Using facial movements to generate a conversational record

Abstract

Systems, methods, and computer program products are disclosed for generating a common record based on differing source inputs. Generating a common record based on differing source inputs may include receiving via at least one sensor first non-audible signals indicative of verbalization of an individual; interpreting first words of the individual at least in part using the first non-audible signals received from the at least one sensor; receiving second signals generated by a source other than the individual; performing speech recognition on the second signals to interpret second words from the source; and using the first words and the second words to generate a record.

Description

Attorney Docket No. 16198.0049-00000USING FACIAL MOVEMENTS TO GENERATE A CONVERSATIONAL RECORD CROSS REFERENCES TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority of U.S. 19 / 033,020 filed January 21, 2025, which is incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure generally relates to the field of discerning information from neuromuscular activity. One example is to discern communications by detecting facial skin movements that occur during subvocalization. Other examples include enabling control based neuromuscular activity and discerning changes in neuromuscular activity over time.BACKGROUND

[0003] The human brain and neural activity are complex and involve many subsystems. One of those subsystems is the facial region used by humans for communication with others. From birth, humans are trained to activate craniofacial muscles to articulate sounds. Even before full language ability evolves, babies use facial expressions, including micro-expressions, to convey deeper information about themselves. After language abilities are learned, however, speech is the main technique that humans use to communicate.

[0004] The normal process of vocalized speech uses multiple groups of muscles and nerves, from the chest and abdomen, through the throat, and up through the mouth and face. To utter a given phoneme, motor neurons activate muscle groups in the face, larynx, and mouth in preparation for propulsion of air flow out of the lungs, and these muscles continue moving during speech to create words and sentences. Without this air flow, no sounds are emitted from the mouth. Silent speech occurs when the air flow from the lungs is absent, while the muscles in the face, larynx, and mouth articulate the desired sounds or move in a manner enabling interpretation.

[0005] Some of the disclosed embodiments are directed to providing a new approach for extracting meaning from neuromuscular activity, one that detects facial skin micromovements that occur during subvocalization, such as, silent speech.SUMMARY

[0006] Embodiments consistent with the present disclosure provide systems, methods, and devices for detection and usage of facial movements.

[0007] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for identifying individuals using facial skin micromovements. These embodiments may involve operating a wearable coherent light source configured to project light towards a facial region a head of an individual; operating at least one detector configured to receive coherent lightAttorney Docket No. 16198.0049-00304reflections from the facial region and to output associated reflection signals; analyzing the reflection signals to determine specific facial skin micromovements of the individual; accessing memory correlating a plurality of facial skin micromovements with the individual; searching for match between the determined specific facial skin micromovements and at least one of the plurality of facial skin micromovements in the memory; if a match is identified, initiating a first action; and if a match is not identified, initiating a second action different from the first action.

[0008] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for interpreting facial skin movements. These embodiments may involve projecting light on a plurality of facial region areas of an individual, wherein the plurality of areas includes at least a first area and a second area, the first area being closer to at least one of a zygomaticus muscle or a risorius muscle than the second area; receiving reflections from the plurality of areas; detecting first facial skin movements corresponding to reflections from the first area and second facial skin movements corresponding to reflections from the second area; determining, based on differences between the first facial skin movements and the second facial skin movements, that the reflections from the first area closer to the at least one of a zygomaticus muscle or a risorius muscle are a stronger indicator of communication than the reflections from the second area; based on the determination that the reflections from the first area are a stronger indicator of communication, processing the reflections from the first area to ascertain the communication, and ignoring the reflections from the second area.

[0009] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for performing identity verification operations based on facial micromovements. These embodiments may involve receiving in a trusted manner, reference signals for verifying correspondence between a particular individual and an account at an institution, the reference signals being derived based on reference facial micromovements detected using first coherent light reflected from a face of the particular individual; storing in a secure data structure, a correlation between an identity of the particular individual and the reference signals reflecting the facial micromovements; following storing, receiving via the institution, a request to authenticate the particular individual; receiving real-time signals indicative of second coherent light reflections being derived from second facial micromovements of the particular individual; comparing the real-time signals with the reference signals stored in the secure data structure to thereby authenticate the particular individual; and upon authentication, notifying the institution that the particular individual is authenticated.

[0010] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for continuous authentication based on facial skin micromovements. These embodiments may involve receiving during an ongoing electronic transaction, first signals representing coherent light reflections associated with first facial skin micromovements during a first time period; determining, using the first signals, an identity of a specific individual associated with the first facial skin micromovements; receiving during the ongoing electronic transaction second signalsAttorney Docket No. 16198.0049-00304representing coherent light reflections associated with second facial skin micromovements, the second signals being received during a second time period following the first time period; determining, using the second signals, that the specific individual is also associated with the second facial skin micromovements; receiving during the ongoing electronic transaction third signals representing coherent light reflections associated with third facial skin micromovements, the third signals being received during a third time period following the second time period; determining, using the third signals, that the third facial skin micromovements are not associated with the specific individual; and initiating an action based on the determination that the third facial skin micromovements are not associated with the specific individual.

[0011] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for performing thresholding operations for interpretation of facial skin micromovements. These embodiments may involve detecting facial micromovements in an absence of perceptible vocalization associated with the facial micromovements; determining an intensity level of the facial micromovements; comparing the determined intensity level with a threshold; when the intensity level is above the threshold, interpreting the facial micromovements; and when the intensity level falls beneath the threshold, disregarding the facial micromovements.

[0012] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for establishing nonvocalized conversations. These embodiments may involve establishing a wireless communication channel for enabling a nonvocalized conversation via a first wearable device and a second wearable device, wherein both the first wearable device and the second wearable device each contain a coherent light source and a light detector configured to detect facial skin micromovements from coherent light reflections; detecting by the first wearable device first facial skin micromovements occurring in an absence of perceptible vocalization; transmitting a first communication via the wireless communication channel from the first wearable device to the second wearable device, wherein the first communication is derived from the first facial skin micromovements and is transmitted for presentation via the second wearable device; receiving a second communication via the wireless communication channel from the second wearable device, wherein the second communication is derived from second facial skin micromovements detected by the second wearable device; and presenting the second communication to a wearer of the first wearable device.

[0013] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for initiating content interpretation operations prior to vocalization of content to be interpreted. These embodiments may involve receiving signals representing facial skin micromovements; determining from the signals at least one word to be spoken prior to vocalization of the at least one word in an origin language; prior to the vocalization of the at least one word, instituting an interpretation of the at least one word; and causing the interpretation of the at least one word to be presented as the at least one word is spoken.Attorney Docket No. 16198.0049-00304

[0014] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for performing private voice assistance operations. These embodiments may involve receiving signals indicative of specific facial skin micromovements reflective of a private request to an assistant, wherein answering the private request requires an identification of a specific individual associated with the specific facial skin micromovements; accessing a data structure maintaining correlations between the specific individual and a plurality of facial skin micromovements associated with the specific individual; searching in the data structure for a match indicative of a correlation between a stored identity of the specific individual and the specific facial skin micromovements; in response to a determination of an existence of the match in the data structure, initiating a first action responsive to the request, wherein the first action involves enabling access to information unique to the specific individual; and if the match is not identified in the data structure, initiating a second action different from the first action.

[0015] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for determining subvocalized phonemes from facial skin micromovements. These embodiments may involve controlling at least one coherent light source in a manner enabling illumination of a first region of a face and a second region of the face; performing first pattern analysis on light reflected from the first region of the face to determine first micromovements of facial skin in the first region of the face; performing second pattern analysis on light reflected from the second region of the face to determine second micromovements of facial skin in the second region of the face; and using the first micromovements of the facial skin in the first region of the face and the second micromovements of the facial skin in the second region of the face to ascertain at least one sub vocalized phoneme.

[0016] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for generating synthesized representations of facial expressions. These embodiments may involve controlling at least one coherent light source in a manner enabling illumination of a portion of a face; receiving output signals from a light detector, wherein the output signals correspond to reflections of coherent light from the portion of the face; applying speckle analysis on the output signals to determine speckle analysis-based facial skin micromovements; using the determined speckle analysis-based facial skin micromovements to identify at least one word prevocalized or vocalized during a time period; using the determined speckle analysis-based facial skin micromovements to identify at least one change in a facial expression during the time period; and during the time period, outputting data for causing a virtual representation of the face to mimic the at least one change in the facial expression in conjunction with an audio presentation of the at least one word.

[0017] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for performing operations for attention-associated interactions based on facial skin micromovements. These embodiments may involve determining facial skin micromovements of anAttorney Docket No. 16198.0049-00304individual based on reflections of coherent light from a facial region of the individual; using the facial skin micromovements to determine a specific engagement level of the individual; receiving data associated with a prospective interaction with the individual; accessing a data structure correlating information reflective of alternative engagement levels with differing presentation manners; based on the specific engagement level and the correlating information, determining a specific presentation manner for the prospective interaction; and associating the specific presentation manner with the prospective interaction for subsequent engagement with the individual.

[0018] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for performing voice synthetization operations from detected facial skin micromovements. These embodiments may involve determining particular facial skin micromovements of a first individual speaking with a second individual based on reflections of light from a facial region of the first individual; accessing a data structure correlating facial micromovements with words; performing a lookup in the data structure of particular words associated with the particular facial skin micromovements; obtaining an input associated with a preferred speech consumption characteristic of the second individual; adopting the preferred speech consumption characteristic; and synthesizing, using the adopted preferred speech consumption characteristic, audible output of the particular words.

[0019] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for performing operations for personal presentation of prevocalization. These embodiments may involve receiving reflection signals corresponding to light reflected from a facial region of an individual; using the received reflections signals to determine particular facial skin micromovements of an individual in an absence of perceptible vocalization associated with the particular facial skin micromovements; accessing a data structure correlating facial skin micromovements with words; performing a lookup in the data structure of particular unvocalized words associated with the particular facial skin micromovements; and causing an audible presentation of the particular unvocalized words to the individual prior to vocalization of the particular words by the individual.

[0020] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for interpreting impaired speech based on facial movements. These embodiments may involve receiving signals associated with specific facial skin movements of an individual having a speech impairment that affects a manner in which the individual pronounces a plurality of words; accessing a data structure containing correlations between the plurality of words and a plurality of facial skin movements corresponding to the manner in which the individual pronounces the plurality of words; based on the received signals and the correlations, identifying specific words associated with the specific facial skin movements; and generating an output of the specific words for presentation, wherein the output differs from how the individual pronounces the specific words.Attorney Docket No. 16198.0049-00304

[0021] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for ongoing verification of communication authenticity based on light reflections from facial skin. These embodiments may involve generating a first data stream representing a communication by a subject, the communication having a duration; generating a second data stream for corroborating an identity of the subject from facial skin light reflections captured during the duration of the communication; transmitting the first data stream to a destination; transmitting the second data stream to the destination; and wherein the second data stream is correlated to the first data stream in a manner such that upon receipt at the destination, the second data stream is enabled for use in repeatedly checking during the duration of the communication that the communication originated from the subject.

[0022] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for noise suppression using facial skin micromovements. These embodiments may involve operating a wearable coherent light source configured to project light towards a facial region of a head of a wearer; operating at least one detector configured to receive coherent light reflections from the facial region associated with facial skin micromovements and to output associated reflection signals; analyzing the reflection signals to determine speech timing based on the facial skin micromovements in the facial region; receiving audio signals from at least one microphone, the audio signals containing sounds of words spoken by the wearer together with ambient sounds; correlating, based on the speech timing, the reflection signals with the received audio signals to determine portions of the audio signals associated with the words spoken by the wearer; and outputting the determined portions of the audio signals associated with the words spoken by the wearer, while omitting output of other portions of the audio signals not containing the words spoken by the wearer.

[0023] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for providing private answers to silent questions. These embodiments may involve receiving signals indicative of particular facial micromovements in an absence of perceptible vocalization; accessing a data structure correlating facial micromovements with words; using the received signals to perform a lookup in the data structure of particular words associated with the particular facial micromovements; determining a query from the particular words; accessing at least one data structure to perform a look up for an answer to the query; and generating a discreet output that includes the answer to the query.

[0024] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for performing control commands based on facial skin micromovements. These embodiments may involve operating at least one coherent light source in a manner enabling illumination of a non-lip portion of a face; receiving specific signals representing coherent light reflections associated with specific non-lip facial skin micromovements; accessing a data structure associating a plurality of non-lip facial skin micromovements with control commands; identifying inAttorney Docket No. 16198.0049-00304the data structure a specific control command associated with the specific signals associated with the specific non-lip facial skin micromovements; and executing the specific control command.

[0025] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for detecting changes in neuromuscular activity overtime. These embodiments may involve establishing a baseline of neuromuscular activity from coherent light reflections associated with historical skin micromovements; receiving current signals representing coherent light reflections associated with current skin micromovements of an individual; identifying a deviation of the current skin micromovements from the baseline of neuromuscular activity; and outputting an indicator of the deviation.

[0026] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for projecting graphical content and for interpreting non-verbal speech. These embodiments may involve operating a wearable light source configured to project light in a graphical pattern on a facial region of an individual, wherein the graphical pattern is configured to visibly convey information; receiving from a sensor, output signals corresponding with a portion of the light reflected from the facial region; determining from the output signals facial skin micromovements associated with non-verbalization; and processing the output signals to interpret the facial skin micromovements .

[0027] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for interpreting facial skin micromovements. These embodiments may involve receiving coherent light reflections from a facial region associated with facial skin micromovements of an individual; outputting reflection signals associated with the light reflections; capturing sounds produced by the individual; outputting audio signals associated with the captured sounds; and using both the reflection signals and the audio signals to generate output corresponding to words articulated by the individual.

[0028] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for interpreting facial skin micromovements. These embodiments may involve receiving during a first time period first signals representing prevocalization facial skin micromovements; receiving during a second time period succeeding the first time period, second signals representing sounds; analyzing the sounds to identify words spoken during the second time period; correlating the words spoken during the second time period with the prevocalization facial skin micromovements received during the first time period; storing the correlations; receiving during a third time period, third signals representing facial skin micromovements received in an absence of vocalization; using the stored correlations to identify language associated with the third signals; and outputting the language.

[0029] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for operating a multifunctional earpiece. These embodiments may involve operating a speaker integrated with an ear-mountable housing associated with the multifunctional earpiece forAttorney Docket No. 16198.0049-00304presenting sound; operating a light source integrated with the ear-mountable housing for projecting light toward skin of the wearer’s face; operating a light detector integrated with the ear-mountable housing and configured to receive reflections from the skin corresponding to facial skin micromovements indicative of pre vocalized words of the wearer; and simultaneously presenting the sound through the speaker, projecting the light toward the skin, and detecting the received reflections indicative of the prevocalized words.

[0030] Some disclosed embodiments may include a driver for integration with a software program and for enabling a neuromuscular detection device to interface with the software program. The driver comprising: an input handler for receiving non-audible muscle activation signals from the neuromuscular detection device; a lookup component for mapping specific ones of the non-audible activation signals to corresponding commands in the software program; a signal processing module for receiving the non-audible muscle activation signals from the input handler, supplying the specific ones of the non-audible muscle activation signals to the lookup component, and receiving an output as the corresponding commands; and a communications module for conveying the corresponding commands to the software program, to thereby enable control within the software program based on non-audible muscular activity detected by the neuromuscular detection device.

[0031] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for performing context-driven facial micromovement operations. These embodiments may involve receiving during a first time period, first signals representing first coherent light reflections associated with first facial skin micromovements; analyzing the first coherent light reflections to determine a first plurality of words associated with the first facial skin micromovements; receiving first information indicative of a first contextual condition in which the first facial skin micromovements occurred; receiving during a second time period, second signals representing second coherent light reflections associated with second facial skin micromovements; analyzing the second coherent light reflections to determine a second plurality of words associated with the second facial skin micromovements; receiving second information indicative of a second contextual condition in which the second facial skin micromovements occurred; accessing a plurality of control rules correlating a plurality of actions with a plurality of contextual conditions, wherein a first control rule prescribes a form of private presentation based on the first contextual condition, and a second control rule prescribes a form of non-private presentation based on the second contextual condition; upon receipt of the first information, implementing the first control rule to privately output the first plurality of words; and upon receipt of the second information, implementing the second control rule to non-privately output the second plurality of words.

[0032] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for extracting reactions to content based on facial skin micromovements. These embodiments may involve during a time period when an individual is consuming content, determining the facial skin micromovements of the individual based on reflections of coherent light from a facialAttorney Docket No. 16198.0049-00304region of the individual; determining at least one specific micro-expression from the facial skin micromovements; accessing at least one data structure containing correlations between a plurality of micro-expressions and a plurality of non-verbalized perceptions; based on the at least one specific micro-expression and the correlations in the data structure, determining a specific non-verbalized perception of the content consumed by the individual; and initiating an action associated with the specific non-verbalized perception.

[0033] Some disclosed embodiments may include systems, methods, and non-transitory computer readable media for removing noise from facial skin micromovement signals. These embodiments may involve during a time period when an individual is involved in at least one non-speech-related physical activity, operating a light source in a manner enabling illumination of a facial skin region of the individual; receiving signals representing light reflections from the facial skin region; analyzing the received signals to identify a first reflection component indicative of prevocalization facial skin micromovements and a second reflection component associated with the at least one non-speech-related physical activity; and filtering out the second reflection component to enable interpretation of words from the first reflection component indicative of the prevocalization facial skin micromovements .

[0034] Consistent with other disclosed embodiments, non-transitory computer-readable storage media may store program instructions, which are executed by at least one processing device and perform any of the methods described herein.

[0035] The foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various disclosed embodiments. In the drawings:

[0037] Fig. 1 is a schematic illustration of a user using a first example speech detection system, consistent with some embodiments of the present disclosure.

[0038] Fig. 2A is a schematic illustration of a user using a second example speech detection system, consistent with some embodiments of the present disclosure.

[0039] Fig. 2B is a perspective view of a user using a third example speech detection system, consistent with some embodiments of the present disclosure.

[0040] Fig. 3 is a schematic illustration of a user using a fourth example speech detection system, consistent with some embodiments of the present disclosure.

[0041] Fig. 4 is a block diagram illustrating some of the components of a speech detection system and a remote processing system, consistent with some embodiments of the present disclosure.

[0042] Fig. 5A and 5B are schematic illustrations of part of the speech detection system as it detects facial skin micromovements, consistent with some embodiments of the present disclosure.Attorney Docket No. 16198.0049-00304

[0043] Fig. 6 is a schematic illustration of a reflection image associated with light reflections received from an area of facial region associated with a single spot, consistent with some embodiments of the present disclosure.

[0044] Fig. 7 is a block diagram of a memory consistent with the disclosed embodiments.

[0045] Fig. 8 is an exemplary alternative action speech detection process diagram consistent with some embodiments of the present disclosure.

[0046] Fig. 9 is a flowchart of an example process for identifying individuals, consistent with some embodiments of the present disclosure.

[0047] Fig. 10 is a flowchart of an example process for identifying individuals using facial skin micromovements, consistent with some embodiments of the present disclosure.

[0048] Fig. 11 is an illustration of two example use cases for interpreting facial skin movements from light reflections, consistent with some embodiments of the present disclosure.

[0049] Fig. 12 is an illustration of another example use case for interpreting facial skin movements from light reflections, consistent with some embodiments of the present disclosure.

[0050] Fig. 13 is a flowchart of an example process for interpreting facial skin movements, consistent with some embodiments of the present disclosure.

[0051] Fig. 14 is a schematic illustration of operation of an exemplary authentication service configured to provide identity verification of an individual based on facial micromovements consistent with some embodiments of the present disclosure.

[0052] Figs. 15, 16A and 16B are simplified illustrations of an exemplary system for identity verification of an individual using facial micromovements consistent with some embodiments of the present disclosure.

[0053] Fig. 17A is a flowchart of an exemplary process for identity verification of an individual using facial micromovements consistent with some embodiments of the present disclosure.

[0054] Fig. 17B is a flowchart of an exemplary process for generating a reference signal for identity verification of an individual consistent with some embodiments of the present disclosure.

[0055] Fig. 18 is a schematic illustration of an exemplary authentication system and service configured to provide continuous authentication of an individual based on facial skin micromovements consistent with some embodiments of the present disclosure.

[0056] Fig. 19 is a simplified illustration of an exemplary system configured to provide continuous authentication of an individual using facial micromovements consistent with some embodiments of the present disclosure.

[0057] Fig. 20 is a flowchart of an exemplary process for continuous authentication of an individual using facial micromovements consistent with some embodiments of the present disclosure.

[0058] Fig. 21 is a flowchart of another exemplary process for continuous authentication of an individual using facial micromovements consistent with some embodiments of the present disclosure.Attorney Docket No. 16198.0049-00304

[0059] Fig. 22 is a flowchart of another exemplary process for continuous authentication of an individual using facial micromovements consistent with some embodiments of the present disclosure.

[0060] Fig. 23 is a flowchart of another exemplary process for continuous authentication of an individual using facial micromovements consistent with some embodiments of the present disclosure.

[0061] Fig. 24 include a series of displacement versus time charts that include threshold levels associated with a number of facial locations, consistent with some embodiments of the present disclosure.

[0062] Fig. 25A and 25B are schematic illustrations of exemplary displacement levels of facial micromovements where a threshold trigger mechanism may be employed, consistent with some embodiments of the present disclosure.

[0063] Fig. 26 is a block diagram of an exemplary speech detection system using thresholds and threshold adjustments as a trigger mechanism, consistent with some embodiments of the present disclosure.

[0064] Fig. 27 is a displacement versus time graph including background noise, consistent with some embodiments of the present disclosure.

[0065] Fig. 28A and 28B show an example of measuring skin potential difference to determine facial micromovements, consistent with some embodiments of the present disclosure.

[0066] Fig. 29 is a flow chart showing an exemplary method for using a threshold to interpret or disregard facial micromovements, consistent with some embodiments of the present disclosure.

[0067] Fig. 30 is a schematic illustration of a system configured to enable nonvocalized conversations between individuals, consistent with some embodiments of the present disclosure.

[0068] Fig. 31 is a schematic illustration of exemplary processing of detected facial skin micromovements of an individual consistent with some embodiments of the present disclosure.

[0069] Fig. 32 is a schematic illustration of another system configured to enable nonvocalized conversations between individuals consistent with some embodiments of the present disclosure.

[0070] Fig. 33 is a flowchart of an exemplary process for establishing nonvocalized conversations consistent with some embodiments of the present disclosure.

[0071] Fig. 34 is a schematic illustration of an exemplary content interpretation process initiated prior to vocalization of content to be interpreted, consistent with some embodiments of the present disclosure.

[0072] Fig. 35 is a flowchart of an example process for initiating content interpretation prior to vocalization of content to be interpreted, consistent with embodiments of the present disclosure.

[0073] Fig. 36 illustrates an exemplary protocol for performing private voice assistance operations with different facial skin micromovements, consistent with embodiments of the present disclosure.

[0074] Fig. 37 illustrates examples of second actions initiated if a match is not identified in an exemplary data structure, consistent with embodiments of the present disclosure.Attorney Docket No. 16198.0049-00304

[0075] Fig. 38 illustrates a flowchart of an example process for performing private voice assistance operations, consistent with embodiments of the present disclosure.

[0076] Fig. 39 is an exemplary diagram illustrating how different areas of facial skin are used to detect subvocalized phonemes, consistent with some embodiments of the present disclosure.

[0077] Fig. 40 illustrates three graphs depicting exemplary alternative timings for completing a process that involves detecting subvocalized phonemes, consistent with embodiments of the present disclosure.

[0078] Fig. 41 is a flowchart of an example process determining subvocalized phonemes from facial skin micromovements, consistent with embodiments of the present disclosure.

[0079] Fig. 42A is one perspective view of a user wearing an example head set and a resulting virtual representation of one facial expression of the user, consistent with some embodiments of the present disclosure.

[0080] Fig. 42B is another perspective view of a user wearing an example headset and a resulting virtual representation of another facial expression of the user, consistent with some embodiments of the present disclosure.

[0081] Fig. 43 is a block diagram illustrating an exemplary operating environment for generating synthesized representations of facial expressions, consistent with some embodiments of the present disclosure.

[0082] Fig. 44 is a block diagram illustrating an exemplary system for generating synthesized representations of facial expressions and / or for determining spoken phonemes from facial skin micromovements, consistent with some embodiments of the present disclosure.

[0083] Fig. 45 is a flow chart illustrating an exemplary method for generating synthesized representations of facial expressions and / or for determining spoken phonemes from facial skin micromovements, consistent with some embodiments of the present disclosure.

[0084] Fig. 46 is a flow chart illustrating another exemplary method for generating synthesized representations of facial expressions and / or for determining spoken phonemes from facial skin micromovements, consistent with some embodiments of the present disclosure.

[0085] Fig. 47 is a schematic illustration of an example process of ascertaining presentation manners based on facial skin micromovements, consistent with some embodiments of the present disclosure.

[0086] Fig. 48 is a schematic illustration of a user using an exemplary system of attention-associated interactions based on facial skin micromovements, consistent with some embodiments of the present disclosure.

[0087] Fig. 49 is a schematic illustration of receipt of a prospective interaction via a smartphone, consistent with some embodiments of the present disclosure.

[0088] Fig. 50 is a flowchart of an example process of ascertaining presentation manners based on facial skin micromovements, consistent with some embodiments of the present disclosure.Attorney Docket No. 16198.0049-00304

[0089] Fig. 51 illustrates a first individual wearing speech detection system while communicating with at least one second individual, consistent with some embodiments of the present disclosure.

[0090] Fig. 52 illustrates a flowchart of an example process for initiating content interpretation prior to vocalization of content to be interpreted, consistent with embodiments of the present disclosure.

[0091] Fig. 53A and 53B are schematic illustrations of audible presentation of unvocalized words prior to vocalization, consistent with some embodiments of the present disclosure.

[0092] Fig. 54 is a block diagram of an exemplary speech detection system using received reflections to determine unvocalized words from facial micromovement causing an audible presentation, consistent with some embodiments of the present disclosure.

[0093] Fig. 55 shows an exemplary schematic illustration of synthesized translation between languages, consistent with some embodiments of the present disclosure.

[0094] Fig. 56 shows exemplary additional functions of personal presentation of prevocalization, consistent with some embodiments of the present disclosure.

[0095] Fig. 57 is a flow chart showing an exemplary method for using received reflections to determine unvocalized words from facial micromovement to cause an audible presentation, consistent with some embodiments of the present disclosure.

[0096] Fig. 58 is a perspective view of an individual using a first example speech detection system, consistent with some embodiments of the present disclosure.

[0097] Figs. 59A and 59B are schematic illustrations of a portion of the speech detection system as it detects facial skin micromovements, consistent with some embodiments of the present disclosure.

[0098] Fig. 60 is a block diagram illustrating exemplary components of the first example of the speech detection system, consistent with some embodiments of the present disclosure.

[0099] Fig. 61 is a flowchart of an exemplary method for determining facial skin micromovements, consistent with some embodiments of the present disclosure.

[0100] Fig. 62 is an illustration of an example system for correcting speech impairment based on facial movements, consistent with some embodiments of the present disclosure.

[0101] Fig. 63 is a flowchart of an example process for correcting speech impairment based on facial movements, consistent with some embodiments of the present disclosure.

[0102] Fig. 64 is a schematic illustration of an exemplary speech detection system that sends two data streams to a destination to verify communication authenticity, consistent with some embodiments of the present disclosure.

[0103] Fig. 65 is a schematic illustration of exemplary functions used to authenticate communication at a destination, consistent with some embodiments of the present disclosure.

