Granted Patent B2

US 12,626,019 · App. 18/800,655 · Granted May 12, 2026

Inventors: Ajay Sharma (Kurukshetra, IN); Arihant Jain (Noida, IN); Prakhar Shrivastav (Kanpur, IN); Rahul Yadav (Alwar, IN); Vipul Gupta (Hisar, IN)

Assignee: Samsung Electronics Co., Ltd.

G06F21/6263G06F11/3438

Abstract

Claims (75)

1 . A method of applying secrecy settings on a user device, the method comprising:

monitoring, by the user device, user activity data based on usage of one or more applications of the user device;

determining, by the user device via a neural network model, whether a user is exhibiting a secretive behavior based on the user activity data and historical behavior of the user;

extracting, by the user device, contextual information from other applications, wherein the contextual information is associated with the user activity data;

determining, by the user device, whether the contextual information is related to the user activity data;

in response to determining that the contextual information is related to the user activity data, processing, by the user device based on the secretive behavior, the contextual information to provide at least one data stream and at least one attribute associated with the contextual information;

in response to determining to process the user activity data according to one of a plurality of predefined secrecy types, determining, by the user device, a predefined secrecy type from among the plurality of predefined secrecy types based on an analysis of the at least one data stream and the at least one attribute; and

applying, by the user device, secrecy settings on the user device based on the predefined secrecy type,

wherein the method further comprises:

clustering, by the user device, the user activity data into at least one predefined category; and

determining, by the user device, whether the user activity data of one of the at least one predefined category is to be processed according to one of the plurality of predefined secrecy types based on a usage pattern of the user device, and

wherein the plurality of predefined secrecy types corresponds to the secretive behavior.

2 . The method of claim 1 , wherein the secretive behavior comprises one of a normal behavior or a secret behavior.

3 . The method of claim 1 , wherein the user activity data comprises at least one of application activity data, user actions, the usage pattern of the user device, demographics information, a call history, or messages.

4 . The method of claim 1 , wherein the at least one attribute associated with the contextual information comprises deletion history and application activity.

5 . The method of claim 1 ,

wherein the contextual information comprises at least one of conversations, application data, application activities, application usage, or search patterns at the user device, and

wherein the at least one data stream comprises at least one of an audio data stream, an image data stream, or a video data stream captured on the user device.

6 . The method of claim 1 , further comprising:

analyzing, by the user device, the at least one data stream and the at least one attribute to determine whether the user activity data should be processed according to one of the plurality of predefined secrecy types.

7 . The method of claim 6 , wherein the analyzing of the at least one data stream and the at least one attribute comprises:

analyzing, by the user device via one or more neural network models, the at least one data stream and the at least one attribute to determine whether the at least one data stream is one of public or private; and

based on a result of determining whether the at least one data stream is one of public or private, determining whether the user activity data should be processed according to one of the plurality of predefined secrecy types.

8 . The method of claim 1 , wherein the applying of the secrecy settings based on the predefined secrecy type comprises modifying at least one of display settings, tracking settings, cookie settings, download settings, or history settings at the user device.

9 . The method of claim 1 , wherein the applying of the secrecy settings based on the predefined secrecy type comprises at least one of:

modifying, by the user device, current user interface content at the user device;

modifying, by the user device, a tracking status of the one or more applications of the user device;

modifying, by the user device, a cookie status of the one or more applications of the user device;

modifying, by the user device, a downloading status of the one or more applications of the user device; or

modifying, by the user device, history storage settings of the one or more applications of the user device.

10 . The method of claim 1 , further comprising:

monitoring, by the user device, the user activity data during a specific time period based on the one or more applications of the user device, wherein the monitoring of the user activity data comprises tracking a sequence of events in the one or more applications of the user device;

clustering, by the user device, the user activity data into at least one category;

determining, by the user device, whether the user activity data of one of the at least one category is processed according to one of the plurality of predefined secrecy types based on the usage pattern of the user device; and

training, by the user device, the neural network model with the user activity data, the at least one category, and the plurality of predefined secrecy types.

11 . The method of claim 1 , further comprising:

in response to determining that the contextual information is not related to the user activity data, determining, by the user device, the predefined secrecy type among the plurality of predefined secrecy types based on the secretive behavior.

