Patent Description:
Face masks (such as face coverings, surgical masks, N95 masks, and the like) are simple and effective ways to help decrease the spread of germs and infectious disease. These masks trap droplets that are released when the wearer sneezes, coughs, or talks. They can also protect the nose and mouth of the mask wearer from contacting respiratory droplets from other individuals. These masks are also used to filter out large particles in the air.

One pain point for the mask wearer is the degraded verbal communication caused by the mask. Speech is often muffled, volume is lessened, and intelligibility is heavily attenuated. This often times makes it difficult for a listener to understand what the mask wearer is saying. The mask wearer may need to speak louder than normal to maintain the loudness level for the listener. However, speech intelligibility may not be improved, and several sounds are still difficult to be perceived. <CIT> describes a respiratory protection device comprising a microphone, a loudspeaker and a processor, the processor being coupled to the microphone and the loudspeaker and programmed to enhance the mask wearer's utterance.

<CIT> describes a respiratory protection device comprising a proximity sensor for detecting unsafe distances to another mask wearer.

Embodiments of the present disclosure are described herein. It is to be understood, however, that the disclosed embodiments are merely examples and other embodiments can take various and alternative forms. The figures are not necessarily to scale; some features could be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the embodiments. As those of ordinary skill in the art will understand, various features illustrated and described with reference to any one of the figures can be combined with features illustrated in one or more other figures to produce embodiments that are not explicitly illustrated or described. The combinations of features illustrated provide representative embodiments for typical applications. Various combinations and modifications of the features consistent with the teachings of this disclosure, however, could be desired for particular applications or implementations.

Face masks (such as face coverings, surgical masks, N95 masks, and the like) are simple and effective ways to help decrease the spread of germs and infectious disease. These masks may be more important now than ever with the spread of COVID-<NUM>. These masks can trap droplets that are released when the wearer sneezes, coughs, or talks. They can also protect the nose and mouth of the mask wearer from contacting respiratory droplets from other individuals. These masks are also used to filter out large particles in the air.

While masks can help stop the spread of infectious disease, they come with their problems. For one, masks make it harder for a person to hear what the mask wearer is saying. Speech is often muffled, volume is reduced, and clarity of the words spoken by the mask wearer is hindered. Even if the mask wearer speaks louder, it may still be hard to understand what the mask wearer is saying.

Therefore, according to various embodiments described herein, a mask <NUM> is disclosed that works to improve intelligibility and make it generally easier for a listener to understand what the mask wearer is saying. The mask <NUM> incorporates electronics to improve the speech. The mask <NUM> may therefore be referred to as a smart mask. The smart mask <NUM> also includes (or is part of) a smart mask system <NUM> that will be described in detail.

The smart mask <NUM> and associated smart mask system <NUM> is configured to receive an utterance or spoken speech from the mask wearer and, based on the utterance, output audio that enhances the utterance to improve the intelligibility of the utterance. Various devices may be provided as part of the mask <NUM> itself or an overall smart mask system <NUM> that includes the smart mask <NUM>. These device may include an audio transceiver <NUM>, one or more processors <NUM> (for purposes of illustration, only one is shown), any suitable quantity and arrangement of memory <NUM> such as non-volatile memory <NUM> (storing one or more programs, algorithms, models, or the like) and/or volatile memory. Accordingly, the smart mask system <NUM> comprises at least one computer (e.g., embodied as at least one of the processors <NUM> and memory <NUM>), wherein the smart mask system <NUM> is configured to carry out the methods described herein. The smart mask system <NUM> may also include a wireless transceiver <NUM>, proximity sensor <NUM>, and light source <NUM> which will also be described in turn.

