening up a payment application or physically interacting at all with the display.
7. The wireless communication device of claim 3, wherein receiving the motion input and performing pattern recognition on the motion input to confirm a user intent to complete a payment process does not require any interaction by the user with a graphical display of the user device.
8. The wireless communication device of claim 1, wherein the computer-readable storage device stores additional instructions which, when executed by the processor, cause the processor to be configured to: initiate, from the wireless communication device and based on a receipt of the user payment data, a query to a third-party database operated by a third party that stores financial data associated with the user, wherein the third party is independent of an entity operating the wireless communication device; andreceive, at the wireless communication device and from the third-party database, a response related to the purchase based on the financial data associated with the user.
9. A method comprising: establishing, via a wireless link, a communication between a wireless communication device and a user device, the communication being associated with a purchase for a product by a user;transmitting, over the wireless link and via a protocol for enabling the purchase and that is used to exchange user payment data and data for navigation or input, a signal to the user device to initiate a payment process on the user device to make the purchase, wherein the signal is configured to cause the user device, automatically and without any other user interaction based on the data for navigation or input, launch a payment application and place the payment application in a state of being configured next to transmit the user payment data to the wireless communication device via the wireless link after a confirmation from the user to finalize the purchase; receiving, via the wireless link, from the user device and at the wireless communication device, the user payment data to make the purchase, wherein the user payment data is obtained based on the user device: (1) receiving the confirmation from the user to finalize the purchase; (2) retrieving, based on the confirmation, the user payment data from a memory of the user device; (3) providing, as part of the payment process, an indication on a display of the user device regarding a status of the payment process; and (4) transmitting, according to the protocol for enabling the purchase, the user payment data to the wireless communication device; andtransmitting, to the user device and via the wireless link, a payment confirmation upon completion of the payment process.
10. The method of claim 9, wherein additional input from the user is received at the user device and comprises both motion input and user input requiring pattern recognition.
11. The method of claim 9, wherein the state of being configured next to transmit the user payment data to the wireless communication device after the confirmation from the user to finalize the purchase comprises the user device being configured to interpret two clicks on a physical button on the user device as an indication of a user intent to continue with the purchase as part of the confirmation.
12. The method of claim 9, further comprising: initiating, from the wireless communication device and based on a receipt of the user payment data, a query to a third-party database operated by a third party that stores financial data associated with the user, wherein the third party is independent of an entity operating the wireless communication device; andreceiving, at the wireless communication device and from the third-party database, a response related to the purchase based on the financial data associated with the user.
13. A method comprising: establishing, via a wireless link, a communication between a wireless communication device and a user device, the communication being associated with a purchase for a product by a user;transmitting, over the wireless link and via a protocol for enabling the purchase in which the protocol for enabling the purchase is used to exchange user payment data, a signal to the user device to cause the user device, automatically to be configured next to transmit the user payment data after a confirmation from the user to finalize the purchase without any user interaction other than the confirmation;receiving, via the wireless link, from the user device and at the wireless communication device, the user payment data to make the purchase, wherein the user payment data is obtained based on the user device: (1) receiving the confirmation from the user to finalize the purchase; (2) retrieving, based on the confirmation, the user payment data from a memory of the user device; (3) providing, as part of the purchase, an indication on a display of the user device regarding a status of the purchase; and (4) transmitting, according to the protocol for enabling the purchase, the user payment data to the wireless communication device; andtransmitting, to the user device and via the wireless link, a payment confirmation upon completion of the purchase.
14. The method of claim 13, wherein the purchase occurs independent of any physical interaction with the display of the user device.
15. The method of claim 14, wherein the confirmation comprises at least one button press and motion input from the user.
16. The method of claim 13, wherein the user device is programmed to interpret two clicks on a physical button on the user device as an indication of a user intent to continue with the purchase.
17. The method of claim 13, wherein the user device further receives motion input from the user as part of a security measure to prevent unauthorized purchases.
18. The method of claim 17, wherein the user device further receives at least one button click on a physical button from the user and receives the motion input from the user as part of the confirmation and independent of the user manually opening up a payment application or physically interacting with the display of the user device.
19. The method of claim 13, further comprising: processing a payment based on the user payment data to complete the purchase of the product.
20. The method of claim 13, wherein the user device further receives motion input from the user as part of the confirmation and as a security measure to prevent unauthorized purchases.
21. The method of claim 13, wherein: the wireless communication device is associated with a merchant that operates a merchant server; andthe user payment data is obtained based on a user account being established at a site that enables payments to be made in a simplified and consistent user interaction with the user device for communicating the user payment data via one or more of: the wireless link to the wireless communication device; an application associated with the merchant server; or at a website associated with the merchant server, without the user registering payment credentials or establishing an account at the merchant server.
22. The method of claim 21, wherein the site establishes a link between the user account and the merchant server as well as other merchant servers to enable the user payment data to be used for payments across the merchant server and the other merchant servers in a consistent and one-click manner without requiring multiple user accounts to be registered at the merchant server or the other merchant servers.

