# US 2021/0343286 A1 · Auto-completion for Multi-modal User Input in Assistant Systems

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- Number: US 2021/0343286 A1
- Document ID: US20210343286A1
- Status: Published application
- Kind: A1
- Application number: 17/368,066
- Filed: 2021-07-06
- Published: 2021-11-04
- Earliest claimed: 2018-04-20
- Assignees: Facebook Technologies, LLC
- Applicants: Facebook Technologies, LLC
- Inventors: William Crosby Presant; Francislav P. Penov; Anuj Kumar
- Web page: https://patentgrep.com/patent/US20210343286A1

## 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 3/167: {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 16/3322: {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 16/90332: {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 21/6245: {Protecting personal data, e.g. for financial or medical purposes}
- 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 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/00: Machine learning
- G06Q 10/40: Business processes related to social networking or social networking services
- G06Q 10/42: Determination of affinities or common interests between users
- G06Q 10/48: using social graphs
- G06V 10/764: using classification, e.g. of video objects
- 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 40/28: {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 5/02: Channels characterised by the type of signal
- 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 51/18: Commands or executable codes
- 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 an initial input in a first modality from a first user at a client system, determining intents and slots corresponding to the initial input, wherein the slots are conditioned on the intents, generating one or more candidate continuation-inputs based on the intents and slots, where the one or more candidate continuation-inputs are in one or more candidate modalities, respectively, wherein the candidate modalities are different from the first modality, and wherein each of the candidate continuation-inputs references entities represented by the slots, and presenting one or more suggested inputs corresponding to one or more of the candidate continuation-inputs at the client system.

## Claims

### Claim 1

A method comprising, by a client system:
receiving, at the client system, an initial input from a first user, wherein the initial input is in a first modality;
determining one or more intents and one or more slots corresponding to the initial input, wherein the one or more slots are conditioned on the one or more intents;
generating, based on the one or more intents and the one or more slots, one or more candidate continuation-inputs, where the one or more candidate continuation-inputs are in one or more candidate modalities, respectively, wherein the candidate modalities are different from the first modality, and wherein each of the candidate continuation-inputs references one or more entities represented by the one or more slots; and
presenting, at the client system, one or more suggested inputs corresponding to one or more of the candidate continuation-inputs.

### Claim 2 (depends on 1)

The method of claim 1, wherein the first modality comprises one of audio, text, image, video, motion, or orientation.

### Claim 3 (depends on 2)

The method of claim 2, wherein the first modality comprises motion, and wherein the initial input comprises a gesture.

### Claim 4 (depends on 2)

The method of claim 2, wherein the first modality comprises orientation, and wherein the initial input comprises a gaze on an object.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
determining that the first user needs one or more suggested inputs.

### Claim 6 (depends on 5)

The method of claim 5, wherein determining that the first user needs one or more suggested inputs is based on a wake-up input from the first user.

### Claim 7 (depends on 6)

The method of claim 6, wherein the wake-up input comprises one or more of a voice utterance, a character string, an image, a video clip, a gesture, or a gaze.

### Claim 8 (depends on 5)

The method of claim 5, wherein the initial input comprises a gaze on an object, and wherein determining that the first user needs one or more suggested inputs is further based on the gaze on the object.

### Claim 9 (depends on 8)

The method of claim 8, wherein generating the one or more candidate continuation-inputs is further based on the object.

### Claim 10 (depends on 5)

The method of claim 5, wherein determining that the first user needs one or more suggested inputs is further based on contextual information associated with the initial input.

### Claim 11 (depends on 5)

The method of claim 5, wherein determining that the first user needs one or more suggested inputs is further based on the one or more intents.

### Claim 12 (depends on 1)

The method of claim 1, further comprising:
identifying one or more entities associated with the one or more intents.

### Claim 13 (depends on 12)

The method of claim 12, wherein generating the one or more candidate continuation-inputs is further based the one or more entities.

### Claim 14 (depends on 1)

The method of claim 1, further comprising:
receiving, at the client system, a user-selected input from the first user, wherein the user-selected input comprises one of the suggested inputs; and
executing one or more tasks based on the user-selected input.

### Claim 15 (depends on 1)

The method of claim 1, further comprising:
receiving, at the client system, a first user-selected input from the first user, wherein the first user-selected input comprises one of the suggested inputs, and wherein the first user-selected input is associated with a first intent;
generating, based on the first user-selected input, one or more additional candidate continuation-inputs, wherein each of the one or more additional candidate continuation-inputs is associated with the first intent;
presenting, at the client system, one or more additional suggested inputs corresponding to one or more of the additional candidate continuation-inputs;
receiving, at the client system, a second user-selected input from the first user, wherein the second user-selected input comprises one of the additional suggested inputs; and
executing one or more tasks based on the second user-selected input.

### Claim 16 (depends on 1)

The method of claim 1, further comprising:
determining the initial input comprises an incomplete input for triggering an execution of one or more tasks corresponding to the one or more intents.

### Claim 17 (depends on 16)

The method of claim 16, wherein each of the candidate continuation-inputs comprises a complete instruction to trigger the execution of a respective task of the one or more tasks.

### Claim 18 (depends on 17)

The method of claim 17, wherein each of the presented suggested inputs comprises a guidance to the first user for executing the complete instruction corresponding to the respective candidate continuation-input.

### Claim 19

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
receive, at a client system, an initial input from a first user, wherein the initial input is in a first modality;
determine one or more intents and one or more slots corresponding to the initial input, wherein the one or more slots are conditioned on the one or more intents;
generate, based on the one or more intents and the one or more slots, one or more candidate continuation-inputs, where the one or more candidate continuation-inputs are in one or more candidate modalities, respectively, wherein the candidate modalities are different from the first modality, and wherein each of the candidate continuation-inputs references one or more entities represented by the one or more slots; and
present, at the client system, one or more suggested inputs corresponding to one or more of the candidate continuation-inputs.

### Claim 20

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, at a client system, an initial input from a first user, wherein the initial input is in a first modality;
determine one or more intents and one or more slots corresponding to the initial input, wherein the one or more slots are conditioned on the one or more intents;
generate, based on the one or more intents and the one or more slots, one or more candidate continuation-inputs, where the one or more candidate continuation-inputs are in one or more candidate modalities, respectively, wherein the candidate modalities are different from the first modality, and wherein each of the candidate continuation-inputs references one or more entities represented by the one or more slots; and
present, at the client system, one or more suggested inputs corresponding to one or more of the candidate continuation-inputs.