The audio transceiver <NUM> may comprise one or more microphones <NUM>, one or more speakers <NUM>, and one or more electronic circuits (not shown) coupled to the microphone(s) <NUM> and speaker <NUM>. The electronic circuit(s) may comprise an amplifier (e.g., to amplify an incoming and/or outgoing analog signal), a noise reduction circuit, an analog-to-digital converter (ADC), a digital-to-analog converter (DAC), and the like. The audio transceiver <NUM> may be coupled communicatively to the processor <NUM> so that audible human speech uttered by the mask wearer may be received into the smart mask system <NUM> via the microphone(s) <NUM> and so that a sound may be generated audibly to a listener via the speaker <NUM> once the dialog system <NUM> has processed the user's speech. This is shown in the diagram illustrated in <FIG>. The microphone <NUM> is configured to receive an utterance spoken from the mask wearer, which can then be converted to digital via an ADC <NUM> for use by the processor <NUM>. The processor <NUM> can then access the memory <NUM> which contains, among other things, the speech enhancement model <NUM> (discussed below) for enhancing the speech of the mask wearer. The processor <NUM> can then send a signal to a DAC <NUM> for conversion into an analog signal so that a sound can exit the speaker <NUM>.

Of course other structure may be implemented in the smart mask system <NUM> that is not illustrated. For example, a noise filter, pre-amplifier, power amplifier, interference remover, and the like may be provided. An input buffer may be provided between the ADC <NUM> and the processor <NUM>, and an output buffer may be provided between the processor <NUM> and the DAC <NUM>.

If one microphone <NUM> is used with the smart mask <NUM>, a single-channel speech enhancement solution can be utilized. If two or more microphones <NUM> are used with the smart mask <NUM>, a microphone array solution can be utilized to enhance the speech intelligibility. Alternatively, a fixed-point DSP solution can be utilized to reduce the power consumption.

The processor(s) <NUM> may be programmed to process and/or execute digital instructions to carry out at least some of the tasks described herein. Non-limiting examples of processor(s) <NUM> include one or more of a microprocessor, a microcontroller or controller, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), one or more electrical circuits comprising discrete digital and/or analog electronic components arranged to perform predetermined tasks or instructions, etc. The processor <NUM> may therefore also be referred to as a controller. In at least one example, the processor <NUM> reads from memory <NUM> and executes multiple sets of instructions which may be embodied as a computer program product stored on a non-transitory computer-readable storage medium (e.g., such as non-volatile memory). Some non-limiting examples of instructions are described in the processes below and illustrated in the drawings. These and other instructions may be executed in any suitable sequence unless otherwise stated. The instructions and the example processes described below are merely embodiments and are not intended to be limiting.

The memory <NUM> may include both non-volatile memory and/or volatile memory. Non-volatile memory may comprise any non-transitory computer-usable or computer-readable medium, storage device, storage article, or the like that comprises persistent memory (e.g., not volatile). Non-limiting examples of non-volatile memory include: read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), optical disks, magnetic disks (e.g., such as hard disk drives, floppy disks, magnetic tape, etc.), solid-state memory (e.g., floating-gate metal-oxide semiconductor field-effect transistors (MOSFETs), flash memory (e.g., NAND flash, solid-state drives, etc.), and even some types of random-access memory (RAM) (e.g., such as ferroelectric RAM). According to one example, non-volatile memory may store one or more sets of instructions which may be embodied as software, firmware, or other suitable programming instructions executable by the processor <NUM> - including but not limited to the instruction examples set forth herein. For example, according to an embodiment, non-volatile memory may store various programs, algorithms, models, or the like, such as a speech recognition model <NUM>, a speech extraction model <NUM>, and/or a speech enhancement model <NUM>, which will be described in turn. According to an example, the memory <NUM> may store any combination of the one or more of the above-cited models (<NUM>-<NUM>) and may not store others. These models <NUM>-<NUM> each may comprise a unique of set instructions, and models <NUM>-<NUM> are merely examples (e.g., one or more other programs may be used instead).