[0104] Fig. 66 is a flow chart showing an exemplary method for using received reflections to verify communication authenticity, consistent with some embodiments of the present disclosure.Attorney Docket No. 16198.0049-00304

[0105] Fig. 67 illustrates an exemplary head mountable system for noise suppression, consistent with some embodiments of the present disclosure.

[0106] Fig. 68 illustrates examples of audio signal processing for noise suppression, consistent with some embodiments of the present disclosure.

[0107] Fig. 69 is a flowchart of an example process for noise suppression, consistent with some embodiments of the present disclosure.

[0108] Fig. 70 illustrates an exemplary system for providing private answers to silent questions, consistent with embodiments of the present disclosure.

[0109] Fig. 71 illustrates examples of image data applications that may be used for providing private answers to silent questions, consistent with embodiments of the present disclosure.

[0110] Fig. 72 illustrates a flowchart of an example process for providing private answers to silent questions, consistent with embodiments of the present disclosure.

[0111] Fig. 73 is a schematic illustration of an individual using a first example speech detection system, consistent with some embodiments of the present disclosure.

[0112] Fig. 74 is a schematic illustration of two individuals each using an example speech detection system, consistent with some embodiments of the present disclosure.

[0113] Fig. 75 is a flowchart of an exemplary method for performing silent voice control, consistent with some embodiments of the present disclosure.

[0114] Fig. 76 is a schematic illustration of an exemplary timeline of the progression of a medical condition that may be detectable by measuring skin micromovements over time, consistent with some embodiments of the present disclosure.

[0115] Fig. 77 is a block diagram of an exemplary system capable of detecting changes in neuromuscular activity over time, consistent with some embodiments of the present disclosure.

[0116] Fig. 78 is a block diagram of exemplary functions for detecting deviation in medical conditions, consistent with some embodiments of the present disclosure.

[0117] Fig. 79 is a flow chart showing an exemplary method for using received light reflections to detect changes in neuromuscular activity overtime, consistent with some embodiments of the present disclosure.

[0118] Fig. 80 is a schematic illustration of using a projected graphical pattern to detect non-verbal information from an individual consistent with some embodiments of the present disclosure.

[0119] Fig. 81 is a schematic illustration of altering a projected graphical pattern consistent with some embodiments of the present disclosure.

[0120] Fig. 82 is a flowchart of an exemplary process of using a projected graphical pattern to detect non-verbal information consistent with some embodiments of the present disclosure.

[0121] Fig. 83 illustrates an exemplary embodiment of a user wearing the head mountable system for interpreting facial skin micromovements.Attorney Docket No. 16198.0049-00304

[0122] Fig. 84 illustrates a flowchart of an example method for interpreting facial skin micromovements .

[0123] Fig. 85A to 85C illustrate exemplary embodiments of training operations to interpret facial skin micromovements in the first through third time periods, consistent with some disclosed embodiments.

[0124] Fig. 86 is a flow diagram of an example of the first through third time periods illustrated in Fig. 85A to 85C with an example additional extended time period, consistent with some disclosed embodiments.

[0125] Fig. 87 is a flowchart of an example method for interpreting facial skin micromovements, consistent with some disclosed embodiments.

[0126] Fig. 88 is a schematic illustration of a user wearing an exemplary headset with added facial micromovement detection capability, consistent with some embodiments of the present disclosure.

[0127] Fig. 89 is a schematic illustration of an exemplary facial micromovement detection process, consistent with some embodiments of the present disclosure.

[0128] Fig. 90 is a flowchart of an example process of operating a multifunctional earpiece, consistent with some embodiments of the present disclosure.

[0129] Fig. 91 is a schematic illustration of a user wearing an exemplary headset of an alternative form factor, consistent with some embodiments of the present disclosure.

[0130] Fig. 92 illustrates a block diagram of an exemplary driver for interfacing with a software program and a device, consistent with disclosed embodiments.

[0131] Fig. 93 illustrates a schematic diagram of an exemplary driver for integration with a software program and neuromuscular detection device, consistent with disclosed embodiments.

[0132] Fig. 94 illustrates a schematic diagram of an exemplary system for integration with a software program and for enabling a device to interface with the software program, consistent with embodiments of the present disclosure.

[0133] Fig. 95 is a block diagram illustrating an exemplary operating environment for generating context-driven facial micromovement output, consistent with some embodiments of the present disclosure.

[0134] Fig. 96 is a block diagram illustrating an exemplary system for generating context-driven facial micromovement output, consistent with some embodiments of the present disclosure.

[0135] Fig. 97 is a flow chart illustrating an exemplary method for generating context-driven facial micromovement output, consistent with some embodiments of the present disclosure.

[0136] Fig. 98 is a flow chart illustrating another exemplary method for generating context-driven facial micromovement output, consistent with some embodiments of the present disclosure.

[0137] Fig. 99 is a schematic illustration of a user wearing an example head set and resulting context-driven outputs based on facial micromovements, consistent with some embodiments of the present disclosure.Attorney Docket No. 16198.0049-00304

[0138] Fig. 100 is a schematic illustration of an example system for extracting reactions to content based on facial skin micromovements, consistent with some embodiments of the present disclosure.

[0139] Fig. 101 includes block diagrams of two example use cases for initiating actions based on reactions to content, consistent with some embodiments of the present disclosure.

[0140] Fig. 102 is a flowchart of an example process for extracting reactions to content based on facial skin micromovements, consistent with some embodiments of the present disclosure.

[0141] Fig. 103 illustrates an individual performing a first non-speech-related activity (e.g., walking) and a second non-speech-related activity (e.g., sitting) while wearing a speech recognition system, consistent with embodiments of the present disclosure.

[0142] Fig. 104 illustrates an exemplary close-up view of the speech detection system of Fig. 103, consistent with embodiments of the present disclosure.

[0143] Fig. 105 illustrates an exemplary comparison between a first signal of an individual performing speech-related facial skin movements while walking, and a second signal of the individual performing speech-related facial skin movements while sitting, consistent with embodiments of the present disclosure.

[0144] Fig. 106 illustrates an exemplary decomposition and classification of an electronic representation of a light signal into a first reflection component indicative of prevocalization facial skin micromovements and a second reflection component associated with at least one non-speech-related physical activity, consistent with embodiments of the present disclosure.

[0145] Fig. 107 illustrates an exemplary second reflection component of a light signal reflecting from the facial region of individual concurrently involved in a first physical activity and a second physical activity, consistent with embodiments of the present disclosure.

[0146] Fig. 108 illustrates a flowchart of example process for removing noise from facial skin micromovement signals, consistent with embodiments of the present disclosure.

[0147] Fig. 109 illustrates another exemplary decomposition and classification of a representation of a light signal to identify a first reflection component indicative of prevocalization facial skin micromovements, consistent with embodiments of the present disclosure.

[0148] Fig. 110 is a schematic illustration of an example system for generating a common record based on differing source inputs, consistent with some embodiments of the present disclosure.

[0149] Fig. 111A is an exemplary first example of a common record, consistent with some embodiments of the present disclosure.

[0150] Fig. 11 IB is an exemplary second example of a common record, consistent with some embodiments of the present disclosure.

[0151] Fig. 112 is a flowchart of an example process for generating a common record based on differing source inputs, consistent with some embodiments of the present disclosure.Attorney Docket No. 16198.0049-00304DETAILED DESCRIPTION

[0152] The following detailed description includes references to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the description to refer to the same or similar parts. While several illustrative embodiments are described herein, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the components illustrated in the drawings, and the illustrative methods described herein may be modified by substituting, reordering, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples. Instead, the proper scope is defined by the appended claims.

[0153] Various terms used in the specification and claims may be defined or summarized differently when discussed in connection with differing disclosed embodiments. It is to be understood that the definitions, summaries and explanations of terminology in each instance apply to all instances, even when not repeated, unless the transitive definition, explanation, or summary would result in inoperability of an embodiment. It is also to be understood that once a term is defined herein, in the absence of an inherent inconsistency, that definition applies to all other uses of the term herein. Moreover, the exemplary embodiments of the figures and their description are not to be considered definitions of claim terms, but rather are non-limiting examples used to illustrate specific embodiments.

[0154] Throughout, this disclosure mentions “embodiments” and “disclosed embodiments,” which refer to examples of inventive ideas, concepts, and / or manifestations described herein. Many related and unrelated embodiments are described throughout this disclosure. The fact that some “disclosed embodiments” are described as exhibiting a feature or characteristic does not mean that other disclosed embodiments necessarily share that feature or characteristic.

[0155] This disclosure employs open-ended permissive language, indicating for example, that some embodiments “may” employ, involve, or include specific features. The use of the term “may,” and other open-ended terminology, is intended to indicate that although not every embodiment may employ the specific disclosed feature, at least one embodiment employs the specific disclosed feature.

[0156] Differing embodiments of this disclosure may involve systems, methods, and / or computer readable media containing instructions. A system refers to at least two interconnected or interrelated components or parts that work together to achieve a common objective, function, or subfunction. A method refers to at least two steps, actions, or techniques to be followed in order to complete a task or a sub-task, to reach an objective, or to arrive at a next step. Computer-readable media containing instructions refers to any storage mechanism that contains program code instructions, for example to be executed by a computer processor. Examples of computer-readable media are further described elsewhere in this disclosure. Instructions may be written in any type of computer programming language, such as an interpretive language (e.g., scripting languages such as HTML and JavaScript), aAttorney Docket No. 16198.0049-00304procedural or functional language (e.g., C or Pascal that may be compiled for converting to executable code), an object-oriented programming language (e.g., Java or Python), a logical programming language (e.g., Prolog or Answer Set Programming), and / or any other programming language.Instructions executed by at least one processor may include implementing one or more program code instructions in hardware, in software (including in one or more signal processing and / or application specific integrated circuits), in firmware, or in any combination thereof, as described earlier. Causing a processor to perform operations may involve causing the processor to calculate, execute, or otherwise implement one or more arithmetic, mathematic, logic, reasoning, or inference steps.

[0157] Some disclosed embodiments may involve detecting facial skin micromovements. The term “facial skin micromovements” broadly refers to skin motions on the face that may be detectable using a sensor, but which might not be readily detectable to the naked eye. The facial skin micromovements include various types of movements, including involuntary movements caused by muscle recruitments and other types of small-scale skin deformations that fall within the range of micrometers to millimeters and fractions of a second to several seconds in duration. In some cases, the facial skin micromovements are part of a larger-scale skin movement visible to the naked eye (e.g., a smile may involve many facial skin micromovements). In other cases, the facial skin micromovements are not part of any larger-scale skin movement visible to the naked eye. While such micromovements may occur over a multi-square millimeter facial area, they may occur in a surface area of the facial skin of less than one square centimeter, less than one square millimeter, less than 0.1 square millimeter, less than 0.01 square millimeter, or an even smaller area. In some embodiments, the facial skin micromovements correspond to one or more muscle recruitments in a facial region of a head of an individual. The facial region may include specific anatomical areas, for example: a part of the cheek above the mouth, a part of the cheek below the mouth, a part of the mid-jaw, a part of the cheek below the eye, a neck, a chin, and other areas associated with specific muscle recruitments that may cause facial skin micromovements. In some embodiments, the specific muscles may be connected to skin tissue and not to any bone. In particular, the specific muscles may be located in a subcutaneous tissue associated with cranial nerve V or cranial nerve VII. As is discussed herein in greater detail, first facial skin micromovement 522A and second facial skin micromovement 522B in Fig. 5A and are non-limiting examples of facial skin micromovements, consistent with the present disclosure.

[0158] When specific muscles contract, the muscles pull on the facial skin and cause movements of the facial skin. Some of the movements that occur when the specific muscles contract may be micromovements. By way of example, the specific muscles that may cause facial skin micromovements in the context of the present disclosure may broadly be split into four groups: orbital, nasal, oral, and tongue. The orbital group of facial muscles contains two muscles associated with the eye socket. These muscles control the movements of the eyelids, important in protecting the cornea from damage. They are both innervated by cranial nerve VII. The nasal group of facial muscles is associated with movements of the nose and the skin around it. There are three muscles inAttorney Docket No. 16198.0049-00304this group, and they are also all innervated by cranial nerve VII. The oral group is the most important group of the facial expressors: responsible for movements of the mouth and lips. Such movements are required in singing and whistling and add emphasis to vocal communication. The oral group of muscles consists of the orbicularis oris, buccinator, and various smaller muscles. In a specific embodiment, a disclosed system may monitor facial skin micromovements that correspond to recruitment of the buccinator muscle. The buccinator muscle is located between the mandible and maxilla relatively deep compared to other muscles of the face. The tongue group of muscles consists of four intrinsic muscles (e.g., the superior longitudinal muscle, the inferior longitudinal muscle, the vertical muscle, and the transverse muscle) used to change the shape of the tongue; and four extrinsic muscles (e.g., the genioglossus, the hyoglossus, the styloglossus, and the palatoglossus) used to change the position of the tongue. Any of the tongue muscles listed above may cause movements of the tongue that may be detected by analyzing detected facial skin micromovements. As is discussed herein in greater detail, muscle fiber 520 in Figs. 5A and 5B is a non-limiting example of a facial muscle that causes micromovements of the facial skin, consistent with the present disclosure.

[0159] Consistent with the present disclosure, facial skin micromovements may be detected during subvocalization. The term “during subvocalization” refers to any speech-related activity that takes place without utterance, before utterance, or preceding an imperceptible utterance. In one embodiment, the speech-related activity may include silent speech (i.e., when air flow from the lungs is absent but the facial muscles articulate the desired sounds). In another embodiment, the speech-related activity may include speaking soundlessly (i.e., when some air flow from the lungs, but words are articulated in a manner that is not perceptible using an audio sensor). In yet another embodiment, the speech-related activity may include prevocalization muscle recruitments (i.e., subvocalization that occurs prior to an onset of vocalization is sometimes referred to herein as prevocalization). In some cases, the prevocalization facial skin micromovements may be triggered by voluntary muscle recruitments that occur when certain craniofacial muscles start to vocalize words. In other cases, the prevocalization facial skin micromovements may be triggered by involuntary facial muscle recruitments that the individual makes when certain craniofacial muscles prepare to vocalize words. By way of example, the involuntary facial muscle recruitments may occur between 0.1 seconds to 0.5 seconds before the actual vocalization. In some cases, a suggested system may use the detected facial skin micromovement occur during subvocalization to identify words that are about to be vocalized. Determining words that the user intends to say before they are actually vocalized may have many benefits because the system does not have to wait for the user to vocally articulate the words to start process the words. In one example, a disclosed system may generate subtitles for live broadcasts without delays. In another example, a disclosed system may translate what the user is saying in realtime to a different language. Additionally, because the disclosed system can detect words before they are vocalized, the actual vocalization of these words is not a requirement. Thus, facial skin micromovements that occur during subvocalization may be detected in an absence of perceptibleAttorney Docket No. 16198.0049-00304vocalization. Movement of facial skin or muscles in an absence of vocalization but which nevertheless conveys speech-related information is referred to herein as silent speech. Detecting silent speech may have various usages, including but not limited to enabling silent communicating with other users, initiating a command, or enabling interaction with a virtual personal assistance. As is discussed herein in greater detail, subvocalization deciphering module 708 in Fig. 7 is a non-limiting example of a software module used for deciphering some subvocalization facial skin micromovements.

[0160] In some embodiments, the detection of the facial skin micromovements occurs using a speech detection system. While the shorthand “speech detection system” is employed, it is to be understood that the system may alternatively or additionally be configured to detect non-speech commands, expressions, or emotions. The system may also be used for user authentication. The speech detection system may include any device of a group of devices operatively coupled together. As used herein, the term “system” includes any device or a group of devices operatively connected together and configured to perform a function. In some embodiments, the system may include a computer (e.g., a desktop computer, a laptop computer, a server, a smart phone, a portable digital assistant (PDA), or a similar device) or plurality of computers or servers operatively connected together (e.g., using wires or wirelessly) to share information and / or data. The computer(s) may include special purpose computers (e.g., hardwired and coded to perform desired functions) or may include general purpose computers (e.g., using software to perform any desired function). In some embodiments, the system may include a cloud server. As described elsewhere in this disclosure, a cloud server may be a computer platform that provides services via a network, such as the Internet. In one embodiment, the speech detection system may include a wearable housing, a coherent light source or a non-coherent light source, a light detector, and a processor. However, the specific list of components mentioned above is not intended to limit systems covered by the present disclosure. As will be appreciated by a person skilled in the art having the benefit of this disclosure, numerous variations and / or modifications may be made to the example speech detection system. For example, not all components may be essential for the detection of facial skin micromovements in all cases. Moreover, the components may be rearranged into a variety of configurations while providing the functionality of various disclosed embodiments. In some cases, a speech detection system according to some embodiments of the disclosure does not have to be wearable, but could be aimed at a skin from a location not connected to a human body. A wearable or a non-wearable system may project coherent light towards a facial region of a user, analyze reflected light, and determine facial skin micromovements. Alternatively, in other cases, a speech detection system according to some embodiments of the disclosure does not have to include a coherent light source. Specifically, the light detector may be an ultra-high resolution image sensor (e.g., more than 120 megapixel) or any other sensor capable of facial micromovement detection, and the detection of the facial skin micromovements may be accomplished using one or more image processing algorithms. As is discussed herein in greater detail, speech detection systems 100 in Figs. 1-3 are non-limiting examplesAttorney Docket No. 16198.0049-00304of a speech detection system, consistent with the present disclosure. As illustrated in these examples, the system includes a wearable housing 110, a light source 410, a light detector 412, and a processing device 400.

[0161] Some disclosed embodiments involve a wearable housing configured to be worn on a head of an individual. The term “wearable housing” broadly includes any structure or enclosure designed for connection to a human head, such as in a manner configured to be worn by a user. Such a wearable housing may be configured to contain or support one or more electronic components or sensors. In one example, the wearable housing is configured for association with a pair of glasses. In another example, the wearable housing is associated with an earbud. The wearable housing may have a cross-section that is button-shaped, P-shaped, square, rectangular, rounded rectangular, or any other regular or irregular shape capable of being worn by a user. Such a structure may permit the wearable housing to be worn on, in, or around a body part associated with a head of the user (e.g., on the ear, in the ear, around the neck). The wearable housing may be made of plastic, metal, composite, a combination of two or more of plastic, metal and composite, or other suitable material. Consistent with disclosure embodiments, the housing may be worn on an ear. There are several ways in which the housing can be attached to the ear: 1. In-the-ear (ITE): the housing may be inserted directly into the ear canal and held in place by the shape of the ear. Examples include earbuds and earplugs. In some cases, the housing may be custom-made to fit the specific shape of an individual's ear and seated in the ear bowl. 2. Behind-the-ear (BTE): the housing may be seated behind the ear and with a small tube that runs to the ear canal. Examples include hearing aids and Bluetooth headsets. 3. Over-the-ear (OTE): the housing may be seated on top of the ear and held in place by a headband or other support. Examples include structures like headphones and earmuffs. 4. Over-the-head (OTH): the housing may be held in place by a headband that goes over the top of the head. In other embodiments, the wearable housing may be attached to a secondary device such as a glasses (sun or corrective vision glasses), a hat, a helmet, a visor, or any other type of head wearable devices. In some cases, the wearable housing may be attached to a secondary device using at least one adaptor. Specifically, the at least one adaptor may be configured to enable the individual to wear the speech detection system in two or more different ways. For example, a single adapter may enable the wearable housing to be attached to glasses and to an earbud. As is discussed herein in greater detail, wearable housings 110 in Fig. 1 and Fig. 2A are non-limiting examples of a wearable housing, consistent with the present disclosure.

[0162] Some embodiments involve a coherent light source configured to project light towards a facial region of the user. Other embodiments involve a non-coherent light source configured to project light towards a facial region of the user. As used herein, the term “light source” broadly refers to any device configured to emit light. The term “coherent light” includes light that is highly ordered and exhibits a high degree of spatial and temporal coherence. This may occur, for example, when the light waves are in phase with each other and have a uniform frequency and wavelength, resulting in a beam of light that is highly directional and has restricted outward spread out as it travels. Alternatively,Attorney Docket No. 16198.0049-00304coherent light may include a scenario when light waves have constant phase difference. In some examples, coherent light may be produced by a coherent light source, such as lasers and other types of light sources that have a narrow spectral range and a high degree of monochromaticity (i.e., the light consists of a single wavelength). In contrast, incoherent light may be produced by a non-coherent light source such as incandescent bulbs and natural sunlight, which have a broad spectral range and a low degree of monochromaticity.

[0163] By way of example, coherent light may include many waves of the same frequency, having different phases and amplitudes, not necessarily in the same time and locations. To control the interference, light phase information may be required to be recognized in advance. In one embodiment, the coherent light source may be a laser such as a solid-state laser, laser diode, a high-power laser, Quantum-Cascade Laser (QCLs), or an alternative light source such as a light emitting diode (LED) -based light source. In addition, the coherent light source may emit light in differing formats, such as light pulses, continuous wave (CW), quasi-CW, and so on. For example, one type of light source that may be used is a vertical -cavity surface -emitting laser (VCSEL). Another type of light source that may be used is an external cavity diode laser (ECDL). In some examples, the light source may include a laser diode configured to emit light at a wavelength between about 650 nm and 1150 nm. Alternatively, the coherent light source may include a laser diode configured to emit light at a wavelength between about 800 nm and about 1020 nm, between about 850 nm and about 950 nm, or between about 1300 nm and about 1700 nm. Unless indicated otherwise, the terms “about” and “substantially the same,” with regard to a numeric value, may include a variance of up to 5% with respect to the stated value. As is discussed herein in greater detail, light source 410 in Fig. 4 and in Figs. 5A and 5B are non-limiting examples of a light source, consistent with the present disclosure. In the context of this disclosure, it should be recognized that the use of a coherent light source is intended as a non-limiting example implementation in the context of speech detection systems, methods, and computer readable media. Many of the embodiments described herein may be practiced with coherent light or non-coherent light, and the reference to either herein by way of example, is not intended to be limiting. For example, even when not explicitly stated, the described and claimed speech detection systems, methods, and computer program products may be configured to measure non-coherent light reflections for detecting facial skin micromovements.

[0164] Some embodiments involve at least one detector configured to receive light reflections from a facial region of the user. The term “light detector,” or simply “detector,” broadly refers to any device, element, or system capable of measuring one or more properties (e.g., power, frequency, phase, pulse timing, pulse duration, or other characteristics) of electromagnetic waves and to generate an output relating to the measured property or properties. Examples of detectors consistent with this disclosure may include: a light sensitive sensor, an imaging sensor, a phase detector, a MEMS senor, a wavemeter, a spectrometer, a spectrophotometer, a homodyne detector, or a heterodyne detector. In some embodiments, the at least one detector may be configured to detect coherent light reflections.Attorney Docket No. 16198.0049-00304Additionally or alternatively, the at least one detector may be configured to detect non-coherent light reflections. The at least one detector may include a plurality of detectors constructed from a plurality of detecting elements. The at least one detector may include a light detector of different types. The at least one detector may include multiple detectors of the same type which may differ in other characteristics (e.g., sensitivity, size). Combinations of several types of detectors may be used for different reasons. Consistent with some embodiments, the at least one detector may measure any form of reflection and of scattering of light, including secondary speckle patterns, different types of specular reflections, diffuse reflections, speckle interferometry, and any other form of light scattering. In some embodiments, the at least one detector is configured to output associated reflection signals from the detected coherent light reflections. In the context of this disclosure, the term “reflection signals” broadly refers to any form of data retrieved from the at least one light detector in response to the light reflections from the facial region. The reflection signals may be any electronic representation of a property determined from the light reflections, or raw measurement signals detected by the at least one light detector. As is discussed herein in greater detail, light detector 412 in Fig. 4 and in Figs. 5A and 5B are non-limiting examples of a light detector, consistent with the present disclosure.

[0165] Some embodiments involve at least one processor configured to use the reflection signals from the detector and determine the facial skin micromovements. The term “at least one processor” may involve any physical device or group of devices having electric circuitry that performs a logic operation on an input or inputs. For example, the at least one processor may include one or more integrated circuits (IC), including an application-specific integrated circuit (ASIC), microchips, microcontrollers, microprocessors, all or part of a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), field-programmable gate array (FPGA), server, virtual server, or other circuits suitable for executing instructions or performing logic operations. The instructions executed by at least one processor may, for example, be pre-loaded into a memory integrated with or embedded into the controller or may be stored in a separate memory. The memory may include a Random Access Memory (RAM), a Read-Only Memory (ROM), a hard disk, an optical disk, a magnetic medium, a flash memory, other permanent, fixed, or volatile memory, or any other mechanism capable of storing instructions. In some embodiments, the at least one processor may include more than one processor. Each processor may have a similar construction, or the processors may be of differing constructions that are electrically connected or disconnected from each other. For example, the processors may be separate circuits or integrated in a single circuit. When more than one processor is used, the processors may be configured to operate independently or collaboratively and may be co-located or located remotely from each other. The processors may be coupled electrically, magnetically, optically, acoustically, mechanically, or by other means that permit them to interact. As is discussed herein in greater detail, processing unit 112 in Fig. 1 and processing device 400 in Fig. 4 are non-limiting examples of at least one processor, consistent with the present disclosureAttorney Docket No. 16198.0049-00304

[0166] In some embodiments, the at least one processor may determine the facial skin micromovements by applying a light reflection analysis. The term “light reflection analysis” involves the evaluation of properties of a surface by analyzing patterns of light scattered off the surface. When light strikes a surface (e.g., the facial skin), some of it is absorbed, some is transmitted, and some is reflected. The amount and type of light that is reflected depends on the properties of the surface and the angle at which the light strikes it. In one example, when a non-coherent light source is used, the light reflection analysis may include scattering analysis which involves measuring the scattering of light from the surface (e.g., the facial skin). In another example, when a coherent light source is used, the light reflection analysis may include a speckle analysis or any pattern-based analysis. By way of example, coherent light shining onto a rough, contoured, or textured surface may be reflected or scattered in many different directions, resulting in a pattern of bright and dark areas called “speckles.” Such analysis may be performed using a computer (e.g., including a processor) to identify a speckle pattern and derive information about a surface (e.g., facial skin) represented in reflection signals received from at least light detector. A speckle pattern may occur as the result of the interference of coherent light waves added together to give a resultant wave whose intensity varies. The detected speckle pattern or any other detected pattern may then be processed to generate reflection image data. As is discussed herein in greater detail, light reflections processing module 706 depicted in Fig. 7 is a non-limiting example of a software module used for determining facial skin micromovements by applying a light reflection analysis.

[0167] Consistent with the present disclosure, the reflection image data may be processed by any image processing algorithms, including classic and / or artificial neural network (ANN) based algorithms such as Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN). In some examples, the reflection image data may be preprocessed by transforming the image data using a transformation function to obtain a transformed speckle image. For example, the transformed reflection image data may include one or more convolutions of the speckle image. The transformation function may include one or more image filters, such as low-pass filters, high-pass filters, band-pass filters, all-pass filters, and so forth. In some examples, the transformation function may comprise a nonlinear function. In some examples, the reflection image data may be preprocessed by smoothing at least parts of the reflection image data, for example using Gaussian convolution, using a median filter, and so forth. In some examples, the reflection image data may be preprocessed to obtain a different representation of the reflection image data. For example, reflection image data may comprise: a representation of at least part of the reflection image data in a frequency domain; a Discrete Fourier Transform of at least part of the reflection image data; a Discrete Wavelet Transform of at least part of the reflection image data; a time / frequency representation of at least part of the reflection image data; a representation of at least part of the reflection image data in a lower dimension; a lossy representation of at least part of the reflection image data; a lossless representation of at least part of the reflection image data; a time-ordered series of any of the above; any combination of the above. InAttorney Docket No. 16198.0049-00304some examples, the reflection image data may be preprocessed to extract edges, and the preprocessed reflection image data may comprise information based on and / or related to the extracted edges. In some examples, the reflection image data may be preprocessed to extract features from the reflection image data. Some examples of such features may comprise information related to: edges, comers, blobs, ridges, Scale Invariant Feature Transform (SIFT) features, temporal features, and more.

