Assisting users with personalized and contextual communication content

• US 10,782,986 B2
• Filed: 04/30/2018
• Issued: 09/22/2020
• Est. Priority Date: 04/20/2018
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Martin, Scott
• Primary Examiner(s): Gay, Sonia L
• Application Number:
  US15/966,455
• Publication Number:
  US 20190325863A1
• Time in Patent Office: 876 Days
• CPC Class Codes: G06F 16/176 Support for shared access to files; File sharing support
• CPC Class Codes: G06F 16/2255 Hash tables
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/243 Natural language query formulation
• CPC Class Codes: G06F 16/24552 Database cache management
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/3322 using system suggestions G06F16/3325 takes precedence
• CPC Class Codes: G06F 16/3323 using document space presentation or visualization, e.g. category, hierarchy or range presentation and selection
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/90332 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06F 16/9038 Presentation of query results
• CPC Class Codes: G06F 16/904 Browsing; Visualisation therefor for navigating the web G06F16/954; browsing optimisation for the web G06F16/957
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9532 Query formulation
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 18/2411 : based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 2216/13 : Prefetching
• CPC Class Codes: G06F 3/011 : Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06F 3/013 : Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06F 3/017 : Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06F 3/167 : Audio in a user interface, e.g. using voice commands for navigating, audio feedback
• CPC Class Codes: G06F 40/205 : Parsing
• CPC Class Codes: G06F 40/274 : Converting codes to words; Guess-ahead of partial word inputs
• CPC Class Codes: G06F 40/295 : Named entity recognition
• CPC Class Codes: G06F 40/30 : Semantic analysis
• CPC Class Codes: G06F 40/40 : Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06F 7/14 : Merging, i.e. combining at least two sets of record carriers each arranged in the same ordered sequence to produce a single set having the same ordered sequence
• CPC Class Codes: G06F 8/31 : Programming languages or programming paradigms
• CPC Class Codes: G06F 9/44505 : Configuring for program initiating, e.g. using registry, configuration files
• CPC Class Codes: G06F 9/4451 : User profiles; Roaming
• CPC Class Codes: G06F 9/453 : Help systems
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 3/006 : based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 5/022 : Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/027 : Frames
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/00 : Administration; Management
• CPC Class Codes: G06Q 50/01 : Social networking
• CPC Class Codes: G06V 10/764 : using classification, e.g. of video objects
• CPC Class Codes: G06V 10/82 : using neural networks
• CPC Class Codes: G06V 20/10 : Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 40/28 : Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113
• CPC Class Codes: G10L 13/00 : Speech synthesis; Text to speech systems
• CPC Class Codes: G10L 13/04 : Details of speech synthesis systems, e.g. synthesiser structure or memory management
• CPC Class Codes: G10L 15/02 : Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 15/063 : Training
• CPC Class Codes: G10L 15/07 : to the speaker
• CPC Class Codes: G10L 15/16 : using artificial neural networks
• CPC Class Codes: G10L 15/1815 : Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 15/1822 : Parsing for meaning understanding
• CPC Class Codes: G10L 15/183 : using context dependencies, e.g. language models
• CPC Class Codes: G10L 15/187 : Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams
• CPC Class Codes: G10L 15/22 : Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 15/26 : Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 17/00 : Speaker identification or verification techniques
• CPC Class Codes: G10L 17/06 : Decision making techniques; Pattern matching strategies
• CPC Class Codes: G10L 17/22 : Interactive procedures; Man-machine interfaces
• CPC Class Codes: G10L 2015/223 : Execution procedure of a spoken command
• CPC Class Codes: G10L 2015/225 : Feedback of the input speech
• CPC Class Codes: H04L 12/2816 : Controlling appliance services of a home automation network by calling their functionalities arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station; in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom H04Q9/00
• CPC Class Codes: H04L 41/20 : Network management software packages
• CPC Class Codes: H04L 41/22 : comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 43/0882 : Utilisation of link capacity
• CPC Class Codes: H04L 43/0894 : Packet rate
• CPC Class Codes: H04L 5/02 : Channels characterised by the type of signal
• CPC Class Codes: H04L 51/02 : using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• CPC Class Codes: H04L 51/046 : Interoperability with other network applications or services
• CPC Class Codes: H04L 51/18 : Commands or executable codes
• CPC Class Codes: H04L 51/216 : Handling conversation history, e.g. grouping of messages in sessions or threads
• CPC Class Codes: H04L 51/222 : using geographical location information, e.g. messages transmitted or received in proximity of a certain spot or area
• CPC Class Codes: H04L 51/52 : for supporting social networking services
• CPC Class Codes: H04L 63/102 : Entity profiles
• CPC Class Codes: H04L 67/10 : in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04L 67/53 : using third party service providers
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 67/5651 : Reducing the amount or size of exchanged application data
• CPC Class Codes: H04L 67/75 : Indicating network or usage conditions on the user display
• CPC Class Codes: H04W 12/08 : Access security

Abstract

In one embodiment, a method includes receiving, from a client system associated with a first user, a first user input by the first user, wherein the first user input is associated with a current dialog session, identifying a first language register associated with the first user based on the first user input, accessing a plurality of language-register models associated with a plurality of language registers stored in a data store, selecting a first language-register model from the plurality of language-register models based on the identified first language register, and generating a first communication content responsive to the first user input, the first communication content being personalized for the first user based on the selected first language-register model.

