# US 10,313,461 B2 · Adjusting pacing of notifications based on interactions with previous notifications > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,313,461 B2 - Document ID: US10313461B2 - Status: Granted - Kind: B2 - Application number: 15/354,948 - Filed: 2016-11-17 - Published: 2018-05-17 - Granted: 2019-06-04 - Earliest claimed: 2016-11-17 - Assignees: Facebook, Inc. - Applicants: Facebook, Inc. - Inventors: Li Ju; Matthew Bryan Folz; Ramanathan Palaniappan; Jonathan Ma; Pawel Bara - Web page: https://patentgrep.com/patent/US10313461B2 ## CPC - G06Q 30/0201: Market modelling; Market analysis; Collecting market data - G06F 11/34: Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation {; Recording or statistical evaluation of user activity, e.g. usability assessment} - G06F 11/3438: {monitoring of user actions (tracking the activity of the user H04L67/535)} - G06F 11/3447: {Performance evaluation by modeling} - G06Q 10/48: using social graphs - G06Q 30/0202: Market predictions or forecasting for commercial activities - H04L 67/306: User profiles - H04L 67/535: {Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34)} - H04L 67/54: Presence management, e.g. monitoring or registration for receipt of user log-on information, or the connection status of the users - H04L 67/55: Push-based network services - G06F 2201/81: Threshold - 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 - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract An online system predicts the user's activity on the online system during a future time interval (e.g., the next day). The system collects activity data, such as actions that the user has taken on the system. The system predicts whether the user is likely to be active during the future time interval based on features extracted from the activity data. This system determines selection of notifications and delivery of notifications based on the predicted time when the user is likely to be active on the online system. The system further records the user's past interactions with notifications, such as whether the user viewed the notification, whether the user interacted with a content item associated with the notification, and so on. If system determines a rate of delivery of notifications to the user based on the frequency of past user interactions with notifications. ## Claims ### Claim 1 A method comprising: identifying a plurality of candidate notifications for sending to a user of an online system, each candidate notification associated with a content item posted on the online system; for each of the plurality of candidate notifications: extracting features associated with the candidate notification, wherein the features comprise a content type for the associated content item, providing the features as input to a model generated using machine learning, the model trained using past user interactions with notifications sent to the user by the online system, and obtaining, as an output from the model, a score indicating a predicted click-through rate for the candidate notification; determining a likelihood of the user actively interacting with the online system during a future time interval; responsive to the determined likelihood of the user actively interacting with the online system during the future time interval being greater than a threshold value, selecting one or more candidate notifications having a predicted click-through rate for the user exceeding a threshold click-through rate, the threshold click-through rate determined based on interactions performed by the user with notifications previously provided to the user; and sending the one or more candidate notifications to a client device associated with the user prior to the future time interval, wherein the sending of the candidate notification is initiated at the online system. ### Claim 2 (depends on 1) The method of claim 1, wherein selecting the one or more candidate notifications to send to the user comprises determining a number of candidate notifications to be sent to the user as a value not exceeding a notification limit. ### Claim 3 (depends on 2) The method of claim 2, wherein the notification limit is a predetermined constant value. ### Claim 4 (depends on 2) The method of claim 2, wherein the notification limit is determined based on the user's past interactions with notifications. ### Claim 5 (depends on 1) The method of claim 1, wherein each candidate notification is further associated with a user performing an action associated with the content item associated with the candidate notification, and wherein the features further comprise a feature derived based on an identifier for the user performing the action. ### Claim 6 (depends on 1) The method of claim 1, wherein each candidate notification is further associated with an entity represented in the online system to which the associated content item is posted, and wherein the features further comprise a feature derived based on an identifier for the entity. ### Claim 7 (depends on 6) The method of claim 6, wherein the entity is one of a group or a brand page. ### Claim 8 (depends on 1) The method of claim 1, further comprising: for each past notification of a plurality of past notifications, each past notification previously provided to the user of the online system and associated with a content item on the online system, determining a set of features corresponding to the past notification, wherein the features comprise a content type for the associated content item; and training the model using the sets of features associated with the plurality of past notifications. ### Claim 9 A non-transitory computer-readable medium including instructions for execution on a processor, the instructions, when executed by the processor, causing the processor to: identify a plurality of candidate notifications for sending to a user of an online