PRIORITY CLAIM

This application is a continuation of Ser. No. 18/423,806, filed Jan. 26, 2024, which is a continuation of U.S. patent application Ser. No. 17/963,586, filed Oct. 11, 2022, which is a continuation of U.S. patent application Ser. No. 17/588,931, filed Jan. 31, 2022, which is a continuation of U.S. patent application Ser. No. 17/179,776, filed Feb. 19, 2021, which is a continuation of U.S. patent application Ser. No. 16/884,416, filed Jul. 14, 2020, which is a continuation of U.S. patent application Ser. No. 16/849,219, filed Apr. 15, 2020, now U.S. Pat. No. 10,825,079, issued Nov. 3, 2020, which is a continuation of U.S. patent application Ser. No. 16/721,970, filed Dec. 20, 2019, which is a continuation of U.S. patent application Ser. No. 16/573,411, filed Sep. 17, 2019, which is a continuation of U.S. patent application Ser. No. 16/445,297, filed Jun. 19, 2019, which is a continuation of U.S. patent application Ser. No. 16/279,685, filed Feb. 19, 2019, which is a continuation-in-part of U.S. patent application Ser. No. 16/126,541, filed Sep. 10, 2018, which is a continuation-in-part of U.S. patent application Ser. No. 15/947,395, filed Apr. 6, 2018, which is a continuation-in-part of U.S. Nonprovisional patent application Ser. No. 15/720,878, filed Sep. 29, 2017, which is a continuation-in-part of U.S. Nonprovisional patent application Ser. No. 15/263,057, filed Sep. 12, 2016, now U.S. Pat. No. 9,824,408, issued Nov. 21, 2017, which is a continuation of U.S. Nonprovisional patent application Ser. No. 15/018,954, filed Feb. 9, 2016, now U.S. Pat. No. 9,734,526, issued Aug. 15, 2017, which is a continuation of U.S. Nonprovisional patent application Ser. No. 14/853,579, filed Sep. 14, 2015, now U.S. Pat. No. 9,396,491, Issued on Jul. 19, 2016, which is a continuation of U.S. Nonprovisional patent application Ser. No. 14/822,368, filed Aug. 10, 2015, now U.S. Pat. No. 9,292,871, Issued on Mar. 22, 2016, which is a continuation of U.S. Nonprovisional patent application Ser. No. 14/672,876, filed Mar. 30, 2015, now U.S. Pat. No. 9,361,638, Issued on Jun. 7, 2016, which claims priority to U.S. Provisional Patent Application No. 61/973,287, filed Apr. 1, 2014 and also is a continuation-in-part of U.S. Nonprovisional patent application Ser. No. 14/230,864, filed 31 Mar. 2014, now U.S. Pat. No. 9,430,794, Issued on Aug. 30, 2016, and also claims priority to U.S. Provisional Patent Application No. 61/972,843, filed Mar. 31, 2014, U.S. Provisional Patent Application No. 61/972,834, filed Mar. 31, 2014, U.S. Provisional Patent Application No. 61/972,848, filed Mar. 31, 2014, U.S. Provisional Patent Application No. 61/972,865, filed Mar. 31, 2014, U.S. Provisional Patent Application No. 61/972,879, filed Mar. 31, 2014, U.S. Provisional Patent Application No. 61/972,861, filed Mar. 31, 2014, U.S. Provisional Patent Application No. 61/972,878, filed Mar. 31, 2014, U.S. Provisional Patent Application No. 61/972,892, filed Mar. 31, 2014, U.S. Provisional Patent Application No. 61/972,890, filed Mar. 31, 2014, the contents of each of which are herein incorporated by reference in their entireties. This application is a continuation of Ser. No. 18/423,806, filed Jan. 26, 2024, which is a continuation of U.S. patent application Ser. No. 17/963,586, filed Oct. 11, 2022, which is a continuation of U.S. patent application Ser. No. 17/588,931, filed Jan. 31, 2022, which is a continuation of U.S. patent application Ser. No. 17/179,776, filed Feb. 21, 2021, which is a continuation of U.S. patent application Ser. No. 16/884,416, filed Jul. 14, 2020, which is a continuation of U.S. patent application Ser. No. 16/849,219, filed Apr. 15, 2020, now U.S. Pat. No. 10,825,079, issued Nov. 3, 2020, which is also a continuation of U.S. patent application Ser. No. 16/801,513, filed Feb. 26, 2020, which is a continuation of U.S. patent application Ser. No. 16/675,341, filed Nov. 6, 2019, which is a continuation of Ser. No. 16/573,411, filed Sep. 17, 2019, the contents of each of which are herein incorporated by reference in their entireties.

US Referenced Citations (426)

