# US 2020/0401423 A1 · Auto-completion for Gesture-input in Assistant Systems

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- Number: US 2020/0401423 A1
- Document ID: US20200401423A1
- Status: Published application
- Kind: A1
- Application number: 17/010,750
- Filed: 2020-09-02
- Published: 2020-12-24
- 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/US20200401423A1

## 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 detecting a user input comprising an incomplete gesture performed by one or more hands of a first user by a client system associated with the first user; selecting one or more candidate gestures from a plurality of pre-defined gestures by the client system based on a personalized gesture-recognition model, wherein each of the candidate gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate gesture, and presenting one or more suggested inputs corresponding to one or more of the candidate gestures at the client system.

## Claims

### Claim 1

A method comprising:
detecting, by a client system associated with a first user, a user input comprising an incomplete gesture performed by one or more hands of the first user;
selecting, by the client system based on a personalized gesture-recognition model, one or more candidate gestures from a plurality of pre-defined gestures, wherein each of the candidate gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate gesture; and
presenting, at the client system, one or more suggested inputs corresponding to one or more of the candidate gestures.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
calculating, by the client system for each of the one or more candidate gestures, a similarity level of the candidate gesture with respect to the incomplete gesture.

### Claim 3 (depends on 2)

The method of claim 2, wherein the similarly level of each candidate gesture with respect to the incomplete gesture is based on a trajectory of the incomplete gesture with respect to the client system.

### Claim 4 (depends on 2)

The method of claim 2, wherein the similarly level of each candidate gesture with respect to the incomplete gesture is based on an orientation of the incomplete gesture with respect to the client system.

### Claim 5 (depends on 2)

The method of claim 2, wherein the similarly level of each candidate gesture with respect to the incomplete gesture is based on an object associated with the incomplete gesture.

### Claim 6 (depends on 2)

The method of claim 2, wherein the similarly level of each candidate gesture with respect to the incomplete gesture is based on contextual information associated with the incomplete gesture.

### Claim 7 (depends on 2)

The method of claim 2, wherein the similarly level of each candidate gesture with respect to the incomplete gesture is based on a position of the incomplete gesture with respect to the client system.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
calculating, by the client system, one or more confidence scores for one or more intents corresponding to the incomplete gesture; and
determining, by the client system, that each of the one or more confidence scores is below a threshold score.

### Claim 9 (depends on 8)

The method of claim 8, wherein the threshold score is based on a wake-up gesture performed by the first user.

### Claim 10 (depends on 8)

The method of claim 8, wherein calculating the one or more confidence scores for the one or more intents corresponding to the incomplete gesture is based on a velocity associated with the incomplete gesture.

### Claim 11 (depends on 8)

The method of claim 8, wherein calculating the one or more confidence scores for the one or more intents corresponding to the incomplete gesture is based on temporal information associated with the incomplete gesture, and wherein the temporal information comprises a pause in the user input.

### Claim 12 (depends on 8)

The method of claim 8, wherein selecting the one or more candidate gestures is further based on the one or more intents.

### Claim 13 (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, by the client system, one or more tasks based on the user-selected input.

### Claim 14 (depends on 1)

The method of claim 1, wherein each pre-defined gesture comprises one or more of pointing, poking, tapping, waving, or swiping.

### 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, by the client system based on the first user-selected input, one or more additional candidate gestures, wherein each of the one or more additional candidate gestures 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 gestures;
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, by the client system, one or more tasks based on the second user-selected input.

### Claim 16

One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
detect, by a client system associated with a first user, a user input comprising an incomplete gesture performed by one or more hands of the first user;
select, by the client system based on a personalized gesture-recognition model, one or more candidate gestures from a plurality of pre-defined gestures, wherein each of the candidate gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate gesture; and
present, at the client system, one or more suggested inputs corresponding to one or more of the candidate gestures.

### Claim 17 (depends on 16)

The media of claim 16, wherein the software is further operable when executed to:
calculate, by the client system for each of the one or more candidate gestures, a similarity level of the candidate gesture with respect to the incomplete gesture.

### Claim 18 (depends on 17)

The media of claim 17, wherein the similarly level of each candidate gesture with respect to the incomplete gesture is based on a trajectory of the incomplete gesture with respect to the client system.

### Claim 19 (depends on 17)

The media of claim 17, wherein the similarly level of each candidate gesture with respect to the incomplete gesture is based on an orientation of the incomplete gesture with respect to the client system.

### 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:
detect, by a client system associated with a first user, a user input comprising an incomplete gesture performed by one or more hands of the first user;
select, by the client system based on a personalized gesture-recognition model, one or more candidate gestures from a plurality of pre-defined gestures, wherein each of the candidate gestures is associated with a confidence score representing a likelihood the first user intended to input the respective candidate gesture; and
present, at the client system, one or more suggested inputs corresponding to one or more of the candidate gestures.