# US 10,481,751 B2 · User experience/user interface based on interaction history > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,481,751 B2 - Document ID: US10481751B2 - Status: Granted - Kind: B2 - Application number: 15/231,428 - Filed: 2016-08-08 - Published: 2016-12-01 - Granted: 2019-11-19 - Earliest claimed: 2013-08-09 - Assignees: Facebook, Inc. - Applicants: Facebook, Inc. - Inventors: Eyal Michael Sharon; Michael Dudley Johnson - Web page: https://patentgrep.com/patent/US10481751B2 ## CPC - G06N 20/00: Machine learning - G06F 3/0481: based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance - G06F 15/17306: {Intercommunication techniques} - G06Q 10/42: Determination of affinities or common interests between users - G06Q 10/48: using social graphs - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - G06F 3/048: Interaction techniques based on graphical user interfaces [GUI] - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract In one embodiment, a method includes accessing a base user experience (UX) including a user interface (UI) corresponding to one or more features of a computing system; determining one or more user categories based at least in part on previous interactions with the UI by a number of users; classifying, using a machine-learning algorithm, a particular user into one or more of the user categories based at least in part on current interactions of the particular user with the UI; and determining, relative to the base UX, one or more modifications to the UI based at least in part on the classification of the particular user into one or more of the user categories. The modifications to the UI modify one or more features of the UX. The method also includes applying the modifications to the UI; and providing the UI as modified for display to the particular user. ## Claims ### Claim 1 A method comprising: by a computing device, accessing a base user experience (UX) comprising a user interface (UI) having one or more graphical elements corresponding to one or more features of a computing system; by the computing device, determining one or more user categories based at least in part on previous interactions with one or more of the graphical elements of the UI by a plurality of users of the computing system; by the computing device, classifying, using a machine-learning algorithm, a particular user into one or more of the user categories based at least in part on current interactions performed by the particular user with the UI; by the computing device, determining, relative to the base UX, one or more modifications to the UI based at least in part on the classification of the particular user into one or more of the user categories, wherein: the modifications to the UI modify one or more features of the graphical elements; one or more of the modifications comprises a graphical element associated with one or more of the modifications; and the graphical element associated with one or more of the modifications is associated with a window illustrating a use of one or more features of the computing system; by the computing device, applying the modifications to the UI; and by the computing device, providing the UI as modified for display to the particular user. ### Claim 2 (depends on 1) The method of claim 1, wherein the determination of one or more of the user categories comprises accessing previous user actions of the plurality of users on the computing system over a pre-determined period of time, wherein the accessed user actions comprise training data for the machine-learning algorithm. ### Claim 3 (depends on 2) The method of claim 2, further comprising: receiving updated training data comprising subsequent user actions; and revaluating the classification of the particular user based on updated training data. ### Claim 4 (depends on 2) The method of claim 2, wherein the classification comprises using the machine-learning algorithm based on information associated with the particular user. ### Claim 5 (depends on 4) The method of claim 4, wherein the information associated with the particular user comprises a location, age, affinities, number of friends, or interactions of the particular user logged over a pre-determined period of time. ### Claim 6 (depends on 5) The method of claim 5, wherein the information associated with the particular user is stored on a social graph of a social-networking system, the social graph comprising a plurality of nodes and edges connecting the nodes, at least one node in the social graph corresponding to the particular user. ### Claim 7 (depends on 1) The method of claim 1, wherein determining the modifications comprises: assigning a value to a particular functionality of the computing based at least in part on an objective; identifying one or more candidate functionalities based on the assigned value; and selecting one or more modifications corresponding to the identified candidate functionality having a highest assigned value relative to the objective. ### Claim 8 (depends on 7) The method of claim 7, wherein the objective is associated with an amount of user engagement with the computing system. ### Claim 9 (depends on 1) The method of claim 1, wherein the computing system is a social-networking system. ### Claim 10 (depends on 1) The method of claim 1, wherein the graphical element associated with one or more of the modifications is associated with a guided tour illustrating one or more features of the computing system. ### Claim 11 (depends on 1) The method of claim 1, wherein one or more of the modifications comprises removing a graphical element illustrating one or more features of the computing system from the UI. ### Claim 12 One or more computer-readable non-transitory storage media embodying software configured when executed to: access a base user experience (UX) comprising a user interface (UI) having one or more graphical elements corresponding to one or more features of a computing system; determine one or more user categories based at least in part on previous interactions with one or more of the graphical elements of the UI by a plurality of users of the computing system; classify, using a machine-learning algorithm, a particular user into one or more of the user categories based at least in part on current interactions performed by the particular user with the UI; determine, relative to the base UX, one or more modifications to the UI based at least in part on the classification of the particular user into one or more of the user categories, wherein: the modifications to the UI modify one or more features of the graphical elements; one or more of the modifications comprises a graphical element associated with one or more of the modifications; and the graphical element associated with one or more of the modifications is associated with a window illustrating a use of one or more features of the computing system; apply the modifications to the UI; and provide the UI as modified for display to the particular user. ### Claim 13 (depends on 12) The media of claim 12, wherein the software is further configured to access previous user actions of the plurality of users on the computing system over a pre-determined period of time, wherein the accessed user actions comprise training data for the machine-learning algorithm. ### Claim 14 (depends on 13) The media of claim 13, wherein the software is further configured to: receive updated training data comprising subsequent user actions; and re-evaluate the classification of the particular user based on updated training data. ### Claim 15 (depends on 13) The media of claim 13, wherein the software is further configured to use the machine-learning algorithm based on information associated with the particular user. ### Claim 16 A device comprising: a processor; and one or more computer-readable non-transitory storage media coupled to the processor and embodying software that: access a base user experience (UX) comprising a user interface (UI) having one or more graphical elements corresponding to one or more features of a computing system; determine one or more user categories based at least in part on previous interactions with one or more of the graphical elements of the UI by a plurality of users of the computing system; classify, using a machine-learning algorithm, a particular user into one or more of the user categories based at least in part on current interactions performed by the particular user with the UI; determine, relative to the base UX, one or more modifications to the UI based at least in part on the classification of the particular user into one or more of the user categories, wherein: the modifications to the UI modify one or more features of the graphical elements; one or more of the modifications comprises a graphical element associated with one or more of the modifications; and the graphical element associated with one or more of the modifications is associated with a window illustrating a use of one or more features of the computing system; apply the modifications to the UI; and provide the UI as modified for display to the particular user. ### Claim 17 (depends on 16) The device of claim 16, wherein the software is further configured to access previous user actions of the plurality of users on the computing system over a pre-determined period of time, wherein the accessed user actions comprise training data for the machine-learning algorithm. ### Claim 18 (depends on 17) The device of claim 17, wherein the software is further configured to: receive updated training data comprising subsequent user actions; and re-evaluate the classification of the particular user based on updated training data.