Number	Name	Date	Kind
4837842	Holt	Jun 1989	A
4912654	Wood	Mar 1990	A
4933872	Vandenberg	Jun 1990	A
5524182	Chari	Jun 1996	A
5608819	Ikeuchi	Mar 1997	A
5644656	Akra	Jul 1997	A
5678677	Baudat	Oct 1997	A
5696838	Chiu	Dec 1997	A
5818978	Al-Hussein	Oct 1998	A
5859925	Yaeger	Jan 1999	A
5892843	Zhou	Apr 1999	A
5960411	Hartman	Sep 1999	A
6360216	Hennessey	Mar 2002	B1
6615408	Kaiser	Sep 2003	B1
6731788	Agnihotri	May 2004	B1
6978035	Kawaguchi	Dec 2005	B2
7014107	Singer	Mar 2006	B2
7028020	Keskar	Apr 2006	B1
7197479	Franciscus de Heer	Mar 2007	B1
7206434	Overton	Apr 2007	B2
7230653	Overton	Jun 2007	B1
7292996	Nobrega et al.	Nov 2007	B2
7327885	Divakaran	Feb 2008	B2
7328176	Tarvydas	Feb 2008	B2
7400768	Mayzlin	Jul 2008	B1
7668823	Oldham	Feb 2010	B2
7769771	Ikeda	Aug 2010	B2
7778994	Raman	Aug 2010	B2
7792705	Bezos	Sep 2010	B2
7801358	Furmaniak	Sep 2010	B2
7853583	Schachter	Dec 2010	B2
7860872	Serjeantson	Dec 2010	B2
8016192	Messerges et al.	Sep 2011	B2
8020188	Terasaki	Sep 2011	B2
8077930	Hauke	Dec 2011	B2
8086495	Ansari	Dec 2011	B2
8135707	Mika	Mar 2012	B2
8190601	Ahari	May 2012	B2
8218892	Bober	Jul 2012	B2
8219553	Bedingfield, Sr.	Jul 2012	B2
8224851	Banda	Jul 2012	B2
8239370	Wong	Aug 2012	B2
8255323	Casey	Aug 2012	B1
8296196	Martine	Oct 2012	B2
8296280	Mo	Oct 2012	B2
8301632	Bovenschulte	Oct 2012	B2
8311957	Kirshenbaum	Nov 2012	B2
8320681	Kim	Nov 2012	B2
8341036	Hartman	Dec 2012	B2
8352320	MacDonald	Jan 2013	B2
8352464	Fotev	Jan 2013	B2
8355997	Kirshenbaum	Jan 2013	B2
8370319	Krynski	Feb 2013	B1
8370330	Priyadarshan	Feb 2013	B2
8373904	Yano	Feb 2013	B2
8375325	Wuttke	Feb 2013	B2
8380570	Agarwal	Feb 2013	B2
8406528	Hatwich	Mar 2013	B1
8406792	Aaltonen	Mar 2013	B2
8417226	Ahtisaari	Apr 2013	B2
8423568	Fuxman	Apr 2013	B2
8458053	Buron	Jun 2013	B1
8468095	DiMartino et al.	Jun 2013	B1
8478240	Aaltonen	Jul 2013	B2
8489582	Jain	Jul 2013	B2
8499164	Ortiz et al.	Jul 2013	B2
8504419	Priyadarshan	Aug 2013	B2
8510309	Priyadarshan	Aug 2013	B2
8510658	Priyadarshan	Aug 2013	B2
8515966	Jammalamadaka	Aug 2013	B2
8548872	Gupta	Oct 2013	B1
8560405	Buron	Oct 2013	B1
8577813	Weiss	Nov 2013	B2
8595203	Monahan	Nov 2013	B2
8595851	Aaltonen	Nov 2013	B2
8631122	Kadam	Jan 2014	B2
8635227	Sankhla	Jan 2014	B2
8639567	Winters	Jan 2014	B2
8639778	Madanes	Jan 2014	B2
8640032	Priyadarshan	Jan 2014	B2
8650173	McDonald	Feb 2014	B2
8655146	Bennett	Feb 2014	B2
8655736	Bharadwaj	Feb 2014	B2
8660912	Dandekar	Feb 2014	B1
8666895	Grigg et al.	Mar 2014	B2
8670652	Yai	Mar 2014	B2
8676682	Kalliola	Mar 2014	B2
8700729	Dua	Apr 2014	B2
8712382	Aaltonen	Apr 2014	B2
8713003	Fotev	Apr 2014	B2
8719091	Kirley	May 2014	B2
8738418	Winters	May 2014	B2
8762475	Cheung et al.	Jun 2014	B2
8762484	Ravichandran	Jun 2014	B2
8763068	Gu	Jun 2014	B2
8775273	Mengerink	Jul 2014	B2
8793260	Haykal	Jul 2014	B2
8856539	Weiss	Oct 2014	B2
8880498	Rubanovich	Nov 2014	B2
8898217	Grigorovici	Nov 2014	B2
8903800	Kakade	Dec 2014	B2
8935340	Aaltonen	Jan 2015	B2
8949342	Ahopelto	Feb 2015	B2
8949889	Erdmann	Feb 2015	B1
8959022	Cheng	Feb 2015	B2
8972286	Grigg	Mar 2015	B2
8972600	Malaby	Mar 2015	B2
8977554	Hertschuh	Mar 2015	B1