[0168] In some embodiments, performing light reflection analysis may include evaluating the reflection image data and / or the preprocessed reflection image data using one or more rules, functions, procedures, artificial neural networks, object detection algorithms, visual event detection algorithms, action detection algorithms, motion detection algorithms, background subtraction algorithms, inference models, and so forth. Some non-limiting examples of such inference models may include: an inference model preprogrammed manually; a classification model; a regression model; a result of training algorithms, such as machine learning algorithms and / or deep learning algorithms, on training examples, where the training examples may include examples of data instances, and in some cases, a data instance may be labeled with a corresponding desired label and / or result; and so forth. In some embodiments, performing speckle analysis may comprise analyzing pixels, voxels, point cloud, range data, etc. included in the reflection image data.

[0169] Some embodiments may involve analyzing the reflection image data to decipher speech. The process of deciphering the speech from the reflection image data may involve identifying patterns or recognizing signatures in the reflection image data. For example, know data, patterns, or signatures may be associated with certain phenomes, combinations of phonemes, words, combinations of words, or any other speech-related component. By recognizing such information in the reflection image data, speech may be deciphered. Such recognition and / or deciphering may be aided by machine learning. For example, machine learning models or algorithms may be employed to recognize and / or understand speech or commands. Some non-limiting examples of machine learning algorithms that may be used include classification algorithms, data regressions algorithms, image segmentation algorithms, visual detection algorithms (such as object detectors, motion detectors, edge detectors, etc.), visual recognition algorithms (such as object recognition, etc.), speech recognition algorithms, mathematical embedding algorithms, natural language processing algorithms, support vector machines, random forests, nearest neighbors algorithms, deep learning algorithms, artificial neural network algorithms, convolutional neural network algorithms, recursive neural network algorithms, linear machine learning models, non-linear machine learning models, ensemble algorithms, and so forth. For example, a trained machine learning algorithm may include an inference model, such as a predictive model, a classification model, a regression model, a clustering model, a segmentation model, an artificial neural network (such as a deep neural network, a convolutional neural network, a recursive neural network, etc.), a random forest, a support vector machine, and so forth. In some examples, the training examples may include example inputs together with the desired outputs corresponding to the example inputs. Further, in some examples, training machine learning algorithmsAttorney Docket No. 16198.0049-00304using the training examples may generate a trained machine learning algorithm, and the trained machine learning algorithm may be used to estimate outputs for inputs not included in the training examples. In some examples, engineers, scientists, processes, and machines that train machine learning algorithms may further use validation examples and / or test examples. For example, validation examples and / or test examples may include example inputs together with the desired outputs corresponding to the example inputs, a trained machine learning algorithm and / or an intermediately trained machine learning algorithm may be used to estimate outputs for the example inputs of the validation examples and / or test examples, the estimated outputs may be compared to the corresponding desired outputs, and the trained machine learning algorithm and / or the intermediately trained machine learning algorithm may be evaluated based on a result of the comparison. In some examples, a machine learning algorithm may have parameters and hyper parameters, where the hyper parameters are set manually by a person or automatically by a process external to the machine learning algorithm (such as a hyper parameter search algorithm), and the parameters of the machine learning algorithm are set by the machine learning algorithm according to the training examples. In some implementations, the hyper-parameters are set according to the training examples and the validation examples, and the parameters are set according to the training examples and the selected hyper-parameters .

[0170] In some examples, deciphering the speech from the reflection image data may involve a trained machine learning algorithm that is used as an inference model that when provided with an input generates an inferred output. For example, a trained machine learning algorithm may include a classification algorithm, the input may include a sample, and the inferred output may include a classification of the sample. In another example, a trained machine learning algorithm may include a regression model, the input may include a sample, and the inferred output may include an inferred value for the sample. In yet another example, a trained machine learning algorithm may include a clustering model, the input may include a sample, and the inferred output may include an assignment of the sample to at least one cluster. In an additional example, a trained machine learning algorithm may include a classification algorithm, the input may include an image, and the inferred output may include a classification of an item depicted in the image. In yet another example, a trained machine learning algorithm may include a regression model, the input may include an image, and the inferred output may include an inferred value for an item depicted in the image (such as an estimated facial skin motion, and so forth). In an additional example, a trained machine learning algorithm may include an image segmentation model, the input may include an image, and the inferred output may include a segmentation of the image. In yet another example, a trained machine learning algorithm may include an object detector, the input may include an image, and the inferred output may include one or more detected objects in the image and / or one or more locations of objects within the image. In some examples, the trained machine learning algorithm may include one or more formulas and / or one or more functions and / or one or more rules and / or one or more procedures, the input may be used asAttorney Docket No. 16198.0049-00304input to the formulas and / or functions and / or rules and / or procedures, and the inferred output may be based on the outputs of the formulas and / or functions and / or rules and / or procedures (for example, selecting one of the outputs of the formulas and / or functions and / or rules and / or procedures, using a statistical measure of the outputs of the formulas and / or functions and / or rules and / or procedures, and so forth). As is discussed herein in greater detail, reflection image 600 in Fig. 6 is a non-limiting example of a visualization of reflection image data, consistent with the present disclosure.

[0171] In some embodiments, artificial neural networks may be configured to analyze inputs and generate corresponding outputs. Some non-limiting examples of such artificial neural networks may include shallow artificial neural networks, deep artificial neural networks, feedback artificial neural networks, feed-forward artificial neural networks, autoencoder artificial neural networks, probabilistic artificial neural networks, time-delay artificial neural networks, convolutional artificial neural networks, recurrent artificial neural networks, long / short term memory artificial neural networks, and so forth. In some examples, an artificial neural network may be configured manually. For example, a structure of the artificial neural network may be selected manually, a type of an artificial neuron of the artificial neural network may be selected manually, a parameter of the artificial neural network (such as a parameter of an artificial neuron of the artificial neural network) may be selected manually, and so forth. In some examples, an artificial neural network may be configured using a machine learning algorithm. For example, a user may select hyper-parameters for the artificial neural network and / or the machine learning algorithm, and the machine learning algorithm may use the hyper-parameters and training examples to determine the parameters of the artificial neural network, for example using back propagation, using gradient descent, using stochastic gradient descent, using mini-batch gradient descent, and so forth. In some examples, an artificial neural network may be created from two or more other artificial neural networks by combining the two or more other artificial neural networks into a single artificial neural network.

[0172] Disclosed embodiments may include and / or access a data structure or data. A data structure consistent with the present disclosure may include any collection of data values and relationships among them. By way of example, a data structure may contain correlations of facial micromovements with words or phonemes, and the at least one processor may perform a lookup in the data structure of particular words or phenomes associated with detected facial skin micromovements. The data may be stored linearly, horizontally, hierarchically, relationally, non-relationally, uni-dimensionally, multidimensionally, operationally, in an ordered manner, in an unordered manner, in an object-oriented manner, in a centralized manner, in a decentralized manner, in a distributed manner, in a custom manner, or in any manner enabling data access. By way of non-limiting examples, data structures may include an array, an associative array, a linked list, a binary tree, a balanced tree, a heap, a stack, a queue, a set, a hash table, a record, a tagged union, ER model, and a graph. For example, a data structure may include an XML database, an RDBMS database, an SQL database, or NoSQL alternatives for data storage / search such as, for example, MongoDB, Redis, Couchbase,Attorney Docket No. 16198.0049-00304Datastax Enterprise Graph, Elastic Search, Splunk, Solr, Cassandra, Amazon DynamoDB, Scylla, HBase, and Neo4J. A data structure may be a component of the disclosed system or a remote computing component (e.g., a cloud-based data structure). Data in the data structure may be stored in contiguous or non-contiguous memory. Moreover, a data structure, as used herein, does not require 995 information to be co-located. It may be distributed across multiple servers, for example, servers that may be owned or operated by the same or different entities. Thus, the term “data structure” as used herein in the singular is inclusive of plural data structures. As is discussed herein in greater detail, data structure 124 in Fig. 1 and data structures 422 and 464 in Fig. 4 are non-limiting examples of a data structure, consistent with the present disclosure.1000

[0173] Consistent with the present disclosure, at least one processor may generate output associated with the determined facial skin micromovements. The term “generating an output” broadly refers to emitting a command, emitting data, and / or causing any type of electronic device to initiate an action. In some embodiments, the output may be sound (e.g., delivered via a speaker configured to fit in the ear of the user), and the sound may be an audible presentation of words associated with silent or 1005 prevocalized speech. In one example, the audible presentation of words may include an answer to a question that the user silently asked a virtual personal assistance. In another example, the audible presentation of words may include synthesized speech (e.g., artificial production of human speech). According to other disclosed embodiments, the output may be directed to a display (e.g., a visual display such as a computer monitor, television, mobile communications device, VR or XR glasses, or 1010 any other device that enables visual perception) and the generated output may include graphics, images, or textual presentations of words associated with prevocalized or vocalized speech (e.g., subtitles). The textual presentation of the words may be presented at the same time words are vocalized. In other embodiments, the output may be directed to a communications device associated with the user and the generated output may be any data exchanged with the communications device.1015 The term “communications device ” is intended to include all possible types of devices capable of exchanging data using a network configured to convey data. In some examples, the communications device may include a smartphone, a tablet, a smartwatch, a personal digital assistant, a desktop computer, a laptop computer, an Internet of Things (loT) device, a dedicated terminal, a wearable communications device, and any other device that enables data communications. As is discussed 1020 herein in greater detail, output determination module 712 in Fig. 7 is a non-limiting example of a software module used for generating output associated with the determined facial skin micromovements .

[0174] Disclosed embodiments may involve exchanging data (e.g., textual data) using a network. The term “communications network,” or simply “network,” may include any type of physical or 1025 wireless computer networking arrangement used to exchange data. For example, a network may be the Internet, a private data network, a virtual private network using a public network, a Wi-Fi network, a LAN or WAN network, a combination of one or more of the foregoing, and / or otherAttorney Docket No. 16198.0049-00304suitable connections that may enable information exchange among various components of the system. In some embodiments, a network may include one or more physical links used to exchange data, such 1030 as Ethernet, coaxial cables, twisted pair cables, fiber optics, or any other suitable physical medium for exchanging data. A network may also include a public switched telephone network (“PSTN”) and / or a wireless cellular network. A network may be a secured network or an unsecured network. In other embodiments, one or more components of the system may communicate directly through a dedicated communication network. Direct communications may use any suitable technologies, including, for 1035 example, BLUETOOTH™, BLUETOOTH LE™ (BLE), Wi-Fi, near-field communications (NFC), or other suitable communication methods that provide a medium for exchanging data and / or information between separate entities. As is discussed herein in greater detail, communications network 126 shown in Fig. 1, is a non-limiting example of a communications network, consistent with the present disclosure.1040

[0175] As used herein, a non-transitory computer-readable storage medium (or similar constructs such as a non-transitory computer-readable media) refers to any type of physical memory on which information or data readable by at least one processor can be stored. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, any other optical data storage medium, any physical 1045 medium with patterns of holes, markers, or other readable elements, a PROM, an EPROM, a FLASH- EPROM or any other flash memory, NVRAM, a cache, a register, any other memory chip or cartridge, and networked versions of the same. The terms “memory” and “computer-readable storage medium” may refer to multiple structures, such as a plurality of memories or computer-readable storage mediums located within a wearable device or at a remote location. Additionally, one or more 1050 computer-readable storage mediums can be utilized in implementing a computer-implemented method. Accordingly, the term computer-readable storage medium should be understood to include tangible items and exclude carrier waves and transient signals.

[0176] Reference is now made to Fig. 1, which illustrates an individual 102 using a speech detection system consistent with some embodiments of the present disclosure. Fig. 1 is a single 1055 exemplary representation, and it is to be understood that some illustrated elements might be omitted, and others may be added within the scope of this disclosure. In the illustrated example implementation, a speech detection system 100 may be mountable on ahead of user 102. Specifically, speech detection system 100 (also referred to herein simply as “the system”) may have the form and appearance of an over-the-ear clip-on headset. Alternatively, the system may be head-mountable in 1060 one of many other ways within the scope of this disclosure, including an in-ear bud, integration into or connectable to a temple of glasses, a head band, or any other mechanism capable of securing the system or a portion thereof to a human head. Speech detection system 100 may be configured to direct projected light 104 (e.g., coherent light) toward respective locations on the face of user 102, thus creating an array of light spots 106 extending over a facial region 108 of the face. Facial region 108Attorney Docket No. 16198.0049-003041065 may have an area of at least 1 cm2, at least 2 cm2, at least 4 cm2, at least 6 cm2, or at least 8 cm2. In some embodiments, the size of facial region 108 may be determined to enable sensing the motion of different parts of the facial muscles. In the depicted example, only one beam of projected light 104 is illustrated, however, it is contemplated that that every spot projected towards facial region 108 may be associated with a corresponding light beam or with one or more light beams. In other embodiments, 1070 the light source may project light in a manner other than an array of spots. For example, a region of the face may be uniformly or non-uniformly illuminated.

[0177] For embodiments that are head-worn, speech detection system 100 may include a wearable housing 110 configured to be worn on a head of user 102. Wearable housing 110 may include or be associated with a processing unit 112 configured to interpret facial skin micromovements; an output 1075 unit 114 configured to fit into the user’s ear and to present audible and / or vibrational output; and optical sensing unit 116 configured to project light toward a non-lip part of the face of user 102 and to detect reflections of the projected light. In the illustrated example, optical sensing unit 116 may be connected to output unit 114 by an arm 118 and thus may be held in a location in proximity to and / or facing the user’s face. According to some disclosed embodiments, optical sensing unit 116 does not 1080 contact the user’s skin at facial region 108, but rather optical sensing unit 116 may be held at a certain distance from the skin surface of facial region 108. The distance of optical sensing unit 116 from the skin surface may be at least 5 mm, at least 7.5 mm, at least 10 mm, at least 15 mm, or at least 20 mm.

[0178] Optical sensing unit 116 may be configured to receive reflections of light 104 from facial region 108 and to output associated reflection signals. Specifically, the reflection signals may be 1085 indicative of light patterns (e.g., secondary speckle patterns) that may arise due to reflection of the coherent light from each of spots 106 within a field of view of speech detection system 100. To cover a sufficiently large facial region 108, the detector of speech detection system 100 may have a wide field of view, for example, the field of view may have an angular width of at least 60°, at least 70°, or at least 90°. Within this field of view, speech detection system 100 may sense and process the signals 1090 reflective of light patterns in all of spots 106 or only a certain subset of spots 106. For example, processing unit 112 may select a subset of spots 106 determined to give the largest amount of useful and reliable information with respect to the relevant movements of the skin surface of user 102 and may avoid processing data from other spots 106. Additional details of the structure and operation of optical sensing unit 116 are described below with reference to Fig. 5.1095

[0179] Consistent with the present disclosure, speech detection system 100 may be capable of detecting facial skin micromovements of user 102 and extract meaning from the detected movements, even without vocalization of speech or utterance of any other sounds by user 102. The extracted meaning may be an identification of user 102 wearing speech detection system 100, an identification of a subvocalization by a user, such as a word silently spoken by user 102, an identification of a word 1100 vocally spoken by user 102, an identification of a phoneme silently spoken by user 102, or an identification of a phoneme vocally spoken by user 102. Similarly, the extract meaning may includeAttorney Docket No. 16198.0049-00304an identification of a heart rate of user 102, an identification of a breathing rate of user 102, and / or other characteristics associated with verbal or non-verbal communication by user 102. In one example, speech detection system 100 may generate output signals that include data associated with 1105 an identification information, a UI command, synthesized audio signal, a textual transcription, or any combination thereof. In one example, the synthesized audio signal may be played back to user 102 via a speaker in output unit 114. This playback may be useful in giving user 102 feedback with respect to the speech output.

[0180] Consistent with the present disclosure, speech detection system 100 may exchange data 1110 (e.g., output signals) with a variety of communications devices associated with users, for example, a mobile communications device 120 or a server 122. The term “communications device” is intended to include all possible types of devices capable of exchanging data using a digital communications network, an analog communication network, or any other communications network configured to convey data. In some examples, the communications device may include a wearable communications 1115 device, such as a smartphone, a tablet, a smartwatch, a personal digital assistant, a laptop computer, an loT device, a dedicated terminal, industrial machinery, a vehicle, a smart house, an appliance, or any other electronic device capable of exchanging information or data with another electronic device. In other examples, the communications device may include a non-wearable communications device, such as a desktop computer, a smart home hub, a router, a server, or any other network-connected 1120 equipment. In some cases, a processing device of mobile communications device 120 or server 122 may supplement or replace some functions of processing unit 112 of speech detection system 100. In some embodiments, the output signals generated by speech detection system 100 may be transmitted via a communication link to mobile communications device 120 or to a cloud server. The term “cloud server” refers to a computer platform that provides services via a network, such as the Internet. In the 1125 example embodiment illustrated in Fig. 1, a server 122 may use one or more virtual machines that may not correspond to individual pieces of hardware. For example, computational and / or storage capabilities may be implemented by allocating appropriate portions of desirable computation / storage power from a scalable repository, such as a data center or a distributed computing environment. In one example configuration, server 122 may be a cloud server that determines neural activity of user 1130 102 based on facial skin micromovements. In one example, server 122 may implement the methods described herein using customized hard-wired logic, one or more Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), firmware, and / or program logic which, in combination with the computer system, cause server 122 to be a special -purpose machine.

[0181] In some embodiments, server 122 may access data structure 124 to determine, for example, 1135 correlations between words and a plurality of facial movements. Data structure 124 may utilize a volatile or non-volatile, magnetic, semiconductor, tape, optical, removable, non-removable, other type of storage device or tangible or non-transitory computer-readable medium, or any medium or mechanism for storing information. Data structure 124 may be part of server 122 or separate fromAttorney Docket No. 16198.0049-00304server 122, as shown. When data structure 124 is not part of server 122, server 122 may exchange 1140 data with data structure 124 via a communication link. Data structure 124 may include one or more memory devices that store data and instructions used to perform one or more features of the disclosed methods. In one embodiment, data structure 124 may include any of a plurality of suitable data structures, ranging from small data structures hosted on a workstation to large data structures distributed among data centers. Data structure 124 may also include any combination of one or more 1145 data structures controlled by memory controller devices (e.g., servers) or software. Consistent with the present disclosure, speech detection system 100 may communicate with mobile communications device 120 or server 122 using a communications network 126 as defined above.

[0182] Reference is now made to Fig. 2A, which illustrates another example implementation of speech detection system 100, in accordance with the present disclosure. In this example, wearable 1150 housing 110 may be integrated with or otherwise attached to a pair of glasses 200 having a frame 202.In this example implementation, glasses 200 may include nasal electrodes 204 and temporal electrodes 206 attached to frame 202 and contacting the user’s skin surface. Electrodes 204 and 206 may receive body surface electromyogram (sEMG) signals, which provide additional information regarding the activation of the user’s facial muscles. Speech detection system 100 may use the 1155 electrical activity sensed by electrodes 204 and 206 together with the output of optical sensing unit 116 in generating, for example, the synthesized audio signals. Additionally or alternatively, speech detection system 100 may include one or more additional optical sensing units 208, similar to optical sensing unit 116, for sensing skin movements in other areas of the user’s face, such as eye movement. These additional optical sensing units may be used together with or instead of optical sensing unit 1160 116. In the illustrated example, optical sensing unit 116 may illuminate a first facial region 108A and optical sensing unit 208 may illuminate a second facial region 108B. First facial region 108A and second facial region 108B may be nonoverlapping.

[0183] In some disclosed embodiments, the speech detection system may be incorporated with, integrated with, or otherwise attached to an extended reality appliance. As used herein, the term 1165 “extended reality appliance” may include any type of device or system that enables a user to perceive and / or interact with an extended reality environment. The term “extended reality environment,” refers to all types of real-and-virtual combined environments and human-machine interactions at least partially generated by computer technology. One non-limiting example of an extended reality environment may be a Virtual Reality (VR) environment. A virtual reality environment may be an 1170 immersive simulated non-physical environment which provides to the user the perception of being present in the virtual environment. Another non-limiting example of an extended reality environment may be an Augmented Reality (AR) environment. An augmented reality environment may involve live direct or indirect views of a physical real-world environment enhanced with virtual computergenerated perceptual information, such as virtual objects with which the user may interact. Another 1175 non-limiting example of an extended reality environment is a Mixed Reality (MR) environment. AAttorney Docket No. 16198.0049-00304mixed reality environment may be a hybrid of physical real-world and virtual environments, in which physical and virtual objects may coexist and interact in real time. Examples of the extended reality appliance may include VR headsets, AR headsets, MR headsets, smart glasses, and wearable projection devices.1180

[0184] Reference is now made to Fig. 2B, illustrating another example implementation of speech detection system 100, in accordance with some embodiments of the present disclosure. In the depicted example, speech detection system 100 may be part of an extended reality appliance 250. Extended reality appliance 250 may include all the sensors discussed above with reference to glasses 200 and more. For example, extended reality appliance 250 may include one or more of a gyroscope, an 1185 accelerometer, a magnetometer, an image sensor, a depth sensors, an infrared sensors, a proximity sensor, and / or any other sensor configured to measure one or more properties associated with the individual wearing extended reality appliance 250 and to generate an output relating to the measured property or properties. In some cases, speech detection system 100 may use the input from any one of the sensors of extended reality appliance 250 to determine the vocalized or subvocalized words that 1190 individual 102 articulated. For example, speech detection system 100 may use input from an image sensor of extended reality appliance 250 together with data from optical sensing unit 116 (See Fig. 1) to extract meaning of facial movements. In other cases, extended reality appliance 250 may generate output that includes a visual and / or audible presentation associated with the words detected by the speech detection system 100. For example, individual 102 may interact with extended reality 1195 appliance 250 using silent commands.

[0185] Reference is now made to Fig. 3, which illustrates another example implementation of speech detection system 100, in accordance with the present disclosure. In the implementation illustrated in Fig. 3, speech detection system 100 may be integrated with mobile communications device 120. Specifically, mobile communications device 120 may include a light detector configured 1200 to detect reflections 300 of light from facial region 108. In this example, the light projected to facial region 108 originates from a non-wearable light source 302 that may be a coherent light source or non-coherent light source. In some configurations, non-wearable light source 302 may be included in mobile communications device 120. Alternatively, non-wearable light source 302 may be separated from mobile communications device 120.1205

[0186] Consistent with the present disclosure, and as depicted in Fig. 3, the pattern of the light projected to facial region 108 may be a single spot 106 large enough to illuminate different portions of facial region 108. For example, spot 106 may include a first portion 304A associated with a first facial muscle and a second portion 304B associated with a second facial muscle. Thereafter, a processing device of mobile communications device 120 may apply a light reflection analysis on received 1210 reflections 300 to determine facial skin micromovements. In particular, the processing device of mobile communications device 120 may determine first facial skin micromovements of first portion 304A and second facial skin micromovements of second portion 304B. The processing device mayAttorney Docket No. 16198.0049-00304use both the first facial skin micromovements and the second facial skin micromovements to extract meaning (e.g., determine speech or a command, or to authenticate user 102) and to generate output.1215 The example implementation of speech detection system 100 illustrated in Fig. 3 may be used when the extracted meaning includes a continuous authentication of user 102. Specifically, speech detection system 100 may provide an authentication service that uses biometrics of facial micromovements for continuous authentication during usage of mobile communications device 120.

[0187] Fig. 4 is a block diagram of an exemplary configuration of speech detection system 100 and 1220 an exemplary configuration of remote processing system 450. It is to be noted that Fig. 4 is a representation of just one embodiment, and it is to be understood that some illustrated elements might be omitted and others added within the scope of this disclosure. In the depicted embodiment, speech detection system 100 comprises processing unit 112 that includes a processing device 400 and a memory device 402; output unit 114 that includes a speaker 404, a light indicator 406, and a haptic 1225 feedback device 408; optical sensing unit 116 that includes at least one light source 410 and at least one light detector 412; an audio sensor 414, a power source 416, one or more additional sensors 418, network interface 420, and data structure 422. Speech detection system 100 may directly or indirectly access a bus 424 (or any other communication mechanism) that interconnects the above-mentioned subsystems and components for transferring information and commands within speech detection 1230 system 100. Some of the subsystems and components listed above are referred to herein in the singular but in alternative configurations may be plural. For example, in some configurations speech detection system 100 may include multiple light sources 410 or multiple light detectors 412.

[0188] Processing device 400, shown in Fig. 4, may constitute any physical device or group of devices having electric circuitry that performs a logic operation on an input or inputs. The instructions 1235 executed by at least one processor may, for example, be pre-loaded into a memory integrated with or embedded into processing device 400, or may be stored in a separate memory (e.g., memory device 402 or data structure 422). As described above, the processing device may include more than one processor. Each processor may have a similar construction, or the processors may be of differing constructions that are electrically connected or disconnected from each other. For example, the 1240 processors may be separate circuits or integrated in a single circuit. When more than one processor is used, the processors may be configured to operate independently or collaboratively and may be colocated or located remotely from each other. The processors may be coupled electrically, magnetically, optically, acoustically, mechanically, or by other means that permit them to interact. Consistent with the present disclosure, at least some of the functionalities described below with regard 1245 to processing device 400 may be executed by a processing device of remote processing system 450.

[0189] Memory device 402, shown in Fig. 4, may include high-speed random-access memory and / or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices, and / or flash memory (e.g., NAND, NOR). Consistent with the present disclosure, the components of memory device 402 may be distributed in more than one unit of speech detectionAttorney Docket No. 16198.0049-003041250 system 100 and / or in more than one memory device. In particular, memory device 402 may be used to store a software product and / or data stored on a non-transitory computer-readable medium. As described above, the terms “memory” and “computer-readable storage medium” may refer to multiple structures, such as a plurality of memories or computer-readable storage mediums located within speech detection system 100 or at a remote location (e.g., at remote processing system 450).1255 Additionally, one or more computer-readable storage mediums can be utilized in implementing a computer-implemented method. Examples of software modules stored in memory device 402 are described below with reference to Fig. 7.

