User interface methods and systems for selecting and presenting content based on user relationships

Abstract
A user-interface method of selecting and presenting a collection of content items based on user navigation and selection actions associated with the content is provided. The method includes associating a relevance weight on a per user basis with content items to indicate a relative measure of likelihood that the user desires the content item. The method includes receiving a user's navigation and selections actions for identifying desired content items, and in response, adjusting the associated relevance weight of the selected content item and group of content items containing the selected item. The method includes, in response to subsequent user input, selecting and presenting a subset of content items and content groups to the user ordered by the adjusted associated relevance weights assigned to the content items and content groups.
Description
BACKGROUND

Field of Invention


This invention generally relates to learning the behavior of a user navigating and selecting content on input and display constrained devices. More specifically, the invention relates to using the learned navigation and selection behavior data to personalize the user's interactions with various service providers and content query systems, e.g., to better find results to queries provided by the user and to order the results for presentation to the user.


Description of Related Art


The acid test for the usability of an information finding system on input constrained and display constrained devices is the effort expended by the user in the discovery of desired information (the discovery of information could be text based search, browsing a content space, or some combination of both). The effort expended by the user is the number of steps involved in interacting with an information finding system to discover the desired information. Each click of a button, or a scroll motion, or the entry of a character, would be perceived by the user as expended effort. The success of any user interface may be determined by this metric.


Minimizing the effort expended to find information (be it search or browse) is a challenging problem on input and display constrained devices such as mobile phones and televisions. The method of discovery the user chooses may vary upon the application context and the user intent—for example, a user may, from past habit, browse through the phonebook to a contact to make a call (especially when the contact list is small), or perform text input when searching for a web site. Browse based navigation is typically used (and is effective) when the user's intent is broad. Furthermore it is a viable form of navigation only when the content space is not very large at any level of navigation of the content space hierarchy—only text-based search is effective for content spaces that are large. Any solution however, needs to solve the “minimal effort” problem for both these forms of discovery.


BRIEF SUMMARY

The invention provides methods and systems for selecting and presenting content based on learned user navigation and selection actions associated with the content.


Under another aspect of the invention, a user-interface method of selecting and presenting a collection of content items in which the presentation s ordered at least in part based on navigation and selection behavior of a user learned over time includes providing a set of content items where each content item has an associated relevance weight on a per user basis. The method also includes organizing the content items into groups based on the informational content of the content items, each group of content items having an associated relevance weight on a per user basis. The method further includes receiving from the user navigation and selection actions, adjusting the associated relevance weight of the selected content item. The method also includes, in response to subsequent input entered by the user, selecting and presenting a subset of content items and content groups to the user where the content items and content groups are ordered at least in part by the adjusted associated relevance weights assigned to the content items and content groups such that content items with greater associated relevance weights are presented as more relevant content items within a content group and groups of content items with greater associated relevance weights are presented as more relevant groups of content items.


Under further aspect of the invention, the context such as geographic location of the user, day, date, and time, in which the user performed the selection action is associated with the adjusted relevance weighting of content items and groups of content items. The adjusted relevance weighting of content items and groups of content items is only used in subsequent searches by the user when the search is performed in the same or similar context.


Under yet another aspect of the invention, the adjusted associated relevance weights are decayed as time passes from the act of adjusting the associated relevance weights.


Under yet another aspect of the invention, the adjusted associated relevance weights are decayed based upon the number of user selections occurring after the act of adjusting the associated relevance weights.


These and other features will become readily apparent from the following detailed description where embodiments of the invention are shown and described by way of illustration.





BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

For a more complete understanding of various embodiments of the present invention, reference is now made to the following descriptions taken in connection with the accompanying drawings in which:



FIG. 1 illustrates a network based information navigation system in accordance with one or more embodiments of the invention.



FIG. 2 illustrates device configuration options to perform navigation of locally or remotely resident information.



FIG. 3A illustrates instances of mobile device interface used to perform navigation of locally or remotely resident information.



FIG. 3B illustrates the various states the user can transition through to arrive at the desired result through navigation of resident information.



FIG. 3C illustrates a 12-key keypad with overloaded keys.



FIG. 4 illustrates a content hierarchy that automatically adjusts its structure (from the user's perspective) over time to match the user's preferences.



FIG. 5 illustrates the user's discovery of information using text search before and after system learns the user's action behavior.



FIG. 6 illustrates the user's discovery of information using browse before and after system learns the user's action behavior



FIG. 7 illustrates the user performing a repetitive banking task before and after system learns the user's action behavior.



FIG. 8 summarizes the basic concept of personalized navigation.



FIG. 9 illustrates a corporate hierarchy that is being navigated by the user.



FIG. 10 illustrates the initial conditions and the personalized navigation as the system continues to learn the user's action behavior.





DETAILED DESCRIPTION

The invention addresses the shortcomings of existing information navigation systems by taking a unified approach to the information finding process, be it search (incremental or full word search) or browse, and helps the user find information of interest by personalizing the information space to match the user's actions and exploiting the relationship of the user to the information space being navigated. A multi-pronged holistic approach of taking into account (1) what the user does with the device (user's intent) (2) when do these interactions happen (3) and where do these interactions happen, provides significant insights into achieving the goal of reducing device interactions, and thereby improving the user experience.


For text-input based discovery content, the key factors to reduce the effort involved in discovering information is to reduce the number of characters the user has to type in to discover the desired information and the number of browse navigations to reach the desired result once it appears on the screen. Incremental text search, combined with the right relevance ordering of results, is key to reducing the effort involved in discovering content through text-input based search. For browse based discovery of content, minimizing the number of navigations (navigating through folders and linear scroll) through the browse hierarchy is key.


Preferred embodiments of the invention capture user preferences, user information navigation behavior, and a user's relationship to an information hierarchy. The learned data is used to personalize the user's interaction with various service providers and the user's interaction with content query systems, e.g., to personalize the navigation and discovery of information by the user. In an illustrative embodiment, the user is searching a phonebook for an individual phone number. In another illustrative embodiment, the user is an employee searching a corporate hierarchy for superiors, peers, and subordinates.


Embodiments of the present invention build on techniques, systems and methods disclosed in earlier filed applications, including but not limited to U.S. patent application Ser. No. 11/136,261, entitled Method and System For Performing Searches For Television Programming Using Reduced Text Input, filed on May 24, 2005; U.S. patent application Ser. No. 11/246,432, entitled Method And System For Incremental Search With Reduced Text Entry Where The Relevance Of Results Is A Dynamically Computed Function of User Input Search String Character Count, filed on Oct. 7, 2005; U.S. patent application Ser. No. 11/235,928, entitled Method and System For Processing Ambiguous, Multiterm Search Queries, filed on Sep. 27, 2005; U.S. patent application Ser. No. 11/509,909, entitled User Interface For Visual Cooperation Between Text Input And Display Device, filed Aug. 25, 2006; and U.S. patent application Ser. No. 11/682,693, entitled Methods and Systems For Selecting and Presenting Content Based On Learned Periodicity Of User Content Selection, filed on Mar. 6, 2007; the contents of each of which are herein incorporated by reference. Those applications taught specific ways to perform incremental searches using ambiguous text input, methods of ordering the search results, and techniques for learning a user's behavior and preferences. The techniques disclosed in those applications can be used with the user's navigation behavior or the user's relationship to an information hierarchy described herein in the same or similar ways in which the techniques are applied to the collections of content items described in those applications. In such a case, the user's behavior or relationship described herein represents a particular type of content item. The present techniques, however, are not limited to systems and methods disclosed in the incorporated patent applications. Thus, while reference to such systems and applications may be helpful, it is not believed necessary to understand the present embodiments or inventions.


Referring to FIG. 1, an overall system for navigation of local and/or network resident information using a wide range of devices in accordance with one or more embodiments of the invention is illustrated. A server farm 101 serves as the source of navigation data and relevance updates with a network 102 functioning as the distribution framework. The distribution could be a combination of wired and wireless connections. The navigation devices could have a wide range of interface capabilities such as a hand-held device 103 (e.g. phone, PDA, or a music/video playback device) with limited display size and optionally an overloaded or small QWERTY keypad, a television 104a coupled with a remote 104b having an overloaded or small QWERTY keypad, or a desktop telephone 105 with limited display and input entry capabilities.