12 . The method of claim 1 , wherein the at least one data stream and the at least one attribute are indicative of at least one of current user actions or data from the other applications.

13 . An electronic device for applying secrecy settings, the electronic device comprising:

memory storing one or more computer programs; and

one or more processors communicatively coupled to the memory;

wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors, cause the electronic device to:

monitor user activity data based on usage of one or more applications of the electronic device,

determine, via a neural network model, whether a user is exhibiting a secretive behavior based on the user activity data and historical behavior of the user,

extract contextual information from other applications, wherein the contextual information is associated with the user activity data,

determine whether the contextual information is related to the user activity data,

in response to determining that the contextual information is related to the user activity data, process, based on the secretive behavior, the contextual information to provide at least one data stream and at least one attribute associated with the contextual information,

in response to determining to process the user activity data according to one of a plurality of predefined secrecy types, determine a predefined secrecy type from among the plurality of predefined secrecy types based on an analysis of the at least one data stream and the at least one attribute, and

apply secrecy settings on the electronic device based on the predefined secrecy type,

wherein the one or more computer programs further include computer-executable instructions that, when executed by the one or more processors, cause the electronic device to:

cluster the user activity data into at least one predefined category, and

determine whether the user activity data of one of the at least one predefined category is to be processed according to one of the plurality of predefined secrecy types based on a usage pattern of the electronic device, and

wherein the plurality of predefined secrecy types corresponds to the secretive behavior.

14 . The electronic device of claim 13 ,

wherein the one or more computer programs further include computer-executable instructions that, when executed by the one or more processors, cause the electronic device to:

create the usage pattern for the electronic device corresponding to received sequence of actions using frequent pattern mining algorithms, and

wherein the frequent patten mining algorithms include a frequent pattern growth (FP-growth) scalable technique in which the FP-growth is provided as an input to K-pattern clustering and similar patterns are grouped by using frequent activity patterns' mining to cluster the user activity data into one or more predefined categories.

15 . The electronic device of claim 13 ,

wherein the one or more computer programs further include computer-executable instructions that, when executed by the one or more processors, cause the electronic device to:

based on the predefined secrecy type being a second predefined secrecy type, perform downloads in a public space of the electronic device, and

based on the predefined secrecy type being a third predefined secrecy type, perform the downloads in a private space of the electronic device, and

wherein files downloaded in the private space are opened/viewed only in a particular application and only by an authenticated user and all other applications are blocked from accessing the files downloaded in the private space.

16 . The electronic device of claim 13 , wherein the plurality of predefined secrecy types include at least three predefined secrecy types comprising a first predefined secrecy type associated with no secrecy or normal mode, a second predefined secrecy type associated with secrecy from network, and a third predefined secrecy type associated with secrecy from other users/applications.

17 . One or more non-transitory computer-readable storage media storing one or more computer programs including computer-executable instructions that, when executed by one or more processors of a user device, cause the user device to perform operations, the operations comprising:

monitoring, by the user device, user activity data based on usage of one or more applications of the user device;

determining, by the user device via a neural network model, whether a user is exhibiting a secretive behavior based on the user activity data and historical behavior of the user;

extracting, by the user device, contextual information from other applications, wherein the contextual information is associated with the user activity data;

determining, by the user device, whether the contextual information is related to the user activity data;

in response to determining that the contextual information is related to the user activity data, processing, by the user device based on the secretive behavior, the contextual information to provide at least one data stream and at least one attribute associated with the contextual information;

in response to determining to process the user activity data according to one of a plurality of predefined secrecy types, determining, by the user device, a predefined secrecy type from among the plurality of predefined secrecy types based on an analysis of the at least one data stream and the at least one attribute; and

applying, by the user device, secrecy settings on the user device based on the predefined secrecy type,

wherein the operations further comprise:

clustering, by the user device, the user activity data into at least one predefined category; and

determining, by the user device, whether the user activity data of one of the at least one predefined category is to be processed according to one of the plurality of predefined secrecy types based on a usage pattern of the user device, and

wherein the plurality of predefined secrecy types corresponds to the secretive behavior.

Assignments (1)

Priority Claims (2)

Continuity (2)

References Cited (42)