The memory <NUM> may optionally include volatile memory, which may comprise any non-transitory computer-usable or computer-readable medium, storage device, storage article, or the like that comprises nonpersistent memory (e.g., it may require power to maintain stored information). Non-limiting examples of volatile memory <NUM> include: general-purpose random-access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), or the like.

Herein, the term memory may refer to either non-volatile or volatile memory, unless otherwise stated. During operation, processor <NUM> may read data from and/or write data to memory <NUM>, which may include volatile or non-volatile memory.

The speech recognition model <NUM> may be any suitable set of instructions that processes audible human speech. According to an example, speech recognition model <NUM> converts mask wearer's human speech into recognizable and/or interpretable words (e.g., textual speech data). A non-limiting example of the speech recognition model <NUM> is a model comprising an acoustic model, a pronunciation model, and a language model - e.g., wherein the acoustic model maps audio segments into phonemes, wherein the pronunciation model connects the phonemes together to form words, and wherein the language model expresses a likelihood of a given phrase. Continuing with the present example, speech recognition model <NUM> may, among other things, receive human speech via microphone(s) <NUM> and determine the uttered words and their context based on the textual speech data.

The speech extraction model <NUM> may be any suitable set of instructions that extract signal speech data from a mask wearer's audible human speech and use the extracted signal speech data to clarify ambiguities that arise from analyzing the text without such signal speech data. Further, in some examples, the speech extraction model <NUM> may comprise instructions to identify sarcasm information in the audible human speech. Thus, the speech extraction model <NUM> facilitates a more accurate interpretation of the audible human speech; consequently, using information determined by the speech extraction model <NUM>, the smart mask system <NUM> may generate more accurate responses.

According to at least one example, the speech extraction model <NUM> uses raw audio (e.g., from the microphone <NUM>) and/or the output of the speech recognition model <NUM>. Signal speech data may comprise one or more of a prosodic cue, a spectral cue, or a contextual cue, wherein the prosodic cue comprises one or more of an accent feature, a stress feature, a rhythm feature, a tone feature, a pitch feature, and an intonation feature, wherein the spectral cue comprises any waveform outside of a range of frequencies assigned to an audio signal of a user's speech (e.g., spectral cues can be disassembled into its spectral components by Fourier analysis or Fourier transformation), wherein the contextual cue comprises an indication of speech context (e.g., circumstances around an event, statement, or idea expressed in human speech which provides additional meaning). Types of extracted signal knowledge (i.e., the signal speech data) will be discussed in detail below.

The speech enhancement model <NUM> may include the speech recognition model <NUM> and/or speech extraction model <NUM>, or may be a standalone model receiving data from either model <NUM>, <NUM>. In other words, each of the models described herein may generally be referred to as a speech enhancement model. The speech enhancement model <NUM> is generally configured to enhance the speech prior to output to the speaker <NUM>. The model-based speech enhancement may be utilized to improve speech intelligibility. The speech enhancement model <NUM> can be trained from several acoustic models to represent mapping between unclear and clear speech sounds. The clear sound may be obtained from decoding the trained model with the highest likelihood score that matches the unclear sound. Ultimately, the clear sound may be output from the processor(s) <NUM> for output to the speaker <NUM>. The sound output from the speaker <NUM> can match the pitch and tone of the real-time utterance of the mask wearer, and the combination of the real un-processed utterance from the mask wearer with the sound output from the speaker <NUM> improves the intelligibility of the sound for better listening.

The speech enhancement model <NUM> may be an end-to-end neural network or any suitable neural network that is trained to generate an enhanced speech signal based on the input utterance. In one example, the speech enhancement model <NUM> utilizes deep learning processes (e.g., end-to-end learning) that includes a training component (e.g., in which the processor(s) <NUM> records all parameters executed by the human operator (e.g., through convolutional neural networks, CNNs)) and an inference component (e.g., in which the processor(s) <NUM> acts upon previously gained experience from the training component.