[0190] Output unit 114, shown in Fig. 4, may cause output from a variety of output devices, such as speaker 404, light indicator 406, and a haptic feedback device 408. Examples of speaker 404 may 1260 include or may be incorporated with a loudspeaker, earbuds, audio headphones, a hearing aid type device, a bone conduction headphone, and any other device capable of converting an electrical audio signal into a corresponding sound. In some embodiments, speaker 404 may be configured to let only user 102 to listen to the generated audio signals. Alternatively, speaker 404 may be configured to emit sound into the open air for anyone nearby to hear. Light indicator 406 may include one or more light 1265 sources, for example, a LED array associated with different colors. Light indicator 406 may be used to indicate the battery status of speech detection system 100 or to indicate its operational mode. Haptic feedback device 408 may include a vibrating motor, linear actuator, vibrational transducer, or any other force feedback device that provide tactile or haptic cues or is capable of converting an electrical signal into corresponding vibrations or force applications.1270

[0191] Optical sensing unit 116, shown in Fig. 4, may include light source 410 and light detector 412. Light source 410 may project coherent light or non-coherent light to facial region 108. As discussed above, light source 410 may be a laser such as a solid-state laser, laser diode, a high-power laser, or an alternative light source such as a light emitting diode (LED) -based light source. In addition, the light source 410, may emit light in differing formats, such as light pulses, continuous 1275 wave (CW), quasi-CW, and so on. In one embodiment, light source 410 may be an infrared laser diode configured to emit an input beam of coherent radiation. Light source 410 may be associated with a beam-splitting element, such as a Dammann grating or another suitable type of diffractive optical element (DOE), for splitting an input beam into multiple output beams, which form respective spots 106 at a matrix of locations extending over facial region 108. In another embodiment (not 1280 shown in the figures) light source 410 may include multiple laser diodes or other emitters, which generate respective groups of the output beams, covering different respective sub-areas within facial region 108. In one embodiment, processing unit 112 may select and actuate only a subset of the emitters, without actuating all the emitters. For example, to reduce the power consumption of speech detection system 100, processing unit 112 may actuate only one emitter or a subset consisting of two 1285 or more emitters that illuminates a specific area on the user’s face that has been found to give the most useful information for generating the desired speech output.Attorney Docket No. 16198.0049-00304

[0192] Light detector 412, shown in Fig. 4, may be used to detect reflections from facial region 108 indicative of facial skin movements. As discussed above, a light detector may be capable of measuring properties of coherent or non-coherent light, such as power, frequency, phase, pulse 1290 timing, pulse duration, and other properties. In some embodiments, light detector 412 may include an array of detecting elements, for example, a set of a charge-coupled device (CCD) sensors and / or a set of complementary metal-oxide semiconductor (CMOS) sensors, with objective optics for imaging facial region 108 onto the array. Due to the small dimensions of optical sensing unit 116 and its proximity to the skin surface, light detector 412 may have a sufficiently wide field of view to detect 1295 many of spots 106 at a high angle of at least 60°, at least 70°, or at least 90°. Light detector 412 may be configured to generate an output relating to the measured properties of the detected light.Consistent with the present disclosure, the output of light detector 412 may include any form of data determined in response to the received light reflections from facial region 108. In some embodiments, the output may include reflection signals that include electronic representation of one or more 1300 properties determined from the coherent or non-coherent light reflections. In other embodiments, the output may include raw measurements detected by at least one light detector 412.

[0193] In some embodiments, light detector 412 may measure one of more optical attributes associated with skin changes. The term “skin changes” refers to any detectable movements, alterations, or modifications that occurred to the skin. Such skin changes may include changes in the 1305 epidermis (i.e., the outermost layer of the skin), changes in the dermis (i.e., the middle layer of the skin), changes in the hypodermis (i.e., the deepest layer of the skin), and changes in deeper muscle tissues. The optical attributes may be measured without contacting the skin of individual 102.Examples of one of more optical attributes of the reflected light that may be measured by light detector 412 may include intensity, frequency, reflection, angle, sharpness, bidirectional reflectance 1310 distribution function, color, brightness, glossiness, transparency, opacity, surface texture, surface relief, surface movement, and other optical attributes derivable from analysis of light reflections. The output of light detector 412 may be used to determine information associated with skin changes. In some embodiments, the information associated with those skin changes may be derived from changes in a distance from the skin to the detector as the skin moves, and in other embodiments the changes 1315 may not be derived from variations in the distance of the skin from light detector 412. For example, the determined speed or angular speed of the changes of the facial skin may be determined by detecting the changes of non-distance measurements (e.g., image sharpness) over time. Thus, in one non-limiting example, optical attributes may be detected from random intensity variations observed when coherent light interacts with a rough or scattering surface, such as human skin. In another non1320 limiting example, optical attributes may be detected based on the interference of light waves, such as when interference patterns are used to measure the phase difference or amplitude changes between two or more optical paths.Attorney Docket No. 16198.0049-00304

[0194] In some embodiments, optical sensing unit 116 may not require reference to parameters of the light source, such as the light source’s wavelength, intensity, or coherence, and may not require a 1325 reference beam (typically used with a beam-splitter) to measure the one or more optical attributes of the reflected light. For example, optical sensing unit 116 may use a single beam to illuminate the skin and then process the light reflections returned to light detector 412. While some speech detection systems may include a single pixel sensor (e.g., a photo diode), in other embodiments, light detector 412 may include one or more multi-pixel sensors (e.g., each pixel sensor includes more than 4 1330 megapixels, more than 10 megapixels, or more than 10 megapixels) that enables producing an image providing spatial information beyond a single point. For example, a reflection image depicted in Fig.6 may be produced from the output of light detector 412. As described throughout the disclosure, output of light detector 412 may be analyzed using image processing methods to determine patterns of light scattered off a surface. For example, features of secondary speckles may be determined.1335

[0195] In some non-limiting examples, optical sensing unit 116 may use a diffractive element to split the outbound beam to multiple beams and may not rely on superposition of coherent light waves to cause interference. In some non-limiting examples, optical sensing unit 116 may be arranged such that light detector 412 may be positioned along a different optical axis from light source 410. In other non-limiting examples, aligning the light source and the sensor along the same optical axis may be 1340 used for maintaining coherence, achieving path length matching, ensuring spatial overlap, and preserving the sensitivity and accuracy of the interference patterns. However, since some implementations of light detector 412 detect a reflection image and not a distance to a point, optical sensing unit 116 may include a first optical axis for outbound light and a second optical axis, not aligned with the first optical axis, for inbound light. In some embodiments, light detector 412 is 1345 configured to measure both sub-microbic speed and depth changes in the ranges of 5-500 microns. In alternative embodiments, light detector 412 is configured to measure changes that are less than a micron. All of the examples provided in this paragraph are alternatives and may be implement in the many alternative embodiments provided herein, depending on the specifics of implementation.

[0196] Audio sensor 414, shown in Fig. 4, may include one or more audio sensors configured to 1350 capture audio by converting sounds to digital information. Some examples of audio sensors may include microphones, unidirectional microphones, bidirectional microphones, cardioid microphones, omnidirectional microphones, onboard microphones, wired microphones, wireless microphones, or any combination of the above. Audio sensor 414 may be configured to capture sounds uttered by user 102, thereby enabling user 102 to use speech detection system 100 as a conventional headphone when 1355 desired. Additionally or alternatively, audio sensor 414 may be used in conjunction with the silent speech sensing capabilities of speech detection system 100. In one embodiment, the audio signals output by audio sensor 414 can be used in changing the operational state of speech detection system 100. For example, processing unit 112 may generate the speech output only when audio sensor 414 does not detect vocalization of words by user 102. In another embodiment, audio sensor 414 may beAttorney Docket No. 16198.0049-003041360 used in a calibration procedure, in which optical sensing unit 116 detects micromovements of the skin while user 102 utters certain phonemes or words. Processing unit 112 may compare the reflection signals output by light detector 412 to the sounds sensed by audio sensor 414 to calibrate optical sensing unit 116. This calibration may include prompting user 102 to shift the position of optical sensing unit 116 to align the optical components in the desired position relative to facial region 108.1365 In yet another embodiment, audio sensor 414 enables on-the-fly training of a neural network of speech detection system 100. For example, speech detection system 100 may be configured to correlate facial skin micromovements with words using audio signals concurrently captured with the micromovements. After recognizing recorded words, speech detection system 100 can perform a look-back to identify facial micromovement that preceded articulation of those words, thereby 1370 training speech detection system 100. In a similar way, speech detection system can be used to train on expressions, commands, user recognition, and emotions.

[0197] Power source 416, shown in Fig. 4, may provide electrical energy to power speech detection system 100. A power source may include any device or system that can store, dispense, or convey electric power, including, but not limited to, one or more batteries (e.g., a lead-acid battery, a 1375 lithium-ion battery, a nickel-metal hydride battery, a nickel-cadmium battery), one or more capacitors, one or more connections to external power sources, one or more power convertors, or any combination of the foregoing. With reference to the example illustrated in Fig. 4, power source 416 may be mobile, which means that speech detection system 100 can be wearable. The mobility of the power source enables user 102 to use speech detection system 100 in a variety of situations. In other 1380 embodiments, power source 416 may be associated with a connection to an external power source (such as an electrical power grid) that may be used to charge power source 416.

[0198] Additional sensors 418, shown in Fig. 4, may include a variety of sensors, for example, image sensors, motion sensors, environmental sensors, Electromyography (EMG) sensors, resistive sensors, ultrasonic sensors, proximity sensors, biometric sensors, or other sensing devices configured 1385 to facilitate related functionalities. For example, speech detection system 100 may include one or more image sensors configured to capture visual information from the environment of user 102 by converting light (not emitted from light source 410) to image data. Consistent with the present disclosure, an image sensor may be included in any device or system capable of detecting and converting optical signals in the near-infrared, infrared, visible, and / or ultraviolet spectrums into 1390 electrical signals. Examples of image sensors may include digital cameras, semiconductor charge- coupled devices (CCDs), active pixel sensors in complementary metal-oxide semiconductor (CMOS), or N-type metal-oxide-semiconductor (NMOS, Live MOS). The electrical signals may be used to generate image data. Consistent with the present disclosure, the image data may include pixel data streams, digital images, digital video streams, data derived from captured images, and data that may 1395 be used to construct one or more 3D images, a sequence of 3D images, 3D videos, or a virtual 3DAttorney Docket No. 16198.0049-00304representation. The image data acquired by the one or more image sensors may be transmitted by wired or wireless transmission to processing unit 112 or to remote processing system 450.

[0199] Speech detection system 100 may also include one or more motion sensors configured to measure motion of user 102. Specifically, a motion sensor may perform at least one of the following: 1400 detect motion of user 102, measure the velocity of user 102, measure the acceleration of user 102, or measure any other action that involves movement. In some embodiments, the motion sensor may include one or more accelerometers configured to detect changes in acceleration (e.g., proper acceleration) and / or to measure acceleration of speech detection system 100. In some embodiments, the motion sensor may include one or more gyroscopes configured to detect changes in the orientation 1405 of speech detection system 100 and / or to measure information related to the orientation of speech detection system 100. In some embodiments, the motion sensors may include one or more using image sensors, LIDAR sensors, radar sensors, or proximity sensors. For example, by analyzing captured images, processing device 400 may determine the motion of speech detection system 100, for example, using ego-motion algorithms. In addition, the processing device may determine the 1410 motion of objects in the environment of speech detection system 100, for example, through object tracking.

[0200] Speech detection system 100 may also include one or more environmental sensors of different types configured to capture data reflective of the environment of user 102. In some embodiments, the environmental sensor may include one or more chemical sensors configured to 1415 perform at least one of the following: measure chemical properties in the environment of user 102, measure changes in the chemical properties in the environment of user 102, detect the present of chemicals in the environment of user 102, and / or measure the concentration of chemicals in the environment of user 102. Examples of measurable chemical properties include: pH level, toxicity, and temperature. Examples of chemicals or phenomena that may be measured include: electrolytes, 1420 particular enzymes, particular hormones, particular proteins, smoke, carbon dioxide, carbon monoxide, oxygen, ozone, hydrogen, and hydrogen sulfide. In other embodiments, the environmental sensor may include one or more temperature sensors configured to detect changes in the temperature of the environment of user 102 and / or to measure the temperature of the environment of user 102. In other embodiments, the environmental sensor may include one or more barometers configured to 1425 detect changes in the atmospheric pressure in the environment of user 102 and / or to measure the atmospheric pressure in the environment of user 102. In other embodiments, the environmental sensor may include one or more light sensors configured to detect changes in the ambient light in the environment of user 102.

[0201] Network interface 420, shown in Fig. 4, may provide two-way data communications to a 1430 network, such as communications network 126. In one embodiment, network interface 420 may include an Integrated Services Digital Network (ISDN) card, cellular modem, satellite modem, or a modem to provide a data communication connection over the Internet. As another example, networkAttorney Docket No. 16198.0049-00304interface 420 may include a Wireless Local Area Network (WLAN) card. In another embodiment, network interface 420 may include an Ethernet port connected to radio frequency receivers and 1435 transmitters and / or optical (e.g., infrared) receivers and transmitters. The specific design and implementation of network interface 420 may depend on the communications network or networks over which speech detection system 100 is intended to operate. For example, in some embodiments, speech detection system 100 may include network interface 420 designed to operate over a GSM network, a GPRS network, an EDGE network, a Wi-Fi or WiMax network, and a Bluetooth network.1440 In any such implementation, network interface 420 may be configured to send and receive electrical, electromagnetic, or optical signals that carry digital data streams or digital signals representing various types of information.

[0202] Data structure 422, shown in Fig. 4, may include any hardware, software, firmware, or combination thereof for storing and facilitating the retrieval of information from a database. The term 1445 “database” may be understood to include a collection of data that may be distributed or nondistributed. A database may include a database management system that controls the organization, storage and retrieval of data contained within the database. As described above, the data included in the database may be stored linearly, horizontally, hierarchically, relationally, non-relationally, uni- dimensionally, multidimensionally, operationally, in an ordered manner, in an unordered manner, in 1450 an object-oriented manner, in a centralized manner, in a decentralized manner, in a distributed manner, in a custom manner, or in any manner enabling data access. In disclosed embodiments, data structure 422 may include correlations of facial micromovements with words, commands, emotions, expressions, and / or biological conditions. The at least one processor may perform a lookup in the data structure to thereby interpret the detected facial skin micromovements. In accordance with one 1455 embodiment, at least some of the data stored in data structure 422 may alternatively or additionally be stored in remote processing system 450.

[0203] Consistent with the present disclosure, speech detection system 100 may be configured to communicate with a remote processing system 450 (e.g., mobile communications device 120 or server 122). Remote processing system 450 may directly or indirectly accesses a bus 452 (or other 1460 communication mechanism) interconnecting subsystems and components for transferring information within remote processing system 450. For example, bus 452 may interconnect a memory interface 454, a network interface 456, a power source 458, a processing device 460, one or more additional sensors 462, a data structure 464, and memory device 466.

[0204] Memory interface 454, shown in Fig. 4, may be used to access a software product and / or 1465 data stored on a non-transitory computer-readable medium or on other memory devices, such as memory devices 402, 466, data structure 422, or data structure 464. Memory device 466 may contain software modules to execute processes consistent with the present disclosure. In particular embodiments, memory device 466 may include a shared memory module 472, a node registration module 473, a load balancing module 474, one or more computational nodes 475, an internalAttorney Docket No. 16198.0049-003041470 communication module 476, an external communication module 477, and a database access module (not shown). Modules 472-477 may contain software instructions for execution by at least one processor (e.g., processing device 460) associated with remote processing system 450. Shared memory module 472, node registration module 473, load balancing module 474, computational module 475, and external communication module 477 may cooperate to perform various operations.1475

[0205] Shared memory module 472 may allow information sharing between remote processing system 450 and other devices related to one or more speech detection systems 100. In some embodiments, shared memory module 472 may be configured to enable processing device 460 to access, retrieve, and store data. For example, using shared memory module 472, processing device 460 may perform at least one of: executing software programs stored on memory devices 402, 466, 1480 data structure 422, or data structure 464; storing information in memory devices 402, 466, Data structure 422, or data structure 464; or retrieving information from memory devices 402, 466, data structure 422, or data structure 464.

[0206] Node registration module 473 may be configured to track the availability of one or more computational nodes 475. In some examples, node registration module 473 may be implemented as: a 1485 software program, such as a software program executed by one or more computational nodes 475, a hardware solution, or a combined software and hardware solution. In some implementations, node registration module 473 may communicate with one or more computational nodes 475, for example, using internal communication module 476. In some examples, one or more computational nodes 475 may notify node registration module 473 of their status, for example, by sending messages: at startup, 1490 at shutdown, at constant intervals, at selected times, in response to queries received from node registration module 473, or at any other determined times. In some examples, node registration module 473 may query about the status of one or more computational nodes 475, for example, by sending messages: at startup, at constant intervals, at selected times, or at any other determined times.

[0207] Load balancing module 474 may be configured to divide the workload among one or more 1495 computational nodes 475. In some examples, load balancing module 474 may be implemented as a software program, such as a software program executed by one or more of the computational nodes 475, a hardware solution, or a combined software and hardware solution. In some implementations, load balancing module 474 may interact with node registration module 473 to obtain information regarding the availability of one or more computational nodes 475. In some implementations, load 1500 balancing module 474 may communicate with one or more computational nodes 475, for example, using internal communication module 476. In some examples, one or more computational nodes 475 may notify load balancing module 474 of their status, for example, by sending messages: at startup, at shutdown, at constant intervals, at selected times, in response to queries received from load balancing module 474, or at any other determined times. In some examples, load balancing module 474 may 1505 query about the status of one or more computational nodes 475, for example, by sending messages: at startup, at constant intervals, at pre-selected times, or at any other determined times.Attorney Docket No. 16198.0049-00304

[0208] Internal communication module 476 may be configured to receive and / or to transmit information from one or more components of remote processing system 450. For example, control signals and / or synchronization signals may be sent and / or received through internal communication 1510 module 476. In one embodiment, input information for computer programs, output information of computer programs, and / or intermediate information of computer programs may be sent and / or received through internal communication module 476. In another embodiment, information received though internal communication module 476 may be stored in memory device 466 or in data structure 464. For example, information retrieved from data structure 464 may be transmitted using internal 1515 communication module 476. In another example, reference signals reflecting facial micromovements of user 102 may be stored in data structure 464 and accessed using internal communication module 476.

[0209] External communication module 477 may be configured to receive and / or to transmit information from one or more speech detection systems 100. For example, control signals may be sent 1520 and / or received through external communication module 477. In one embodiment, information received though external communication module 477 may be stored in memory device 466, in data structure 464, and / or any memory device in the one or more speech detection systems 100. In another embodiment, information retrieved from data structure 464 may be transmitted using external communication module 477 to speech detection system 100 or to any entity with whom user 102 1525 communicates. For example, when user 102 communicate with a financial institution (e.g., a bank) information retrieved from data structure 464 may be transmitted to enable authentication of user 102. In another embodiment, sensor data may be transmitted and / or received using external communication module 477. Examples of such input data may include data received from speech detection system 100, information captured from the environment of user 102 using one or more sensors such as 1530 additional sensors 418 and additional sensors 462.

[0210] In some embodiments, aspects of modules 472-477 may be implemented in hardware, in software (including in one or more signal processing and / or application specific integrated circuits), in firmware, or in any combination thereof, executable by one or more processors, alone, or in various combinations with each other. Specifically, modules 472-477 may be configured to interact with each 1535 other and / or other modules of speech detection system 100 to perform functions consistent with disclosed embodiments. Memory device 466 may include additional modules and instructions or fewer modules and instructions.

[0211] Network interface 456, power source 458, processing device 460, additional sensors 462, and data structure 464, shown in Fig. 4, may share similar functionality with the functionality of 1540 corresponding elements in speech detection system 100, as described above. The specific design and implementation of the above-mentioned components may vary based on the implementation of remote processing system 450. In addition, remote processing system 450 may include more or fewer components. For example, when remote processing system 450 is a mobile communications deviceAttorney Docket No. 16198.0049-00304associated with user 102 (e.g., mobile communications device 120) it may include a speaker, a 1545 microphone, and additional sensors.

[0212] The components and arrangements of speech detection system 100 and remote processing system 450 as illustrated in Fig. 4 are not intended to limit the disclosed embodiments. As will be appreciated by a person skilled in the art having the benefit of this disclosure, numerous variations and / or modifications may be made to the depicted configuration of speech detection system 100 and 1550 remote processing system 450. For example, not all components may be essential for the operation of an input unit in all cases. Any component may be located in any appropriate part of speech detection system 100 or remote processing system 450. Moreover, the components may be rearranged into a variety of configurations while providing the functionality of the disclosed embodiments. For example, some speech detection systems may not include all of the elements as shown in speech 1555 detection system 100 and in remote processing system 450. Other speech detection systems may include additional components and still fall within the scope of this disclosure.

[0213] Figs. 5A and 5B include two schematic illustrations of optical sensing unit 116 as it detects facial skin micromovements in accordance with some embodiments of the present disclosure. The two schematic illustrations show a simplified scenario before muscle recruitment and after muscle 1560 recruitment. As depicted, optical sensing unit 116 may include an illumination module 500, a detection module 502, and, optionally, audio sensor 414. As discussed above and illustrated in Fig. 5, optical sensing unit 116 may be configured not to contact the user’s skin at facial region 108, but rather may be held at a distance D from the skin surface of facial region 108. The distance D of optical sensing unit 116 from the skin surface may be at least 5 mm, at least 7.5 mm, at least 10 mm, 1565 at least 15 mm, or at least 20 mm.

[0214] In the depicted embodiment, illumination module 500 includes light source 410 (e.g., an infrared laser diode) configured to generate an input light beam 504. Illumination module 500 further includes a beam-splitting element 506, such as a Dammann grating or another suitable type of diffractive optical element (DOE), configured to split input beam 504 into multiple output beams 508, 1570 which form respective spots 106A-106E at a pattern (e.g., a matrix of locations) extending over facial region 108. In an alternative embodiment (not shown in the figure), illumination module 500 may include multiple light sources 410, which generate respective groups of output beams 508, covering different respective sub-areas within facial region 108. In this alternative embodiment, processing unit 112 may select and actuate only a subset of the multiple light sources, without actuating all of them.1575 For example, to reduce the power consumption of speech detection system 100, processing unit 112 may actuate only one light source or a group of two or more light sources that illuminate a part of facial region 108.

[0215] Detection module 502 may include light detector 412, which may include an array 510 of optical sensors (e.g., an array of CMOS image sensors) with objective optics 512 for obtaining 1580 reflections 300 of coherent light from facial region 108. Because of the small dimensions of opticalAttorney Docket No. 16198.0049-00304sensing unit 116 and its proximity to the skin surface, detection module 502 may be configured to have a wide field of view to acquire reflections from many spots 106 at a high angle. As mentioned above, the field of view of light detector 412 may have an angular width of at least 60°, at least 70°, or at least 90°. Due to the roughness of the skin surface, the light patterns at spots 106 can be detected at 1585 these high angles, as well.

[0216] Speech detection system 100 may analyze light reflections 300 to determine facial skin micromovements resulting from recruitment of muscle fiber 520. Determining the facial skin micromovements may include determining an amount of the skin movement, determining a direction of the skin movement, and / or determining an acceleration of the skin movement. The determined 1590 facial skin micromovements may include voluntary and / or involuntary recruitment of muscle fiber 520. Muscle fiber 520 may be part of: a zygomaticus muscle, an orbicularis oris muscle, a risorius muscle, genioglossus muscle, or a levator labii superioris alaeque nasi muscle. Processing device 400 may be configured to perform a first speckle analysis on light reflected from a first region of face in proximity to spot 106A to determine that the first region moved by a distance dl, i.e., first facial skin 1595 micromovement 522A; and perform a second speckle analysis on light reflected from a second region of face in proximity to spot 106E to determine that the second region moved by a distance d2, i.e., second facial skin micromovement 522B. Thereafter, processing device 400 may use the determined movements of the first region and the second region to ascertain at least one spoken word. Consistent with disclosed embodiments, distances dl and d2 may be less than 1000 micrometers, less than 100 1600 micrometers, less than 10 micrometers, or less.

[0217] Fig. 6 is a schematic illustration of a reflection image 600 associated with light reflections 300 received from an area of facial region 108 associated with a single spot 106 (e.g., spot 106A depicted in Fig. 5). In disclosed embodiments, processing device 400 may receive reflection signals indicative of coherent light reflections from facial region 108. The reflection signals may be 1605 represented by reflection image 600. Thereafter, processing device 400 may determine the facial skin micromovements by applying a light reflection analysis. When light source 410 is a coherent light source, the light reflection analysis may include a speckle analysis or any pattern-based analysis. Such analysis may be performed by processing device 400 or processing device 460 to identify a speckle pattern and derive thereof movement of a corresponding area of facial region 108.1610

[0218] In the depicted example, a speckle 602 appears in reflection image 600 after recruitment of muscle fiber 520. The detected speckle or any other detected pattern may then be processed to generate reflection image data. With reference to the example discussed above, assuming reflection image 600 reflects spot 106A, the reflection image data may include data indicating that the first region moved by a distance dl. In some cases, the reflection image data may be processed by any 1615 image processing algorithms (e.g., CNN and RNN) to determine skin movements of at least two areas within facial region 108. Thereafter, processing device 400 may use one or more machine learningAttorney Docket No. 16198.0049-00304(ML) algorithms and artificial intelligence (Al) algorithms to decipher the reflection image data and to extract meaning from the facial skin micromovement.

[0219] As shown in Fig. 7, memory device 700 may contain software modules to execute processes 1620 consistent with the present disclosure. In particular, memory device 700 may include an illumination control module 702, a sensors communication module 704, a light reflections processing module 706, an artificial neural network (ANN) training module 710, a subvocalization deciphering module 708, an output determination module 712, and a database structure access module 714. The disclosed embodiments are not limited to any particular configuration of memory 700. Further, processing 1625 device 400 and / or processing device 460 may execute the instructions stored in any of modules 702- 714 included in memory device 700. It is to be understood that references in the following discussions to a processing device may refer to processing device 400 of speech detection system 100 and processing device 460 of remote processing system 450 individually or collectively. Accordingly, steps of any of the following processes associated with modules 702-714 may be performed by one or 1630 more processors associated with speech detection system 100.

[0220] Consistent with disclosed embodiments, illumination control module 702, sensors communication module 704, light reflections processing module 706, subvocalization deciphering module 708, ANN training module 710, output determination module 712, and database access module 714 may cooperate to perform various operations. For example, illumination control module 1635 702 may determine light characteristics for illuminating facial region 108. Sensors communication module 704 may receive coherent light reflections from facial region 108 and output associated reflection signals. Light reflections processing module 706 may process the reflection signals to determine facial skin micromovements. Subvocalization deciphering module 708 and database access module 714 may cooperate to extract meaning (e.g., determine silently spoken words) from the facial 1640 skin micromovements. In some cases, ANN training module 710 may use the determined silently spoken words and the determined facial skin micromovements to train an artificial network. Output determination module 712 may generate a presentation of the determined words.

[0221] Illumination control module 702 may regulate the operation of light source 410 to illuminate facial region 108. In some embodiments, illumination control module 702 may determine 1645 values for characteristics of projected light 104 such as light intensity, pulse frequency, duty cycle, illumination pattern, light flux, or any other optical characteristic. In a specific embodiment, as long as user 102 is not speaking, speech detection system 100 may operate in a first illumination mode (e.g., low frame rate) to conserve power of its battery. While speech detection system 100 operates at this first illumination mode, it may process the images to detect at least one trigger in the reflection 1650 signals (e.g., a movement of the face) indicative of speech. When such trigger is detected, illumination control module 702 may cause the coherent light source to operate in a second illumination mode (e.g., high frame rate) to enable detection of changes in the coherent light patterns (e.g., speckle) that occur due to silent speech. Illumination control module 702 may also configured toAttorney Docket No. 16198.0049-00304change one or more characteristics of projected light 104 based on various types of triggers. The 1655 various types of triggers may be detected by analysis of data from sensors communication module 704.