8977625	Buehrer	Mar 2015	B2
8983978	Priyadarshan	Mar 2015	B2
9031927	Kaushal	May 2015	B2
9038000	Haynes, II	May 2015	B2
9064281	Ballard	Jun 2015	B2
9066130	Buron	Jun 2015	B1
9092289	Bouthillier	Jul 2015	B2
9100826	Weiss	Aug 2015	B2
9107064	Ramalingam	Aug 2015	B1
9113214	Andrews	Aug 2015	B2
9129199	Spodak et al.	Sep 2015	B2
9137576	McRae	Sep 2015	B2
9141504	Greenzeiger	Sep 2015	B2
9146665	Gandhi	Sep 2015	B2
9148771	Zentner	Sep 2015	B2
9182243	Van Os	Nov 2015	B2
9183280	Achuthan	Nov 2015	B2
9183588	Yankovich	Nov 2015	B2
9189549	Rubinstein	Nov 2015	B2
9218419	Wu	Dec 2015	B2
9224167	Lampert	Dec 2015	B2
9230395	Mattice et al.	Jan 2016	B2
9242619	Choi	Jan 2016	B2
9251144	Du	Feb 2016	B2
9256795	Gray	Feb 2016	B1
9275334	Sharma	Mar 2016	B2
9276991	DeRoller	Mar 2016	B2
9277022	Lee	Mar 2016	B2
9292871	Isaacson	Mar 2016	B2
9329692	Bai	May 2016	B2
9332302	Briggs	May 2016	B2
9338327	Miyata	May 2016	B2
9342835	Fordyce, III	May 2016	B2
9342930	Kraft	May 2016	B1
9348935	Doig	May 2016	B2
9374431	Kannan	Jun 2016	B2
9383815	Kim	Jul 2016	B2
9412127	Dumon	Aug 2016	B2
9418672	Pylappan	Aug 2016	B2
9424002	Bruno	Aug 2016	B2
9424598	Kraft	Aug 2016	B1
9430766	Kraft	Aug 2016	B1
9430794	Isaacson	Aug 2016	B2
9443017	Borisovich	Sep 2016	B2
9451010	Spitz	Sep 2016	B2
9477985	Poon	Oct 2016	B2
9483755	Lemay	Nov 2016	B2
9495699	Mangaru	Nov 2016	B2
9501750	Fujioka	Nov 2016	B2
9508006	Shamir	Nov 2016	B2
9516304	Friel	Dec 2016	B2
9519715	Dedhia	Dec 2016	B2
9519771	Fadell et al.	Dec 2016	B2
9529926	Wu	Dec 2016	B2
9535990	Skolicki	Jan 2017	B2
9536161	Lish	Jan 2017	B1
9557891	Iwabuchi	Jan 2017	B2
9558272	Kalinina	Jan 2017	B2
9560415	Good	Jan 2017	B2
9576251	Forman	Feb 2017	B2
9578358	Skolicki	Feb 2017	B1
9589059	Levens	Mar 2017	B2
9607330	Spitz	Mar 2017	B2
9613160	Houle	Apr 2017	B2
9658931	Simons	May 2017	B2
9659067	De Sousa	May 2017	B2
9665895	Alston	May 2017	B2
9684741	Li	Jun 2017	B2
9704180	Kobayashi	Jul 2017	B2
9736028	Ansari	Aug 2017	B2
9754174	Sugiyama	Sep 2017	B2
9767520	Isaacson	Sep 2017	B2
9799046	Stoll	Oct 2017	B2
9800727	Chakrabarty	Oct 2017	B1
9824355	Aabye et al.	Nov 2017	B2
9830137	Matthew	Nov 2017	B2
9832519	Avedissian	Nov 2017	B2
9841282	VonDerheide	Dec 2017	B2
9852400	Ruan	Dec 2017	B2
9858297	Park	Jan 2018	B2
9858340	Frey	Jan 2018	B1
9870131	Kulikov	Jan 2018	B2
9875489	Spitz	Jan 2018	B2
9881083	Kalinina	Jan 2018	B2
9883008	Ravichandran	Jan 2018	B2
9898162	Jisrawi	Feb 2018	B2
9899063	Avedissian	Feb 2018	B2
9922381	Isaacson	Mar 2018	B2
9924235	Ansari	Mar 2018	B2
9959872	Barreira	May 2018	B2
9965604	Smith	May 2018	B2
9966649	Christie	May 2018	B2
9996602	Chong	Jun 2018	B2
10019430	Rossi	Jul 2018	B2
10037582	Carey	Jul 2018	B2
10043185	Van Os et al.	Jul 2018	B2
10042514	Delacroix	Aug 2018	B2
10055768	Briggs	Aug 2018	B2
10069940	Smith	Sep 2018	B2
10070185	Ho	Sep 2018	B1
10073794	Thomas	Sep 2018	B2
10078621	Luo	Sep 2018	B2
10083369	Tosic	Sep 2018	B2
10089412	Kosarev	Oct 2018	B2
10121186	Isaacson	Nov 2018	B2
10127531	Fu	Nov 2018	B2
10127595	Hipschman	Nov 2018	B1
10152756	Isaacson	Dec 2018	B2
10175860	Marantz	Jan 2019	B2
10176506	Ohara	Jan 2019	B2
10181147	Stoll	Jan 2019	B2
10185972	Churchill	Jan 2019	B2