The speech enhancement model <NUM> can utilize a noise-estimation algorithm as part of its speech enhancement. In such a system, a spectrum of background noise is estimated as deviating from the spoken utterances, and those background noise spectra are removed from the sound signals sent to be output by the speaker <NUM>. In an embodiment, the multi-channel Wiener filter (MWF) is utilized. In an embodiment, a distributed adaptive node-specific signal estimation (DANSE) algorithm which is a distributed realization of the MWFs of the individual nodes of the smart mask (e.g., each microphone and associated processing architecture), and which allows the nodes to cooperate by exchanging pre-filtered and compressed signals while eventually converging to the same centralized MWF solutions as if each node would have access to all the microphone signals in the smart mask. In an embodiment, a generalized eigenvalue decomposition (GEVD)-based DANSE can be utilized in which each node incorporates a GEVD-based low-rank approximation of the speech correlation matrix in its local MWF. In an embodiment, a double buffering (e.g., Ping-Pong buffer) can be implemented in two buffers, one Ping and one Pong, each receive data, buffer the data, and transmit the data back for further buffering by the other buffer. For example, if one receiving buffer (e.g., Ping) is already full, data from a buffer port (e.g., multichannel buffered serial ports, McBSP) can be transferred to the receiving buffer (e.g., Pong) and the controller can convey the processed data in the transmitting Pong buffer to the McBSP to send out. At the same time, the processor processes the data in the Ping receiving buffer and put the enhanced data into the Ping transmitting buffer.

Once speech or an utterance is recognized by model <NUM>, and optionally extracted by model <NUM>, the speech or utterance may be enhanced by enhancement model <NUM> implementing either digital signal processing machine learning, or other methods described herein, and eventually the enhanced speech is output by the speaker <NUM>. The speech enhancement model <NUM> is configured to output those spoken words in a clear fashion with little or no noise or other interference. Once converted to analog (e.g., via DAC <NUM>), sound can be output from the speaker <NUM> that includes the amplified, clear spoken words in a pitch and frequency that matches the real-time spoken utterance from the mask wearer.

The models described herein (e.g., models <NUM>-<NUM>) may be configured to perform digital signal processing using software pre-programmed and fixed in the memory <NUM>. The models <NUM>-<NUM> may also implement machine learning functions, in which the memory <NUM> stores one or more learning engines executable by the processor(s) <NUM> to process data of utterances received from the microphone <NUM>, and develop utterance or spoken word metadata on the mask wearer. Machine learning generally refers to the ability of a computer application to learn without being explicitly programmed. In particular, a computer application performing machine learning (sometimes referred to as a learning engine) is configured to develop an algorithm based on training data. For example, to perform supervised learning, the training data includes example inputs and corresponding desired (e.g., actual) outputs, and the learning engine progressively develops a model that maps inputs to the outputs included in the training data. Machine learning can be performed using various types of methods and mechanisms including, but not limited to, decision tree learning, association rule learning, artificial neural networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, and genetic algorithms.

It will be appreciated that computer programs, algorithms, models, or the like may be embodied in any suitable instruction arrangement. For example, one or more of the speech recognition model <NUM>, speech extraction model <NUM>, speech enhancement model <NUM>, and any other additional suitable programs, algorithms, or models may be arranged as a single software program, multiple software programs capable of interacting and exchanging data with one another via processor(s) <NUM>, etc. Further, any combination of the above programs, algorithms, or models may be stored wholly or in part on memory <NUM>.

The wireless transceiver <NUM> can be integrated into the mask <NUM> or external to the mask <NUM> but part of the smart mask system <NUM>. The wireless transceiver <NUM> enables communication between the smart mask system <NUM> and another device, such as a mobile device (e.g., smart phone, smart watch, other wearable devices, etc.). This can enable communication from the smart mask <NUM> to an electronic personal assistant on the device, such as Siri™ for Apple™ devices or Google Assist™ (Google Now ™) for Android™ devices. As such, the wireless transceiver <NUM> may be configured to communicate with the device via Bluetooth, Bluetooth Low Energy (BLE), Z-Wave, ultra-high frequency (UHF), ZigBee or other communication protocol based on IEEE <NUM>. <NUM>, and the like.