Referring to FIG. 2, multiple device configurations for search are illustrated. In one configuration, a navigation device 103 has a display 201, a processor 202, a volatile memory 203, a text input interface 204 which is on-device, remote connectivity 205 to a server 101 through a network 102, and a local persistent storage 206. In another device configuration the device 103 may not have the local persistent storage 206. In such a scenario, the device 103 would have remote connectivity 205 to submit the query to the server 101 and retrieve results from it. In another configuration of the device 103, it may not have remote connectivity 205. In this scenario the navigation database may be locally resident on the local persistent storage 206. The persistent storage 206 may be a removable storage element, such as SD, SmartMedia, CompactFlash card etc. In a configuration of the device with remote connectivity 205 and the local persistent storage 206 for navigation, the device may use the remote connectivity for search/browse relevance data update or for the case where the navigation database is distributed on the local storage 206 and on the server 101.



FIG. 3A illustrates a mobile device 300A interface for navigating locally or remotely resident information. The user enters text using a keypad 302A and the entered text is displayed in a text field 303A. The navigation interface on the device is a navigation button 305A that facilitates movement minimally in horizontal and vertical directions. The results are displayed in a results area 301A corresponding to the input incremental text query or browse action. The user can scroll through the results using a scroll interface 304A using the navigation buttons 305A. An alternate scroll interface 307A is shown in a browse only device 306A (e.g. a music player like iPod where content is remotely resident and the user remotely navigates this content). The browse results are shown in the display 308A.



FIG. 3B illustrates the various states of navigation actions a user could transition in order to get to the desired result. The user has the freedom to choose, either exclusively or a combination of, text entry and browse forms of discovery of the result(s) of interest. The actual path taken however may be influenced by both the user's intent and the information that is displayed. For instance, the user may start by entering text 301B, and may scroll through the displayed results 302B, pick a non-terminal node 303B and traverse the children of the non-terminal 302B. When the user discovers a result, he selects it 303B and performs an appropriate action 304B. This is discussed in further detail in FIG. 5 and FIG. 6.



FIG. 3C illustrates a 12-key keypad with overloaded keys. As explained in greater detail in the incorporated earlier filed applications a keypad with overloaded keys may be used to perform text input for incremental searches.


Personalized Navigation Based on the User's Navigation Behavior



FIG. 4 graphically illustrates one embodiment of personalized navigation with the data hierarchy changing over time as a node is repetitively acted upon. As described in more detail below, and based on techniques described in one or more of the incorporated applications, the relevance weight for a particular node is influenced by the context of each repetitive action taken upon the node, including the time and location of the action. Therefore, the first discoverable node in the phonebook list can be based on both a user's past navigation behavior along with the current search location and time. For example U.S. patent application Ser. No. 11/682,693, entitled Methods and Systems For Selecting and Presenting Content Based On Learned Periodicity Of User Content Selection, filed on Mar. 6, 2007, describes techniques used to infer future behavior of the user from past behavior.


For illustrative purposes FIG. 4 shows a data hierarchy 400 represented as a tree—it could have been any other form of organization such as a graph. The initial condition of a search space, such as a phonebook, is shown as data nodes (D1-DK). Each data node is in turn a hierarchy of nodes of different depths. If a user were to navigate the tree purely as in a browse based fashion, then the number of steps (the cost of navigation) to reach a node at depth “i” in a pure browse based fashion is Σ(Ldi+Ni) (1<=i<=I) where L is the cost of linear traversal to a node at level “i” where the traversed node is an ancestor of the node of interest, and N is the cost of descending from an ancestor of the node to the first child in its immediate descendants list. This cost is primarily due to the linearization of the data hierarchy as the user descends down the tree by a pure browse action.


For example, a data hierarchy 401 is representative of a phonebook with data node Dk representing John Doe, child node CkJ representing John Doe's mobile phone number, and the other child node siblings to child node CkJ representing John Doe's other contact information such as home and office numbers. The data hierarchy also contains node D1 representing John Adams and node D2 representing John Brown. The user interface could, from a rendering perspective, display both a contact (e.g. data nodes D1 through Dk) and the associated primary contact number (e.g. child nodes C11 through Ck1). For example, when the user searches for “John” the result set 402 would contain D1 (John Adams) and C11 (John Adams' primary contact number), D2 (John Brown) and C21 (John Brown's primary contact number), up to and including Dk (John Doe) and Ck1 (John Doe's primary contact number). The user would have the option to either see other contact numbers for John Doe by descending down the tree, or directly making a call to the primary contact number initially presented.


If the user is interested in John Doe's mobile phone number, node CkJ, the user may discover the number using a text search or browse based navigation. In addition, if the user repetitively searches for or browses to node CkJ, the relevance weight assigned to this node would continue to strengthen with each repetitive action taken upon it. The increased relevance weight assigned to the node would be used to reorder the view of the navigation hierarchy from the user's perspective. As illustrated 401 prior to the learned preference and increased relevance weight, the node CkJ would be the jth entry presented in John Doe's list of contact numbers. As illustrated in 403, after the increased relevance weight is applied, node CkJ would bubble up to be the first entry within node Dk, e.g. becoming the first phone number in John Doe's contact folder. The result set 404 displayed for data node Dk (John Doe) would now present John Doe's mobile phone number as the first entry in the result set.


As illustrated in 405, node CkJ's weight would continue to strengthen with usage and eventually this node would become the first discoverable node in the phonebook list. After learning has taken place the result set 406 would have Dk (John Doe) and CkJ (John Doe's mobile number) as the first entry. The remainder of the result set, absent any other user selections, would contain D1 (John Adams) and C11 (John Adams' primary contact number), and D2 (John Brown) and C21 (John Brown's primary contact number).


Repetitive actions with regular patterns eventually result in the user not even having to do much. The relevant nodes receive an increased weight and the contact number would be rendered on the phone display at the appropriate time and location. It is important to note that this strengthening of the relevance weight of the node happens regardless of the type of navigation, either search or browse. Both result in the same form of reorganized view of the navigation hierarchy. For example, if the user always searches for John Doe and calls him, the increase in relevance weight of John Doe would result in John Doe being discovered with fewer characters. Finally, if the repetitive pattern is very regular, the text input step may even be eliminated. The first node in the phonebook context would contain John Doe's contact information and the user would just have to select the contact without entering any incremental text.


While the above illustration focuses on reordering for highly repetitive tasks, the system could also perform reordering of the user's view of the content space based on the broader knowledge of the user's tastes learned from the user's action patterns. For example, if the user always searches for action genre movies, then those movies could be given more relevance so as to be discovered more easily.



FIG. 5 illustrates a user performing text input based incremental search on a mobile phone. The user types in ‘R’ (501), which results in the rendering of results starting with R. FIGS. 502-504 illustrate the user scrolling down to the last row to select RANDOLF to make a call. If this “search-call” action were a repetitive action (with time and location also considered), the system would learn this over time 505 and boost the relevance of RANDOLF so that the user can find this contact easily. Furthermore, if there is another person “RAMA” who is also called in a regular manner but in a different time window, then the entry of the same character “R” would bring up RANDOLF at 9:00 am (which is typically when a call is made to Randolf) and “Rama” at 5:00 pm (which is typically when a call is made to this person). Over time, this repetitive action would strengthen enough to even obviate the user to have to enter the character “R”. Around 9:00 am in the morning, the phone would display “Randolf” on the top for easy access, and “Rama” around 5:00 pm. Thus, in this example, the presentment of the data hierarchy at 9:00 am is different from the presentment of the data hierarchy at 5:00 pm.


The time window identified for repetitive actions may be defined in advance or may be determined dynamically according to the frequency of the repetitive actions. For example, the time window may be set as 15-minute periods occurring during each day or the system may determine a larger window is appropriate for a particular day. The time window may also be differentiated by day of the week or date, e.g., different nodes may be of higher relevance during the week as compared to their relevance during the weekend. Finally, the system may interface with external applications and determine an ideal time window based on the data in the application. For example, the system may take data from a calendar application and boost the relevance of nodes based upon a weekly, monthly, or annual event (such as a birthday of a family member or a monthly project meeting).