10198753	Niepert	Feb 2019	B2
10210559	Stoll	Feb 2019	B2
10231076	Stuart	Mar 2019	B1
10235714	Paulrajan	Mar 2019	B2
10242398	Woo	Mar 2019	B2
10248967	Liu	Apr 2019	B2
10268994	Spitz	Apr 2019	B2
10269021	Levy	Apr 2019	B2
10311499	Kim	Jun 2019	B1
10319013	Moring	Jun 2019	B2
10346895	Stoll	Jul 2019	B2
10360619	Nichols	Jul 2019	B2
10373383	Werner	Aug 2019	B1
10380166	Lee	Aug 2019	B2
10380673	Beach	Aug 2019	B2
10395293	Mangtani	Aug 2019	B1
10397326	Thomas	Aug 2019	B2
10402886	Naito	Sep 2019	B2
10402889	Mukherjee	Sep 2019	B1
10403394	Ansari	Sep 2019	B2
10410234	Madden	Sep 2019	B1
10410272	Johnson	Sep 2019	B1
10430849	Hipschman	Oct 2019	B1
10459919	Zhong	Oct 2019	B2
10460085	Fujioka	Oct 2019	B2
10467287	Hume	Nov 2019	B2
10497037	Isaacson	Dec 2019	B2
10503796	Stein	Dec 2019	B2
10504126	Kulkarni	Dec 2019	B2
10506278	Avedissian	Dec 2019	B2
10509823	Murata	Dec 2019	B2
10511580	Isaacson	Dec 2019	B2
10521837	Mossoba	Dec 2019	B1
10534511	Garcia	Jan 2020	B2
10536414	Dye	Jan 2020	B2
10565562	Saha	Feb 2020	B2
10579602	Wang	Mar 2020	B2
10580049	Graylin	Mar 2020	B2
10580056	Stoll	Mar 2020	B2
10594870	Satyavolu	Mar 2020	B2
10614313	Catalano	Apr 2020	B2
10621653	Isaacson	Apr 2020	B2
10643266	Isaacson	May 2020	B2
10650358	Edwards	May 2020	B1
10664793	Rehn	May 2020	B1
10671931	Bansal	Jun 2020	B2
10678878	Li	Jun 2020	B2
10679269	Wang	Jun 2020	B2
10701127	Spitz	Jun 2020	B2
10706456	Goodwin	Jul 2020	B2
10726472	Isaacson	Jul 2020	B2
10748206	Cooper	Aug 2020	B1
10775254	Hipschman	Aug 2020	B2
10776847	Comar	Sep 2020	B1
10789631	Spitz	Sep 2020	B2
10803506	Kim	Oct 2020	B2
10832310	Isaacson	Nov 2020	B2
10846112	Smith, III	Nov 2020	B2
10852151	Butts, III	Dec 2020	B2
10860949	Shamsi	Dec 2020	B2
10866646	Thomas-Brigden	Dec 2020	B2
10872193	Lu	Dec 2020	B2
10885496	Sonone	Jan 2021	B2
10909586	Avedisssian	Feb 2021	B2
10922737	Guo	Feb 2021	B2
10963915	Madden	Mar 2021	B2
10977319	Bright	Apr 2021	B2
11004092	MacIlwaine	May 2021	B2
11004096	Thomas	May 2021	B2
11004139	Isaacson	May 2021	B2
11017163	Wang	May 2021	B2
11030633	Reid	Jun 2021	B2
11042898	Mehanian	Jun 2021	B2
11049164	Agrawal	Jun 2021	B2
11068892	Buis	Jul 2021	B2
11068969	Wilson	Jul 2021	B2
11080777	Isaacson	Aug 2021	B2
11087356	Blom	Aug 2021	B2
11113082	Redin	Sep 2021	B2
11132749	Konson	Sep 2021	B1
11138375	Avedissian	Oct 2021	B2
11144906	Girish	Oct 2021	B2
11151507	Kang	Oct 2021	B2
11170003	Wu	Nov 2021	B2
11205207	Jain	Dec 2021	B2
11227315	Briggs	Jan 2022	B2
11250493	Isaacson	Feb 2022	B2
11265912	Hopen	Feb 2022	B2
11269905	Fitzpatrick	Mar 2022	B2
11270064	Collart	Mar 2022	B2
11270323	Gerard	Mar 2022	B2
11282131	Isaacson	Mar 2022	B2
11308544	Akbarpour	Apr 2022	B2
11314746	Kadam	Apr 2022	B2
11315174	Walker	Apr 2022	B2
11316688	Ansari	Apr 2022	B2
11317159	Crossley	Apr 2022	B2
11363106	Nelluri	Jun 2022	B2
11367282	Wang	Jun 2022	B2
11386452	Ostrover	Jul 2022	B1
11386454	Ripper	Jul 2022	B1
11423463	Belcher	Aug 2022	B2
11435873	Sharma	Sep 2022	B1
11436654	Coldwell	Sep 2022	B2
20020128981	Kawan	Sep 2002	A1
20020164031	Piikivi	Nov 2002	A1
20030055723	English	Mar 2003	A1
20040103197	Benson	May 2004	A1
20040254891	Blinn	Dec 2004	A1