The wireless transceiver <NUM> is also communicatively connected to the processor(s) <NUM> and interactive with the speech enhancement models implemented. For example, the speech that is recognized by the models described herein in digital form can be communicated to the electronic personal device for commands. This can clarify the commands given to the personal assistant for better processing by the personal assistant. As described herein, any mask or face covering can interfere with the intelligibility of utterances; by enhancing the speech and then sending a signal to the electronic personal assistant based on the enhanced speech, the electronic personal assistant is better able to understand and process the commands given by the mask wearer.

In other embodiments, the wireless transceiver <NUM> is coupled to the processor(s) <NUM> in a way that the utterance from the mask wearer is transmitted to the mobile device without any speech enhancement. For example, the utterance can be merely passed through the system <NUM> from microphone <NUM> and ADC <NUM>, and transmitted as a digital signal to the mobile device without relying on any models <NUM>-<NUM>, etc. Thus an unfiltered and raw voice is converted to a digital signal and transmitted to the mobile device.

The proximity sensor <NUM> and light source <NUM> can also optionally be integrated into the mask <NUM>, or external to the mask <NUM> but part of the smart mask system <NUM>. The proximity sensor <NUM> is configured to detect the existence of a nearby object within a threshold distance (e.g., about <NUM> (six feet)) from the sensor. The light source <NUM> may activate in response to the proximity sensor <NUM> indicating an object is closer to the sensor than the threshold distance. This can warn both the mask wearer and the external object (e.g., another person) that they are unsafely close to one another. Current Center for Disease Control (CDC) guidelines recommend <NUM> (six feet) be maintained between conversing people, and the use of the proximity sensor <NUM> and light source <NUM> can be a tool to warn the people to keep their distance safely.

The proximity sensor may be any type of sensor configured to detect the presence of nearby objects without any physical contact. It may, for example, emit an electromagnetic field or beam of electromagnetic radiation (e.g., infrared) and look for changes in the field or return signal. In other embodiments, the proximity sensor <NUM> may be embodied as a capacitive sensor, inductive sensor, magnetic sensor, optical sensor (e.g., photocell), or may utilize radar, sonar, or the like. A signal from the proximity sensor <NUM> is output to the processor <NUM>, which accesses predetermined rules stored in memory <NUM> regarding the threshold distance, for example. In response to the signal from the proximity sensor indicating a distance between the sensor <NUM> and an external object being less than the stored threshold distance, the processor <NUM> can activate the light source <NUM> (e.g., light-emitting diode, LED, or the like) mounted on the mask <NUM>.

In one embodiment, the light source <NUM> will emit a first color in response to the processor <NUM> recognizing that the mask wearer is speaking, via the microphone <NUM>. The light source <NUM> may emit a second color (different than the first color) in response to the processor <NUM> recognizing that a nearby object is closer than the threshold distance, via the proximity sensor <NUM>. For example, as the mask wearer is talking, the light source <NUM> may emit a yellow light, and if the proximity sensor <NUM> detects a person within the threshold distance (e.g., about <NUM> (six feet)) the light source <NUM> may change to emit a red light.

<FIG> illustrates an exploded perspective view of a main body <NUM> of the mask <NUM>, according to an embodiment. The main body <NUM> includes a back frame <NUM> and a front cover <NUM> configured to attach to the back frame <NUM>. The attachment between the back frame <NUM> and the front cover <NUM> can be a hinge connection or the like, maintaining connection or contact in a certain region at all times such that electronic wiring and the like can pass therethrough even when the back frame <NUM> and front cover <NUM> are open or separated to receive a filter <NUM>. While the back frame <NUM> and front cover <NUM> are shown as separate components, it should be understood that in other embodiments they are a single unit. The back frame <NUM>, front cover <NUM>, and filter <NUM> can be referred to as a mask assembly.