Similarly, location of the user may influence the relevance of a node. For example, if the user is at work, the relevance of business contact information may be increased. Location may be determined by a variety of methods well known in the art, e.g., the user's device may have GPS capabilities.



FIG. 6 illustrates the learning of a browse based repetitive navigation behavior. Steps 601 through 604 illustrate a user initially performing scroll (or page down operations) through a lexicographically sorted list of contacts to reach the desired contact, Randolf. The system learns this action behavior 605 over time, and automatically displays “Randolf s” number on the screen for easier access. Similarly, if the contact “Rama” were typically called at a different time window, then “Rama” would be displayed for easy access during that period of day. Thus from a lexicographic ordering (initial condition) the system learns the user's action patterns over time, to render orderings that reflect the most likely actions the user would perform for that particular time and location.



FIG. 7 illustrates another instance where a search and browse based navigation to reach an action is learned by the system and optimized to provide a better user experience. The user performs an incremental search 701, entering “BANKE” which results in discovering the bank of choice “BANKEX.” The user then browses to the bank finance portion of the bank “B. FINANCE,” 702 followed by selecting the payment item “PAY” from the submenu 703. The first step of making a payment is pin entry 704. As the system learns 705 the user's actions over time, the user's effort expended is reduced both in the number of characters used to get to the desired bank (“BA”) and the number of browse actions the user needs to perform—the bill pay option is shown along with the bank in the result. The user now has to enter three fewer characters during the incremental text search, and does not need to browse through two submenus to select the payment item. This form of rendering an aggregate node, along with the most likely child node that user may act upon, gives the user the choice to act on both items without further effort. The user can select the pay bill option or choose to browse all the other choices.



FIG. 8 summarizes the basic concept of personalizing the content space of the user based on learned behavior. The user's navigation through the content space 801 and actions on discovered content 802 is learned by the system. Each time a user performs an action on a node, the relevance of that node (for that particular time and location), with respect to the user is altered. The view of the content hierarchy from the user's perspective is altered to match the user's learned behavior and preferences.


Personalized Navigation Based on the User's Relationship to the Information Hierarchy



FIG. 9 illustrates an instance of a corporate employee hierarchy tree with a user 904 at a particular level in this hierarchy, specifically level 2. Tom Dalton 901 is at level 0, Tom Clancy 902 is at level 1, Tom Jones 903 is a peer of the user 904 at level 2, and Tom Crawford 905 is a direct report of the user 904 on level 3. In the initial conditions state, a user who just joins a company or moves to a new position within a company, can benefit from the present invention, which adjusts the organization tree to help the user 904 easily find the members of the group at his level, his direct reports, or his manager. The user can easily find the person he is looking for by entering just a few characters of an incremental search or with minimal steps using a pure browse search.


When the user 904 searches for a particular person by entering text, e.g., “TOM”, the system automatically lists the results in descending order of the proximity of the matched employee(s) in relationship to the user's position in the hierarchy. However, after learning, the nodes that are immediate descendents to the user's node may trump the user's sibling nodes, since the immediate descendents may be direct reports. Additionally, if the user 904 is discovering the information using an incremental search, e.g., “TO”, results may be shown with matches from different nodes as clusters for each level with one match displayed with the aggregate node (e.g. TOM CLANCY at Level 1, TOM CRAWFORD Level 3, TOM DALTON level 0). The system may provide a means to navigate these aggregate nodes, so the user can quickly get to any level. If the user is navigating the tree purely by a browse means, then the employees at the user's level (or his immediate reports) will be listed first as aggregates followed by other levels. This form of navigation would be more user-friendly than a pure lexicographically ordered browse tree.


The user search experience is also improved, in comparison to pure organization based clustering, by reordering the information hierarchy to match the user's repetitive action behavior. For example, if the user 904 repetitively navigates to a sibling node to perform an action (e.g. navigating to the node for Tom Jones 903 to place a phone call), then the ordering of the user's siblings would be adjusted over time to reduce this navigation distance by bringing that node closer to the user. This approach can also be used for any node that is at any level. For example, if the user 904 always navigates to the node for Tom Clancy 902 to place a phone call, then that node is reordered at its own level to come up quicker. Additionally with time, the nodes that are frequently visited in the hierarchy would move closer to the user's home node 904.


The navigation process within the corporate employee hierarchy tree could have been text-based search or browse based navigation. Over time the nodes that are frequently visited in the hierarchy would move closer to the user's home node within the hierarchy, thus easing their discovery either by search or browse. If the search were an incremental search, over time personalization would reduce the number of characters required for discovering the node. If the search was a browsed based navigation, over time personalization would reduce the number of user selections required for discovering the node.



FIG. 10 illustrates the evolution of the navigation system with time 1002 as the navigation hierarchy is reordered to match the user's action behavior (e.g. making a phone call after discovering a node of interest). In an embodiment of the invention, the initial conditions 1001 would start with the “locus of relevance centered” at the user's position in the organization hierarchy. As the user navigates hierarchy and selects specific content items, the hierarchy would evolve to bring referenced nodes closer to user's locus of relevance. For example, using the method described in FIG. 4 above, this could be accomplished by assigning initial relevance weights to nodes in the navigation hierarchy based on the users position within a corporate hierarchy. The initial relevance weights could be assigned such that, prior to learning, the results would be ordered to return peers, then subordinates, then supervisors, and finally persons unrelated to the user in the corporate hierarchy. As the user navigates and selects content items from the navigation hierarchy the relevance weights of particular nodes, with respect to the particular user performing the search, would be increased. When the user makes subsequent searches, the nodes with higher relevance weights would be presented higher in the search results.


In another embodiment of the invention the locus of relevance would always remain at the root of the organization hierarchy, with the user's nodes of interest hoisted to the root for easy access. This method of reordering would be meaningful for information finding in an entertainment space, where no prior knowledge of the user's interest is known, and hence there is no a priori relationship between the user and the content navigation hierarchy.


Another instance of automatic adjustment of circle of relevance is where the user is part of a defined group, for example, where the user is a member of an Instant Messaging group or an online community group, such as a Yahoo group. The system would automatically increase the relevance weights of the members of the group in relation to the user. Here the adjustment of the circle of relevance is done by the system merely by the participation of the user in these groups and no explicit action by the user is required. This is similar to the corporate setting where a user can be grouped with his or her peers, or where a user can be grouped with all other employees with offices on the same floor in a building.


Additionally, the system can take advantage of dynamic groups created for projects spanning employees in the corporate hierarchy. The members of these dynamically created groups would also move closer to the “locus of relevance” of the user. These groups could have been created explicitly in the corporate database, or the system may interface with external applications, such as a mailing list in an email application, in order to discover these dynamic groups. Once a dynamically created group is detected, again using the techniques described above, the relevance weights of the members of that group can be adjusted such that group members are returned higher in the result set, overriding the default corporate hierarchy. For example, after a new emailing list for a project is created, the relevance weights of the members of that project can be adjusted and the results would be ordered to return project members, then peers, then subordinates, then supervisors, and finally persons unrelated to the user in the corporate hierarchy.


Automatic adjustment of locus or circle of relevance would also be applied in a transitive manner between individuals or groups of individuals based on the actions of the individuals. For example, in a community, if a Susie calls Barbara often, and Barbara calls Kate often, then the likelihood of Susie calling Kate increases over time. Hence, when Susie makes a search or performs a browse, the relevance of ordering of Kate is increased, such that Susie can discover Kate more easily. In this case, when Susie navigates and selects the contact information for Barbara, the relevance weight for that node is adjusted. In addition, the relevance weights for any nodes that Barbara has selected, e.g. Kate, are also increased with respect to Susie. The contact information for both Barbara and Kate will now be returned higher in the result set for any subsequent searches by Susie.


In an embodiment of the invention the locus of relevance would also be adjusted over time by the system taking into account the actions taken by groups of individuals. For example, if members of two groups in an organization hierarchy communicate often with each other (e.g. the action taken by users in this case being making a phone call), then the two groups would come closer to each other in the navigation hierarchy. So when searches are done by a member of one of these groups, the system would give a higher relevance to people from the other group with which the communication was high—this would facilitate the discovery of the desired result with fewer characters in the case of incremental search. Similarly, in a browse based discovery, the other group would be found closer to the user's own group in the organization hierarchy.