system, each candidate notification associated with a content item posted on the online system; for each of the plurality of candidate notifications: extract features associated with the candidate notification, wherein the features comprise a content type for the associated content item, provide the features as input to a model generated using machine learning, the model trained using past user interactions with notifications sent to the user by the online system, and obtain, as an output from the model, a score indicating a predicted click-through rate for the candidate notification; determine a likelihood of the user actively interacting with the online system during a future time interval; responsive to the determined likelihood of the user actively interacting with the online system during the future time interval being greater than a threshold value, selecting one or more candidate notifications having a predicted click-through rate for the user exceeding a threshold click-through rate, the threshold click-through rate determined based on interactions performed by the user with notifications previously provided to the user; and sending the one or more candidate notifications to a client device associated with the user prior to the future time interval, wherein the sending of the candidate notification is initiated at the online system. ### Claim 10 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein selecting the one or more candidate notifications to send to the user comprises determining a number of candidate notifications to be sent to the user as a value not exceeding a notification limit. ### Claim 11 (depends on 10) The non-transitory computer-readable medium of claim 10, wherein the notification limit is a predetermined constant value. ### Claim 12 (depends on 10) The non-transitory computer-readable medium of claim 10, wherein the notification limit is determined based on the user's past interactions with notifications. ### Claim 13 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein each candidate notification is further associated with a user performing an action associated with the content item associated with the candidate notification, and wherein the features further comprise a feature derived based on an identifier for the user performing the action. ### Claim 14 (depends on 9) The non-transitory computer-readable medium of claim 9, wherein each candidate notification is further associated with an entity represented in the online system to which the associated content item is posted, and wherein the features further comprise a feature derived based on an identifier for the entity. ### Claim 15 (depends on 14) The non-transitory computer-readable medium of claim 14, wherein the entity is one of a group or a brand page. ### Claim 16 (depends on 9) The non-transitory computer-readable medium of claim 9, further comprising: for each past notification of a plurality of past notifications, each past notification previously provided to the user of the online system and associated with a content item on the online system, determining a set of features corresponding to the past notification, wherein the features comprise a content type for the associated content item; and training the model using the sets of features associated with the plurality of past notifications. ### Claim 17 A system comprising: a processor configured to execute instructions; a non a non-transitory computer-readable medium including instructions for execution by the processor, the instructions causing the processor to: identify a plurality of candidate notifications for sending to a user of an online system, each candidate notification associated with a content item posted on the online system; for each of the plurality of candidate notifications: extract features associated with the candidate notification, wherein the features comprise a content type for the associated content item, provide the features as input to a model generated using machine learning, the model trained using past user interactions with notifications sent to the user by the online system, and obtain, as an output from the model, a score indicating a predicted click-through rate for the candidate notification; determine a likelihood of the user actively interacting with the online system during a future time interval; responsive to the determined likelihood of the user actively interacting with the online system during the future time interval being greater than a threshold value, selecting one or more candidate notifications having a predicted click-through rate for the user exceeding a threshold click-through rate, the threshold click-through rate determined based on interactions performed by the user with notifications previously provided to the user; and sending the one or more candidate notifications to a client device associated with the user prior to the future time interval, wherein the sending of the candidate notification is initiated at the online system. ### Claim 18 (depends on 17) The system of claim 17, wherein selecting the one or more candidate notifications to send to the user comprises determining a number of candidate notifications to be sent to the user as a value not exceeding a notification limit. ### Claim 19 (depends on 18) The system of claim 18, wherein the notification limit is a predetermined constant value. ### Claim 20 (depends on 17) The system of claim 17, further comprising: for each past notification of a plurality of past notifications, each past notification previously provided to the user of the online system and associated with a content item on the online system, determining a set of features corresponding to the past notification, wherein the features comprise a content type for the associated content item; and training the model using the sets of features associated with the plurality of past notifications.