公开(公告)号：US11038974B1

公开(公告)日：2021-06-15

申请号：US16036827

申请日：2018-07-16

Applicant: Facebook, Inc.

Inventor： Emmanouil Koukoumidis ,  Fuchun Peng ,  Jason Schissel

IPC: G06F15/173 ,  H04L29/08 ,  G06Q50/00

Abstract: In one embodiment, a method includes, by one or more computing devices, receiving, from a client system associated with a first user of an online social network, an indication of a trigger action by the first user, wherein the trigger action is associated with a user activity, determining a first user context based on the trigger action, accessing one or more recommended content objects associated with the first user context, calculating a recommendation score for each recommended content object, generating one or more content suggestions comprising one of the one or more recommended content objects, respectively, each content suggestion corresponding to a recommended content object having a recommendation score above a threshold recommendation score, and sending, to the client system in response to the trigger action, instructions for presenting one or more of the content suggestions to the first user.

公开(公告)号：US11245646B1

公开(公告)日：2022-02-08

申请号：US16192538

申请日：2018-11-15

Applicant: Facebook, Inc.

Inventor： Emmanouil Koukoumidis ,  Michael Robert Hanson ,  Mohsen M Agsen

IPC: H04L12/58 ,  G06F40/295 ,  G10L15/22 ,  G06N20/00

Abstract: In one embodiment, a method includes, by one or more computing systems, receiving, from a client system associated with a first user, a first user input from the first user, identifying one or more entities referenced by the first user input, determining a classification of the first user input based on a machine-learning classifier model, generating several candidate conversational fillers based on the classification of the first user input and the one or more identified entities, wherein each candidate conversational filler references at least one of the one or more identified entities, ranking the candidate conversational fillers based on a relevancy of the candidate conversational filler to the first user input and a decay model hysteresis, and sending instructions for presenting a top-ranked candidate conversational filler as an initial response to the first user.

公开(公告)号：US20210224346A1

公开(公告)日：2021-07-22

申请号：US17224487

申请日：2021-04-07

Applicant: Facebook, Inc.

Inventor： Fuchun Peng ,  Kun Han ,  Wenhai Yang ,  Cheng Zhang ,  Vivek Narayanan ,  Emmanouil Koukoumidis

IPC: G06F16/9535 ,  G06F16/2457 ,  G06N20/00 ,  G06F16/248

Abstract: In one embodiment, a method includes receiving an indication of a trigger action by a first user at a client system, wherein the trigger action is associated with a priming content object, identifying related content objects associated with the priming content object, selecting recommended content objects based on the priming content object, the related content objects, and profile information of the first user, wherein each of the selected recommended content objects comprises entity information of entities associated with the priming content object, and presenting content suggestions at the client system, wherein each content suggestion comprises one of the selected recommended content objects.

公开(公告)号：US11010436B1

公开(公告)日：2021-05-18

申请号：US15967279

申请日：2018-04-30

Applicant: Facebook, Inc.

Inventor： Fuchun Peng ,  Kun Han ,  Wenhai Yang ,  Cheng Zhang ,  Vivek Narayanan ,  Emmanouil Koukoumidis

IPC: G06F16/24 ,  G06F16/9535 ,  G06N20/00 ,  G06F16/248 ,  G06F16/2457 ,  H04L29/08

Abstract: In one embodiment, a method includes receiving an indication of a trigger action by a first user, wherein the trigger action is associated with a priming content object, identifying one or more related content objects associated with the priming content object, generating a first feature vector representing the priming content object, the one or more related content objects, and profile information of the first user, accessing a plurality of second feature vectors representing a plurality of recommended content objects, respectively, selecting one or more of the recommended content objects based on comparisons between the first feature vector and the respective second feature vectors representing the recommended content objects, and sending, to a client system in response to the trigger action, instructions for presenting one or more content suggestions to the first user, wherein each content suggestion comprises one of the selected recommended content objects.

公开(公告)号：US20220092131A1

公开(公告)日：2022-03-24

申请号：US17543539

申请日：2021-12-06

Applicant: Facebook, Inc.

Inventor： Emmanouil Koukoumidis ,  Michael Robert Hanson ,  Rajen Subba ,  Heidi Young ,  Rushin Shah ,  Jinsong Yu ,  Benoit F. Dumoulin ,  Jeremy Gillmor Kahn ,  Chandrasekhar Iyer

IPC: G06F16/951 ,  G06F40/40 ,  G06N20/00

Abstract: In one embodiment, a method includes receiving a user query associated with dialog-intents at a client system, executing tasks corresponding to the dialog-intents, generating a multi-perspective response by a stitching model based on two or more of execution results of the tasks, wherein the multi-perspective response comprises a natural-language response combining the two or more execution results, and presenting the multi-perspective response at the client system.

公开(公告)号：US11086858B1

公开(公告)日：2021-08-10

申请号：US16222957

申请日：2018-12-17

Applicant: Facebook, Inc.

Inventor： Emmanouil Koukoumidis ,  Michael Robert Hanson ,  Mohsen Agsen

IPC: G06F16/242 ,  G06N20/00 ,  G06F16/2455 ,  G06F16/2457 ,  G10L15/22

Abstract: In one embodiment, a method includes, by one or more computing systems, receiving, from a client system associated with a user, an initial portion of a user input, wherein the initial portion comprises a partial request, and wherein the initial portion is received while the user is continuing to provide further input, generating, responsive to receiving the initial portion of the user input, one or more speculative queries based on the partial request and a machine-learning predictive model, wherein each speculative query is a predicted complete request based on the partial request, calculating a confidence score for each speculative query based on the predictive model, ranking the one or more speculative queries based on their respective confidence scores and associated costs, executing one or more of the speculative queries based on their ranks, and caching one or more results of the executed one or more speculative queries.