For example, consider a corporate hierarchy where Able and Baker are members of the accounting department, Charlie and Dawn are members of the tax department, and Eugene is a member of the legal department. If Able calls Charlie on a regular basis then the accounting and tax departments become closer to each other in the navigation hierarchy. Here the relevance weights for all members of both departments are adjusted, not just those for Able and Charlie. So when Baker searches the corporate hierarchy members of the tax department will have a higher relevance than members of the legal department. This is due to the contacts, over time, between members of the two departments, e.g. the contacts between Able and Charlie, and the associated adjustments to the relevance weights for all members of both departments.


Having described preferred embodiments of the present invention, it should be apparent that modifications can be made without departing from the spirit and scope of the invention. For example, the relative weighting of nodes has been used herein in the context of a phone book. However, embodiments of the invention can be implemented for any form of node based content space, such as genres of movies.

Claims
  • 1. A method for generating for display identifiers of videos, the method comprising: retrieving, using control circuitry, from a database at a server, data indicating a relationship between a first user and a second user, wherein the data is based on information indicating a frequency at which the first user and the second user interacted during a first period of time;making a determination that a video is relevant to the first user;in response to determining that the video is relevant to the first user, assigning a first relevance weight of the video to the second user based on the frequency at which the first user and the second user interacted during the first period of time;receiving information indicating a frequency at which the first user and the second user interacted during a second period of time;determining that the frequency at which the first user and the second user interacted during the second period of time is more than the frequency at which the first user and the second user interacted during the first period of time;in response to determining that the frequency at which the first user and the second user interacted during the second period of time is more than the frequency at which the first user and the second user interacted during the first period of time: increasing the first relevance weight of the video to the second user; andgenerating, for display in a user interface of the second user, an identifier of the video, wherein placement of the identifier of the video in the user interface is based on the first relevance weight of the video to the second user.
  • 2. A system for generating for display identifiers of videos, the system comprising control circuitry configured to: retrieve, from a database at a server, data indicating a relationship between a first user and a second user, wherein the data is based on information indicating a frequency at which the first user and the second user interacted during a first period of time;make a determination that a video is relevant to the first user;in response to determining that the video is relevant to the first user, assign a first relevance weight of the video to the second user based on the frequency at which the first user and the second user interacted during the first period of time;receive information indicating a frequency at which the first user and the second user interacted during a second period of time;determine that the frequency at which the first user and the second user interacted during the second period of time is more than the frequency at which the first user and the second user interacted during the first period of time;in response to determining that the frequency at which the first user and the second user interacted during the second period of time is more than the frequency at which the first user and the second user interacted during the first period of time: increase the first relevance weight of the video to the second user; andgenerate, for display in a user interface of the second user, an identifier of the video, wherein placement of the identifier of the video in the user interface is based on the first relevance weight of the video to the second user.
  • 3. The method of claim 1, further comprising calculating a second relevance weight that reflects an amount of relevance of the video to the first user.
  • 4. The method of claim 1, wherein the data indicating the relationship between the first user and the second user is based on information indicating that the first user and the second user belong to a same group.
  • 5. The method of claim 1, wherein the first determination is based on receiving a selection of the video by the first user.
  • 6. The method of claim 1, wherein the first determination is based on receiving a search for the video performed by the first user.
  • 7. The method of claim 1, comprising: recording the amount of interaction between the first user and the second user dining the first period of time.
  • 8. The method of claim 1, further comprising: receiving, using the control circuitry, a selection of the identifier of the video from the second user; andin response to receiving the selection, generating the video for display for the second user.
  • 9. The system of claim 2, wherein the control circuitry is further configured to calculate a second relevance weight that reflects an amount of relevance of the video to the first user.
  • 10. The system of claim 9, wherein the first determination is based on the second relevance weight.
  • 11. The system of claim 2, wherein the data indicating the relationship between the first user and the second user is based on information indicating that the first user and the second user belong to a same group.
  • 12. The system of claim 2, wherein the control circuitry is further configured to make the first determination based on receiving a selection of the video by the first user.
  • 13. The system of claim 2, wherein the control circuitry is further configured to make the first determination based on receiving a search for the video performed by the first user.
  • 14. The system of claim 2, wherein the control circuitry is further configured to: record the amount of interaction between the first user and the second user during the first period of time.
  • 15. The system of claim 2, wherein the control circuitry is further configured to: receive a selection of the identifier of the video from the second user; andgenerate the video for display for the second user in response to receiving the selection.
  • 16. The method of claim 3, wherein the first determination is based on the second relevance weight.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior U.S. patent application Ser. No. 14/175,189, filed on Feb. 7, 2014, entitled User Interface Methods and Systems for Selecting and Presenting Content Based on User Relationships, currently allowed, which is a continuation of prior U.S. patent application Ser. No. 13/765,335, filed on Feb. 12, 2013, entitled User Interface Methods and Systems For Selecting and Presenting Content Based On User Relationships, now U.S. Pat. No. 8,688,746, which is a continuation of prior U.S. patent application Ser. No. 13/479,820, filed on May 24, 2012, entitled User Interface Methods and Systems For Selecting and Presenting Content Based On User Relationships, now U.S. Pat. No. 8,423,583, which is a continuation of prior U.S. patent application Ser. No. 13/336,660, filed on Dec. 23, 2011, entitled User Interface Methods and Systems For Selecting and Presenting Content Based On User Navigation and Selection Actions Associated With The Content, now U.S. Pat. No. 8,375,069, which is a continuation of U.S. patent application Ser. No. 13/034,034, filed on Feb. 24, 2011, entitled User Interface Methods and Systems For Selecting and Presenting Content Based On User Navigation and Selection Actions Associated With The Content, now U.S. Pat. No. 8,086,602, which is a continuation of prior U.S. patent application Ser. No. 12/326,485 filed on Dec. 2, 2008, entitled User Interface Methods and Systems For Selecting and Presenting Content Based On User Navigation and Selection Actions Associated With The Content, now U.S. Pat. No. 7,899,806, which is a continuation of U.S. patent application Ser. No. 11/738,101 filed on Apr. 20, 2007, entitled User Interface Methods and Systems For Selecting and Presenting Content Based On User Navigation and Selection Actions Associated With The Content, now U.S. Pat. No. 7,461,061, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Application No. 60/793,537, entitled A System and Method for Personalized Navigation and Discovery of Information on Input and Display Constrained Devices, filed Apr. 20, 2006, and U.S. Provisional Application No. 60/793,540, entitled A System and Method for Personalized Navigation and Discovery of Information Utilizing Users Relationship to the Information Hierarchy on Input and Display Constrained Devices, filed Apr. 20, 2006, the contents of all of which are incorporated by reference herein. This application is related to U.S. patent application Ser. No. 11/738,138, entitled User Interface Methods and Systems For Selecting and Presenting Content Based On Relationships Between the User and Other Members of An Organization, filed Apr. 20, 2007, now U.S. Pat. No. 7,539,676.