[0222] Sensors communication module 704 may regulate the operation of light detector 412, audio sensor 414, and additional sensors 418 to receive captured measurements from one or more sensors, integrated with, or connected to, speech detection system 100. In one embodiment, sensors1660 communication module 704 may use the signals received from one or more sensors to generate sensor data associated with user 102. In one example, sensors communication module 704 may receive reflection signals from light detector 412 and may generate a first data stream of reflections images from which the facial skin micromovements in the facial region may be determined. In another example, sensors communication module 704 may receive audio signals from audio sensor 414 and 1665 may generate a second data stream from which the words vocally spoken by user 102 may be determined. In another example, sensors communication module 704 may receive motion signals from a motion sensor included in additional sensors 418 and generate a third data stream from which an activity that user 102 is engaged with may be determined. Sensors communication module 704 may convey the sensor data to other software modules for processing.1670

[0223] Light reflections processing module 706 may process the sensor data received from sensors communication module 704 in preparation for speech deciphering. In one embodiment, light reflections processing module 706 may receive from sensors communication module 704 reflection signals indicative of coherent light reflections from facial region 108 that originates from light detector 412. The reflection signals may by represented by a reflection image (e.g., reflection image 1675 600) that can be processed by at least one image processing algorithm to extracts the skin motion at a set of pre-selected locations on the face of user 102. The number of locations to inspect may be an input to the image processing algorithm. In some cases, the locations on the skin that are extracted for coherent light processing may be taken from a list of points of interest. The list of points of interest specifies anatomical locations that correspond with the zygomaticus muscle, the orbicularis oris 1680 muscle, the risorius muscle, genioglossus muscle, or the levator labii superioris alaeque nasi muscle.In layman’s terms, the list of points of interest may include specific points in the cheek above mouth, in the chin, in mid-jaw, in the cheek below mouth, in the high cheek, and in the back of the cheek. Consistent with the present disclosure, the list of points of interest may be dynamically updated with more points on the face that are extracted during a training phase. The entire set of locations may be 1685 ordered in descending order such that any subset of the list (in order) minimizes the word error rate (WER) with respect to the chosen number of locations that are inspected. In another embodiment, light reflections processing module 706 may crop each of the coherent light spots that were extracted from the raw image frames around the coherent light spots, and the algorithm process only the cropped images. Typically, the process of coherent light spot processing involves reducing by two the 1690 order of magnitude of a size of full frame image pixels (of -1.5MP) that are received from sensorsAttorney Docket No. 16198.0049-00304communication module 704, with a very short exposure. Exposure may be dynamically set and adapted to be able to capture only coherent light reflections and not skin segments. The cropped images of the coherent light spots may depict coherent light patterns. In other embodiments, light reflections processing module 706 may apply image processing algorithm on the reflection image. For 1695 example, light reflections processing module 706 may improve the images’ contrast, by removing noise using a threshold to determine black pixels and computing a characteristic metric of the coherent light, such as scalar speckle energy measure, e.g., an average intensity. In addition, light reflections processing module 706 may analyze changes in time in the reflections pattern (e.g., in average speckle intensity). Alternatively, other metrics may be used such as the detection of specific 1700 coherent light patterns. Thereafter, light reflections processing module 706 may assign a sequence of values of the characteristic metric of the coherent light, which may be calculated frame-by-frame and aggregated to generate reflection image data indicative of facial skin micromovements. Light reflections processing module 706 may convey the reflection image data indicative of facial skin micromovements to other software modules for processing.1705

[0224] Subvocalization deciphering module 708 may use machine learning (ML) algorithms and artificial intelligence (Al) algorithms to decipher the reflection image data indicative of facial skin micromovements received from light reflections processing module 706. Consistent with the present disclosure, deciphering the reflection image data may include extracting meaning from the detected facial skin micromovements. In one embodiment, subvocalization deciphering module 708 may use a 1710 trained ANN to correlate words with the facial skin micromovements. Different types ANNs may be used, such as a classification NN that eventually outputs words, and a sequence-to-sequence NN which outputs a sentence (word sequence). In some embodiments, during normal speech of the user, system 100 may simultaneously sample the voice of user 102 and the facial movements. Automatic speech recognition (ASR) and Natural Language Processing (NLP) algorithms may be applied by 1715 subvocalization deciphering module 708 on the actual voice, and the outcome of these algorithms may be used for optimizing the parameters of the algorithms used by subvocalization deciphering module 708. These parameters may include the weights of the various neural networks, as well as the spatial distribution of laser beams for optimal performance. In addition, subvocalization deciphering module 708 may limit the output of the algorithms to a pre-defined word set may significantly increase the 1720 accuracy of word detection in cases of ambiguity, i.e., when two different words result in similar micromovements on the facial skin. The used word set can be personalized overtime, adjusting the dictionary to the actual words used by the specific user, with their respective frequency and context. In addition, subvocalization deciphering module 708 may use the context of a conversation between user 102 and a callee. The context may be determined from the input of the words and sentences 1725 extraction algorithms to increase the accuracy by eliminating out-of-context options. The context of the conversation may be understood by applying Automatic speech recognition (ASR) and Natural Language Processing (NLP) algorithms on the side of user 102 and on the side of the callee.Attorney Docket No. 16198.0049-00304

[0225] ANN training module 710 may be used to train an ANN to perform silent speech deciphering, in accordance with embodiments of the disclosure. To train an ANN such as the one that 1730 may be used by subvocalization deciphering module 708 may require several thousands of examples.To achieve this, ANN training module 710 may rely on a large group of persons (e.g., a group of reference human subjects). In one example, subvocalization deciphering module 708 may perform fine adjustments to the ANN such that it is customized to user 102. In this manner, within minutes or less of wearing speech detection system 100, subvocalization deciphering module 708 may be ready 1735 for deciphering the facial skin micromovements. ANN training module 710 can be used to train two different ANN types: a classification neural network that eventually outputs words, and a sequence- to-sequence neural network which outputs a sentence (word sequence). To do so, ANN training module 710 may upload from a memory training data, such as silent speech data received from light reflections processing module 706 that was gathered from multiple reference human subjects. The 1740 silent speech data may be collected from a wide variety of people (people of varying ages, genders, ethnicities, physical disabilities, etc.). It is to be noted that the number of examples required for learning and generalization may be task-dependent. For word / utterance prediction (within a closed group) at least several thousands of examples may be gathered. Thereafter, ANN training module 710 may augment the image processed training data to get more artificial data for the training process. In 1745 particular, the augmented data may include image processed coherent light patterns, with some of the image processing steps described herein. The data augmentation process may include the steps of (i) time dropout, where amplitudes at random time points are replaced by zeros; (ii) frequency dropout, where the signal is transformed into the frequency domain, and random frequency chunks are filtered out; (iii) clipping, where the maximum amplitude of the signal at random time points is clamped. This 1750 clipping may add a saturation effect to the data; (iv) noise addition, where Gaussian noise is added to the signal, and speed change, where the signal is resampled to achieve a slightly lower or slightly faster signal.

[0226] The augmented dataset may go through a feature extraction process. In this process, ANN training module 710 may compute time domain silent speech features. For this purpose, for example, 1755 each signal may be split into low and high frequency components, x low and x high, and windowed to create time frames, for example, using a frame length of 27ms and shift of 10ms. For each of the frame five time-domain features and the nine frequency domain features, a total of 14 features per signal may be computed. Specifically, the time-domain features may be represented as follows:> > <1760 where ZCR is the zero-crossing rate. In addition, in this example, the magnitude values used are from a 16-point short Fourier transform, i.e., frequency domain features and all features are normalized to zero mean unit variance.Attorney Docket No. 16198.0049-00304

[0227] Thereafter, ANN training module 710 may split the data into training, validation, and test sets. The training set may be the data used to train the model. Hyperparameter tuning may be done 1765 using the validation set, and final evaluation may be done using the test set. The model architecture may be task dependent. Two different examples describe training two networks for two conceptually different tasks. A first task may include signal transcription, i.e., translating silent speech to text by generating a word, a phoneme, or a letter. This first task may be addressed by using a sequence-to- sequence model. A second task may include predicting a word or an utterance, i.e., categorizing 1770 utterances uttered by users into a single category within a closed group. This second task may be addressed by using a classification model. The disclosed sequence-to-sequence model may be composed of an encoder, which may transform the input signal into high level representations (embeddings), and a decoder, which produces linguistic outputs (i.e., characters or words) from the encoded representations. The input entering the encoder may be a sequence of feature vectors. In one 1775 example, the input may enter the first layer of the encoder, a temporal convolution layer, which may down-sample the data to achieve a good performance. The model may use an order of a hundred of such convolution layers.

[0228] In some embodiments, the outputs from the temporal convolution layer at each time step may be passed to three layers of bidirectional recurrent neural networks (RNN). ANN training module 1780 710 may employ long short-term memory (LTSM) as units in each RNN layer. Each RNN state may be a concatenation of the state of the forward RNN with the state of the backward RNN. The decoder RNN may be initialized with the final state of the encoder RNN (concatenation of the final state of the forward encoder RNN with the first state of the backward encoder RNN). At each time step, the decoder RNN may receive as input the preceding word, encoded one-hot and embedded in a 150- 1785 dimensional space with a fully connected layer. The decoder RNN output may be projected through a matrix into the space of words or phonemes (depending on the training data). The sequence-to- sequence model may condition the next step prediction on the previous prediction. During learning, a log probability may be maximized:max^ logP(yi|x, y<j; 0)i1790 where y<i is the ground truth of the previous prediction. The classification neural network may be composed of the encoder as in the sequence-to-sequence network and an additional fully connected classification layer on top of the encoder output. The output may be projected into the space of closed words and the scores may be translated into probabilities for each word in the dictionary. The results of the above entire procedure may include two types of trained ANNs, expressed in computed 1795 coefficients. The coefficients may be stored in a data structure associated with speech detection system 100 (e.g., data structure 422 and data structure 464). In day-to-day use, ANN training module 710 may receive up to date coefficients for the trained ANN. The first ANN task may be the signal transcription, i.e., translating silent speech to text by word / phoneme / letter generation. The secondAttorney Docket No. 16198.0049-00304ANN task may be word / utterance prediction, i.e., categorizing utterances uttered by users into a single 1800 category within closed group.

[0229] Output determination module 712 may regulate the operation of output unit 114 and the operation of network interface 420 to generate output using speaker 404, light indicator 406, haptic feedback device 408, and / or to send data to a remote computing device. In some embodiments, the output generated by output determination module 712 may include various types of output associated 1805 with silent speech determined from detected facial skin micromovements. Specifically, output determination module 712 may synthesize vocalization of words determined from the facial skin movements by subvocalization deciphering module 708. The synthesis may emulate a voice of user 102 or emulate a voice of someone other than user 102 (e.g., a voice of a celebrity or preselected template voice). The vocalization of the words may be presented via speaker 404 or transmitted to the 1810 remote computing device via network interface 420. Alternatively, output determination module 712 may generate a textual output from the facial skin movements by subvocalization deciphering module 708. The textual output may be transmitted to the remote computing device via network interface 420. According to another embodiment, the output generated by output determination module 712 may relate to the operation of speech detection system 100. In some cases, light indicator 406 may include 1815 a light indicator that shows the battery status of speech detection system 100. For example, the light indicator may start to blink when speech detection system 100 has low battery. Additional examples of the types of output that may be generated by output determination module 712 are described throughout the present disclosure.

[0230] Database access module 714 may cooperate with data structures 422 and 464 to retrieve 1820 stored data. The retrieved data may include, for example, correlations between a plurality of words and a plurality of facial skin movements, correlations between a specific individual and a plurality of facial skin micromovements associated with the specific individual, and more. As described above, subvocalization deciphering module 708 may use a trained ANN to perform silent speech deciphering. The trained ANN may use data stored in data structures 422 and 464 to extract meaning 1825 from detected facial skin micromovements. Data structures 422 and 464 may include separate databases, including, for example, a vector database, raster database, tile database, viewport database, and / or a user input database. The data stored in data structures 422 and 464 may be received from modules 702-712 or other components of speech detection system 100. Moreover, the data stored in data structures 422 and 464 may be provided as input using data entry, data transfer, or data 1830 uploading.

[0231] Modules 702-714 may be implemented in software, hardware, firmware, a mix of any of those, or the like. Processing devices of speech detection system 100 and remote processing system 450 may be configured to execute the instructions of modules 702-714. In some embodiments, aspects of modules 702-714 may be implemented in hardware, in software (including in one or more signal 1835 processing and / or application specific integrated circuits), in firmware, or in any combination thereof,Attorney Docket No. 16198.0049-00304executable by one or more processors, alone, or in various combinations with each other. Specifically, modules 702-714 may be configured to interact with each other and / or other modules associated with speech detection system 100 to perform functions consistent with disclosed embodiments.

[0232] Nowadays, image-based facial recognition technology is commonly used as a biometric 1840 authentication method in many communications devices. It allows users to unlock their devices, make payments, and access apps or accounts using their face as a unique identifier. But image-based facial recognition technology is not always reliable and has limitations that can make it less effective in certain situations. For example, image-based facial recognition systems can be impacted by factors such as poor lighting conditions, low-quality images, and occlusions such as masks or accessories. 1845 These factors may lead to inaccurate or incomplete matches. Additionally, image recognition algorithms may exhibit bias, leading to misidentifications based on various factors like race, gender, or age. Moreover, false positives and false negatives are common issues in image-based facial recognition technology; thus, individuals may be misidentified as someone else or not recognized at all. The following disclosure suggests a new and improved technological solution for providing a 1850 reliable biometric authentication that may overcome inherent deficiencies of image-based facial recognition technology.

[0233] Some disclosed embodiments of the present disclosure may be configured to detect facial skin micromovements of an individual, use the detected facial skin micromovements to identify the individual, and determine an action to initiate based on the identification of the individual.1855

[0234] The description that follows refers to Figs. 8 to 10 to illustrate exemplary implementations for identifying individuals using facial skin micromovements, consistent with some disclosed embodiments. Figs. 8 to 10 are intended merely to facilitate conceptualization of exemplary implementations for performing operations for identifying individuals using facial skin micromovements and do not limit the disclosure to any particular implementation.1860

[0235] Some disclosed embodiments involve a head mountable system for identifying individuals using facial skin micromovements. Consistent with this disclosure, a head mountable system may be understood to include any component or combination of components that can be attached to a head, as exemplified and described elsewhere in this disclosure. The term “identifying individuals” refers to a process for determining whether an individual is known to the system. Specifically, the identification 1865 process may involve comparing detected characteristics of an individual with known characteristics of that individual to identify, verify, or authenticate that individual. Consistent with the present disclosure, the individual may be identified based on the individual’s facial skin micromovements. The term “facial skin micromovements” may be understood as described and exemplified elsewhere in this disclosure. In some cases, the head mountable system may access data indicative of reference 1870 facial skin micromovements and use that data to determine whether an individual currently using the head mountable system is the same individual associated with the reference facial skin micromovements. Depending on implementation, the probability that the identification processAttorney Docket No. 16198.0049-00304described below would result in misidentification of an individual based on his / her facial skin micromovements may be less than one in 10,000, less than one in 100,000, or less than one in 1875 1,000,000.

[0236] Some disclosed embodiments involve a wearable housing configured to be worn on a head of an individual. The term “wearable housing” may be understood as described and exemplified elsewhere in this disclosure. Consistent with some disclosed embodiments, the head mountable system includes at least one coherent light source associated with the wearable housing. The term 1880 “coherent light source” may be understood as described and exemplified elsewhere in this disclosure.The term “associated with the wearable housing” may relate to any component that is linked, incorporated, affiliated with, connected to, or related to the wearable housing. For example, the light source may be mounted to the wearable housing with screws adhesive, clips, heat and pressure, or any other known way to attach two elements. Or, the light source may be partially or fully contained 1885 within the housing. In an alternative embodiment, the light source may be associated with the housing through a wired or wireless connection. Light source 410 in Fig. 4 is one example of a coherent light source.

[0237] Consistent with some disclosed embodiments, the at least one coherent light source may be configured to project light towards a facial region of the head. Projecting coherent light may include 1890 radiating coherent light in a direction toward a portion of the face. The coherent light may be a monochromatic wave having a well-defined phase relationship across its wavefront in a defined direction, such as towards a facial region of the head. A facial region of the head refers to any anatomical part of the human body above the shoulders. The facial region may include at least some of the following: forehead, eyes, cheeks, ears, nose, mouth, chin, and neck. Examples of facial regions 1895 are illustrated in Figs. 1-3 (e.g., facial region 108). For example, as illustrated in Fig.l and Fig. 2, coherent light source 410 included in optical sensing unit 116 is attached to wearable housing 110 and may direct light towards the facial region. The head mountable system may also include at least one detector associated with the wearable housing. The terms “detector” and “associated with the wearable housing” may be understood as described and exemplified elsewhere in this disclosure. The 1900 at least one detector may be configured to receive coherent light reflections from the facial region and to output associated reflection signals. Receiving coherent light reflections may refer to detecting, acquiring, obtaining, or otherwise measuring electromagnetic waves (e.g., in the visible or invisible spectrum) reflected from the facial region and impinging on the at least one detector. Outputting associated reflection signals may include sending, transmitting, producing, and / or providing 1905 information representing or corresponding to the coherent light reflections. For example, projecting coherent light on facial skin that does not move may result in first reflection signals indicative of the coherent light reflections. But even small micromovements of the facial skin may cause the at least one detector to output second reflection signals differing from the first reflection signals. The changes between the first and second reflection signals may be used to determine specific facial skinAttorney Docket No. 16198.0049-003041910 micromovements. By way of one example, light detector 412 in Fig. 4 is associated with a wearable housing 110 and is employed to determine facial skin micromovements.

[0238] Consistent with some disclosed embodiments, the head mountable system includes at least one processor. The term “processor” may be understood as described and exemplified elsewhere in this disclosure. The processor may be employed to provide some or all of the functionality described 1915 herein. Processing device 400 in Fig. 4 is one example of at least one processor provided for purposes of achieving at least some of the functionality described herein.

[0239] Some disclosed embodiments involve analyzing reflection signals to determine specific facial skin micromovements of an individual. The term “analyzing” refers to examining, investigating, scrutinizing, and / or studying. Reflection signals may be analyzed to determine if they are recognized 1920 or whether they correlate with other information. For example, the reflection signals (or a data set derived from the reflection signals, may be analyzed, for example, to determine a correlation, association, pattern, or lack thereof within the data set or with respect to a different data set.Specifically, the reflection signals received from the at least one detector may be analyzed, for example, using one or more processing techniques, such as light pattern analysis (as described and 1925 exemplified elsewhere in this disclosure). Other processing techniques may include convolutions, fast Fourier transforms, edge detection, pattern recognition, object detection algorithms, clustering, artificial intelligence, machine and / or deep learning, and any other processing technique for determining specific facial skin micromovements of the individual. In some examples, a machine learning model may be trained using training examples to determine facial skin micromovements 1930 based on reference reflection data. An example of such training example may include a sample reflection data stream, together with a label indicating associated facial skin micromovements. The trained machine learning model may be used to analyze the received reflection signals relative to the reference reflection data to determine the facial skin micromovements. In some examples, at least part of the reflection signals may be analyzed to calculate a convolution of the at least part of the reflection 1935 signals and thereby obtain a result value of the calculated convolution. Further, in response to the result value of the calculated convolution being a first value, a first facial skin micromovements may be determined, and in response to the result value of the calculated convolution being a second value, a second different facial skin micromovements may be determined. For example, reflection signals received by the at least one detector may be analyzed as described elsewhere in this disclosure, and 1940 facial skin micromovements associated with the question “what is my mom’s birthday?” may be determined. Additional details and examples on how the at least one processor may analyze the reflection signals to determine specific facial skin micromovements are described herein with reference to light reflections processing module 706.

[0240] Consistent with some disclosed embodiments, at least some of the specific facial skin 1945 micromovements in the facial region may include micromovements of less than 100 microns or less than 50 microns. In other words, the output of the process of determining the specific facial skinAttorney Docket No. 16198.0049-00304micromovements may be accurate enough to distinguish changes in facial skin in the range of 10 to 100 microns. In some embodiments, these changes may be detected over atime period of 0.01 to 0.1 seconds. In some disclosed embodiments, the determined specific facial skin micromovements may 1950 correspond to a facial expression (e.g., smile, scowl, worried) or to a facial muscular action corresponding to a physiological event (e.g., sneeze, laugh, yawn). In other embodiments, the facial skin micromovements may correspond to a phenome, syllable, word, or phrase that is pre-vocalized or vocalized, as described below. In yet other embodiments, the facial skin micromovements may correspond to a biological process such as pulse or respiration rate. In further embodiments, the facial 1955 skin micromovements may correspond to a combination of one or more of the foregoing.

[0241] Consistent with some disclosed embodiments, the specific facial skin micromovements may correspond to prevocalization muscle recruitments. As described elsewhere herein, prevocalization or subvocalization refers to the effects of facial muscle movement in an absence of audible vocalization or prior to an occurrence of vocalization. Facial skin micromovements correspond to prevocalization 1960 muscle recruitment, when the prevocalization muscle recruitments are the direct or indirect cause of the facial skin micromovements. In some case, prevocalization muscle recruitment may cause facial skin micromovements prior to an onset of vocalization. By way of example, the prevocalization muscle recruitments may occur between 0.1 seconds to 0.5 seconds before the actual vocalization. In some cases, the prevocalization muscle recruitment may include voluntary muscle recruitments that 1965 occur when an individual start to vocalize words. In other cases, the prevocalization muscle recruitment may include involuntary facial muscle recruitments that occur when certain craniofacial muscles prepare to vocalize words.

[0242] Consistent with some disclosed embodiments, the specific facial skin micromovements may correspond to muscle recruitment during pronunciation of at least one word or a portion thereof. For 1970 example, the at least one word may correspond to a predefined expression, a password, or a secret passphrase. As discussed above, actual vocalization depends on whether air is emitted from the lungs and into the throat. Without this air flow, no sounds are emitted. Because prevocalization muscle recruitment occurs before and separately from the muscles that convey the air flow, the prevocalization muscle recruitment may occur when there is subsequent vocalization or when there is 1975 no subsequent vocalization.

[0243] Fig. 8 illustrates an exemplary speech detection process. In the illustrated example, speech detection system 100 may analyze the reflection signals associated with the question “what is my mom’s birthday?” to determine specific facial skin micromovements 800 associated with an unknown individual 802.1980

[0244] Some disclosed embodiments involve accessing memory correlating a plurality of facial skin micromovements with the individual. The term “accessing memory” refers to retrieving or examining electronically stored information. This may occur, for example, by communicating with or connecting to electronic devices or components in which data is electronically stored. Such data mayAttorney Docket No. 16198.0049-00304be organized, for example, in a data structure for the purpose of reading stored data (e.g., acquiring 1985 relevant information) or for the purpose of writing new data (e.g., storing additional information). In some cases, the accessed memory may be part of a speech detection system or part of a remote processing device (e.g., cloud server) that may be accessed by the speech detection system. In some examples, the at least one processor may access the memory, for example, at startup, at shutdown, at constant intervals, at selected times, in response to queries received from the at least one processor, or 1990 at any other determined times. The memory may store data that correlates a plurality of facial skin micromovements with the individual. The stored data may be any electronic representation of the facial skin micromovements, any electronic representation of one or more properties determined from the facial skin micromovements, or raw measurement signals detected by the at least one light detector and representing the facial skin micromovements. Correlating a plurality of facial skin 1995 micromovements with the individual may include storing relationships between facial skin micromovements and an identifier of the individual in a memory or data structure. This may allow for efficient retrieval and identification of the individual based on these relationships. For example, the memory may be associated with a built-in mechanism for linking or associating facial skin micromovements with an identifier of the individual. In one example, correlations may be stored 2000 between specific phenomes, syllables, words, or phrases and associated skin micromovements.Depending on implementation, these correlations may be unique to the individual or specific to a population or subpopulation associated with the individual, (e.g., micromovements associated with certain parts of speech may vary across individuals, countries, dialects, or based on different regional accents.) Correlating a plurality of facial skin micromovements with the individual may occur through 2005 any one of the above examples. If the intention is to verify a personal identity of a specific individual, then a comparison may occur to a database of correlations associated with that specific individual (e.g., based on samples previously capture from that individual.) Alternatively, if the intention is to identify the individual as part of a population or sub-population, then pre-stored data associated with that population or subpopulation may be accessed.2010

[0245] Consistent with the present disclosure, the fact that the plurality of facial skin micromovements correlates with the individual means that the plurality of facial skin micromovements can either uniquely identify the individual or identify the individual as part of a particular population or sub-population. In one exemplary embodiment for uniquely identifying an individual, the probability that the plurality of facial skin micromovements would be identical for two 2015 different individuals may be less than one in 10,000, less than one in 100,000, less than one in 1,000,000, or less than one in 10,000,000, depending on implementation.

[0246] Consistent with some disclosed embodiments, the memory may correlate a plurality of facial skin movements with a plurality of individuals. Specifically, the memory may be designed to store relationships between facial skin micromovements with a plurality of identifiers associated with 2020 a plurality of individuals. For example, specific correlations may be stored for each of manyAttorney Docket No. 16198.0049-00304individuals such that when a current signal is received, it may be compared with the various stored correlations to uniquely identify an individual associated with the stored correlation. In some disclosed embodiments, for each of the plurality of individuals the memory may store at least 10, at least 50, or at least 100 data entries associated with different facial skin micromovements. In some 2025 examples, the plurality of individuals may be related, e.g., the plurality of individuals may be family members or part of the same organization. In other examples, the plurality of individuals may be unrelated but include a common attribute, e.g., individuals from the same group age, or individuals associated with a same language dialect.

[0247] Consistent with some disclosed embodiments, the at least one processor may be configured 2030 to distinguish the plurality of individuals from each other based on reflection signals unique to each of the plurality of individuals. Distinguishing the plurality of individuals from each other means that the at least one processor may be able to determine which individual is responsible for the received reflection signals. For example, the at least one processor may identify that a certain sentence was spoken by a particular individual and not by any other individuals contained in the database. The at 2035 least one processor may be configured to distinguish the plurality of individuals from each other by detecting reflection signals unique to each individual. Unique reflection signals means that no two individuals have the same reflection signals. For example, the unique reflection signals may be associated with a distinctive sequence of facial skin micromovements that occurs when the individual vocalizes or prevocalizes one or more phonemes, syllables, words or phrases, such as a passphrase. In 2040 one example, the speech detection system may be used by a group of individuals and for each individual the speech detection system may store personal settings. In one embodiment, the at least one processor may detect, during a first time period, first facial skin micromovements of a first individual and at a subsequent second time period, detect second facial skin micromovements of a second individual. Upon identifying the first individual using the first facial skin micromovements, 2045 the at least one processor may initiate a first action (e.g., applying personal settings associated with the first individual), and upon identifying the second individual using the second facial skin micromovements, the at least one processor may initiate a second action (e.g., applying personal settings associated with the second individual). Or, if a correlation is identified for a particular individual, access to an application may be provided; while access may be denied if a correlation is 2050 not identified.

