Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION Claims 1-20 are pending. Claims 1, 8, and 15 are independent. This Application was published as U.S. 20240203440 . Apparent priority: 1 5 October 2020 . The instant Application is filed as a Divisional of application no. 18/129469, issued into US 11,875,813 on claims that were withdrawn in the parent application pursuant to a restrict ion requirement and correspond to withdrawn claims 18-27 of the parent. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim s 1- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pu (U.S. 20210306765) in view of Mesgarani (U.S. 20190066713) . Pu is a U.S. patent application publication corresponding to the journal article cited by the EPO office as an x reference and submitted by an IDS. ( PU WENGIANG ET AL: “Evaluation of Joint Auditory Attention Decoding and Adaptive Binaural Beamforming Approach for Hearing Devices with At tention Switching , ” I CASSP 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON ACQUSTICS, SPEECH AND SIGNAL PROCESSING ( I CASSP), IEEE, 4 May 2020 (2020-05-04), pages 8728-8732 .) Regarding Claim 1 , Pu teaches: 1. A method for speech separation comprising: [Pu is directed to source separation (SP) .] obtaining, by a device, a combined sound signal for signals combined from multiple sound sources in an area in which a person is located; [Pu, the audio being captured by the microphone 102 is a mixed audio signal. Figure 1, Hearing Aid 104 includes or is connected to an EEG and the EEG is connected to the person’s brain and therefore the person is located in the same area as the microphone 102. Figure 3 shows the “experiment participant” in the middle of the room and the talkers are located all around him at P11 .. and also P5 and P6 that are front and back. “[0024] FIG. 1 is a block diagram of an exemplary embodiment of an ear-mounted hearing system 100 according to the present invention…. In an embodiment, there are K speech sources, also called talkers, in an environment with a plurality of talkers, including target talker and background talkers (who simulate background noise)…. ” “[0006] Additional source separation process: In the AA decoding step, in order to obtain the envelope information of every speech source, it is necessary to extract the information of each speech source from the mixed input speech signals received from the microphone, which arouses an additional demand for blind source separation and needs to be implemented by an additional and complex signal processing method.” See also [0054]-[0055] with respect to Figure 3.] obtaining, by the device, neural signals for the person , the neural signals being indicative of one or more target sound sources , from the multiple sound sources, the person is attentive to; [Pu, Figure 1, EEG is the neural signal that indicates to which talker the wearer of the hearing aid is trying to listen (“target talker” / “target sound source.”) “[0024] FIG. 1 is a block diagram of an exemplary embodiment of an ear-mounted hearing system 100 according to the present invention. The hearing system 100 comprises an electroencephalogram-assisted beam former 108 and a linear transformation module 110. The hearing system 100 further comprises a microphone 102, a hearing system processing circuit 104 and a loudspeaker 106. The electroencephalogram-assisted beam former 108 may be arranged in a hearing system processing circuit 104 and may also be arranged in separation of the hearing system processing circuit 104. In the description below, the electroencephalogram-assisted beam former 108 arranged in the hearing system processing circuit 104 is taken as an example. In an embodiment, there are K speech sources, also called talkers, in an environment with a plurality of talkers, including target talker and background talkers (who simulate background noise). When one of the target talkers talks with the user of the ear-mounted hearing system, the target talker is called an attended talker, while other target talkers are considered interferences (also called unattended talkers). The microphone 102 shown in FIG. 1 represents M microphones, all receiving an input sound and generating an electrical signal representing the input sound. The processing circuit 104 processes (one or more) microphone signals to generate an output signal. The loudspeaker 106 uses the output signal to generate an output sound including the speech. In various embodiments, an input sound may comprise various components, a speech and a noise interference, as well as a sound fed back by the loudspeaker 106 via a sound feedback path. ….”] determining a separation filter based, at least in part, on the neural signals obtained for the person; and [Pu, Figure 1, the “separation filter” is taught by the “adaptive filter” of Pu: “[0024] … The processing circuit 104 comprises an adaptive filter to reduce noise and sound feedback. In the embodiment shown, the adaptive filter comprises an electroencephalogram-assisted beam former 108… Further, FIG. 1 also shows a linear transformation module 110, which is configured to receive EEG signals from the head of the user of the hearing system through a sensor on the head of the user and perform linear transformation on the received EEG signals to obtain an optimized linear transformation coefficient. The linear transformation coefficient reconstructs the envelope of speech sources according to the EEG signals to obtain a reconstructed envelope of the speech sources, and the linear transformation module 110 is further configured to transfer the reconstructed envelope to the electroencephalogram-assisted beam former 108. In various embodiments, w hen the hearing system 100 is implemented, the processing circuit 104 receives microphone signals, and the electroencephalogram-assisted beam former 108 uses the microphone signals from the hearing system and the reconstructed envelope to provide adaptive binaural beam forming . ”] applying, by the device, the separation filter to a representation of the combined sound signal to derive a resultant separated signal representation associated with sound from the one or more target sound sources the person is attentive