US Referenced Citations (380)
Number Name Date Kind
252873 Freese Jan 1882 A
1261167 Russell Apr 1918 A
4045777 Mierzwinski et al. Aug 1977 A
4453217 Boivie Jun 1984 A
4760528 Levin Jul 1988 A
4797855 Duncan, IV et al. Jan 1989 A
4893238 Venema Jan 1990 A
5224060 Ma Jun 1993 A
5337347 Halstead-Nussloch et al. Aug 1994 A
5369605 Parks Nov 1994 A
5487616 Ichbiah Jan 1996 A
5532754 Young et al. Jul 1996 A
5557686 Brown et al. Sep 1996 A
5600364 Hendricks Feb 1997 A
5623406 Ichbiah Apr 1997 A
5635989 Rothmuller Jun 1997 A
5732216 Logan et al. Mar 1998 A
5745889 Burrows Apr 1998 A
5774588 Li Jun 1998 A
5802361 Wang et al. Sep 1998 A
5805155 Allibhoy et al. Sep 1998 A
5818437 Grover et al. Oct 1998 A
5828420 Marshall et al. Oct 1998 A
5828991 Skiena et al. Oct 1998 A
5835087 Herz et al. Nov 1998 A
5859662 Cragun et al. Jan 1999 A
5872834 Teitelbaum Feb 1999 A
5880768 Lemmons et al. Mar 1999 A
5896444 Perlman et al. Apr 1999 A
5912664 Eick et al. Jun 1999 A
5930788 Wical Jul 1999 A
5937422 Nelson et al. Aug 1999 A
5945928 Kushler et al. Aug 1999 A
5945987 Dunn Aug 1999 A
5953541 King et al. Sep 1999 A
6005565 Legall et al. Dec 1999 A
6005597 Barrett et al. Dec 1999 A
6006225 Bowman et al. Dec 1999 A
6008799 Van Kleeck Dec 1999 A
6009459 Belfiore et al. Dec 1999 A
6011554 King et al. Jan 2000 A
6041311 Chislenko et al. Mar 2000 A
6047300 Walfish et al. Apr 2000 A
6075526 Rothmuller Jun 2000 A
6133909 Schein et al. Oct 2000 A
6169984 Bogdan Jan 2001 B1
6184877 Dodson et al. Feb 2001 B1
6189002 Roitblat Feb 2001 B1
6204848 Nowlan et al. Mar 2001 B1
6223059 Haestrup Apr 2001 B1
6239794 Yuen et al. May 2001 B1
6260050 Yost et al. Jul 2001 B1
6266048 Carau, Sr. Jul 2001 B1
6266814 Lemmons et al. Jul 2001 B1
6269361 Davis et al. Jul 2001 B1
6286064 King et al. Sep 2001 B1
6292804 Ardoin et al. Sep 2001 B1
6307548 Flinchem et al. Oct 2001 B1
6307549 King et al. Oct 2001 B1
6360215 Judd et al. Mar 2002 B1
6377945 Risvik Apr 2002 B1
6383080 Link et al. May 2002 B1
6385602 Tso et al. May 2002 B1
6388714 Schein et al. May 2002 B1
6392640 Will May 2002 B1
6438579 Hosken Aug 2002 B1
6438751 Voyticky et al. Aug 2002 B1
6463586 Jerding Oct 2002 B1
6466933 Huang et al. Oct 2002 B1
6529903 Smith et al. Mar 2003 B2
6543052 Ogasawara Apr 2003 B1
6564213 Ortega et al. May 2003 B1
6564313 Kashyap May 2003 B1
6564378 Satterfield et al. May 2003 B1
6594657 Livowsky Jul 2003 B1
6600496 Wagner et al. Jul 2003 B1
6614422 Rafii et al. Sep 2003 B1
6614455 Cuijpers et al. Sep 2003 B1
6615248 Smith Sep 2003 B1
6622148 Noble et al. Sep 2003 B1
6631496 Li et al. Oct 2003 B1
6662177 Martino et al. Dec 2003 B1
6664980 Bryan et al. Dec 2003 B2
6708336 Bruette Mar 2004 B1
6721713 Guheen Apr 2004 B1
6721954 Nickum Apr 2004 B1
6732369 Schein et al. May 2004 B1
6734881 Will May 2004 B1
6735695 Gopalakrishnan et al. May 2004 B1
6756997 Ward, III et al. Jun 2004 B1
6757906 Look et al. Jun 2004 B1
6766526 Ellis Jul 2004 B1
6772147 Wang Aug 2004 B2
6785671 Bailey et al. Aug 2004 B1
6801909 Delgado et al. Oct 2004 B2
6835602 Norskov et al. Dec 2004 B2
6839702 Patel et al. Jan 2005 B1
6839705 Grooters Jan 2005 B1
6850693 Young et al. Feb 2005 B2
6865575 Smith et al. Mar 2005 B1
6865746 Herrington et al. Mar 2005 B1
6907273 Smethers Jun 2005 B1
6965374 Villet et al. Nov 2005 B2
6999959 Lawrence et al. Feb 2006 B1
7013304 Schuetze et al. Mar 2006 B1
7117207 Kerschberg et al. Oct 2006 B1
7130866 Schaffer Oct 2006 B2
7136845 Chandrasekar et al. Nov 2006 B2
7136854 Smith et al. Nov 2006 B2
7146627 Ismail et al. Dec 2006 B1
7149983 Robertson et al. Dec 2006 B1
7165098 Boyer et al. Jan 2007 B1
7185335 Hind et al. Feb 2007 B2
7191238 Ucjoda Mar 2007 B2
7213256 Kikinis May 2007 B1
7225180 Donaldson et al. May 2007 B2
7225184 Carrasco et al. May 2007 B2
7225455 Bennington et al. May 2007 B2
7269548 Fux et al. Sep 2007 B2
7293231 Gunn et al. Nov 2007 B1
7451470 Zimmerman Nov 2008 B2
7461061 Aravamudan et al. Dec 2008 B2
7487151 Yamamoto Feb 2009 B2
7509313 Colledge et al. Mar 2009 B2
7529741 Aravamudan et al. May 2009 B2
7529744 Srivastava et al. May 2009 B1
7536384 Venkataraman et al. May 2009 B2
7536854 Da-Silva et al. May 2009 B2
7539676 Aravamudan et al. May 2009 B2
7548915 Ramer et al. Jun 2009 B2
7594244 Scholl et al. Sep 2009 B2
7644054 Garg et al. Jan 2010 B2
7657526 Aravamudan et al. Feb 2010 B2
7679534 Kay et al. Mar 2010 B2
7680882 Tiu, Jr. et al. Mar 2010 B2
7683886 Willey Mar 2010 B2
7685197 Fain et al. Mar 2010 B2
7712053 Bradford et al. May 2010 B2
7725485 Sahami et al. May 2010 B1
7725486 Tsuzuki et al. May 2010 B2
7739280 Aravamudan et al. Jun 2010 B2
7756895 Emigh Jul 2010 B1
7757250 Horvitz et al. Jul 2010 B1
7774294 Aravamudan et al. Aug 2010 B2
7774341 Aravamudan et al. Aug 2010 B2
7779011 Venkataraman et al. Aug 2010 B2
7788266 Venkataraman et al. Aug 2010 B2
7792815 Aravamudan et al. Sep 2010 B2
7835998 Aravamudan et al. Nov 2010 B2
7904924 de Heer et al. Mar 2011 B1
8046801 Ellis et al. Oct 2011 B2
8107397 Bagchi et al. Jan 2012 B1
8527510 Chen Sep 2013 B2
20020042791 Smith et al. Apr 2002 A1
20020052873 Delgado et al. May 2002 A1
20020083448 Johnson Jun 2002 A1
20020133481 Smith et al. Sep 2002 A1
20030011573 Villet et al. Jan 2003 A1
20030014753 Beach et al. Jan 2003 A1
20030023976 Kamen et al. Jan 2003 A1
20030037043 Chang et al. Feb 2003 A1
20030046698 Kamen et al. Mar 2003 A1
20030055894 Yeager et al. Mar 2003 A1
20030066079 Suga Apr 2003 A1
20030217121 Willis Nov 2003 A1
20030226146 Thurston et al. Dec 2003 A1
20030229900 Reisman Dec 2003 A1
20030237096 Barrett et al. Dec 2003 A1
20040013909 Shimizu et al. Jan 2004 A1
20040021691 Dostie et al. Feb 2004 A1
20040024777 Schaffer Feb 2004 A1
20040034652 Hofmann et al. Feb 2004 A1
20040046744 Rafii et al. Mar 2004 A1
20040049783 Lemmons et al. Mar 2004 A1
20040054520 Dehlinger et al. Mar 2004 A1
20040073432 Stone Apr 2004 A1
20040073926 Nakamura et al. Apr 2004 A1
20040078815 Lemmons et al. Apr 2004 A1
20040078816 Johnson Apr 2004 A1
20040078820 Nickum Apr 2004 A1
20040083198 Bradford et al. Apr 2004 A1
20040093616 Johnson May 2004 A1
20040103434 Ellis May 2004 A1
20040111745 Schein et al. Jun 2004 A1
20040128686 Boyer et al. Jul 2004 A1
20040139091 Shin Jul 2004 A1
20040143569 Gross et al. Jul 2004 A1
20040155908 Wagner Aug 2004 A1
20040163032 Guo et al. Aug 2004 A1
20040194141 Sanders Sep 2004 A1
20040205065 Petras et al. Oct 2004 A1
20040216160 Lemmons et al. Oct 2004 A1
20040220926 Lamkin et al. Nov 2004 A1
20040221308 Cuttner et al. Nov 2004 A1