[0248] By way of one example with reference to Fig. 8, memory 804 may store a plurality of reference facial skin micromovements (e.g., 806A, 806B, 806C, and 806D) associated with user 102. In the figure, only four reference facial skin micromovements are illustrated, but as will be appreciated by a person skilled in the art having the benefit of this disclosure, a greater number of 2055 reference facial skin micromovements may be stored as reference data to identify individuals. For example, the plurality of reference facial skin micromovements may be for all known phonemes, or for at least 1,000 words. In addition, memory 804 may be designed to store a plurality of referenceAttorney Docket No. 16198.0049-00304facial skin micromovements for multiple users, thus enabling the processor to distinguish the plurality of individuals from each other based on reflection signals unique to each of the multiple individuals.2060

[0249] Some disclosed embodiments involve searching for match between the determined specific facial skin micromovements and at least one of the plurality of facial skin micromovements in the memory. The term “searching for a match” may refer to finding one or more records that satisfy a given set of search criteria. Different types of search algorithms may be used to search for the match, such as a linear search, a binary search, tree-based search, and various types of database searches. In 2065 addition, an artificial intelligence model may be employed and used to search for a match in a dataset accessible to the Al model, as described in the following paragraph. In some cases, the initiated search may be used for finding which of the plurality of facial skin micromovements was most likely generated by a same individual that generated the specific facial skin micromovements. A likelihood level or a certainty level of a match may be determined to provide an indication of probability or 2070 degree of confidence in the determination that the identification hypothesis is correct, i.e., that a reference facial skin micromovements stored in the memory was indeed generated by a same individual that generated the specific facial skin micromovements. In some disclosed embodiments, a match may be considered to be found when the likelihood level or the certainty level is, by way of example only, greater than 90%, greater than 95%, or greater than 99%.2075

[0250] Consistent with the present disclosure, the at least one processor may use an artificial neural network (such as a deep neural network, a convolutional neural network) to identify a match. The artificial neural network may be configured manually, using machine learning methods, or by combining other artificial neural networks. Other ways that the at least one processor may use to identify a match includes comparing the determined specific facial skin micromovements with the 2080 plurality of facial skin micromovements in the memory; taking the difference between the determined specific facial skin micromovements with the plurality of facial skin micromovements in the memory and comparing it to a threshold value; calculating at least one statistical value (e.g., mean, variance, or standard deviation) and comparing the at least one statistical value to a threshold; calculating the distance between two vectors in a multi-dimensional space, wherein, if the distance is below a certain 2085 threshold, a match is identified; calculating the cosine of the angle between two vectors in a multidimensional space, wherein, if the cosine value is above a certain threshold, a match is identified; and any other known way of identifying a match in a database.

[0251] By way of one example with reference to Fig. 8, searching for a match may result in a first outcome 808A indicating that match is identified and a second outcome 808B that indicates that 2090 match is not identified.

[0252] Some disclosed embodiments involve initiating a first action if a match is identified, and initiating a second action different from the first action if a match is not identified. The term “initiating” may refer to carrying out, executing, or implementing one or more operative steps. For example, the at least one processor may initiate execution of a program code instructions or cause aAttorney Docket No. 16198.0049-003042095 message to be sent to another processing device to achieve a targeted (e.g., deterministic) outcome or goal. The action may be an initiated response to a determination if a match between the determined specific facial skin micromovements with the plurality of facial skin micromovements is found in the memory. The term “action’ may refer to the performance or execution of an activity or task. For example, performing an action may include executing at least one program code instruction to 2100 implement a function or procedure. The action may be user-defined or system-defined (e.g., software and / or hardware), or any combination thereof. At least one processor may select which action to initiate (e.g., first action or second action) and may determine to initiate the selected action based on the results of the search for match and based on various criteria. The various criteria may include user experiences (e.g., preferences, such as based on context, location, environmental conditions, use type, 2105 user type), user requirements (e.g., context limitations, urgency or priority of the purpose behind the action), device requirements (e.g., computation capacity, computation limitations, presentation limitations, memory capacity, or memory limitations), communication network requirements (e.g., bandwidth, latency). For example, after a match is found, a first action of sending an audio message may be initiated. The artificial voice used to generate the audio message may be selected based on the 2110 various criteria listed above. The action may be initiated by at least one processor configured with the speech detection system, a different local processing device (e.g., associated with a device in proximity to the speech detection system), and / or by a remote processing device (e.g., associated with a cloud server), or any combination thereof. Thus, “initiating an action responding to the search results” may include performing or implementing one or more operations in response to the outcome 2115 of the search for a match between the determined specific facial skin micromovements and at least one of the plurality of facial skin micromovements in the memory.

[0253] Consistent with some disclosed embodiments, the first action institutes at least one predetermined setting associated with the individual. The term “predetermined setting” refers to any configurations or preferences associated with an operation software of a related computing device, or 2120 any other software installed on the computing device. Examples of such predetermined settings may include language settings, default actions, preferred output modes, types of notifications, permissions, display brightness, volume levels, default apps, network settings, and any other option selectable by the user. Consistent with the present disclosure, when a match is identified, the at least one processor may institute (i.e., appoint, establish, or set up) a specific setting associated with the identified 2125 individual. Stating that a predetermined setting is associated with the individual means that data reflecting the individual’s selection of the predetermined setting is stored in a database, a data structure, lookup table, or a linked list. In one example, the predetermined settings may govern what the speech detection system should do upon detecting silent speech. Specifically, after a match is identified, the speech detection system may automatically translate words spoken silently in English 2130 to French and synthesize them with an artificial voice that sounds like the identified individual.Attorney Docket No. 16198.0049-00304

[0254] Consistent with some disclosed embodiments, the first action (i.e., when the individual is identified) includes unlocking a computing device, and the second action (i.e., when the individual is not identified) includes presentation of a message indicating that the computing device remains locked. The computing device may be any electronic device to which access is restricted. For 2135 example, the computing device may be a laptop, PC, tablet, smartphone, wearable electronics, electronic door lock, entry gate, application, system, vehicle, communications device (e.g., mobile communications device 120). In one embodiment, the computing device may be at least a portion of speech detection system 100. The term “unlocking a computing device” generally refers to the process of gaining access to a device that has a security mechanism in place to prevent unauthorized access.2140 For example, upon identifying the individual, the at least one processor may send data to mobile communications device 120 (e.g., a passcode) that causes mobile communications device 120 to unlock. The message indicating that the computing device remains locked may be provided by the computing device or by any other device in any known manner, for example, the message may be provided audible, textually, or virtually. For example, when the individual in not identified, speech 2145 detection system 100 may present a message that mobile communications device 120 remains locked.

[0255] Consistent with some disclosed embodiments, the first action (i.e., when the individual is identified) provides personal information, and the second action (i.e., when the individual is not identified) provides public information. Personal information includes data that is specific to an individual or information that an entity (e.g., user, person, organization or other data owner) may not 2150 wish to share with another entity. For example, it may include any information that, if revealed to a non-authorized entity, may cause harm, loss, or injury to an individual or entity associated therewith. Some examples of personal information (e.g., sensitive data) may include identifying information, location information, genetic data, information related to health, financial, business, personal, family, education, political, religious, and / or legal matters, and / or sexual orientation or gender identification.2155 Public information may include any information other than personal information and may be found in public databases, such as the Internet. For example, following receiving a query from the individual, speech detection system 100 may use the specific facial skin micromovements to generate a response that either includes personal information (when the individual is identified) or includes public information (when the individual is not identified).2160

[0256] Consistent with some disclosed embodiments, the first action (i.e., when the individual is identified) authorizes a transaction, and the second action (i.e., when the individual is not identified) provides information indicating that the transaction is not authorized. Authorizing a transaction refers to the process of granting approval or permission for an activity to occur. In some cases, authorizing a transaction may involve verifying the legitimacy of a transaction request and confirming the identity 2165 of an individual by finding a match. Examples of transactions may include financial transactions (e.g., withdrawal or deposit from a bank account, purchase or sale of goods or services using a credit card, transfer of funds between accounts, payment of bills, wire transfer, or electronic funds transfer), non-Attorney Docket No. 16198.0049-00304financial transactions (e.g., booking a flight, making a hotel reservation, ordering products online, renting a car, enrolling in a subscription, updating an address, or phone number), business transactions 2170 (e.g., ordering supplies, billing customers for products or services rendered, approving refunds, or processing invoices), and government transactions (e.g., applying for a passport or visa, paying taxes or fines, registering a vehicle, obtaining a driver's license, obtaining permits for business operations). When a match is not found, information may be provided to indicate that the transaction is not authorized. The information may be provided via a speech detection system or via a mobile 2175 communications device. For example, when speech detection system 100 is linked to a virtual wallet, upon receiving a request to pay, speech detection system 100 may prompt individual to silently say a password. Thereafter, speech detection system 100 may use the determined specific facial skin micromovements to determine the password and compare the determined password with a previously stored password stored in association with the user. When the determined password matches the 2180 stored password, speech detection system 100 may authorize the payment (i.e., when the individual is identified). Alternatively, when the determined password does not match the stored password, speech detection system 100 may not authorize the payment (i.e., when the individual is not identified).

[0257] Consistent with some disclosed embodiments, the first action (i.e., when the individual is identified) permits access to an application, and the second action (i.e., when the individual is not 2185 identified) prevents access to the application. Permitting access to an application may refer to the process of granting authorization to an individual to use a particular software application or to use electronic hardware. The software application may be installed in a speech detection system or in any computing device associated with the individual (e.g., the individual’s smartphone). For example, a calendar application of an individual may be accessed in response to detected query, such as: “What 2190 was the name of the person I met with last Wednesday?” from an identified individual. If the individual is not identified, access to the calendar application would be prohibited and therefore the query may not be answered.

[0258] Consistent with some disclosed embodiments, a head mountable system includes an integrated audio output, wherein at least one of the first action or at least one of the second action 2195 includes outputting audio via the audio output. The term integrated audio output means that the head mountable system includes internal audio hardware configured to generate sounds without the need for an external audio interface. For example, the head mountable system may include an audio chipset that can convert digital audio signals into analog signals and built-in speakers or headphone jack. Additional examples of the integrated audio output may include or may be associated with a 2200 loudspeaker, earbuds, audio headphones, a hearing aid type device, and any other device capable of converting an electrical audio signal into a corresponding sound. For example, the first action may be emitting sounds into the open air using an audio output device, such as loudspeaker, for anyone nearby to hear, and the second action may be emitting sounds using an audio output device such as earbuds for letting only the individual listen to the generated audio signals.Attorney Docket No. 16198.0049-003042205

[0259] By way of one example with reference to Fig. 8, first action 810A may be initiated when a match is found (i.e., that individual 802 is identified as user 102), and second action 810B may be initiated when a match is not found (i.e., that individual 802 is not identified as user 102).

[0260] Consistent with some disclosed embodiments, a match may be identified upon determination by the at least one processor of a certainty level. As described elsewhere in this 2210 disclosure, the determination of the certainty level provides an indication of the confidence that the identification hypothesis is correct. In other words, and with reference to Fig. 8, the certainty level provides an indication that unknown individual 802 is user 102. Consistent with some disclosed embodiments, when the certainty level is initially not reached, the at least one processor may analyze additional reflection signals to determine additional facial skin micromovements, and arrive at the 2215 certainty level based at least in part on analysis of the additional reflection signals. Fig. 9 (as discussed below) depicts an example implementation of these embodiments.

[0261] Fig. 9 depicts a flowchart of an example process 900 executed by a processing device of speech detection system 100 (e.g., processing device 400) for identifying individuals above a certainty level. For purposes of illustration, in the following description, reference is made to certain2220 components of speech detection system 100. It will be appreciated, however, that other implementations are possible and that other components may be used to implement example process 900. It will also be readily appreciated that the example process 900 can be altered to modify the order of steps, delete steps, or further include additional steps.

[0262] Process 900 begins when the processing device receives reflections from a facial region 2225 (block 902), then the processing device analyzes the reflections to determine specific facial skin micromovements (block 904), and searches for match between the determined specific facial skin micromovements and at least one reference facial skin micromovements (block 906). If a match was not found (decision block 908), the processing device may initiate a second action (block 910), and the process continues by receiving additional reflection signals (block 912), analyzing them to 2230 determine additional facial skin micromovements, and searching for a match to identify individual 802. If a match was found (decision block 908), the processing device may determine a certainty level for the match (block 914) and compare the determined certainty level to a threshold (decision block 916). If the certainty level is greater than a threshold, the processing device may initiate a first action (block 918) and the process continues for receiving additional reflection signals (block 912), 2235 analyzing (block 904), and searching (block 906). But, if the certainty level is less than a threshold, the processing device may initiate the second action (block 910).

[0263] Consistent with some disclosed embodiments, at least one processor continuously compares new facial skin micromovements with the plurality of facial skin micromovements in the memory to determine an instantaneous level of certainty. In this context, the term “continuously compares” 2240 means constantly or regularly compares new facial skin micromovements with the plurality of facial skin micromovements in the memory over a period of time (e.g., during a phone call). In this context,Attorney Docket No. 16198.0049-00304continuous comparison includes intervals between comparisons such as multiple times a second or multiple times a minute. The term “instantaneous level of certainty” refers to a degree of confidence in an identity of individual associated with the new facial skin micromovements. For example, during 2245 a phone call with a banker, the system may regularly compare new facial skin micromovements to make sure that the same authorized individual remains on the line. Consistent with some disclosed embodiments, when the instantaneous certainty level is below a threshold, the at least one processor is configured to initiate an associated action. The fact that the instantaneous certainty level is below a threshold means that there is a risk that someone else - other than the identified individual - is 2250 responsible for the new facial skin micromovements. The associated action refers to an action associated with the fact that the instantaneous certainty level is now below a threshold and may include the second action or stopping the first action. Specifically, in some embodiments, after initiating the first action, when the instantaneous certainty level is below a threshold, the at least one processor is configured to stop the first action. For example, the first action may be authorizing a 2255 transaction in the bank by speaking with a banker over the phone and providing the banker with ongoing confirmation of the identity of the individual over the phone. But, once the instantaneous certainty level drops below the threshold, which may indicate that someone other than the individual is talking to the banker, the transaction may be stopped. In some cases, the second action may include stopping the first action.2260

[0264] With reference to Fig. 9, after the first action was initiated at block 918, additional reflections are received, and the analyzing step (block 904) and the searching step (block 906) are executed. If the determined instantaneous certainty level associated with the additional reflections is below the threshold, then the first action may be stopped by initiating the second action.

[0265] Consistent with some disclosed embodiments, initiating the first action may be associated 2265 with an event, and the at least one processor may continuously compare new facial skin micromovements during the event. The term “event” in this context may refer to an occurrence of an action, activity, change of state, or any other type of detectable development or stimulus. The term “during the event” means any time from a time when the event was detected up until a time the event ends. In one example, the event can be a purchase at point of sale (POS) where the user puts on the 2270 device to approve the transaction. In another example, the event may be associated with an online activity (e.g., a financial transaction, a wagering session, an account access session, a gaming session, an exam, a lecture, or an educational session). In another example, the event may include maintaining a secured session with access to a resource (e.g., a file, a folder, a database, a computer program, a computer code, or computer settings).2275

[0266] Fig. 10 illustrates a flowchart of an exemplary process 1000 for identifying individuals using facial skin micromovements, consistent with embodiments of the present disclosure. In some disclosed embodiments, process 1000 may be performed by at least one processor (e.g., processing device 400 or processing device 460) to perform operations or functions described herein. In someAttorney Docket No. 16198.0049-00304embodiments, some aspects of process 1000 may be implemented as software (e.g., program codes or 2280 instructions) that are stored in a memory (e.g., memory device 402 or memory device 466) or a non- transitory computer readable medium. In some embodiments, some aspects of process 1000 may be implemented as hardware (e.g., a specific-purpose circuit). In some embodiments, process 1000 may be implemented as a combination of software and hardware.

[0267] Referring to Fig. 10, process 1000 includes a step 1002 of projecting light towards a facial 2285 region of a head of an individual. For example, the at least one processor may operate a wearable coherent light source (e.g., light source 410) to illuminate facial region 108 (e.g., using multiple output beams 508). Process 1000 includes a step 1004 of receiving coherent light reflections from the facial region and to output associated reflection signals. For example, the at least one processor may operate at least one detector (e.g., at least one detector 412) to receive coherent light reflections (e.g., 2290 light reflections 300) from facial region 108. Process 1000 includes a step 1006 of analyzing the reflection signals to determine specific facial skin micromovements of the individual. For example, using light reflections processing module 706 and subvocalization deciphering module 708 to determine the specific facial skin micromovements. Process 1000 includes a step 1008 of accessing memory correlating a plurality of facial skin micromovements with the individual. Process 1000 2295 includes a step 1010 of searching for a match between the determined specific facial skin micromovements and at least one of the plurality of facial skin micromovements in the memory. Process 1000 includes a step 1012 of initiating an action based on a determination whether a match is found or not. Specifically, if a match is identified, initiating a first action (e.g., first action 810A), and if a match is not identified, initiating a second action (e.g., second action 810B) different from the first 2300 action.

[0268] In accordance with one implementation, a speech detection system projects a pattern of light on facial skin (e.g., a cheek) of a user. Thereafter, the speech detection system may detect light reflections from various locations of the facial skin. Notably, reflections associated with specific areas may be more relevant for extracting meaning (e.g., determining communication) than other areas. The 2305 specific areas may be those that are located closer to particular facial muscles. Identifying the specific locations may pose challenges because each user has unique facial features, and the position of the light source and / or detector relative to the user’s face may change during every usage and even during ongoing operations. The following paragraphs describes systems, methods, and computer program products for identifying the locations of those specific areas, using the light reflections from the 2310 specific areas to extract meaning, and ignoring light reflections from other areas to conserve processing resources.

[0269] Some disclosed embodiments involve interpreting facial skin movements. The term “interpreting facial skin movements” refers to extracting meaning from detected skin movements, as described elsewhere in this disclosure. In one example, interpreting facial skin movements may 2315 include determining one or more vocalized or subvocalized words from the facial skin movements orAttorney Docket No. 16198.0049-00304determining a facial expression (e.g., happy, sad, anger, fear, surprise, disgust, contempt, or other emotion) of the individual. In another example, interpreting facial skin movements may include determining an identity of the individual. These facial skin movements may be detectable as described elsewhere in this disclosure.2320

[0270] Some disclosed embodiments involve projecting light on a plurality of facial region areas of an individual, wherein the plurality of areas includes at least a first area and a second area. The term “projecting” includes controlling a light source (e.g., a coherent light source) such that it emits light in a given direction (e.g., toward a portion of the face), as discussed elsewhere in this disclosure. The term “individual” includes a person who uses a speech detection system (or another person to whom 2325 the light source is projected), as described elsewhere in this disclosure. The term “facial region area” or simply “area” in the context of the face includes a portion of the face of the individual, as described elsewhere in this disclosure. For example, a facial region area may have a size of at least 1 cm2, at least 2 cm2, at least 4 cm2, at least 6 cm2, or at least 8 cm2. Consistent with some disclosed embodiments, the projected light illuminates a plurality of facial region areas. For example, the 2330 plurality of areas includes 4, 8, 16, 32, or any other numbers of areas. In some cases, the projected light may include at least one spot, as described elsewhere in this disclosure. The at least one spot may illuminate more than one facial region area, for example, as illustrated in Fig. 3, a single spot 106 may illuminate different portions of facial region 108. For example, spot 106 may include a first portion 304A associated with a first facial muscle and a second portion 304B associated with a second 2335 facial muscle. Alternatively, a single facial region area may be illuminated by multiple light spots.Some of the plurality of areas may be spaced apart from each other while others of the plurality of areas may be overlapping with each other. The term “spaced apart” may refer to being nonoverlapping or separated by at least some distance. Thus, spaced apart areas may refer to two or more facial region areas that do not overlap with each other and have even a very small gap in between. For 2340 example, stating that a first facial region area is spaced apart from a second facial region area may include distances between the first and second region of at least 5 mm, at least 10 mm, at least 15 mm, or any other desired distance. In some embodiments the distance may be less than 1 mm, or between 1mm and 5mm. In some cases, only a portion of a facial region area may be illuminated by the projected light. In other cases, all of the facial region areas may be illuminated by the projected light.2345 By way of example, Figs. 11 and 12 illustrate illuminating plurality of facial region areas of an individual using a plurality of spots. As illustrated, each of facial areas 1100A and 1100B are illustrated by more than one light spot.

[0271] Some disclosed embodiments involve illuminating at least a portion of the first area and at least a portion of the second area with a common light spot. As used herein, the term “at least a 2350 portion” and / or grammatical equivalents thereof can refer to any fraction of a whole amount. For example, “at least a portion” can refer to at least about 1%, 5%, 10%, 20%, 40%, 65%, 90%, 95%, 99%, 99.9%, or 100% of a whole amount, or any other fraction. The term “common light spot” meansAttorney Docket No. 16198.0049-00304that a single (common) light spot may cover some or all of the first area and the second area. The common light spot may illuminate at least a portion of the first area and the second area. In one 2355 example, the common light spot may illuminate 30% of the first area and 10% of the second area. In another example, the common light spot may illuminate 100% of the first area and 100% of the second area. Controlling the at least one coherent light source may include illuminating a continuous area on the face that includes the first area and the second area. By way of one example, as illustrated in Fig. 3 single light spot 106 may illuminate two or more facial areas (e.g., 304A and 304B).2360

[0272] Some disclosed embodiments involve illuminating the first area with a first group of spots and illuminating the second area with a second group of sports distinct from the first group of spots. The term “group of spots” refers to more than one light spot. The number of spots in the group of spots may range from two to 64 or more. For example, the group of spots may include 4 spots, 8 spots, 16 spots, 32 spots, 64 spots, or any number of spots greater than two. There may be variations 2365 in illumination characteristics between spots or within the group of spots, as discussed elsewhere in this disclosure. Illuminating an area with a group of spots may refer to illuminating some or all of a facial area region by two or more spots. In one example, the group of spots may illuminate at least 15% of the area, at least 40% of the area, or at least 70% of the area. A first area may be illuminated by a first group of spots and a second area may be illuminated by a second group of spots distinct 2370 from the first group of spots. In this context, the term “distinct” means that the first group of spots is distinguishable from the second group of spots. For example, the first group of spots may include at least one spot not included in the second group of spots. By way of example, Figs. 11 and 12 illustrate a first area facial regions 1100A illuminated by a first group of spots 1108A and a second area 1100B illuminated by a second group of sports 1108B distinct from the first group of spots.2375

[0273] Some disclosed embodiments involve operating a coherent light source (as described elsewhere in this disclosure) located within a wearable housing (as described elsewhere in this disclosure) in a manner enabling illumination of the plurality of facial region areas. Enabling illumination, as used herein, may refer to a process of controlling a light source to generate at least one light beam and directing the at least one light beam toward the plurality of facial region areas. For 2380 example, enabling illumination may also include utilizing a beam-splitting element (as described elsewhere in this disclosure) configured to split an input beam into multiple output beams (as described elsewhere in this disclosure) extending over a portion of a face. In an alternative embodiment, enabling illumination may include utilizing multiple light sources which generate respective groups of output beams, covering different respective sub-areas within a portion of a face.2385 Figs. 1 and 2 illustrate an example implementation of speech detection system (e.g., speech detection system 100) in which at least one facial region area (e.g., facial region 108) is illuminated by a plurality of light spots (e.g., light spots 106). The plurality of light spots may be generated by optical sensing unit 116 that includes at least one light source 410 and at least one light detector 412 and located in a wearable housing 110.Attorney Docket No. 16198.0049-003042390

[0274] Some disclosed embodiments involve operating a coherent light source (as described elsewhere in this disclosure) located remote from a wearable housing (as described elsewhere in this disclosure) in a manner enabling illumination of the plurality of facial region areas (as described elsewhere in this disclosure). The term “located remote” indicates that two objects are separated from each other and with a physical distance between them such that they do not appear physically as a 2395 unified component. For example, the coherent light source may be part of device other than the speech detection system and located more than 1 cm from a wearable housing of the speech detection system. As another example, the coherent light source may be located more than 3 cm from a wearable housing of the speech detection system. It should be understood that the distances 1 cm and 3 cm are exemplary and nonlimiting and other distances may be used. Fig. 3 illustrate an example 2400 implementation of speech detection system in which a plurality of facial region areas (e.g., first portion 304 A of facial region 108 and second portion 304B of facial region 108 ) are illuminated by a coherent light source located remote from the wearable housing (e.g., a non-wearable light source 302).

[0275] In some disclosed embodiments, the first area is closer to at least one of a zygomaticus 2405 muscle or a risorius muscle than the second area. The phrase “a first area is closer to a muscle than a second area” means that a distance of the first area to a specific muscle is less than a distance of the second area to a specific muscle. For example, the distances may be measured from an edge of an area to an edge of specific muscle, from a center of an area to a center of a specific muscle, or any combination thereof. In this context, the center of a shape (i.e., the first area, the second area, or a 2410 specific muscle) may be a geometric center, which is the point which corresponds to the mean position of all the points in shape; a circumscribed center, which is the center of the smallest circle that completely encloses the 2D shape; an incenter, which is the center of the inscribed circle that is tangent to all sides of the 2D shape, or any other reference point previously defined. As discussed, the first area is closer to at least one of a zygomaticus muscle or a risorius muscle than a second area. In 2415 other words, the disclosed embodiments capture two example use cases, the first example use case is that the first area is closer to the zygomaticus muscle than the second area. The second example use case is that the first area is closer to the risorius muscle than the second area. By way of example, Fig.11 illustrates one implementation of the first and second example use cases. Specifically, the first use case is illustrated with regards to individual 102A and the second use case is illustrated with regards 2420 to individual 102B.

[0276] Fig. 11 illustrates two example use cases for interpreting facial skin movements. In both example use cases, a plurality of facial region areas 1100 of individual 102 may be illuminated by at least one light source (e.g., light source 410, not shown). The depicted plurality of areas includes at least a first area 1100A and a second area 1100B. In the first example use case involving individual 2425 102A, first area 1100A is closer to the zygomaticus muscle than second area 1100B, and in the secondAttorney Docket No. 16198.0049-00304example use case involving individual 102B, first area 1100A is closer to the risorius muscle than second area 1100B.

[0277] Some disclosed embodiments involve receiving reflections from the plurality of areas. The term “receiving” may include obtaining, retrieving, acquiring, or otherwise gaining access to data or 2430 signals. In some cases, receiving may include reading data from memory and / or obtaining data from a computing device via a (e.g., wired and / or wireless) communications channel. In other cases, receiving may include detecting electromagnetic waves (e.g., in the visible or invisible spectrum) and generating an output relating to measured properties of the electromagnetic waves. In a first embodiment, at least one processor may receive data indicative of light reflected from the plurality of 2435 areas from at least one detector. In a second embodiment, at least one detector may receive light rays reflected from the plurality of areas. The term “reflections” refers to one or more light rays bouncing off a surface (e.g., the individual’s face) or data derived from the one or more light rays bouncing off the surface. For example, the reflections may include light detected by a light detector after it was deflected from an object. The light detected by the light detector may be generated by at least one 2440 coherent light source of the disclosed speech detection system and / or may be generated from sources other than the disclosed speech detection system. By way of one example, light detector 412 in Figs.5A and 5B is employed to receive reflections 300 that originated from light generated by light source 410.