The back frame <NUM> may have an opening <NUM> with a border <NUM> surrounding the opening <NUM>. Likewise, the front cover <NUM> may have an opening <NUM> with a border <NUM> surrounding the opening <NUM>. The openings <NUM>, <NUM> are present receive a filter <NUM> or mask material that covers the openings <NUM>, <NUM> as will be described. The border <NUM> of the back frame <NUM> provides an attachment area for the border <NUM> of the front cover <NUM>. The attachment area (e.g., the side of the border <NUM> facing the front cover <NUM>) may be a planar surface, and/or may be provided with a suitable attachment for the border <NUM> of the front cover <NUM>. For example, the border <NUM> may have an adhesive, hook and loop (e.g., Velcro ™), hooks, tabs, latches, or other such attachments to allow the wearer to easily remove or detach the front cover <NUM> from the back frame <NUM> to access and replace the filter <NUM>, for example.

Either one or both of the borders <NUM>, <NUM> may have surface features formed thereon that provide a grip for the filter <NUM> to attach to. For example, the borders <NUM>, <NUM> (or at least the surfaces of the borders facing one another) can be made of a vinyl, foam (e.g., vinyl foam, polyethylene foam, closed-cell neoprene, etc.) high-density urethane, or other suitable material that provide a grip to hold the filter <NUM> against it.

Rather than a removable attachment, the front cover <NUM> may be attached to the back frame <NUM> in a more permanent manner (e.g., stitching, gluing, co-molding, fastened, etc.). Or, the front cover <NUM> and the back frame <NUM> may be a single unitary component. In either embodiment, the front cover <NUM> and/or the back frame <NUM> may define a receptacle (e.g., an opening, a pocket, etc.) sized and configured to receive the filter <NUM> therein. In use, the mask wearer can open the main body <NUM> (e.g., by opening the hinge connection between the back frame <NUM> and the front cover <NUM>, or otherwise at least partially disconnecting the front cover <NUM> from the back frame <NUM>) or simply access the receptacle defined between the back frame <NUM> and the front cover <NUM>. The mask wearer can then remove the filter <NUM> therein, and replace with another filter. This preserves the main body <NUM> of the smart mask (allowing it for reuse) while allowing the filter to be replaced as needed.

The back frame <NUM> and the front cover <NUM> may be made of a rigid material, such as plastic. The plastic may be form-fitted to the shape of the wearer's face. For example, the back frame <NUM> may be molded or otherwise formed to take the shape of the wearer's face. Alternatively, the back frame <NUM> and front cover <NUM> may be made of a flexible material configured to seal against the wearer's face. In such an embodiment, the back frame <NUM> (or at least the surface facing the wearer) may be made of a rubber or thin plastic film or membrane that can bend and flex to the contours of the wearer's face. Alternatively, the back frame <NUM> may be made of a rigid material (e.g., plastic) on a front side and a thin or more flexible material on the back side for contacting the wearer's face. In other words, the main body <NUM>, which comprises the back frame <NUM> and the front cover <NUM>, may include a first material with a first flexibility on a front side, and a second material with a second flexibility (greater than the first) on a back side for sealing with the face. The first material can house the electronic components and provide a rigid surface for contacting the front cover <NUM>, while the second material can seal with the wearer's face.

The back frame <NUM> may include ear loops <NUM> configured to wrap around the ears of the mask wearer to hold the smart mask in place on the wearer's face. This is but one example of maintaining the mask on the wearer's face. In another embodiment, the loops are replaced with straps or bands configured to tie together behind the wearer's face rather than wrap around the wearer's ears.