20040231003 Cooper et al. Nov 2004 A1
20040254911 Grasso et al. Dec 2004 A1
20040260574 Gross Dec 2004 A1
20040261021 Mittal et al. Dec 2004 A1
20050015366 Carrasco et al. Jan 2005 A1
20050038702 Merriman et al. Feb 2005 A1
20050060743 Ohnuma et al. Mar 2005 A1
20050071874 Elcock et al. Mar 2005 A1
20050079895 Kalenius et al. Apr 2005 A1
20050086234 Tosey Apr 2005 A1
20050086691 Dudkiewicz et al. Apr 2005 A1
20050086692 Dudkiewicz et al. Apr 2005 A1
20050097170 Zhu et al. May 2005 A1
20050125307 Hunt et al. Jun 2005 A1
20050129199 Abe Jun 2005 A1
20050160458 Baumgartner Jul 2005 A1
20050174333 Robinson et al. Aug 2005 A1
20050187945 Ehrich et al. Aug 2005 A1
20050192944 Flinchem Sep 2005 A1
20050204388 Knudson et al. Sep 2005 A1
20050210020 Gunn et al. Sep 2005 A1
20050210383 Cucerzan et al. Sep 2005 A1
20050210402 Gunn et al. Sep 2005 A1
20050223308 Gunn et al. Oct 2005 A1
20050240580 Zamir et al. Oct 2005 A1
20050246311 Whelan et al. Nov 2005 A1
20050246324 Paalasmaa et al. Nov 2005 A1
20050251827 Ellis et al. Nov 2005 A1
20050256756 Lam et al. Nov 2005 A1
20050262542 DeWeese et al. Nov 2005 A1
20050267994 Wong et al. Dec 2005 A1
20050273812 Sakai Dec 2005 A1
20050278175 Hyvonen Dec 2005 A1
20050283468 Kamvar et al. Dec 2005 A1
20060004892 Lunt et al. Jan 2006 A1
20060010477 Yu Jan 2006 A1
20060010478 White et al. Jan 2006 A1
20060010503 Inoue et al. Jan 2006 A1
20060013487 Longe et al. Jan 2006 A1
20060015588 Achlioptas Jan 2006 A1
20060015906 Boyer et al. Jan 2006 A1
20060020662 Robinson Jan 2006 A1
20060036640 Tateno et al. Feb 2006 A1
20060041843 Billsus et al. Feb 2006 A1
20060044277 Fux et al. Mar 2006 A1
20060053449 Gutta Mar 2006 A1
20060059044 Chan et al. Mar 2006 A1
20060069616 Bau Mar 2006 A1
20060075429 Istvan et al. Apr 2006 A1
20060090182 Horowitz et al. Apr 2006 A1
20060090185 Zito et al. Apr 2006 A1
20060090812 Summerville May 2006 A1
20060098899 King et al. May 2006 A1
20060101499 Aravamudan et al. May 2006 A1
20060101503 Venkataraman et al. May 2006 A1
20060101504 Aravamudan et al. May 2006 A1
20060106782 Blumenau et al. May 2006 A1
20060112162 Marot et al. May 2006 A1
20060117019 Sylthe et al. Jun 2006 A1
20060136379 Marino et al. Jun 2006 A1
20060150216 Herz et al. Jul 2006 A1
20060156233 Nurmi Jul 2006 A1
20060161520 Brewer et al. Jul 2006 A1
20060163337 Unruh Jul 2006 A1
20060167676 Plumb Jul 2006 A1
20060167859 Verbeck Sibley et al. Jul 2006 A1
20060173818 Berstis et al. Aug 2006 A1
20060176283 Suraqui Aug 2006 A1
20060184989 Slothouber Aug 2006 A1
20060190308 Janssens et al. Aug 2006 A1
20060190966 McKissick et al. Aug 2006 A1
20060195435 Laird-McConnell et al. Aug 2006 A1
20060206454 Forstall et al. Sep 2006 A1
20060206815 Pathiyal et al. Sep 2006 A1
20060242178 Butterfield et al. Oct 2006 A1
20060242607 Hudson Oct 2006 A1
20060248078 Gross et al. Nov 2006 A1
20060248573 Pannu et al. Nov 2006 A1
20060256070 Moosavi et al. Nov 2006 A1
20060256078 Flinchem et al. Nov 2006 A1
20060259344 Patel et al. Nov 2006 A1
20060259479 Dai Nov 2006 A1
20060261021 Stagnaro Nov 2006 A1
20060271552 McChesney et al. Nov 2006 A1
20060271959 Jacoby et al. Nov 2006 A1
20060274051 Longe et al. Dec 2006 A1
20060282856 Errico et al. Dec 2006 A1
20060285665 Wasserblat Dec 2006 A1
20070005526 Whitney et al. Jan 2007 A1
20070005563 Aravamudan et al. Jan 2007 A1
20070005576 Cutrell et al. Jan 2007 A1
20070016476 Hoffberg et al. Jan 2007 A1
20070016862 Kuzmin Jan 2007 A1
20070027852 Howard et al. Feb 2007 A1
20070027861 Huentelman et al. Feb 2007 A1
20070027871 Arbajian Feb 2007 A1
20070043750 Dingle Feb 2007 A1
20070044122 Scholl et al. Feb 2007 A1
20070050337 Venkataraman et al. Mar 2007 A1
20070050348 Aharoni et al. Mar 2007 A1
20070061244 Ramer et al. Mar 2007 A1
20070061317 Ramer et al. Mar 2007 A1
20070061321 Venkataraman et al. Mar 2007 A1
20070061753 Ng et al. Mar 2007 A1
20070061754 Ardhanari et al. Mar 2007 A1
20070064626 Evans Mar 2007 A1
20070067272 Flynt et al. Mar 2007 A1
20070074131 Assadollahi Mar 2007 A1
20070088681 Aravamudan et al. Apr 2007 A1
20070094024 Kristensson et al. Apr 2007 A1
20070100650 Ramer et al. May 2007 A1
20070121843 Atazky et al. May 2007 A1
20070130128 Garg et al. Jun 2007 A1
20070136745 Garbow et al. Jun 2007 A1
20070143567 Gorobets Jun 2007 A1
20070150606 Flinchem et al. Jun 2007 A1
20070157242 Cordray et al. Jul 2007 A1
20070168544 Sciammarella Jul 2007 A1
20070174249 James Jul 2007 A1
20070182595 Ghasabian Aug 2007 A1
20070186242 Price et al. Aug 2007 A1
20070199025 Angiolillo et al. Aug 2007 A1
20070208613 Backer Sep 2007 A1
20070208718 Javid et al. Sep 2007 A1
20070214162 Rice Sep 2007 A1
20070219984 Aravamudan et al. Sep 2007 A1
20070219985 Aravamudan et al. Sep 2007 A1
20070226649 Agmon Sep 2007 A1
20070240045 Fux et al. Oct 2007 A1
20070242178 Kawasaki et al. Oct 2007 A1
20070250866 Yamada Oct 2007 A1
20070255693 Ramaswamy et al. Nov 2007 A1
20070256070 Bykov et al. Nov 2007 A1
20070260703 Ardhanari et al. Nov 2007 A1
20070266021 Aravamudan et al. Nov 2007 A1
20070266026 Aravamudan et al. Nov 2007 A1
20070266406 Aravamudan et al. Nov 2007 A1
20070271205 Aravamudan et al. Nov 2007 A1
20070276773 Aravamudan et al. Nov 2007 A1
20070276821 Aravamudan et al. Nov 2007 A1
20070276859 Aravamudan et al. Nov 2007 A1
20070288456 Aravamudan et al. Dec 2007 A1
20070288457 Aravamudan et al. Dec 2007 A1
20080016240 Balandin Jan 2008 A1
20080065617 Burke et al. Mar 2008 A1
20080071771 Venkataraman et al. Mar 2008 A1
20080077577 Byrne et al. Mar 2008 A1
20080086704 Aravamudan Apr 2008 A1
20080109401 Sareen et al. May 2008 A1
20080114743 Venkataraman et al. May 2008 A1
20080134043 Georgis et al. Jun 2008 A1
20080147711 Spiegelman Jun 2008 A1
20080177717 Kumar et al. Jul 2008 A1
20080195601 Ntoulas et al. Aug 2008 A1
20080209229 Ramakrishnan et al. Aug 2008 A1
20080255977 Altberg et al. Oct 2008 A1
20080275719 Davis et al. Nov 2008 A1
20080301732 Archer et al. Dec 2008 A1
20080313564 Barve et al. Dec 2008 A1
20090077496 Aravamudan et al. Mar 2009 A1
20090112989 Anderson et al. Apr 2009 A1
20090133070 Hamano et al. May 2009 A1
20090151002 Zuniga et al. Jun 2009 A1
20090164263 Marlow et al. Jun 2009 A1
20090198688 Venkataraman et al. Aug 2009 A1
20090217203 Aravamudan et al. Aug 2009 A1
20090271358 Lindahl et al. Oct 2009 A1
20100030578 Siddique et al. Feb 2010 A1
20100030638 Davis, III et al. Feb 2010 A1
20100121845 Aravamudan et al. May 2010 A1
20100153380 Garg et al. Jun 2010 A1
20100241625 Aravamudan et al. Sep 2010 A1
20100293160 Aravamudan et al. Nov 2010 A1