Claims

1. A method comprising, by one or more computing systems:

receiving, from a client system associated with a first user, a first user input by the first user, wherein the first user input is associated with a current dialog session;

identifying a first language register associated with the first user based on the first user input;

accessing a plurality of language-register models associated with a plurality of language registers stored in a data store, wherein the plurality of language-register models are each personalized with respect to the first user;

selecting a first language-register model from the plurality of language-register models based on the identified first language register; and

generating a first communication content responsive to the first user input, the first communication content being personalized for the first user based on the selected first language-register model.

2. The method of claim 1, further comprising:

identifying an intent and one or more slots associated with the first user input;

determining an action based on the identified intent;

executing the action to retrieve information associated with the one or more slots from one or more information sources;

accessing a plurality of language-generation templates stored in the data store, wherein each language-generation template comprises one or more slots corresponding to the one or more slots associated with the first user input; and

selecting a language-generation template from the plurality of language-generation templates based on the identified intent.

3. The method of claim 2, wherein the one or more information sources comprise one or more of:

one or more first-party agents;

or

one or more third-party agents.

4. The method of claim 2, wherein generating the communication content for the first user is further based on the retrieved information and the selected language-generation template.

5. The method of claim 4, wherein generating the communication content for the first user comprises:

generating a list of candidate words based on the identified language register and the retrieved information;

pruning the list of candidate words; and

inserting, from the pruned list of candidate words, one of the candidate words into each slot of the selected language-generation template.

6. The method of claim 1, further comprising:

receiving, from the client system, a second user input by the first user, wherein the second user input is associated with the current dialog session;

identifying a second language register associated with the first user based on the second user input;

accessing the plurality of language-register models associated with the plurality of language registers stored in the data store;

selecting a second language-register model from the plurality of language-register models based on the identified second language register; and

generating a second communication content responsive to the second user input, the second communication content being personalized for the first user based on the selected second language-register model.

7. The method of claim 1, wherein the first language register is identified based on a machine-learning model.

8. The method of claim 1, wherein the first user is in the current dialog session with a second user, and wherein the current dialog session comprises a message thread between the first user and the second user.

9. The method of claim 8, further comprising:

sending, to the client system, instructions for presenting the generated first communication content to the first user, wherein the generated first communication content is operable to allow the first user to select the generated first communication content;

receiving, from the client system, a selection of the generated first communication content from the first user; and

inserting the generated first communication content in the message thread between the first user and the second user.

10. The method of claim 1, wherein the first user is in the current dialog session with an assistant xbot, and wherein the current dialog session comprises a message thread between the first user and the assistant xbot.

11. The method of claim 10, further comprising:

inserting the generated first communication content in the message thread between the first user and the assistant xbot.

12. The method of claim 1, wherein the first user input comprises one or more of:

a character string;

an audio clip;

an image;

or

a video.

13. The method of claim 1, wherein the generated first communication content comprises one or more of:

a character string;

an audio clip;

an image;

or

a video.

14. The method of claim 1, wherein the plurality of language-register models are trained based on a word-embedding model.

15. The method of claim 14, wherein the word-embedding model is based on convolutional neural network.

16. The method of claim 1, wherein each language-register model is trained based on a plurality of training samples associated with the first user, the training samples comprising one or more of news feed posts, news feed comments, a user profile, or messages.

17. The method of claim 16, further comprising clustering the plurality of training samples into one or more groups of training samples based on one or more criteria.

18. The method of claim 17, wherein the one or more criteria comprise one or more of age, relationship, location, education, interest, or native language.

19. The method of claim 17, further comprising training, for each group, a language-register model for the group based on the training samples associated with the group.

20. The method of claim 1, wherein identifying the first language register comprises:

generating a first feature vector representing the first user input;

accessing a plurality of second feature vectors representing the plurality of language registers stored in the data store;

calculating a plurality of similarity scores between the first feature vector and the respective second feature vector; and

identifying the first language register based on the plurality of similarity scores.

21. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

receive, from a client system associated with a first user, a first user input by the first user, wherein the first user input is associated with a current dialog session;

identify a first language register associated with the first user based on the first user input;

access a plurality of language-register models associated with a plurality of language registers stored in a data store, wherein the plurality of language-register models are each personalized with respect to the first user;

select a first language-register model from the plurality of language-register models based on the identified first language register; and

generate a first communication content responsive to the first user input, the first communication content being personalized for the first user based on the selected first language-register model.

22. A system comprising:

one or more processors; and

a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to;

receive, from a client system associated with a first user, a first user input by the first user, wherein the first user input is associated with a current dialog session;

identify a first language register associated with the first user based on the first user input;

access a plurality of language-register models associated with a plurality of language registers stored in a data store, wherein the plurality of language-register models are each personalized with respect to the first user;

select a first language-register model from the plurality of language-register models based on the identified first language register; and

generate a first communication content responsive to the first user input, the first communication content being personalized for the first user based on the selected first language-register model.