[0278] By way of example with reference to the two uses cases depicted in Fig. 11, a reflection 2445 image 1102A may represent the reflections received from the first area 1100A, and reflection image 1102B may represent the reflections received from the second area 1100B. As illustrated, in the first example use case, reflection image 1102A represents the reflections received from an area closer to the zygomaticus muscle; and in the second example use case, image 1102A represents the reflections received from an area closer to the risorius muscle.2450

[0279] Some disclosed embodiments involve detecting first facial skin movements corresponding to reflections from the first area and second facial skin movements corresponding to reflections from the second area. The term “detecting” in this context refers to the process of discovering, identifying, or determining the existence of light reflections (or signals associated therewith). In one example, a change in the position of facial skin may be detected. As discussed elsewhere in this disclosure, the 2455 detection process may involve using various techniques or technologies to determine the existence of the pattern or the event. In some cases, the process of detecting facial skin movement may involve determining if there is any movement that occurred and to record information representing the detected movement. For example, at least one processor may detect facial skin movements by applying a light reflection analysis on received reflections. In other cases, detecting facial skin 2460 movements may include determining times in which facial skin movements occurred. In other cases, detecting facial skin movements may include determining data representing the facial skin movements (e.g., direction, velocity, acceleration). The term “facial skin movements” broadly refers any type ofAttorney Docket No. 16198.0049-00304movements prompted by recruitment of underlying facial muscles. The facial skin movements include facial skin micromovements — as described elsewhere in this disclosure — and larger-scale skin 2465 movements generally visible and detectable to the naked eye without the need for magnification (e.g., a smile, a yawn, a frown). The term “the facial skin movements corresponding to reflections from a specific area” means that the detected facial skin movements took place in a specific area of the face from which reflections were received. For example, detecting first facial skin movements corresponding to reflections from the first area means that the first facial skin movements may be 2470 detected by analyzing reflections received from the first area; and detecting second facial skin movements corresponding to reflections from the second area means that the second facial skin movements may be detected by analyzing reflections received from the second area.

[0280] In some disclosed embodiments, detecting the first facial skin movements involves performing a first speckle analysis on light reflected from the first area, and detecting the second 2475 facial skin movements involves performing a second speckle analysis on light reflected from the second area. The term “performing” refers to the act of carrying out a task, activity, or function. The term “speckle analysis” may be understood as described elsewhere in this disclosure. Consistent with the present disclosure, performing a speckle analysis may include detecting a speckle pattern, or any other patterns in signals received from a light reflected from a facial region area. For example, 2480 performing a speckle analysis may include identifying secondary speckle patterns that arise due to reflection of the coherent light from each area. In other embodiments, detecting facial skin movements may involve performing a pattern-based analysis or an image-based analysis additionally or alternatively from performing a speckle analysis.

[0281] Consistent with some disclosed embodiments, the first speckle analysis and the second 2485 speckle analysis occur concurrently by the at least one processor, the term “occur concurrently” means that two or more events occur during coincident or overlapping time periods, either where one begins and ends during the duration of the other, or where a later one starts before the completion of the other. In some cases the two or more events may be speckle analyses (or any pattern-based analysis). In order for the first speckle analysis and the second speckle analysis to occur concurrently, 2490 the at least one processor may include a plurality of processors or a multi -core processor that allows multiple speckle analyses to be executed simultaneously.

[0282] By way of example with reference to the two uses cases depicted in Fig. 11, first facial skin movements 1104A may correspond to reflections from the first area 1100A and second facial skin movements 1104B may correspond to reflections from the second area 1100B. For example, in the 2495 first example use case, first facial skin movements 1104A correspond to reflections received from an area closer to the zygomaticus muscle; and in the second example use case, second facial skin movements 1104B correspond to reflections received from an area closer to the risorius muscle.

[0283] Some disclosed embodiments involve determining, based on differences between the first facial skin movements and the second facial skin movements, that the reflections from the first areaAttorney Docket No. 16198.0049-003042500 closer to the at least one of a zygomaticus muscle or a risorius muscle are a stronger indicator of communication than the reflections from the second area. Determining refers to ascertaining. For example, from the differences between the first and second facial skin movements, the processor may determine which is closer to the associated muscle. The differences between the first facial skin movements and the second facial skin movements may include any distinctions, variations, or 2505 dissimilarities between the first facial skin movements and the second facial skin movements. The differences between the first facial skin movements and the second facial skin movements may be determined using at least one of the following techniques: surface alignment, point-to-point comparison, surface registration, topological analysis, or any other technique for determining differences between two data sets. For example, the differences between the first facial skin 2510 movements and the second facial skin movements may include differences in the movement intensity, movement trajectory, the movement speed, and / or various changes in topography the facial skin. Based on the differences, the at least one processor may determine that reflections from a first area are a stronger indicator of communication than the reflections from a second area. The term “communication” refers to the process of conveying information through various mediums, such as 2515 spoken language, words, body language, gestures, or signals. For example, the communication may include verbal cues (e.g., words, phrases, and language) and non-verbal cues (e.g., body language, facial expressions, gestures, and eye contact). The term “indicator of communication” refers to a measure or sign reflective of an information conveyed by the individual. For example, the statement that reflections from the first area are a stronger indicator of communication than the reflections from 2520 a second area means that it may be easier to determine that the individual intends to convey information and what communication the individual intends to convey from the first facial skin movements than from the second facial skin movements. For example, the reflections from the first area may be a stronger indicator of communication than the reflections from a second area because the facial skin micromovements determined from the reflections from the first area may be associated 2525 with a higher velocity, a higher displacement, or a higher other parameter indicating that the individual intents to convey information and / or the content of the information that the individual intends to convey. Consistent with disclosed embodiments, in the first example use case, when the first area is closer to the zygomaticus muscle, the first facial skin movements may reflect movements with a velocity on the order of one to ten pm / ms, and the second facial skin movements may reflect 2530 smaller movements, if any. In the second example use case, when the first area is closer to the risorius muscle, the first facial skin movements may reflect movements on the order of 0.5-2 mm, and the second facial skin movements reflect smaller movements, if any.

[0284] Consistent with some disclosed embodiments, the differences between the first facial skin movements and the second facial skin movements include differences of less than 100 microns. The 2535 term “differences of less than 100 microns” means that the changes between a first parameter that represents the first facial skin movements and a second parameter that represents second facial skinAttorney Docket No. 16198.0049-00304movements is less than 100 microns. In one example, the first parameter may be a magnitude of a first displacement change vector associated with the first facial skin movements and a second parameter may be a magnitude of a second displacement change vector associated with the second facial skin 2540 movements. A displacement change is a vector that quantifies the distance and direction changes between two measurements of the facial skin. For example, the differences between the first facial skin movements and the second facial skin movements include differences of less than 50 microns, less than 10 microns, or less than 1 micron. In other embodiments, the differences between the first facial skin movements and the second facial skin movements include differences of less than 1 2545 millimeter. Accordingly, the determination that the reflections from the first area are a stronger indicator of communication than the reflections from the second area is based on the differences of less than 1 millimeter, less than 100 microns, less than 50 microns, less than 10 microns, or less than 1 micron.

[0285] Some disclosed embodiments involve, based on the determination that the reflections from 2550 the first area are a stronger indicator of communication, processing the reflections from the first area to ascertain the communication. The term “processing” refers to the act of performing operations or transformations on data or information to achieve a desired outcome. For example, processing may include manipulating, analyzing, or altering inputs in a systematic way to produce meaningful outputs. The term “processing reflections” means extracting information from signals representing the 2555 received reflections. For example, processing reflections may include actions, such as: filtering, amplifying, modulating, and applying light reflection analysis as described elsewhere in this disclosure. Based on the determination that the reflections from the first area are a stronger indicator of communication, the reflections from the first area are processed to ascertain the communication. The term “ascertain the communication” means determining speech or facial expressions associated 2560 with non-verbal communication from facial movements, as described elsewhere in this disclosure.Consistent with the present disclosure, the reflections from the first area may be processed to create images of speckle patterns. Even at fast exposure times, such as 10 ms, the velocity of motion of the skin may be sufficient to make the speckle pattern change during each frame so that the bright pixels are blurred and washed out. The degree of speckle blur of a given spot in a given frame, as manifested 2565 by the loss of contrast in the image, for example, may be indicative of the instantaneous velocity of motion of the skin in the small area of the cheek under the spot. Processing the reflections from the first area may also include extracting quantitative image features from the images of speckle patterns. Vectors of these features, extracted from successive image frames, may be input to a neural network in order to ascertain the communication. Details of neural network architectures and training 2570 algorithms that may be used for this purpose are described elsewhere in this disclosure. An example feature that may be extracted for the purpose of ascertaining the communication may include speckle contrast. Any suitable measure of contrast may be used for this purpose, for example, the mean square value of the luminance gradient taking over the area of the speckle pattern. High contrast in theAttorney Docket No. 16198.0049-00304speckle pattern of a given spot from the first area may be indicative that the corresponding location of 2575 the cheek is stationary, while reduced contrast may be indicative of motion. The contrast decreases with increasing velocity of motion. Contrast features of this sort may be typically extracted from multiple spots distributed over the first area. Additionally, or alternatively, other features may be extracted from the speckle images and input to the neural network. Examples of such features may include total brightness of the speckle pattern and orientation of the speckle pattern, for instance, as 2580 computed by a Sobel filter. By way of one example, subvocalization deciphering module 708 in Fig. 7 may be used for processing the reflections from the first area to ascertain the communication.

[0286] Consistent with some disclosed embodiments, the communication ascertained from the reflections from the first area includes words articulated by the individual. “Ascertaining words articulated by the individual” refers to understanding words that are either vocalized or subvocalized 2585 by the individual. By processing the signals resulting from reflections, words can be ascertained as discussed elsewhere herein. By way of example, the word “Hello” in Fig. 11 represents the words articulated by individual 102A or individual 102B that may be ascertained from the reflections from the first area.

[0287] Consistent with some disclosed embodiments, the communication ascertained from the 2590 reflections from the first area includes non-verbal cues of the individual. The term “non-verbal cues” refers to the various forms of communication that occur without the use of spoken words. Some examples of non-verbal cues may include facial expressions, body language, gestures, eye contact, tone of voice, postures, and other subtle signals that convey meaning in interpersonal interactions. For example, non-verbal cues, such as facial expressions, may be used to communicate basic emotions 2595 like happiness, sadness, anger, fear, surprise, and disgust. As discussed elsewhere in this disclosure, the at least one processor may determine a non-verbal cue by analyzing reflection signals representing facial skin micromovements in the first facial area. By way of example, the emoji in Fig. 11 represents the non-verbal cues that may be ascertained from the reflections from the first area.

[0288] Some disclosed embodiments involve, based on the determination that the reflections from 2600 the first area are a stronger indicator of communication, ignoring the reflections from the second area.In this context, the term “ignoring the reflections” means that the processing actions on the signals representing the received reflections from the second area are less than the processing actions on the signals representing the received reflections from the first area. In one embodiment, signals representing the received reflections from the second area may be filtered, amplified, and analyzed to 2605 determine the second facial skin movements, but some quantitative features may not be extracted because the communication may not be ascertained from signals representing the received reflections from the second area. In another embodiment which also involves “ignoring,” during a first time frame, reflections from both the first area and the second area may be processed to determine which area is closer to the zygomaticus muscle or the risorius muscle. Thereafter, during a subsequentAttorney Docket No. 16198.0049-003042610 second time frame, and upon determining that the first area is closer to the zygomaticus muscle or the risorius muscle, reflections from the second area may be automatically discarded.

[0289] According to some disclosed embodiments, ignoring the reflections from the second area includes omitting use of the reflections from the second area to ascertain the communication. The term “omitting use” refers to not using information associated with reflections from the second area 2615 when determining the meaning of the communication.

[0290] By way of example with reference to the two uses cases depicted in Fig. 11, reflection image 1102A may be processed to ascertain communication 1106 from facial skin movements 1104A associated with the zygomaticus muscle or the risorius muscle, and reflection image 1102B may ignored, e.g., not used or omitted in ascertaining the communication. As depicted, the ascertained 2620 communication may include at least one word 1106A (articulated silently or vocally by individual 102A or individual 102B) and / or at least one facial expression 1106B that serves as an example of a non-verbal cue.

[0291] Some disclosed embodiments involve determining, based on differences between the first facial skin movements and the second facial skin movements, that the first area is closer than the 2625 second area to the subcutaneous tissue associated with cranial nerve V or with cranial nerve VII. The term “subcutaneous tissue” refers to the layer of tissue located beneath the skin and above the underlying muscles and bones. It is composed of fat cells, connective tissue, blood vessels, nerves, and other structures. Cranial nerve V, also known as the trigeminal nerve, is a sensory nerve for the face that control of jaw muscles. Cranial nerve VII controls facial expressions and carries taste 2630 sensation from the front of the tongue. Based on differences between the first facial skin movements and the second facial skin movements (as described above), a determination may be made that the first area is closer than the second area to the subcutaneous tissue associated with cranial nerve V or with cranial nerve VII.

[0292] Some disclosed embodiments involve operating a coherent light source in a manner 2635 enabling bi-mode illumination of the plurality of facial region areas. The term “coherent light source” may be understood as described elsewhere in this disclosure. Operating a coherent light source in this context refers to regulating, supervising, instructing, allowing, and / or enabling the coherent light source to illuminate at least part of a face. For example, the coherent light source may be controlled to illuminate a region of a face in a specific mode of illumination when turned on in response to a 2640 trigger. Bi-mode illumination refers to a capability of the coherent light source to illuminate an object using at least two different modes of illumination. The term “mode of illumination” refers to a specific configuration or settings of the coherent light source. Each of the two modes may be associated with different values of illumination parameters, such as light intensity, illumination pattern, pulse frequency, duty cycle, light flux. Light source 410 in Fig. 4 is one example of either a 2645 single mode or multi-mode (e.g., bi-mode) light source.Attorney Docket No. 16198.0049-00304

[0293] In some disclosed embodiments, a first light intensity of the first mode of illumination differs from a second light intensity of the second mode of illumination. In some disclosed embodiments, a first illumination pattern of the first mode of illumination differs from a second illumination pattern of the second mode of illumination. Light intensity refers to a brightness level of 2650 an illumination and an illumination pattern refers to an arrangement, distribution, or sequence of coherent or non-coherent light emitted from a source or reflected off a surface. The light pattern may be created by a specific design, shape, or configuration of light sources to create a particular visual or non-visual effect on the portion of the face. Examples of illumination patterns may include a grid of light spots having the same size, a grid of light spots having the various sizes, a single light spot, or 2655 any other pattern.

[0294] Some disclosed embodiments involve analyzing reflections associated with a first mode of illumination to identify one or more light spots associated with the first area, and analyzing reflections associated with a second mode of illumination to ascertain the communication. The term “identifying one or more light spots associated with the first area” means determining which of the light spots 2660 projected by the coherent light source are located in the first area. For example, identifying the one or more light spots associated with the first area may be implemented by comparing light intensity at a particular location with boundaries of the first area, based on image analysis of the face of the individual, or by any other processing method. In one example, the first mode of illumination may include a first illumination pattern (e.g., 64 light spots) and the second mode of illumination may 2665 include a second illumination pattern (e.g., 32 light spots). By way of example, with reference to the first example use case depicted in Fig. 11, the first mode of illumination may be used to identify eight light spots included within first area 1100A associated with the zygomaticus muscle. Thereafter, the second mode of illumination (e.g., 4 light spots) may be used to illuminate first area 1100A in a manner that enables ascertaining the communication from received reflections.2670

[0295] Consistent with some disclosed embodiments, the first area is closer than the second area to the zygomaticus muscle, and the plurality of areas further include a third area closer to the risorius muscle than each of the first area and second area. The terms “plurality of areas” and “closer to” may be understood as described elsewhere in this disclosure. By way of example with reference to Fig. 12, the plurality of facial areas 1100 includes the first area 1100A closer to the zygomaticus muscle than 2675 second area 1100B, and a third area 1100C closer to the risorius muscle than each of the first area 1100A and second area 1100B. In some disclosed embodiments, based on a determination that individual 102C is engaged in silent speech, a processing device of the speech detection system may process the reflections from the first area 1100A to ascertain the communication, and ignore the reflections from the second area 1100B and the third area 1100C. In other embodiments, based on a 2680 determination that individual 102C is engaged in voiced speech, a processing device of the speech detection system may process the reflections from third area 1100C to ascertain the communication, and ignore the reflections from the second area 1100B and the first area 1100A.Attorney Docket No. 16198.0049-00304

[0296] Some disclosed embodiments involve analyzing reflected light from the first area when speech is generated with perceptible vocalization (i.e., voiced speech) and analyzing reflected light 2685 from the third area when speech is generated in an absence of perceptible vocalization (i.e., silent speech). In other words, rather than monitoring the entire cheek and processing reflections from a plurality of areas, the speech detection system may process reflections received from a subset of the cheek area (e.g., only a few square millimeters or centimeters) in these two areas to detect both silent and voiced speech. Furthermore, when the plurality of areas are illuminated by multiple light sources 2690 (e.g., an array of laser diodes) only the light sources that illuminate these two areas may be actuated, thus reducing power consumption. If a large movement of the speech detection system relative to the skin is detected, a different set of light sources may be actuated. In some disclosed embodiments, different modes of processing may be applied to ascertain silent speech from voiced speech. For example, during silent speech, the first area being closer to the zygomaticus muscle may exhibit 2695 movements with a velocity on the order of one to ten pm / ms. Therefore, features of the images of the speckles themselves may change rapidly, and these features may be analyzed to generate an output. But during voiced speech, the third area being closer to the risorius muscle may exhibit movements on the order of 0.5-2 mm. Thus, the locations of the spots on the cheek may shift laterally due to the movement of the cheek. In this case, the lateral movements of the spots may be indicative of changes 2700 in the distance of the spots from the speech detection system, which may thus function as a sort of depth sensor. The two processing modes — speckle sensing and depth sensing — may be used individually in detecting silent and voiced speech, respectively. Alternatively, or additionally, these two processing modes may be used together to improve the precision and specificity of measurement, for example, by applying measurements of voiced speech by a given user to learn the patterns of 2705 microscopic movement that will occur in silent speech by the same user.

[0297] Fig. 13 illustrates a flowchart of an exemplary process 1300 for identifying individuals using facial skin micromovements, consistent with embodiments of the present disclosure. In some disclosed embodiments, process 1300 may be performed by at least one processor (e.g., processing device 400 or processing device 460) to perform operations or functions described herein. In some 2710 disclosed embodiments, some aspects of process 1300 may be implemented as software (e.g., program codes or instructions) that are stored in a memory (e.g., memory device 402 or memory device 466) or a non-transitory computer-readable medium. In some disclosed embodiments, some aspects of process 1300 may be implemented as hardware (e.g., a specific-purpose circuit). In some disclosed embodiments, process 1300 may be implemented as a combination of software and hardware.2715

[0298] Referring to Fig. 13, process 1300 includes a step 1302 of projecting light on a plurality of facial region areas of an individual. For example, the at least one processor may operate a wearable coherent light source (e.g., light source 410) to illuminate at least a first area (e.g., first area 1100A) and a second area (e.g., second area 1100A). The first area may be closer to at least one of a zygomaticus muscle or a risorius muscle than the second area. Process 1300 includes a step 1304 ofAttorney Docket No. 16198.0049-003042720 receiving reflections from the plurality of areas. For example, the at least one processor may operate at least one detector (e.g., at least one detector 412) to receive coherent light reflections (e.g., light reflections 300) from the plurality of areas 1100. Process 1300 includes a step 1306 of detecting first facial skin movements corresponding to reflections from a first area and second facial skin movements corresponding to reflections from a second area. For example, the at least one processor 2725 may use light reflections processing module 706 to detect the first facial skin movements, the second facial skin movements corresponding to reflections from the second area. Process 1300 includes a step 1308 of determining that the reflections from the first area are a stronger indicator of communication than the reflections from the second area. For example, the determination of step 1308 may be based on differences between the first facial skin movements and the second facial skin movements. Process 2730 1300 includes a step 1310 of processing the reflections from the first area to ascertain the communication and ignoring the reflections from the second area. For example, the determination of step 1310 may be based on the determination that the reflections from the first area are a stronger indicator of communication. At least one word 1106A and at least one facial expression 1106B are examples of the ascertained communication.2735

[0299] The embodiments discussed above for interpreting facial skin movements may be implemented through non-transitory computer-readable medium such as software (e.g., as operations executed through code), as methods (e.g., process 1300 shown in Fig. 13), or as a system (e.g., speech detection system 100 shown in Figs. 1-3). When the embodiments are implemented as a system, the operations may be executed by at least one processor (e.g., processing device 400 or processing 2740 device 460, shown in Fig. 4).

[0300] In some embodiments, an authentication or identity verification service provider uses biometrics, such as signals indicative of facial skin micromovements of an individual, for authentication purposes. For example, the authentication service provider may use the individual’s facial skin micromovements to verify the identity of the individual. The intensity and order of muscle 2745 activation (e.g., muscle fiber recruitment) over the facial region of an individual differs between individuals. Muscle activation or recruitment is the process of activating motor neurons to produce various levels of muscle contraction. Skin micromovements of an individual may be affected by the muscles, the structure of the muscle fibers, characteristics of the skin, characteristics of the sub skin (e.g., blood vessel structure, fat structure, hair structure, etc.), etc. The iris is an example of visible 2750 muscles of an individual. The iris is the colored tissue at the front of the eye that contains the pupil in the center and helps control the size of the pupil to let more or less light into the eye. While the iris of every individual is round, the structure of each individual’s iris may be unique and may be stable through the life of the individual. This is the same for sub-skin muscles and their activations. Facial skin micromovements may create a unique biometric signature of an individual that may be used to 2755 identify an individual. For the sake of brevity, in the discussion below, facial skin micromovements may simply be referred to as facial micromovements. Institutions that require customer identityAttorney Docket No. 16198.0049-00304verification (a / k / a authentication) may subscribe to the authentication service provided by the provider to authenticate individuals (e.g., customers) before providing access to a service or a facility that the institution provides. Such institution may include financial institutions (e.g., banks and 2760 brokerage services), subscription services (e.g., that provide media content, research or other information), online gaming sites, other online platforms, government agencies, and other organizations that require user authentication and verification, or any other entity or service that desires customer authentication. Authentication is the process of verifying or validating the identity of an individual.2765

[0301] Some disclosed embodiments involve identity verification of an individual based on the individual’s facial micromovements. The verification may occur via a system, computer readable media, or a method. The term “identity verification” is a process of determining who an individual is. It may also refer to a process of confirming or denying whether an individual is who that person claims to be. For example, in some embodiments, systems of the current disclosure may determine 2770 who an individual is based on that individual’s facial micromovements. And in some embodiments, systems of the current disclosure may determine (e.g., confirm or deny) whether the individual is actually who he / she is purported to be based on the individual’s facial micromovements.

[0302] Fig. 14 is a schematic illustration of one exemplary embodiment that includes a system for providing identity verification of an individual based on the individual’s facial micromovements. As 2775 illustrated in Fig. 14 (and in Figs. 1-4), a detection system 100 associated with an individual 102 may detect and communicate, e.g., directly or via mobile communications device 120, signals indicative (or representative) of the individual’s facial micromovements to a cloud server 122 using a communications network 126. In some embodiments, as described elsewhere in this disclosure, server 122 may access data structure 124 to determine, for example, correlations between words and facial 2780 micromovements of the individual. In some embodiments, cloud server 122 may also be configured to verify the identity of the individual based on the received signals. In some embodiments, an authentication service provider (or an identity verification service provider) may use a system, such as server 122, for providing identity verification of the individual based on the individual’s facial micromovements. In some embodiments, as shown in Fig. 14, an institution 1400 and a speech 2785 detection system 100 associated with an individual 102 may communicate with each other and cloud server 122 using communications network 126 to request and receive identity verification of the individual.

[0303] Figs. 15, 16A, and 16B are simplified block diagrams showing different aspects of an exemplary system 1500 for providing identity verification (or identity authentication) based on facial 2790 skin micromovements (or facial micromovements) of an individual. It is to be noted that only elements of authentication system 1500 that are relevant to the discussion below are shown in these figures. Embodiments within the scope of this disclosure may include additional elements or fewer elements. As shown in Fig. 15, system 1500 includes a processor 1510 and a memory 1520. AlthoughAttorney Docket No. 16198.0049-00304only one processor and one memory are illustrated in Fig.15, in some embodiments, processor 1510 2795 may include more than one processor and memory 220 may include multiple devices. These multiple processors and memories may each be of similar or different constructions and may be electrically connected or disconnected from each other. Although memory 1520 is shown separate from processor 1510 in Fig. 15, in some embodiments, memory 1520 may be integrated with processor 1510. In some embodiments, memory 1520 may be remotely located from system 1500 and may be accessible by 2800 system 1500. Memory 1520 may include any device for storing data and / or instructions, such as, for example, a Random Access Memory (RAM), a Read-Only Memory (ROM), a hard disk, an optical disk, a magnetic medium, a flash memory, other permanent, fixed, or volatile memory. In some embodiments, memory 1520 may be non-transitory computer-readable storage medium that stores instructions that when executed by processor 1510 causes processor 1510 to perform identity 2805 verification operations based on facial micromovements. In some embodiments, some or all the functionalities of processor 1510 and memory 1520 may be executed by a remote processing device and memory (for example, processing device 400 and memory device 402 of remote processing system 450, see Fig. 4).

[0304] Some disclosed embodiments involve receiving in a trusted manner, reference signals for 2810 verifying correspondence between a particular individual and an account at an institution. The term “receiving” may include retrieving, acquiring, or otherwise gaining access to, e.g., data. Receiving may include reading data from memory and / or receiving data from a computing device via a (e.g., wired and / or wireless) communications channel. At least one processor may receive data via a synchronous and / or asynchronous communications protocol, for example by polling a memory buffer 2815 for data and / or by receiving data as an interrupt event. The term “signals” or “signal” may refer to information encoded for transmission via a physical medium or wirelessly. Examples of signals may include signals in the electromagnetic radiation spectrum (e.g., AM or FM radio, Wi-Fi, Bluetooth, radar, visible light, lidar, IR, Zigbee, Z-wave, and / or GPS signals), sound or ultrasonic signals, electrical signals (e.g., voltage, current, or electrical charge signals), electronic signals (e.g., as digital 2820 data), tactile signals (e.g., touch), and / or any other type of information encoded for transmission between two entities via a physical medium or wirelessly (e.g., via a communications network). In some embodiments, the signals may include, or may be representative of, “speckles,” reflection image data, or light reflection analysis data (e.g., speckle analysis, pattern-based analysts, etc.) described elsewhere in this disclosure.2825

[0305] Receiving signals in a “trusted” manner refers to receiving reliable signals. For example, receiving the signals in a manner such that the truth and / or validity of the signals can be relied upon. In some embodiments, when receiving signals in a trusted manner, there may be some level of assurance that the signals are valid or are what they are expected to be. In some embodiments, receiving signals in a trusted manner may indicate that these signals are transmitted in a secure 2830 manner such that the signals may not be easily intercepted by and / or deciphered by a third party. InAttorney Docket No. 16198.0049-00304general, signals may be sent and received in a trusted manner using any known secure transmission method. In some embodiments, receiving signals in a trusted manner may refer to receiving encrypted signals. The signals may be encrypted using any now-known or later-developed encryption technology (e.g., Wired Equivalent Privacy (WEP), Wi-Fi Protected Access (WPA), Wi-Fi Protected 2835 Access Version 2 (WPA2), Wi-Fi Protected Access Version 3 (WPA3), etc.). In some embodiments, the encrypted signals may include (one or more) keys that may be used to decrypt the encrypted signals by methods known in the art.