20100306194 Evans Dec 2010 A1
20100325111 Aravamudan et al. Dec 2010 A1
20110043652 King et al. Feb 2011 A1
20110047213 Manuel Feb 2011 A1
20110076994 Kim et al. Mar 2011 A1
20110113249 Gelbard et al. May 2011 A1
20110137789 Kortina et al. Jun 2011 A1
20110179081 Ovsjanikov et al. Jul 2011 A1
20110214148 Gossweiler, III et al. Sep 2011 A1
20120042386 Backer Feb 2012 A1
20120221505 Evans et al. Aug 2012 A1
20120226761 Emigh et al. Sep 2012 A1
20140016872 Chao Jan 2014 A1
Foreign Referenced Citations (43)
Number Date Country
1 763 233 Mar 2007 EP
1841219 Oct 2007 EP
2003250146 Sep 2003 JP
2004254077 Sep 2004 JP
2005505070 Feb 2005 JP
2005520268 Jul 2005 JP
2005275740 Oct 2005 JP
2006024212 Jan 2006 JP
2006510270 Mar 2006 JP
2007158925 Jun 2007 JP
2007274605 Oct 2007 JP
2009534761 Sep 2009 JP
2010-503931 Feb 2010 JP
WO-19940014284 Jun 1994 WO
WO-9856173 Dec 1998 WO
WO-200054264 Sep 2000 WO
WO-200070505 Nov 2000 WO
WO-2001046843 Jun 2001 WO
WO-0193096 Dec 2001 WO
WO 02082814 Oct 2002 WO
WO-2003030528 Apr 2003 WO
WO-2004010326 Jan 2004 WO
WO-2004031931 Apr 2004 WO
WO-2004054264 Jun 2004 WO
WO-2004052010 Jun 2004 WO
WO-2005033967 Apr 2005 WO
WO-2005054982 Jun 2005 WO
WO-2005084235 Sep 2005 WO
WO-2006052966 May 2006 WO
WO-2006052959 May 2006 WO
WO-2006074305 Jul 2006 WO
WO-2007025148 Mar 2007 WO
WO-2007025149 Mar 2007 WO
WO-2007062035 May 2007 WO
WO-2007118038 Oct 2007 WO
WO-2007124429 Nov 2007 WO
WO-2007124436 Nov 2007 WO
WO-2007131058 Nov 2007 WO
WO-2008034057 Mar 2008 WO
WO-2008091941 Jul 2008 WO
WO-2008063987 Aug 2008 WO
WO-2008148012 Dec 2008 WO
WO-2009070193 Jun 2009 WO
Non-Patent Literature Citations (84)
Entry
U.S. Appl. No. 11/738,138, filed Apr. 20, 2007.
U.S. Appl. No. 09/332,244, filed Jun. 11, 1999, Ellis et al.
U.S. Appl. No. 11/324,202, filed Dec. 29, 2005, Yates, et al.
U.S. Appl. No. 11/412,549, filed Apr. 27, 2006, Ellis et al.
U.S. Appl. No. 11/541,299, filed Sep. 29, 2006, Shannon et al.
U.S. Appl. No. 60/548,589, filed Feb. 27, 2004, Flinchem et al.
U.S. Appl. No. 11/324,202, filed Dec. 29, 2005, Yates.
A Model of a Trust-Based Recommendation System on a Social Network—Published Date: Oct. 18, 2007.
C. de Kerchove and P. Dooren. The PageTrust Algorithm: how to rank web pages when negative links are allowed? In Proc. SIAM Int. Conf. on Data Mining, pp. 346352, 2008.
Computing and Applying Trust in Web-Based Social Networks—Published Date: Apr. 11, 2005 htto://test.lib.umd.edu/drum/bitstream/1903/2384/1/umi-umd-2244.pdf.
International Search Report and Written Opinion, International Application No. PCT/US06/25249, dated Jan. 29, 2008 (7 pages).
International Search Report and Written Opinion, International Application No. PCT/US06/33204, dated Sep. 21, 2007 (6 pages).
International Search Reportand Written Opinion, International Application No. PCT/US06/40005, dated Jul. 3, 2007 (8 pages).
International Search Report and Written Opinion, International Application No. PCT/US07/65703, dated Jan. 25, 2008 (5 pages).
International Search Report and Written Opinion, International Application No. PCT/US07/67100, dated Mar. 7, 2008 (6 pages).
International Search Report, International Patent Application No. PCT/US07/67114, dated Jul. 2, 2008 (6 pages).
International Search Report dated May 28, 2009, Application No. PCT/US2008/0011646 (4 pages) (now WO 2009/070193 ).
J. Kunegis, A. Lornrnatzsch, and C. Bauckhage. The slashdot zoo: mining a social network with negative edges. In WWW '09: Proceedings ofthe 18th international conference on World wide web, pp. 741-750, 2009.
L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical Report, Stanford University, 1998.
Office Action dated Jul. 21, 2010 in U.S. Appl. No. 11/986,461.
Office Action dated Jul. 8, 2010 in U.S. Appl. No. 11/986,463.
Guha, R., et al, “Propagation of Trust and Distrust” , WWW2004, May 17-22, 2004, pp. 403-412.
S. Kamvar, M. Schlosser, and H. Garcia-Molina. The EigenTrust algorithm for reputation management in P2P networks. In Proc. Int. Conf. on World Wide Web, pp. 640-651, 2003.
Trust- and Distrust-Based Recommendations for Controversial Reviews—Published Date: 2009.
Trust-Based Recommendations for Publications—A Multi-Layer Network Approach—Published Date: 2006 httQ://www.uni-bamberg.de/fileadmin/uni/fakultaeten/wiailehrstuehle/kulturinformatik!Publikationen/Hess Trust Based Recommendations for Publications—A Multi-Layer Network Aooroach.pdf.
Trust-Based Recommendation Systems: An Axiomatic Approach—Published Date: Apr. 21-25, 2008.
Ardissono, L. et al., “User Modeling and Recommendation Techniques for Personalized Electronic Program Guides,” Personalized Digital Television, Editors: Ardissono, et al., Kluwer Academic Press, 2004 (27 pages).
Benes, V.E., “Mathematical Theory of Connecting Networks and Telephone Traffic,” Academic Press, NY, 1965 (4 pages).
Dalianis, “Improving search engine retrieval using a compound splitter for Swedish,” Abstract of presentation at Nodalida 2005—15th Nordic Conference on Computational Linguistics, Joensuu Finland, May 21-22, 2005. Retrieved Jan. 5, 2006 from http://phon.joensuu.fi/nodalida/abstracts/03.shtml, 3 pages.
Digital Video Broadcasting, http://www.dvb.org (Oct. 12, 2007) (2 pages).
Duff, I.S. et al., “Direct Methods for Sparse Matrices,” Monographs on Numerical Analysis, Oxford Science Publications, Clarendon Press, Oxford, 1986 (7 pages).
GADD, Phonix: The Algorith, Program, vol. 24(4), Oct. 1990 (pp. 363-369).
Garey, M.R. et al., “Computers and Intractability a Guide to the Theory of NP-Completeness,” W.H. Freeman and Co., New York, 1979 (2 pages).
Good, N. et al., Combining Collaborative Filtering with Personal Agents for Better Recommendations, in Proc. of the 16th National Conference on Artificial Intelligence, pp. 439-446, Orlando, Florida, Jul. 18-22, 1999.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2005/040415, dated Nov. 27, 2006, 6 pages.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2005/040424, dated Nov. 21, 2006, 6 pages.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2005/040517, dated Jun. 13, 2008, 4 pages.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2006/025249, dated Jan. 29, 2008, 6 pages.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2006/033204, dated Sep. 21, 2007, 8 pages.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2006/033257, dated Mar. 26, 2008, 5 pages.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2006/033258, dated Mar. 26, 2008 (6 pages).
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2006/040005 dated Jul. 3, 2007 (8 pages).
