# US 12,118,371 B2 · Assisting users with personalized and contextual communication content

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 12,118,371 B2
- Document ID: US12118371B2
- Status: Granted
- Kind: B2
- Application number: 17/557,790
- Filed: 2021-12-21
- Published: 2022-04-14
- Granted: 2024-10-15
- Earliest claimed: 2018-04-20
- Assignees: Meta Platforms, Inc.
- Applicants: Meta Platforms, Inc.
- Inventors: Scott Martin
- Web page: https://patentgrep.com/patent/US12118371B2

## CPC

- G06Q 10/00: Administration; Management
- G06F 3/011: {Arrangements for interaction with the human body, e.g. for user immersion in virtual reality (blind teaching G09B21/00)}
- G06F 3/013: {Eye tracking input arrangements (G06F3/015 takes precedence)}
- 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)}
- G06F 3/0237: {using prediction or retrieval techniques}
- G06F 3/167: {Audio in a user interface, e.g. using voice commands for navigating, audio feedback}
- 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
- G06F 8/31: {Programming languages or programming paradigms}
- G06F 9/44505: {Configuring for program initiating, e.g. using registry, configuration files}
- G06F 9/4451: {User profiles; Roaming}
- G06F 9/453: {Help systems}
- G06F 16/176: Support for shared access to files; File sharing support
- G06F 16/2255: {Hash tables}
- G06F 16/2365: {Ensuring data consistency and integrity}
- G06F 16/243: {Natural language query formulation}
- G06F 16/24552: {Database cache management}
- G06F 16/24575: {using context}
- G06F 16/24578: {using ranking}
- G06F 16/248: Presentation of query results
- G06F 16/285: {Clustering or classification}
- G06F 16/3322: {using system suggestions (G06F16/3325 takes precedence)}
- G06F 16/3323: {using document space presentation or visualization, e.g. category, hierarchy or range presentation and selection}
- G06F 16/3329: Natural language query formulation
- G06F 16/3344: {using natural language analysis}
- G06F 16/338: Presentation of query results
- G06F 16/4393: {Multimedia presentations, e.g. slide shows, multimedia albums}
- G06F 16/90332: {Natural language query formulation or dialogue systems}
- G06F 16/90335: {Query processing}
- G06F 16/9038: Presentation of query results
- G06F 16/904: Browsing; Visualisation therefor (for navigating the web G06F16/954; browsing optimisation for the web G06F16/957)
- G06F 16/951: Indexing; Web crawling techniques
- G06F 16/9532: Query formulation
- G06F 16/9535: Search customisation based on user profiles and personalisation
- G06F 18/2411: based on the proximity to a decision surface, e.g. support vector machines
- G06F 18/2414: {Smoothing the distance, e.g. radial basis function networks [RBFN]}
- G06F 40/205: Parsing
- G06F 40/274: Converting codes to words; Guess-ahead of partial word inputs
- G06F 40/295: Named entity recognition
- G06F 40/30: Semantic analysis
- G06F 40/40: Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30)
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G06N 5/027: {Frames}
- G06N 20/00: Machine learning
- G06Q 10/42: Determination of affinities or common interests between users
- G06Q 10/48: using social graphs
- G06V 10/82: using neural networks
- 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)
- G06V 40/172: {Classification, e.g. identification}
- G06V 40/28: {Recognition of hand or arm movements, e.g. recognition of deaf sign language (static hand signs G06V40/113)}
- G10L 15/02: Feature extraction for speech recognition; Selection of recognition unit
- G10L 15/063: {Training}
- G10L 15/07: to the speaker
- G10L 15/16: using artificial neural networks
- G10L 15/1815: {Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning}
- G10L 15/1822: {Parsing for meaning understanding}
- G10L 15/183: using context dependencies, e.g. language models
- G10L 15/187: Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams
- G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue
- G10L 15/26: Speech to text systems (G10L15/08 takes precedence)
- G10L 17/06: Decision making techniques; Pattern matching strategies
- G10L 17/22: Interactive procedures; Man-machine interfaces
- 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)}
- H04L 41/20: {Network management software packages}
- H04L 41/22: comprising specially adapted graphical user interfaces [GUI]
- H04L 43/0882: Utilisation of link capacity
- H04L 43/0894: Packet rate
- H04L 51/02: using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
- H04L 51/216: Handling conversation history, e.g. grouping of messages in sessions or threads
- H04L 51/52: for supporting social networking services
- H04L 63/102: {Entity profiles}
- 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 67/5651: Reducing the amount or size of exchanged application data
- H04L 67/75: Indicating network or usage conditions on the user display
- H04W 12/08: Access security
- G06F 2216/13: Prefetching
- G10L 13/00: Speech synthesis; Text to speech systems
- G10L 13/04: Details of speech synthesis systems, e.g. synthesiser structure or memory management
- G10L 17/00: Speaker identification or verification techniques
- G10L 2015/223: {Execution procedure of a spoken command}
- G10L 2015/225: {Feedback of the input speech}
- H04L 51/046: Interoperability with other network applications or services
- H04L 51/222: using geographical location information, e.g. messages transmitted or received in proximity of a certain spot or area
- H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46)
- H04L 67/53: using third party service providers
- 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)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

In one embodiment, a method includes receiving one or more voice inputs from a first user, determining a first language register associated with the first user based on the one or more voice inputs, selecting a second language register for a voice response based on the one or more voice inputs, generating the voice response based on the second language register, and providing the voice response in response to the one or more voice inputs.