[0306] As used herein, the term “reference signals” refers to signals that are used as the basis for ascertaining something. For example, the reference signals may be baseline signals used for 2840 comparison purposes, e.g., to determine if a characteristic of the signal has changed. In some embodiments, the reference signals may be representative of one or more properties or characteristics of an individual. For example, the reference signals may be representative of one or more properties / characteristics of the facial micromovements of an individual. In some embodiments, the reference signals may be (or may be a representation of) a speckle pattern (e.g., reflection image 600 2845 of Fig. 6) or another light reflection pattern output by speech detection system 100 associated with an individual. In some embodiments, the reference signal may include, or may be representative of, one or more features of the facial micromovements of an individual. In some embodiments, the reference signal may be (or may include) characteristics or features extracted from a light reflection pattern of the individual. In some embodiments, one or more algorithms may be used to extract these 2850 characteristics or features of an individual’s facial micromovements that are embodied in the reference signals. These extracted features may include fiducial and / or non-fiducial features. Fiducial features may include measurable characteristics of the individual’s facial micromovements (e.g., temporal or amplitude onset, peak (minimum or maximum), offset, spacing, time difference between peaks, and other measurable characteristics). On the other hand, non-fiducial features extraction may 2855 apply time and / or frequency analysis to obtain statistical features of the individual’s facial micromovements. In some embodiments, the reference signals may be representative of multiple biometric signals (e.g., a combination of facial micromovements along with one or more of pulse, cardiac signals, ECG, temperature, pressure, or other biometric signals) of an individual. It is also contemplated that, in some embodiments, the detected facial micromovement signals or the light 2860 reflection pattern output by speech detection system 100 may itself be used as the individual’s reference signals.

[0307] The reference signals may be configured to enable verification of the correspondence between a particular individual and an account at an institution. The term “correspondence” refers to the degree of similarity, connection, equivalence, match, or connection. For example, in some 2865 embodiments, the reference signals of a particular individual may be used to determine the equivalence, similarity, match, or connection between that individual and an account (e.g., of a customer) of the institution. The institution may retain in an associative way, biometric or other dataAttorney Docket No. 16198.0049-00304of a customer, and that data or related data may be contained within the reference signals. The term “institution” refers to any establishment or organization without limitation. In some embodiments, the 2870 institution may be an organization that provides some type of service, for example, to multiple individuals who may each have an account at the institution. In some embodiments, the institution may be a financial organization (e.g., a bank, stock brokerage, mutual fund, etc.) where multiple customers may have accounts (e.g., cash accounts, money market accounts, stock accounts, online accounts, safety deposit boxes, etc.). In some embodiments, the institution may be a company 2875 associated with online activity (e.g., gaming activity, betting activity, exam / test provider, education / class provider, etc.), or a university or education institution where multiple students have accounts (to access classes, billing statements, etc.). In some embodiments, the institution may be a health care provider (e.g., hospital, clinic, testing lab, etc.) or an insurance provider (e.g., insurance company) where multiple patients or customers have accounts, a company where multiple employees 2880 have accounts, etc. In other embodiments the institution may be a government agency or body. The reference signal may be received from any source (e.g., the individual, the institution, etc.).

[0308] In some embodiments, an institution may engage an authentication service provider and / or subscribe to the authentication service to verify the identity of an individual (or customer) in association with providing a service to the individual (for example, before allowing access to an 2885 account, etc.). The authentication service provider may use a system (such as system 1500 of Figs. 15, 16A, and 16B) to verify the identity of the individual using the reference signals. In some embodiments, the system may have access to the reference signals of all the customers of the institution (e.g., all the account holders of a bank, all the students enrolled for a class at a university, etc.). For example, in some embodiments, as illustrated in Fig. 16, reference signals 1502 of all the 2890 customers (e.g., account holders) of an institution 1400 (e.g., a bank) may be sent to system 1500 (e.g., during enrollment). System 1500 may securely store correlations 1504 of the reference signals 1502 with the identity of the different customers in a secure data structure (such as data structure 124) accessible by system 1500. In some embodiments, the customer’s name and / or other identifying information (account number, or other information that identifies the individual associated with the 2895 reference signals) may also be stored and associated with the reference data in the stored correlations 1504. As will be explained in more detail later, system 1500 may use the stored reference signals and correlations to authenticate individuals. For example, as illustrated in Fig. 16B, when an individual engages in a transaction (e.g., attempts to access a customer’s account) with the institution 1400, the institution 1400 may request 1506 the authentication service provider (or system 1500) to authenticate 2900 the individual (e.g., verify the identity of the individual, confirm that the individual is the customer associated with the account, etc.). System 1500 may receive real-time facial micromovement signals 1508 of the individual when the individual is engaged in the transaction, and the system 1500 may compare 1512 the received real-time signals 1508 with the stored reference signals 1502 or correlations 1504 to determine whether the individual is a customer. For example, system 1500 mayAttorney Docket No. 16198.0049-003042905 compare the two signals to determine if one or more characteristics of the received signals correspond to, or sufficiently match, characteristics of the stored reference signals to determine if the received signals are associated with a customer authorized to access the account.

[0309] Consistent with some disclosed embodiments, the reference signals may be derived based on reference facial micromovements detected using first coherent light reflected from a face of the 2910 particular individual. The term “reference” in “reference facial micromovements” indicate that these facial micromovements are used to generate the reference signals. As explained elsewhere in this disclosure, “coherent light” includes light that is highly ordered and exhibits a high degree of spatial and temporal coherence. As explained in detail elsewhere in this disclosure, when coherent light strikes the facial skin of an individual, some of it is absorbed, some is transmitted, and some is 2915 reflected. The amount and type of light that is reflected depends on the properties of the skin and the angle at which the light strikes it. For example, coherent light shining onto a rough, contoured, or textured skin surface may be reflected or scattered in many different directions, resulting in a pattern of bright and dark areas called “speckles.” In some embodiments, when coherent light is reflected from the face of an individual, the light reflection analysis performed on the reflected light may 2920 include a speckle analysis or any pattern-based analysis to derive information about the skin (e.g., facial skin micromovements) represented in the reflection signals. In some embodiments, a speckle pattern may occur as the result of the interference of coherent light waves added together to give a resultant wave whose intensity varies. In some embodiments, the detected speckle pattern (or any other detected pattern) may be processed to generate reflection image data from which the reference 2925 signals may be generated.

[0310] As explained elsewhere in this disclosure with reference to Figs. 1-6, speech detection system 100 associated with an individual may detect facial micromovements of the individual. For example, with specific reference to Figs. 5-7, in some embodiments, speech detection system 100 may analyze reflections 300 of coherent light from facial region 108 of the individual to determine facial 2930 micromovements (e.g., amount of the skin movement, direction of the skin movement, acceleration of the skin movement, speckle pattern, etc.) resulting from recruitment of muscle fiber 520 and output signals representative of the detected facial micromovements. In some embodiments, the determined facial skin micromovements may correspond to muscle activation.

[0311] Consistent with some disclosed embodiments, the reference signals for authentication may 2935 correspond to muscle activation during pronunciation of at least one word. The term “authentication” (and other constructions of this term such as authenticate) refers to determining the identity of an individual or to determining whether an individual is, in fact, who the individual purports to be. In some embodiments, authentication is a security process that relies on the unique characteristics of individuals to identify who they are or to verify they are who they claim to be. For example, 2940 authentication may be a security measure that matches the biometric features of an individual, for example, looking to access a resource (e.g., a device, a system, a service). As used herein, the termAttorney Docket No. 16198.0049-00304“pronunciation” (or other constructions such as pronounces, pronouncing, etc.) refers to when the individual actually utters (or vocalizes) the at least one word (or a syllable, etc.) or before the individual actually utters the word(s) (e.g., during silent speech or pre-vocalization). As explained 2945 elsewhere in this disclosure, speech-related muscle activity occurs prior to vocalization (e.g., when air flow from the lungs is absent but the facial muscles articulate the desired sounds, when some air flows from the lungs but words are articulated in a manner that is not perceptible using an audio sensor, etc.). For example, with reference to Figs. 15, 16A, and 16B, reference signals 1502 that may be used for verifying correspondence between a particular individual and an account at an institution may 2950 correspond to signals caused by muscle activation that occurs during vocalization or prior to vocalization (e.g., during silent speech) of the at least one word. It should be noted that real-time signals 1508 (described below) may also be generated in a similar manner.

[0312] Some disclosed embodiments involve muscle activation associated with at least one specific muscle that includes a zygomaticus muscle, an orbicularis oris muscle, a risorius muscle, a2955 genioglossus muscle, or a levator labii superioris alaeque nasi muscle. “Muscle activation” refers to tension, force, and / or movement of a muscle. Such activation may occur when the brain recruits the muscle. In some embodiments, as explained elsewhere in this disclosure, muscle activation or muscle recruitment is the process of activating motor neurons to produce muscle contraction. As also explained elsewhere in this disclosure, facial skin micromovements include various types of voluntary 2960 and involuntary movements (for example, that fall within the range of micrometers to millimeters and a time duration of fractions of a second to several seconds) caused by muscle recruitment or muscle activation. Some muscles such as the quadriceps (which is powerful muscle group responsible for displaying force very quickly) have a high ratio of muscle fibers to motor neurons. Other muscles such as the eye muscles, have much lower ratios as they use more precise, refined movement leading 2965 to small-scale skin deformations. As explained elsewhere in this disclosure, the zygomaticus muscle, the orbicularis oris muscle, the risorius muscle, the genioglossus muscle, and the levator labii superioris alaeque nasi muscle may articulate specific points in the individual’s cheek above mouth, chin, mid-jaw, cheek below mouth, high cheek, and the back of the cheek. In some embodiments, the reference signals for authentication may be based on facial micromovements detected (e.g., based on 2970 reflections of coherent light) from the face of the individual when the individual is engaged in normal activity (e.g., speaking normally, silently reading something, etc.). In some embodiments, the reference signals may be generated based on facial skin micromovements when the individual speaks or silently speaks (pronounces, articulates, enunciates, etc.) selected word(s), syllable(s), or phrases.

[0313] Consistent with some disclosed embodiments, the identity verification operations may 2975 further include presenting the at least one word to the particular individual for pronunciation. As used herein, the term “presenting” refers generally to making something known. For example, in some embodiments, the individual may be presented with a word by visually displaying the word to the individual and the individual may attempt pronounce the displayed word. In some embodiments, theAttorney Docket No. 16198.0049-00304word or words may also be audibly presented to the individual and the individual may repeat or 2980 attempt to repeat the word and signals may be generated when the individual vocalizes the presented word(s) or prior to vocalization of the word(s). In some embodiments, one or more figures representing one or more words (e.g., dog, cat) may be presented to the individual for pronunciation.

[0314] For example, the individual may be presented with one or more words (a word, a sentence, etc.) to pronounce, and reference signals 1502 (and / or real-time signals 1508) may be generated based 2985 on facial micromovements resulting from the individual pronouncing one or more of the presented words or one or more syllables in the word(s). The one or more words may be presented to the individual for pronunciation in any manner and on any device. For example, with reference to Fig. 14, in some embodiments, the word(s) used to generate reference signals 1502 (and / or real-time signals 1508) may be displayed to the individual textually on a display screen 1402 of mobile2990 communications device 120, and reference signals 1502 (and / or real-time signals 1508) may be generated when the user pronounces the displayed word(s). In some embodiments, the at least one word may be graphically presented to the user. For example, an image (e.g., picture, cartoon, etc.) representing a word (e.g., dog, cat, etc.) may be displayed to the individual and reference signal 1502 (and / or real-time signals 1508) may be generated when the individual pronounces the word2995 represented by the image. In general, any word (e.g., a random word) or words may be presented to the individual to pronounce.

[0315] Consistent with some disclosed embodiments, presenting the at least one word to the particular individual for pronunciation includes textually presenting the at least one word. For example, presenting the word “dog” may be presented by textually displaying the word “dog.” In 3000 some embodiments, presenting the word “dog” may occur by graphically showing an image (picture, cartoon, line drawing, or another similar pictorial display) of a dog. For example, the individual may be presented with one or more words (a word, a sentence, etc.) to pronounce, and reference signals 1502 (and / or real-time signals 1508) may be generated based on facial micromovements resulting from individual pronouncing one or more of the presented words or one or more syllables in the 3005 word(s). One or more words may be presented to the individual for pronunciation in any manner and on any device. For example, in some embodiments, the word(s) may be displayed to the individual textually on a display screen 1402 of mobile communications device 120, and reference signals 1502 (and / or real-time signals 1508) may be generated when the user pronounces the displayed word(s). In some embodiments, the at least one word may be graphically presented to the user. For example, an 3010 image (e.g., picture, cartoon, etc.) representing a word (e.g., dog, cat, etc.) may be displayed to the individual and reference signal 1502 (and / or real-time signals 1508) may be generated when the individual pronounces the word represented by the image. In general, any word (e.g., a random word) or words may be presented to the individual to pronounce.

[0316] Consistent with some disclosed embodiments, presenting the at least one word to the 3015 particular individual for pronunciation includes audibly presenting the at least one word. For example,Attorney Docket No. 16198.0049-00304one or more word may be presented to an individual by audibly sounding the word(s), for example, on a speaker. For example, with reference to Fig. 16, when an individual is setting up an account at an institution, one or more words may be presented to the individual to pronounce and the reference signals 1502 may be generated based on the resulting facial micromovements. As another example, 3020 when engaging in a transaction (e.g., setting up an account or trying to access an account) with institution 1400 using mobile communications device 120, the word(s) used to generate the reference signal 1502 (and / or the real-time signals 1508) may be audibly presented to the individual using a speaker of device 120, the output unit 114 of speech detection system 100, or another speaker. And the speech detection system 100 associated with the individual may generate reference signals 1502 3025 (and / or the real-time signals 1508) based on muscle activation when the user pronounces the word(s) or one or more syllables in the word(s).

[0317] It should be noted that although mobile communications device 120 is described as being used to audibly, textually, and / or graphically display the word(s) used to generate reference signals 1502 and / or the real-time signals 1508 to the individual, this is merely exemplary. In general, the 3030 word(s) may be presented to the individual on any device. For example, in some embodiments, the words may be visually (e.g., textually, graphically, etc.) presented on a screen 1600 (see Fig. 16B) of any device that the individual has access to (e.g., a visual display of, e.g., a smartphone, a tablet, a smartwatch, a personal digital assistant, a desktop computer, a laptop computer, an Internet of Things (loT) device, a dedicated terminal, a wearable communications device, VR / XR glasses, etc.).3035 Similarly, the words may be audibly presented to the individual on any device (e.g., a speaker of any one of the devices described above, etc.). It is also contemplated that in some embodiments, instead of presenting the word(s) used to generate the reference and / or real-time signals 1502, 1508 to the user, a question or a prompt that generates the word(s) may be presented (e.g., audibly, textually, graphically, etc.) to the user. For example, a query such as, for example, “what is your password?” “what is the 3040 city of your birth?” etc. may be presented to the individual, and reference signals 1502 (and / or realtime signals 1508) may be generated from the response. In some embodiments, both the reference signals 1502 and the real-time signals 1508 may be generated by presenting the same word(s) or syllable(s) to the individual to pronounce.

[0318] Consistent with some disclosed embodiments, the presented at least one word may be a 3045 password. In general, a “password” may be any word or a string of characters. In some embodiments, the password may be a string of characters, one or more words, or a phrase that must be used to gain admission to something. For example, when an individual sets up an account at an institution, the individual may be asked to pronounce (e.g., vocalize or pre-vocalize) a password for the account, and reference signals 1502 may be generated based on the resulting facial micromovements. As another 3050 example, in an embodiment where the individual is trying to access a customer’s account at a financial institution, the individual may be asked to pronounce the password associated with the account, for example, by presenting a query (e.g., “what is your password?”). And, reference signalAttorney Docket No. 16198.0049-003041502 and / or real-time signals 1508 may be generated based on reflections of coherent light from the individual’s face when the individual pronounces the password.3055

[0319] In some embodiments, the reference signals for authentication may correspond to muscle activation during pronunciation of one or more syllables. For example, the reference signals may be generated when the individual pronounces (vocalizes or pre-vocalizes) a syllable, such as, for example, a vowel or any other syllable. Although not a requirement, in some embodiments, one or more syllables (e.g., vowels or any other characters), or one or more words containing the syllables, 3060 may be presented to the individual and the reference signals 1502 (and / or real-time signals 1508) for authentication may be generated by system 1500 based on facial micromovements when the individual pronounces the one or more syllables.

[0320] Some disclosed embodiments involve storing, in a secure data structure, a correlation between an identity of the particular individual and the reference signals reflecting the facial 3065 micromovements. A “secure data structure” is a location where data or information may be stored securely without being subject to unauthorized access. Unauthorized access may include access by members within an organization (e.g., institution, authentication service provider, etc.) not authorized to access the stored data or access by members outside the organization. A data structure consistent with the present disclosure may include any collection of data values and relationships among them.3070 The data may be stored linearly, horizontally, hierarchically, relationally, non-relationally, uni- dimensionally, multidimensionally, operationally, in an ordered manner, in an unordered manner, in an object-oriented manner, in a centralized manner, in a decentralized manner, in a distributed manner, in a custom manner, or in any manner enabling data access. By way of non-limiting examples, data structures may include an array, an associative array, a linked list, a binary tree, a 3075 balanced tree, a heap, a stack, a queue, a set, a hash table, a record, a tagged union, ER model, and a graph. For example, a data structure may include an XML database, an RDBMS database, an SQL database or NoSQL alternatives for data storage / search such as, for example, MongoDB, Redis, Couchbase, Datastax Enterprise Graph, Elastic Search, Splunk, Solr, Cassandra, Amazon DynamoDB, Scylla, HBase, and Neo4J. A data structure may be a component of the disclosed system or a remote 3080 computing component (e.g., a cloud-based data structure). Data in the data structure may be stored in contiguous or non-contiguous memory. Moreover, a data structure, as used herein, does not require information to be co-located. It may be distributed across multiple servers, for example, which may be owned or operated by the same or different entities. Thus, the term “data structure” as used herein in the singular is inclusive of plural data structures.3085

[0321] In some embodiments, the secure data structure may be a secure database. The stored information may be encrypted in the secure data structure. As explained elsewhere in this disclosure, the term “database” may be a collection of data that may be distributed or non-distributed. In some embodiments, the secure data structure may be a secure enclave (also known as Trusted Execution Environment). A secure enclave is a computing environment that provides isolation for code and dataAttorney Docket No. 16198.0049-003043090 from the operating system using either hardware-based isolation or isolating an entire virtual machine by placing the hypervisor within the Trusted Computing Base (TCB). A trusted computing base (TCB) may be a computing system that provides a secure environment for operations. This includes its hardware, firmware, software, operating system, physical locations, built-in security controls, and prescribed security and safety procedures. A hypervisor, also known as a virtual machine monitor or 3095 VMM, is software that creates and runs virtual machines (VMs). A hypervisor allows one host computer to support multiple guest VMs by virtually sharing its resources, such as memory and processing. Even users with physical or root access to the machines and operating system may not be able to access the contents of the secure enclave or tamper with the execution of code inside the enclave. A secure enclave provides CPU hardware -level isolation and memory encryption on a server 3100 by isolating application code and data and encrypting memory. Secure enclaves are at the core of confidential computing. In some embodiments, sets of security-related instruction codes may be built into the processors to protect the stored data. The data in the security enclave may be protected because the enclave is decrypted on the fly only within the processor, and then only for code and data running within the enclave itself. With suitable software, a secure enclave may enable the encryption 3105 of stored data and provide full stack security to the stored data. In some embodiments, secure enclave support may be incorporated into the one or more processors of system 1500 (such as processor 1510). In some embodiments, the secure data structure may include encrypted key / value storage. The secure data structure may, in some embodiments, be on a dedicated chip, in a separate IC circuit, or on part of processor 1510. In some embodiments, the secure data structure may include remote 3110 authentication. For example, corresponding authentication keys may be stored locally on system 1500 and on a remote server, and access may be provided to the stored database based on a successful comparison of the two authentication keys.

[0322] Consistent with some disclosed embodiments, a correlation between an identity of the particular individual and the reference signals (reflecting the facial micromovements of that 3115 individual) may be stored in the secure data structure. “Correlation” refers to a relationship or a connection between the identity of an individual and that individual’s reference signals. For example, the correlation is a measure that expresses the extent to which the two are related. In some embodiments, a representation (or a signature) of the received reference signals of the individual may be stored as the correlation. Although not a requirement, in some embodiments, the stored signature 3120 may be reduced size version of the received reference signals. In some embodiments, an encrypted version of the signature may be stored in the secure data structure. A “hash” of the received reference signal may be stored as the correlation in some embodiments. As would be recognized a person of ordinary skill in the art, a hash is a unique digital signature generated from an input signal (e.g., the received reference signals reference signals) using, for example, commercially available algorithms.3125 A hashed / encrypted signature of the individual may be stored as the correlation, for example, in a secure data structure to reduce the possibility of unauthorized access to the data. In someAttorney Docket No. 16198.0049-00304embodiments, the correlation may be, or include, features or characteristics of the reference signals extracted, for example, using feature extraction algorithms. In some embodiments, the correlation may include significant information or landmarks (e.g., position and orientation of peaks and / or 3130 valleys, spatial and / or temporal gap between peaks and / or valleys) in the reference signals. In some embodiments, encrypted reference signals themselves may be stored as the correlation. Since the stored correlation is a representation of the individual’s facial micromovements that are affected by that individual’s person traits (e.g., muscle fiber structure, blood vessel structure, tissue structure, etc.), the stored correlation may uniquely identify the individual that the reference signals correspond 3135 to. In some embodiments, the correlation may include the identity (e.g., name, account number, or other identifying information) of the individual that the reference signal corresponds to or is associated with. In one exemplary embodiment, as illustrated in Fig. 15, system 1500 stores a correlation 1504 of the reference signals 1502 of an individual in a secure data structure in memory 1520. As illustrated in Figs. 16A and 16B, in another exemplary embodiment, system 1500 stores 3140 correlations 1504 of different individual’s (e.g., Tom, Amy, Ron, etc.) reference signals 1502 in a secure data structure in a remote database (e.g., data structure 124).

[0323] Some disclosed embodiments involve, following storing, receiving via the institution, a request to authenticate the particular individual. As described earlier, the term “authenticate” refers to determining the identity of an individual or to determining whether an individual is, in fact, who the 3145 individual (implicitly or explicitly) purports to be. In some embodiments, authentication is a security process that relies on the unique characteristics of individuals to identify who they are or to verify they are who they claim to be. For example, authentication is a security measure that matches the biometric features of an individual, for example, looking to access a resource (e.g., a device, a system, a service). In some embodiments, access to the resource is granted only when ...

Claims

Attorney Docket No. 16198.0049-0030419355CLAIMS1. A non-transitory computer readable medium containing instructions that when executed by at least one processor cause the at least one processor to perform operations for generating a common record 19360 based on differing source inputs, the operations comprising:receiving via at least one sensor first non-audible signals indicative of verbalization of an individual;interpreting first words of the individual at least in part using the first non-audible signals received from the at least one sensor;19365 receiving second signals generated by a source other than the individual;performing speech recognition on the second signals to interpret second words from the source; andusing the first words and the second words to generate the record.19370 2. The non-transitory computer readable medium of claim 1, wherein the at least one sensor includes at least one light detector, and the first non-audible signals are indicative of facial skin movements.

3. The non-transitory computer readable medium of claim 1, wherein the at least one sensor includes at least one electrophysiological sensor, and the first non-audible signals are indicative of facial 19375 muscle activity or facial nerve activity.

4. The non-transitory computer readable medium of claim 1, wherein the second signals are received via at least one additional sensor including a microphone or a camera.19380 5. The non-transitory computer readable medium of claim 4, wherein the microphone includes a directional microphone for distinguishing between a first group of words spoken by the individual and a second group of words spoken by the source other than the individual.

6. The non-transitory computer readable medium of claim 1, wherein the second signals are received 19385 via a communication device.

7. The non-transitory computer readable medium of claim 1, wherein the generated record is an audio record.Attorney Docket No. 16198.0049-0030419390 8. The non-transitory computer readable medium of claim 1, wherein the operations further include using voice recognition to distinguish between a first group of words spoken by the individual and a second group of words generated by the source.

9. The non-transitory computer readable medium of claim 1, wherein the generated record is a textual 19395 record.

10. The non-transitory computer readable medium of claim 1, wherein the source is an additional individual and the record is a transcription of a conversation between the individual and the additional individual.1940011. The non-transitory computer readable medium of claim 10, wherein the transcription includes a chronological recount of the conversation between the individual and the additional individual.

12. The non-transitory computer readable medium of claim 1, wherein the source is an electronic 19405 device outputting audible speech and the record includes a transcription of the audible speech.

13. The non-transitory computer readable medium of claim 12, wherein the electronic device includes at least one of a phone, a television, a radio, a public address system, or an loT device.19410 14. The non-transitory computer readable medium of claim 1, wherein the operations further include projecting light from at least one light source on a non-lip portion of a face of the individual, such that the first non-audible signals are indicative of light reflections from the non-lip portion of the face.

15. The non-transitory computer readable medium of claim 2, wherein the at least one light detector 19415 includes a multi-pixel sensor array, and the operations further include generating images from the first non-audible signals and determining the first words from the images.

16. The non-transitory computer readable medium of claim 15, wherein the operations further include performing speckle analysis on the first non-audible signals to identify a speckle pattern, deriving 19420 information from the speckle pattern, and determining the first words from the derived information.

17. The non-transitory computer readable medium of claim 1, wherein the first non-audible signals used to interpret the first words are indicative of activity that occurred during subvocalization.19425 18. The non-transitory computer readable medium of claim 9, wherein the textual record is generated in a manner that visually differentiates the first words from the second words.Attorney Docket No. 16198.0049-0030419. The non-transitory computer readable medium of claim 18, wherein visually differentiating the first words from the second words includes applying differing visual characteristics to the first words 19430 and the second words.

20. The non-transitory computer readable medium of claim 1, wherein interpreting the first words includes interpreting a first group of words articulated by the individual in an absence of perceptible vocalization by the individual and interpreting a second group of words vocalized by the individual.1943521. The non-transitory computer readable medium of claim 20, wherein the record is generated in a manner visually differentiates the first group of words from the second group of words.

22. The non-transitory computer readable medium of claim 9, wherein the source includes a plurality 19440 of additional individuals and the textual record is generated in a manner visually differentiates from each other the second words spoken by the plurality of additional individuals.

23. The non-transitory computer readable medium of claim 9, wherein the operations further include determining a prompt from the first words and using the prompt and the second words to generate the 19445 textual record.

24. The non-transitory computer readable medium of claim 23, further comprising deriving metadata associated with the prompt for enabling later retrieval of the textual record using the metadata.19450 25. The non-transitory computer readable medium of claim 23, wherein the prompt is related to a specific group of the second words and the operations include highlighting the specific group of the second words in the textual record in response to the prompt.

26. The non-transitory computer readable medium of claim 23, wherein the prompt is related to a 19455 specific group of the second words and the operations include generating a task associated with the specific group of the second words based on the prompt.

27. A method for generating a common record based on differing source inputs, the method comprising:19460 receiving via at least one sensor first non-audible signals indicative of verbalization of an individual;interpreting first words of the individual at least in part using the first non-audible signals received from the at least one sensor;Attorney Docket No. 16198.0049-00304receiving second signals generated by a source other than the individual;19465 performing speech recognition on the second signals to interpret second words from the source; andusing the first words and the second words to generate the record.

28. A head mountable system for generating a common record based on differing source inputs, the 19470 head mountable system comprising:a wearable housing configured to be worn on a head of an individual;at least one sensor incorporated with the wearable housing and configured to receive first non-audible signals indicative of verbalization of the individual;at least one additional sensor incorporated with the wearable housing for receiving second 19475 signals generated by a source other than the individual;at least one processor configured to:interpret first words of the individual at least in part using the first non-audible signals received from the at least one sensor;perform speech recognition on the second signals to interpret second words from the 19480 source; anduse the first words and the second words to generate the record.19485

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