# US 9,519,408 B2 · Systems and methods for guided user actions > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,519,408 B2 - Document ID: US9519408B2 - Status: Granted - Kind: B2 - Application number: 14/145,596 - Filed: 2013-12-31 - Published: 2015-07-02 - Granted: 2016-12-13 - Earliest claimed: 2013-12-31 - Assignees: Google Inc. - Applicants: Google Inc. - Inventors: Garth Shoemaker; Michael Eugene Aiello - Web page: https://patentgrep.com/patent/US9519408B2 ## CPC - G06F 3/0484: for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range - G06F 3/04886: by partitioning the display area of the touch-screen or the surface of the digitising tablet into independently controllable areas, e.g. virtual keyboards or menus - G06N 20/00: Machine learning - H04M 1/72448: with means for adapting the functionality of the device according to specific conditions - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04M: TELEPHONIC COMMUNICATION (circuits for controlling other apparatus via a telephone cable and not involving telephone switching apparatus G08) ## Abstract Systems and methods for guided user actions are described, including detecting a first action performed by a user; gathering information associated with the first action; retrieving a predictive model based on the information; determining an applicability level of the predictive model to the first action, the predictive model suggests a second action; providing the second action in a user interface when the applicability level meets a threshold level; and receiving input from the user selecting the second action or a third action. ## Claims ### Claim 1 A computer-implemented method, comprising: detecting a first action performed by a user; gathering information associated with the first action; applying machine learning to generate a predictive model and a privacy preference of the user, based on the information and a past sharing action associated with the information, wherein the machine learning is applied as a black box that receives the information and outputs the predictive model, and wherein a plurality of pipelines are provided for a plurality of respective domains, each of the pipelines applying the machine learning; determining an applicability level of the predictive model to the first action, wherein the predictive model suggests a second action; providing the second action in a user interface for the applicability level exceeding a first threshold level associated with a degree of sharing of content associated with the first action; receiving input from the user selecting the second action or a third action for the applicability level exceeding the first threshold level and not exceeding a second threshold level that exceeds the first threshold level; and for the applicability level exceeding the second threshold level, automatically selecting the second action or the third action based on the provided second action without receiving the input from the user. ### Claim 2 (depends on 1) The method of claim 1, wherein the first action comprises browsing one or more online sites in an open mode, and the second action comprises predicting a user preference to browse in a private mode based on an identity of the one or more online sites, and providing the user with an option to switch from the open mode to a private mode, wherein the machine learning is applied to fine-tune the system based on the input of the user associated with the provided option. ### Claim 3 (depends on 1) The method of claim 1, wherein the first action comprises identifying content and the second action comprises selecting an application to process the content. ### Claim 4 (depends on 1) The method of claim 1, wherein the first action comprises accessing a website and the second action comprises selecting a mode of a browser to access the website. ### Claim 5 (depends on 1) The method of claim 1, wherein the plurality of pipelines comprise a first pipeline associated with browsing activity, a second pipeline associated with sharing activity on an online social circle of a social network, and a third pipeline associated with email communication. ### Claim 6 (depends on 1) The method of claim 1, wherein the determining the applicability level of the predictive model to the first action comprises: performing a weighted calculation of attributes associated with the predictive model; and determining a combination of at least some of the weighted calculation of the attributes meet the first threshold level. ### Claim 7 (depends on 1) The method of claim 1, wherein input from the user selecting the second action or a third action modifies the predictive model. ### Claim 8 A non-transitory computer readable medium having stored therein computer executable instructions for: detecting a first action performed by a user; gathering information associated with the first action; applying machine learning to generate a predictive model and a privacy preference of the user, based on the information and a past sharing action associated with the information, wherein the machine learning is applied as a black box that receives the information and outputs the predictive model, and wherein a plurality of pipelines are provided for a plurality of respective domains, each of the pipelines applying the machine learning; determining an applicability level of the predictive model to the first action, wherein the predictive model suggests a second action; providing the second action in a user interface for the applicability level exceeding a first threshold level associated with a degree of sharing of content associated with the first action; receiving input from the user selecting the second action or a third action for the applicability level exceeding the first threshold level and not exceeding a second threshold level that exceeds the first threshold level; and for the applicability level exceeding the second threshold level, automatically selecting the second action or the third action based on the provided second action without receiving the input from the user. ### Claim 9 (depends on 8) The medium of claim 8, wherein the first action comprises browsing one or more online sites in an open mode, and the second action comprises predicting a user preference to browse in a private mode based on an identity of the one or more online sites, and providing the user with an option to switch from the open mode to a private mode, wherein the machine learning is applied to fine-tune the system based on the input of the user associated with the provided option. ### Claim 10 (depends on 8) The medium of claim 8, wherein the first action comprises identifying content and the second action comprises selecting an application to process the content. ### Claim 11 (depends on 8) The medium of claim 8, wherein the first action comprises accessing a website and the second action comprises selecting a mode of a browser to access the website. ### Claim 12 (depends on 8) The medium of claim 8, wherein the plurality of pipelines comprise a first pipeline associated with browsing activity, a second pipeline associated with sharing activity on an online social circle of a social network, and a third pipeline associated with email communication. ### Claim 13 (depends on 8) The medium of claim 8, wherein the determining the applicability level of the predictive model to the first action comprises: performing a weighted calculation of attributes associated with the predictive model; and determining a combination of at least some of the weighted calculation of the attributes meet the first threshold level. ### Claim 14 At least one computing device comprising storage and a processor configured to perform: detecting a first action performed by a user; gathering information associated with the first action; applying machine learning to generate a predictive model and a privacy preference of the user, based on the information and a past sharing action associated with the information, wherein the machine learning is applied as a black box that receives the information and outputs the predictive model, and wherein a plurality of pipelines are provided for a plurality of respective domains, each of the pipelines applying the machine learning; determining an applicability level of the predictive model to the first action, wherein the predictive model suggests a second action; providing the second action in a user interface for the applicability level exceeding a first threshold level associated with a degree of sharing of content associated with the first action; receiving input from the user selecting the second action or a third action for the applicability level exceeding the first threshold level and not exceeding a second threshold level that exceeds the first threshold level; and for the applicability level exceeding the second threshold level, automatically selecting the second action or the third action based on the provided second action without receiving the input from the user. ### Claim 15 (depends on 14) The at least one computing device of claim 14, wherein the first action comprises browsing one or more online sites in an open mode, and the second action comprises predicting a user preference to browse in a private mode based on an identity of the one or more online sites, and providing the user with an option to switch from the open mode to a private mode, wherein the machine learning is applied to fine-tune the system based on the input of the user associated with the provided option. ### Claim 16 (depends on 14) The at least one computing device of claim 14, wherein the first action comprises identifying content and the second action comprises selecting an application to process the content. ### Claim 17 (depends on 14) The at least one computing device of claim 14, wherein the first action comprises accessing a website and the second action comprises selecting a mode of a browser to access the website. ### Claim 18 (depends on 14) The at least one computing device of claim 14, wherein the plurality of pipelines comprise a first pipeline associated with browsing activity, a second pipeline associated with sharing activity on an online social circle of a social network, and a third pipeline associated with email communication. ### Claim 19 (depends on 14) The at least one computing device of claim 14, wherein the determining the applicability level of the predictive model to the first action comprises: performing a weighted calculation of attributes associated with the predictive model; and determining a combination of at least some of the weighted calculation of the attributes meet the first threshold level. ### Claim 20 (depends on 14) The at least one computing device of claim 14, wherein input from the user selecting the second action or a third action modifies the predictive model.