## Claims

### Claim 1

A method comprising, by one or more computing systems:
receiving one or more voice inputs from a first user;
determining a first language register associated with the first user based on the one or more voice inputs;
selecting a second language register for a voice response based on the one or more voice inputs;
generating, by a selected language-register model from a plurality of language-register models corresponding to a plurality of language registers, respectively, the voice response based on the second language register, wherein the selected language-register model corresponds to the second language register, wherein the plurality of language-register models are each trained based on a plurality of groups of training samples, respectively, and wherein the plurality of groups of training samples correspond to the plurality of language registers, respectively; and
providing the voice response in response to the one or more voice inputs,
wherein the plurality of language register models are each personalized with respect to the first user, and wherein the voice response is personalized with respect to the first user.

### Claim 2 (depends on 1)

The method of claim 1, wherein the first language register and the second language register are the same language register.

### Claim 3 (depends on 1)

The method of claim 1, wherein the first language register and the second language register are different language registers.

### Claim 4 (depends on 1)

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

### Claim 5 (depends on 4)

The method of claim 4, wherein the word-embedding model is based on a convolutional neural network.

### Claim 6 (depends on 1)

The method of claim 1, wherein the plurality of groups of training samples are associated with the first user, the training samples in each of the plurality of groups comprising one or more of news feed posts, news feed comments, a user profile, or messages.

### Claim 7 (depends on 6)

The method of claim 6, wherein the plurality of groups corresponds to one or more criteria, wherein the one or more criteria comprise one or more of age, relationship, location, education, interest, or native language, and wherein each group is associated with a language register model for the group.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
identifying an intent and one or more slots associated with the one or more voice inputs;
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 a data store, wherein each language-generation template comprises one or more slots corresponding to the one or more slots; and
selecting a language-generation template from the plurality of language-generation templates based on the identified intent.

### Claim 9 (depends on 8)

The method of claim 8, 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.

### Claim 10 (depends on 8)

The method of claim 8, wherein the voice response is generated further based on the retrieved information and the selected language-generation template.

### Claim 11 (depends on 10)

The method of claim 10, wherein generating the voice response comprises:
generating a list of candidate words based on the second 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.

### Claim 12 (depends on 1)

The method of claim 1, wherein determining the first language register or selecting the second language register is further based on a machine-learning model.

### Claim 13 (depends on 1)

The method of claim 1, wherein the first user is in a 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.

### Claim 14 (depends on 13)

The method of claim 13, wherein the voice response is operable to allow the first user to select the voice response, wherein the method further comprises:
receiving a selection of the voice response from the first user; and
inserting the voice response in the message thread between the first user and the second user.

### Claim 15 (depends on 1)

The method of claim 1, wherein the first user is in a 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.

### Claim 16 (depends on 15)

The method of claim 15, further comprising:
inserting the voice response in the message thread between the first user and the assistant xbot.

### Claim 17 (depends on 1)

The method of claim 1, further comprising identifying the second language register, wherein the identifying comprises:
generating a first feature vector representing the one or more voice inputs;
accessing a plurality of second feature vectors representing the plurality of language registers stored in a data store;
calculating a plurality of similarity scores between the first feature vector and the respective second feature vector; and
identifying the second language register based on the plurality of similarity scores.

### Claim 18

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
receive one or more voice inputs from a first user;
determine a first language register associated with the first user based on the one or more voice inputs;
select a second language register for a voice response based on the one or more voice inputs;
generate, by a selected language-register model from a plurality of language-register models corresponding to a plurality of language registers, respectively, the voice response based on the second language register, wherein the selected language-register model corresponds to the second language register, wherein the plurality of language-register models are each trained based on a plurality of groups of training samples, respectively, and wherein the plurality of groups of training samples correspond to the plurality of language registers, respectively; and
provide the voice response in response to the one or more voice inputs,
wherein the plurality of language register models are each personalized with respect to the first user, and wherein the voice response is personalized with respect to the first user.

### Claim 19

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 one or more voice inputs from a first user;
determine a first language register associated with the first user based on the one or more voice inputs;
select a second language register for a voice response based on the one or more voice inputs;
generate, by a selected language-register model from a plurality of language-register models corresponding to a plurality of language registers, respectively, the voice response based on the second language register, wherein the selected language-register model corresponds to the second language register, wherein the plurality of language-register models are each trained based on a plurality of groups of training samples, respectively, and wherein the plurality of groups of training samples correspond to the plurality of language registers, respectively; and
provide the voice response in response to the one or more voice inputs,
wherein the plurality of language register models are each personalized with respect to the first user, and wherein the voice response is personalized with respect to the first user.