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2007/065703 dated Jan. 25, 2008 (5 pages).
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2007/067100, dated Mar. 7, 2008 (5 pages).
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2007/067114, dated Jul. 2, 2008, 6 pages.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2007/068064, dated Jul. 7, 2008, 9 pages.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2007/084500, dated May 20, 2008, 6 pages.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2008/051789, dated Jul. 14, 2008 (7 Pages).
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2008/064730, dated Sep. 8, 2008, 5 pages.
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2012/034780, dated Jul. 16, 2012 (2 pages).
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2006/045053, dated Jul. 24, 2004 (10 pages).
International Search and Written Opinion issued by the U.S. Patent and Trademark Office as the International Searching Authority for International Application No. PCT/US2007/078490, dated Jul. 3, 2008 (6 pages).
J. Kleinberg, Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM) 46(5):604-632, 1999.
Kurapati, et al., “A Multi-Agent TV Recommender,” In Proceedings of the UM 2001Workshop “Personalization in Future TV,” 2001, 8 pages.
Lindgren, B.W. et al., “Introduction to Probability and Statistics,” MacMillan Publishing Co., New York, New York, 1978 (23 pages).
Luenberger, D.G., “Linear and Nonlinear Pogramming,” Second Ed., Addison-Wesley Publishing Company, Reading, MA, 1989 (51 pages).
Mackenzie et al. “Letterwise: Prefix-Based Disambiguation for Mobile Text Input, Proceedings of the ACM Symposium on User Interface Software and Technology—UIST 2001” (pp. 111-120).
Matthom, “Text Highlighting in Search Results”, Jul. 22, 2005. Retrieved from www.matthom.com/archive/2005/0l/22/text-highlighting-in-search-results on Jun. 23, 2006 (4 pages).
Mokotoff, Soundexing and Genealogy, Retrieved from www.avotaynu.com/soundex.html on Mar. 19, 2008, last updated Sep. 8, 2007 (6 pages).
Murray et al., “Inferring Demographic Attributes of Anonymous Internet Users,” WEBKDD '99 LNAI, 1836, pp. 7-20, 2000.
Nardi, et al., “Integrating Communication and Information Through Contact Map,” Communications of the ACM, vol. 45, No.4, Apr. 2002, 7 pages, retrieved from URL:http://portal.acm.org/citation.cfm?id+505251.
Nemhauser, G.L. et al., “Integer and Combinational Optimization,” John Wiley and Sons, New York, 1988 (2 pages).
Office Action for U.S. Appl. No. 11/204,546 dated Sep. 17, 2009, 34 pages.
Office Action for U.S. Appl. No. 11/204,546 dated Jul. 8, 2008, 30 pages.
Office Action for U.S. Appl. No. 11/204,546 dated Mar. 3, 2009, 26 pages.
Press Release from Tegic Communications, “Tegic Communications is awarded patent for Japanese T9(R) text input software from the Japan Patent Office,” Oct. 12, 2004. Retrieved Nov. 18, 2005 from http://www.tegic.com/press view.html?release num=55254242 (4 pages).
Review of Personalization Technologies: Collaborative Filtering vs. ChoiceStream's Attributized Bayesian Choice Modeling, Technology Brief, ChoiceStream Technologies, Cambridge, MA, Feb. 2004, 13 pages.
Roe, et al., “Mapping UML Models Incorporating OCL Constraints into Object-Z,” Technical Report, Sep. 2003, Department of Computing, Imperial College London, retrieved on Jul. 12, 2007 (17 pages).
Silfverberg et al. “Predicting Text Entry Speed on Mobile Phones.” Proceedings of the ACM Conference on Human Factors in Computing Systems—CHI 2000. (pp. 9-16).
Supplemental European Search Report and Written Opinion for EP05826129.8, dated Aug. 11, 2009, 15 pages.
Supplemental European Search Report and Written Opinion for EP06838179.7, dated Dec. 9, 2009, 7 pages.
Supplemental European Search Report and Written Opinion for EP07761026.9 dated Jan. 28, 2010, 8 pages.
Supplemental European Search Report and Written Opinion for EP07842499, dated Aug. 26, 2010, 6 pages.
Supplemental Partial European Search Report for EP05826114.0 dated Aug. 20, 2009, 13 pages.
Talbot, David. “Soul of a New Mobile Machine.” Technology Review May/Jun. 2007. (pp. 46-53).
Turski, et al., “Inner Circle—People Centered Email Client,” CHI 2005 Conference on Human Factors in Computing Sysems, Apr. 2005, pp. 1845-1848, 4 pages, retrieved from URL:http://portal.acm.org/citation.cfm?id+1056808.1057037.
Villani, et al., Keystroke Biometric Recognition Studies on Long-Text Input under Ideal and Application-Oriented Conditions, Proceedings of StudenUFaculty Research Day, CSIS, Pace University, May, 2006, pp. C3.1-C3.8.
Wikipedia's entry for Levenshtein distance (n.d.). Retrieved Nov. 15, 2006 from http://en.wikipedia.org/wiki/Levenshtein distance (9 pages).
Written Opinion of the International Searching Authority, International Application No. PCT/US06/25249, dated Jan. 29, 2008 (4 pages) Combined With ISR to Make One Document (Apr. 27, 2015).
Written Opinion of the International Searching Authority, International Application No. PCT/US06/33204, dated Sep. 21, 2007 (3 pages). Combined With ISR to Make One Document (Apr. 27, 2015).
Written Opinion of the International Searching Authority, International Application No. PCT/US06/40005, dated Jul. 3, 2007 (4 pages). Combined With ISR to Make One Document (Apr. 27, 2015).
Written Opinion of the International Searching Authority, International Application No. PCT/US07/65703, dated Jan. 25, 2008 (4 pages). Combined With ISR to Make One Document (Apr. 27, 2015).
Written Opinion of the International Searching Authority, International Application No. PCT/US07/67100, dated Mar. 7, 2008 (3 pages). Combined With ISR to Make One Document (Apr. 27, 2015).
Zimmerman, et al., “TV Personalization System Design of a TV Show Recommender Engine and Interface,” In Liliana Ardissono, Alfred Kobsa, Mark Maybury (eds) Personalized Digital Television: Targeting Programs to Individual Viewers, Kluwer: 27-52; 2004, 29 pages.
Related Publications (1)
Number Date Country
20150324367 A1 Nov 2015 US
Provisional Applications (2)
Number Date Country
60793537 Apr 2006 US
60793540 Apr 2006 US
Continuations (7)
Number Date Country
Parent 14175189 Feb 2014 US
Child 14793074 US
Parent 13765335 Feb 2013 US
Child 14175189 US
Parent 13479820 May 2012 US
Child 13765335 US
Parent 13336660 Dec 2011 US
Child 13479820 US
Parent 13034034 Feb 2011 US
Child 13336660 US
Parent 12326485 Dec 2008 US
Child 13034034 US
Parent 11738101 Apr 2007 US
Child 12326485 US