20050212752	Marvit	Sep 2005	A1
20060014524	Leforestier et al.	Jan 2006	A1
20060041485	Tarvydas	Feb 2006	A1
20060064411	Gross	Mar 2006	A1
20060143094	Kohout	Jun 2006	A1
20060224973	Albrecht	Oct 2006	A1
20070050406	Byers	Mar 2007	A1
20070073599	Perry	Mar 2007	A1
20070106570	Hartman	May 2007	A1
20070112647	Borders	May 2007	A1
20070168260	Cunescu et al.	Jul 2007	A1
20070294240	Steele	Dec 2007	A1
20080005118	Shakib	Jan 2008	A1
20080017704	VanDeburg et al.	Jan 2008	A1
20080071763	Ferrenq	Mar 2008	A1
20080103879	Armstrong	May 2008	A1
20080140523	Mahoney	Jun 2008	A1
20080167946	Bezos	Jul 2008	A1
20080201304	Sue	Aug 2008	A1
20080240490	Finkelstein	Oct 2008	A1
20080244721	Barrus	Oct 2008	A1
20090006375	Lax	Jan 2009	A1
20090037284	Lewis	Feb 2009	A1
20090037291	Dawson	Feb 2009	A1
20090089260	Chong	Apr 2009	A1
20090094104	Anuar	Apr 2009	A1
20090132347	Anderson	May 2009	A1
20090157450	Athsani	Jun 2009	A1
20090157479	Caldwell	Jun 2009	A1
20090172746	Aldrey	Jul 2009	A1
20090196465	Menon	Aug 2009	A1
20090234849	Erera	Sep 2009	A1
20090235196	MacBeth	Sep 2009	A1
20090271735	Anderson	Oct 2009	A1
20090300476	Vogel et al.	Dec 2009	A1
20090307003	Benyamin	Dec 2009	A1
20100114654	Lukose	May 2010	A1
20100131347	Sartipi	May 2010	A1
20100145861	Law	Jun 2010	A1
20100280909	Zhang	Nov 2010	A1
20100309512	Onoda	Dec 2010	A1
20110029400	Scipioni	Feb 2011	A1
20110189981	Faith et al.	Aug 2011	A1
20110282785	Chin	Nov 2011	A1
20110289598	Chastagnol	Nov 2011	A1
20120084210	Farahmand	Apr 2012	A1
20120136756	Jitkoff	May 2012	A1
20120191569	Shah	Jul 2012	A1
20120203776	Nissan	Aug 2012	A1
20120221437	Yoo	Aug 2012	A1
20120233020	Eberstadt	Sep 2012	A1
20120233170	Musgrove et al.	Sep 2012	A1
20120284130	Lewis	Nov 2012	A1
20120310738	Mesaros	Dec 2012	A1
20130006733	Fisher	Jan 2013	A1
20130013427	Gonsalves	Jan 2013	A1
20130013499	Kalgi	Jan 2013	A1
20130066987	Levinson	Mar 2013	A1
20130066988	Levinson	Mar 2013	A1
20130076788	Ben Zvi	Mar 2013	A1
20130231969	Van Pelt	Sep 2013	A1
20130254115	Pasa et al.	Sep 2013	A1
20130262315	Hruska	Oct 2013	A1
20130290149	Rashwan	Oct 2013	A1
20130297425	Wallaja	Nov 2013	A1
20130307765	Li	Nov 2013	A1
20130316647	Leica	Nov 2013	A1
20130325980	Ohayon	Dec 2013	A1
20130339229	Li	Dec 2013	A1
20130345959	Van Os	Dec 2013	A1
20140019367	Khan	Jan 2014	A1
20140025521	Alsina	Jan 2014	A1
20140032297	Germann	Jan 2014	A1
20140095583	Houle	Apr 2014	A1
20140136334	Lagassey	May 2014	A1
20140136990	Gonnen	May 2014	A1
20140189514	Hillard	Jul 2014	A1
20140207462	Bangalore	Jul 2014	A1
20140229339	Massiere	Aug 2014	A1
20140249968	Mackinnon	Sep 2014	A1
20140279266	Lampert	Sep 2014	A1
20140285435	Bezos	Sep 2014	A1
20140297362	Kumar	Oct 2014	A1
20140297537	Kassemi	Oct 2014	A1
20150052061	Anderson	Feb 2015	A1
20150058191	Khan et al.	Feb 2015	A1
20150088655	Taylor	Mar 2015	A1
20150088686	Glassberg	Mar 2015	A1
20150095238	Khan et al.	Apr 2015	A1
20150142640	Kneen	May 2015	A1
20150149168	Stent	May 2015	A1
20150317698	Kalyvas	Nov 2015	A1
20150324789	Dvorak	Nov 2015	A1
20150348017	Allmen	Dec 2015	A1
20150348029	Van Os	Dec 2015	A1
20160042362	Rowe	Feb 2016	A1
20210241256	Caldwell	Aug 2021	A1