The filter <NUM> be made of a suitable material for properly filtering contagious diseases. For example, the filter <NUM> may be an N95 (e.g., <NUM>% efficient at filtering <NUM> particles), N99 or N100 respirator. Alternatively, the filter <NUM> may be made of a cloth, or can itself be a surgical mask. The filter <NUM> may be made of an electrostatic non-woven polypropylene fiber, or can also be made of polystyrene, polycarbonate, polyethylene or polyester. The filter <NUM> may have its own loops configured to wrap around the wearer's ears, for example if the filter <NUM> itself is a surgical mask. If so, the filter <NUM> can be placed within the main body <NUM> of the smart mask <NUM> with its loops outside of the main body <NUM> to enable the loops to be available to wrap around the wearer's ears.

In one example use, a user can open the main body <NUM> by disconnecting the front cover <NUM> from the back frame <NUM>. The user can remove an old filter if one was there. The user can then attach a new filter <NUM> to the back frame <NUM> such that it covers the opening <NUM>. The material of the border <NUM> described above can give the filter <NUM> an anti-slip engagement with the back frame <NUM>. The front cover <NUM> can then be placed over the filter <NUM> and attached to the back frame <NUM> such that the opening <NUM> of the front cover <NUM> aligns with the opening <NUM> of the back frame <NUM>, and only the filter <NUM> is present in that area of the smart mask <NUM>. Any ear loops or other head attachments of the filter <NUM> can be wrapped around the user's head or ears, as with the ear loops <NUM> of the main body <NUM>.

In another example of use, the main body <NUM> is a single integral unit with a slot or pocket for changing out filters <NUM>. The user can access a pocket or slot in the main body <NUM>, retrieve the old filter and discard it. A new filter <NUM> can then be placed in the slot or pocket, between the back frame <NUM> and front cover <NUM>. The interior of the slot or pocket can include material described above with respect to the borders <NUM>, <NUM> to provide a grip for the filter <NUM>. The ear loops <NUM> or the like of the main body <NUM> can then be fastened around the user's ears.

<FIG> illustrates a front view of smart mask <NUM>, specifically the front cover <NUM> viewed from a standpoint in front of the smart mask <NUM> as worn by a wearer. This view also shows a filter <NUM> attached to the main body <NUM>, covering the openings <NUM>, <NUM> described above. The proximity sensor <NUM>, light source <NUM>, and speaker <NUM> are shown here fixed within the front cover <NUM>. The proximity sensor <NUM>, light source <NUM>, and speaker <NUM> may all be powered by wire to a battery (not shown) which can be fixed or otherwise located within either the back frame <NUM> or the front cover <NUM> of the smart mask. The battery may also power the other components, such as the processor <NUM>, memory <NUM>, and the like. Likewise, the proximity sensor <NUM>, light source <NUM> and speaker <NUM> may be connected by wire to the processor <NUM>, which can be located in either the back frame <NUM> or front cover <NUM>. The speaker <NUM> may be embedded into the front cover <NUM>, at a bottom of the front cover in a location that is aligned with the wearer's mouth.

The output volume of the speaker <NUM> can be dynamically adjusted by the controller <NUM> based on the volume of the original speech uttered by the mask wearer. According to the invention, the output volume of the speaker <NUM> is dynamically adjusted by the controller <NUM> based on the distance between the mask wearer and another person as indicated by the proximity sensor <NUM>. For example, if the proximity sensor detects an external object being about <NUM> (<NUM> feet) away, the controller <NUM> may amplify the volume output by the speaker <NUM> accordingly, and may reduce the volume output by the speaker <NUM> as the distance is reduced. In an embodiment, a threshold distance may be set (e.g., about <NUM> (six feet)) in which, if the distance to the other person as detected by the proximity sensor <NUM> is less than that threshold, the speaker <NUM> will no longer output sound.

<FIG> illustrates a back view of the smart mask <NUM>, specifically the back frame <NUM> viewed from a standpoint from the mask wearer. In other words, <FIG> shows the side of the smart mask <NUM> that faces the wearer. This view shows two microphones <NUM> embedded or otherwise fixed within the back frame <NUM>, facing the wearer. Additional microphones can be utilized. The microphone(s) <NUM> capture audio signals uttered by the mask wearer for processing by the processor according to the teachings herein.