Foreign Referenced Citations (1)

Number	Date	Country
WO 2012075304	Jun 2012	WO

Non-Patent Literature Citations (49)

Entry
Wang, Hong, and Evangelos Kranakis. “Secure wireless payment protocol.” Ottawa-Carleton Institute for Computer Science, Carleton University, Ottawa, Canada, May 15, (2004). (Year: 2004).
U.S. Appl. No. 61/884,926, filed Sep. 30, 2013, Khan et al. (Corres. to 2015/0095238).
U.S. Appl. No. 61/989,107, filed May 6, 2014, Khan et al. (Corres. to 2015/0095238).
U.S. Appl. No. 62/002,721, filed May 23, 2014, Khan et al. (Corres. to 2015/0095238).
U.S. Appl. No. 62/004,182, filed May 28, 2014, Khan et al. (Corres. to 2015/0095238).
www.travelocity.com, [online], Mar. 21, 2012 [retrieved from archive.org on Aug. 26, 2014].
www.tripadvisor.com, [online], Mar. 15, 2012 [retrieved from archive.org on Aug. 26, 2014].
Van den Poel, D. and W. Buckinx, “Predicting online-purchasing behavior,” Jul. 28, 2004 [online]. European Journal of Operational Research 166 pp. 557-575. Retrieved from www.sciencedirect.com.
Hudaib, Adam Ali Zare. “E-payment Security Analysis in Depth.” International Journal of Computer Science and Security (IJCSS) 8.1 (2014): 14. (Year: 2014).
Luttge, Karsten, “E-charging api: outsource charging to a payment service provider”, Intelligent Network Workshop, 2001, IEEE. (Year: 2001).
Kraft, Ben; Mannes, Eric; and Moldow, Jorday, “Security research of a social payment app.”, 2014 (Year 2014).
Constantinides, Efthymios, “Foundations of Social Media Marketing”, Procedia-Social and Behavioral Sciences, 148 (2014), pp. 40-57. (Year: 2014).
Malaga, Ross A. “Search engine optimization—black and white hat approaches.” Advances in Computers, vol. 78, Elsevier, 2010, 1-29. (Year: 2010).
Naylor, Rebecca Walker, Cait Poynor Lamberton, and Patricia M. West. “Beyond the “like” button: The impact of mere virtual presence on band evaluations and purchase intentions in social media settings.” Journal of Marketing, 76.6, (2012): 105-120. (Year: 2012).
Lopes, Christian T., et al., “Cytoscape Web: an interactive web-based network browser,” Bioinformatics—Applications Note, vol. 26, No. 18, pp. 2347-2348, 2010.
Dogru, Tarik, et al., “Blockchain Technology & it Implications for the Hospitality Industry,” Boston University, Winter 2018.
Miraz, Mahdi H., et al., “Analysis of Users' Behaviour and Adoption Trends of Social Media Payment Platforms,” 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE), IEEE, 2019.
Google Wallet Make Your Phone Your Wallet by Google—YouTube, https://www.youtube.com/watch?v=qRVQTQIJV0g, 238 views—Feb. 18, 2013, retrieved on Feb. 11, 2022.
GV-R ISIS Mobile Payment Demo—YouTube, https://www.youtube.com/watch?v=VIfRHCDTzql, 913 views—Aug. 8, 2012, retrieved Feb. 11, 2022.
Introducing the Isis Mobile Wallet™—YouTube, https://www.youtube.com/watch?v=id_Wrt-lyFU, 326 views—Dec. 6, 2013, retrieved Feb. 11, 2022.
ISIS Mobile Wallet for Verizon—Wave to Pay (Hands-on Video)—YouTube, https://www.youtube.com/watch?v=djHgUhOqTYI, 21,654 views—Jan. 12, 2013, retrieved Feb. 11, 2022.
Dileep Kumar and Yeonseung Ryu, “A Brief Introduction of Biometrics and Fingerprint Payment Technology”, International Journal of Advanced Science and Technology, vol. 4, Mar. 2009, pp. 25-28, (Year: 2009).
Nicholas Akinyokun and Vanessa Teague, “Security and Privacy Implications of NFC—enabled Contactless Payment Systems”, Proceedings of the 12th International Conference on Availability, Reliability and Security, 10 pages, (Year: 2017).
Meng, Jian and Ye, Liang. “Secure mobile payment model based on WAP,” 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, IEEE, 2008. (Year: 2008).
Spaid, Brian I., and Flint, Daniel J., “The meaning of shopping experiences augmented by mobile internet devices,” Journal of Marketing Theory and Practice, vol. 22, No. 1, 2014, pp. 73-90. (Year: 2014).
Ondrus, Jan and Yves Pigneur, “An assessment of NFC for future mobile payment systems”, International Conference on the Management of Mobile Business (ICMB), IEEE, 2007. (Year: 2007).
Hutter, K et al., (2013). “The impact of user interactions in social media on brand awareness and purchase intention: The case of MINI on facebook.” The Journal of Product and Brand Management, 22(5), 342-351. Doi:https://doi.org/10.1108/JPBM-05-2013-0299. (Year: 2013).
Gupta, Alok et al., “An empirical study of consumer switching from traditional to electronic channels: A purchase-decision process perspective.” International Journal of Electronic Commerce, 8.3 (2004): 131-161. (Year: 2004).
Lipsman, Andrew et al., “The power of the “like”: How brands reach (and influence) fans through social-media marketing”, Journal of Advertising research, 52.1 (2012): 40-52. (Year: 2012).
Sawant, Ms Rupali, et al., “The RFID based smart shopping cart”, International Journal of Engineering Research and General Science, 3.2 (2015): 275-280. (Year:2015).
Baek, Hyochan, et al., “A model for detecting cryptocurrency transactions with discernible purpose”, 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), IEEE, 2019. (Year: 2019).
Hajli, M. Nick. “A study of the impact of social media on consumers”, International Journal of Market Research, 56.3 (2014) 387-404. (Year: 2014).
Lohse, Gerald L., and Peter Spiller. “Internet retail store design: How the user interface influences traffic and sales.” Journal of Computer-Mediated Communication 5.2 (1999) (Year: 1999).
File History of U.S. Pat. No. 11,468,497 (“'497 Patent File History”), Exhibit 1002.
Declaration of Herbert Cohen (“Cohen Decl.”), Exhibit 1003.
Curriculum Vitae of Herbert Cohen, Exhibit 1008.
IPhone User Guide for iOS 5.0 Software, Apple (2011) (“iPhone User Guide”), Exhibit 1012.
Goodin, Dan, Fingerprints as passwords: New iPhone Touch ID gets mixed security verdict (Updated), ArsTechnica (Sep. 2013) available at https://arstechnica.com/information-technology/2013/09/fingerprints-as-passwords-new-iphone-touch-id-gets-mixed-security-verdict/ (“Goodin”), Exhibit 1013.
Wehner, Mike, Apple reveals Touch ID, a fingerprint sensor built into the iPhone5s, Engadget (Sep. 2013) available at https://www.engadget.com/2013-09-10-apple-reveals-touch-id-a-fingerprint-sensor-built-into-the-ipho.html (“Wehner”), Exhibit 1015.
Cunningham, Andrew, Apple unveils 64-bit iPhone 5S with fingerprint scanner, $199 for 16GB, ArsTechnica (Sep. 2013) available at https://arstechnica.com/2013/09/apple-unveils-64-bit-iphone-5s/ (“Cunningham”), Exhibit 1015.
Reuters, Crack iPhone's fingerprint scanner, win cash and booze, CNBC (Sep. 2013) available at https://www.cnbc.com/2013/09/19/crack-iphones-fingerprint-scanner-win-case-and-booze.html (“Reuters”), Exhibit 1016.
Monticello Enterprises LLC v. Starbucks Corporation, Case No. 6:23-cv-00763, Doc. No. 1. (Nov. 9, 2023) (“Starbucks Complaint”), Exhibit 1031.
Monticello Enterprises LLC v. Petco Health & Wellness Company, Inc. et al., Case No. 6:23-cv-00761, Doc. No. 1. (Nov. 9, 2023) (“Petco Complaint”).
Monticello Enterprises LLC v. Macy's Inc., Case No. 6:23-cv-00753, Doc. No. 1. (Nov. 7, 2023) (“Macy's Complaint”), Exhibit 1033.
Monticello Enterprises LLC v. Starbucks Corporation, Case No. 6:23-cv-00763, Doc. No. 1, (Nov. 9, 2023) (“Starbucks Response to MTD”), Exhibit 1034.
Monticello Enterprises LLC v. Petco Health & Wellness Company, Inc. et al., Case No. 6:23-cv-00761, Doc. No. 1. (Nov. 9, 2023) (“Petco Response to MTD”), Exhibit 1035.
Monticello Enterprises LLC v. Macy's Inc., Case No. 6:23-cv-00753, Doc. No. 1. (Nov. 7, 2023) (“Macy's Response to MTD”), Exhibit 1036.
Claim Chart Comparing the claims of the '497 Patent to Prior Art, Exhibit AA.
Wang, Hong and Evangeos Kranakis, “Secure wireless payment protocol,” Ottawa-Carleton Institute for Computer Science, Carleton University, Ottawa, Canada, May 15, 2004. (Year: 2004).

Related Publications (1)

	Number	Date	Country
	20240257220 A1	Aug 2024	US

Provisional Applications (10)

Number	Date	Country
61973287	Apr 2014	US
61972834	Mar 2014	US
61972890	Mar 2014	US
61972865	Mar 2014	US
61972878	Mar 2014	US
61972861	Mar 2014	US
61972843	Mar 2014	US
61972848	Mar 2014	US
61972892	Mar 2014	US
61972879	Mar 2014	US

Continuations (17)

	Number	Date	Country
Parent	18423806	Jan 2024	US
Child	18592618		US
Parent	17963586	Oct 2022	US
Child	18423806		US
Parent	17588931	Jan 2022	US
Child	17963586		US
Parent	17179776	Feb 2021	US
Child	17588931		US
Parent	16884416	Jul 2020	US
Child	17179776		US
Parent	16849219	Apr 2020	US
Child	16884416		US
Parent	16801513	Feb 2020	US
Child	16849219		US
Parent	16721970	Dec 2019	US
Child	16849219		US
Parent	16675341	Nov 2019	US
Child	16801513		US
Parent	16573411	Sep 2019	US
Child	16721970		US
Parent	16573411	Sep 2019	US
Child	16675341		US
Parent	16445297	Jun 2019	US
Child	16573411		US
Parent	16279685	Feb 2019	US
Child	16445297		US
Parent	15018954	Feb 2016	US
Child	15263057		US
Parent	14853579	Sep 2015	US
Child	15018954		US
Parent	14822368	Aug 2015	US
Child	14853579		US
Parent	14672876	Mar 2015	US
Child	14822368		US

Continuation in Parts (5)

	Number	Date	Country
Parent	16126541	Sep 2018	US
Child	16279685		US
Parent	15947395	Apr 2018	US
Child	16126541		US
Parent	15720878	Sep 2017	US
Child	15947395		US
Parent	15263057	Sep 2016	US
Child	15720878		US
Parent	14230864	Mar 2014	US
Child	14672876		US

System and method for providing an improved payment process over a wireless link

Information

Patent Number

Date Filed

Date Issued

Inventors

Original Assignees

Examiners

CPC

Field of Search

CPC

International Classifications

Disclaimer

Term Extension

Abstract