In an alternative example not falling within the scope of the claims, the speaker <NUM> may be embedded in the back frame <NUM> rather than the front cover <NUM>. This embodiment may be utilized if, for example, the front cover <NUM> is smaller than the back frame <NUM>. The front cover <NUM> may have a corresponding opening or cut-out in a location where the speaker <NUM> is so as to not interfere with the sound output by the speaker <NUM>.

One or more additional microphones can be placed outside the front cover <NUM> of the smart mask <NUM> configured to capture environmental audio signals. These audio signals can be provided to the processor <NUM> to allow filtering of background acoustics for better audio processing of the utterances.

In one embodiment, the smart mask <NUM> may implement or provide a health monitor system for the wearer. For example, the microphone(s) <NUM> may receive audio data indicative of breathing patterns. The controller or processor <NUM> can process such audio and determine that the breathing patterns of the wearer has changed, or increase in rate or change in pitch. If the rate of breathing, or the change in the rate of breathing has exceeding a corresponding threshold, a notification can be sent to the wearer's mobile device (e.g., via transceiver <NUM>), or the light source <NUM> can be activated to alert the wearer and others of a potential health issue.

The processes, methods, or algorithms disclosed herein can be deliverable to/implemented by a processing device, controller, or computer, which can include any existing programmable electronic control unit or dedicated electronic control unit. Similarly, the processes, methods, or algorithms can be stored as data and instructions executable by a controller or computer in many forms including, but not limited to, information permanently stored on non-writable storage media such as ROM devices and information alterably stored on writeable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media. The processes, methods, or algorithms can also be implemented in a software executable object. Alternatively, the processes, methods, or algorithms can be embodied in whole or in part using suitable hardware components, such as Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software and firmware components.

Claim 1:
A smart mask system (<NUM>) comprising:
a main body (<NUM>) including:
a back frame (<NUM>) defining a first opening (<NUM>) and a first border (<NUM>) surrounding the first opening (<NUM>), the first border (<NUM>) providing a surface configured to engage a replaceable filter (<NUM>), and
a front cover (<NUM>), wherein
the front cover (<NUM>) is at least partially removably attached to the back frame (<NUM>), the front cover (<NUM>) defines a second opening (<NUM>) aligned with the first opening (<NUM>) of the back frame (<NUM>) when the front cover (<NUM>) is attached to the back frame (<NUM>), and the front cover (<NUM>) further defines a second border (<NUM>) surrounding the second opening (<NUM>), wherein at least a portion of the second border (<NUM>) of the front cover (<NUM>) is configured to removably attach to the first border (<NUM>) of the back frame (<NUM>) to enable replacement of the filter (<NUM>), or
the main body (<NUM>) is a single unitary component and a receptacle for receiving the filter (<NUM>) is formed in the main body (<NUM>);
a microphone (<NUM>) embedded in the back frame (<NUM>) and configured to receive an utterance from a mask wearer;
a speaker (<NUM>) embedded in the front cover (<NUM>);
a processor (<NUM>) located in the main body (<NUM>) and coupled to the microphone (<NUM>) and the speaker (<NUM>), the processor (<NUM>) programmed to (i) receive signals representing the utterance, (ii) execute a speech enhancement model (<NUM>) stored in memory (<NUM>) to enhance the utterance, and (iii) output signals to cause the speaker (<NUM>) to output sound based on the enhancement of the utterance; and
a proximity sensor (<NUM>) embedded in the main body (<NUM>), coupled to the processor (<NUM>), and configured to detect a distance from the proximity sensor (<NUM>) to a person,
wherein the processor (<NUM>) is configured to dynamically adjust the output volume of the speaker (<NUM>) based on the distance between the mask wearer and another person as indicated by the proximity sensor (<NUM>).