to; [Pu, Figure 1, EEG signals help determine the attended talker/speaker to whom the wearer of the hearing aid wants to listen: “[0024] …In an embodiment, there are K speech sources, also called talkers, in an environment with a plurality of talkers, including target talker and background talkers (who simulate background noise). When one of the target talkers talks with the user of the ear-mounted hearing system, the target talker is called an attended talker, while other target talkers are considered interferences (also called unattended talkers). … The loudspeaker 106 uses the output signal to generate an output sound including the speech. …”] wherein the combined sound signal comprises sound components corresponding to multiple receiving channels, and [Pu, Figure 2A shows the multiple channels on the left had side as the “Multi-channel signal” including y1(t), y2(t), yM(t). “[0025] … FIG. 2A shows microphone signals y1(t), y2(t) and yM(t) …”] wherein determining the separation filter comprises: [As provided above the the “separation filter” is taught by the “adaptive filter” of Pu which is shown at the “electroencephalogram-assisted beam former 108” in Figure 1 and as the electroencephalogram-assisted beam former” block in Figure 2A which is copied from the associate Pu paper and does not include “[0024] … The processing circuit 104 comprises an adaptive filter to reduce noise and sound feedback. In the embodiment shown, the adaptive filter comprises an electroencephalogram-assisted beam former 108 …” ] applying multiple encoders to the sound components corresponding to the multiple receiving channels, with each of the encoders applied to each of the sound components; [Pu, Figure 2A, the STFT block on the left that is receiving the multi-channel signal teaches the “encoders” of the Claim considering that: “[0034] … is a diagonal matrix for compensating a synthesis window used in short-time Fourier transform (STFT) that i s used to express a plurality of input signals ….” “[0053] … As shown in FIGS. 2A and 2B, this embodiment shows three channels of speech signals only in the form of an example, but the present invention is not limited to this. I n the first step S 1 , input speech signals y1(t), y2(t) and yM(t) received at the microphone are appropriately transformed through STFT to signals y1(l, ω), y2(l, ω) and yM(l, ω) in the time-frequency domain, and EEG signals are appropriately transformed through pre-trained linear transformation to the time domain and are input to the electroencephalogram-assisted beam former. …”] for each of the multiple receiving channels, combining output components of the multiple encoders associated with respective ones of the multiple receiving channels; and [ Pu, Figure 2A, the outputs of the STFT are combined in a weighted combination where the weights are provided by Linear Transformation of the EEG signal at the middle box: Electroencephalogram-assisted beam former. “[0053] … In the second step S2, appropriately transformed signals (including transformed input speech signals and transformed EEG signals) are combined to use the above optimization standard to design a beam former , for the purpose of enhancing the speech of the attended talker with the AA information contained in the EEG signals to obtain beam-forming weight coefficients ω1(ω), ω2(ω) and ωM(ω)….” “[0009] … a device for constructing an optimization model and solving the optimization model to obtain a beam-forming weight coefficient for performing linear or non-linear combination on the plurality of input signals, …”] deriving estimated separation functions based on the combined output components for each of the multiple receiving channels, each of the derived estimated separation functions configured to separate the combined output components for each of the multiple receiving channels into separated sound components associated with groups of the multiple sound sources . [Pu, Figure 2A, the beam former that is generated includes the “estimated separation functions” and is based on the combined outputs of the STFT applied to the multiple channel inputs. “[0053] … In the last step S3, the beam-forming weight coefficients obtained by the designed electroencephalogram-assisted beam former are used to perform combination on the signals y1(l, ω), y2(l, ω) and yM(l, ω) that have undergone SFTF to synthesize a beam forming output through ISTFT.”] Pu does not teach “ multiple” encoders. Further, w hile Pu teaches the broadly claimed “encoders” of the Claim, the “separation filter” of the instant Application is based on “a Time-domain Audio Separation Network (TasNet)” that is the subject of an earlier reference having the first-named inventor in common with the instant Application and assigned to the assignee of the instant Application. Mesgarani teaches: wherein determining the separation filter comprises: [Mesgarani, Figures 2 and 3. Figure 2 input of the “speaker mixture 210” included Spkk1+Spk2 and Figure 3, 310: “[0106] With reference now to FIG. 3, a flowchart of an example procedure 300 for attentional selection of a speaker in a multi-speaker environment. The procedure 300 includes obtaining 310, by a device, a combined or mixed sound signal for signals combined from multiple sound sources in an area in which a person is located . As noted, the device of procedure 300 (that performs that various operation of the procedure 300) may include one or more of, for example, a hearing device, a hearing enhancement device (to allow better hearing in a very noisy environment), a noise cancellation device, a virtual reality device, and/or other types of hearing augmentation devices . In some embodiments, the combined signal may be obtained through use of a single receiving sensor (e.g., a single microphone), although the procedure 300 may be performed using multiple receiving sensor which may be part of the device implementing the procedure 300.”] applying multiple encoders to the sound components corresponding to the multiple receiving channels, with each of the encoders applied to each of the sound components; [ Mesgarani, Figure 2, “DNN Speaker Separation” including DNN Spk1 … DNN SpkN. “[0086] With continued reference to FIG. 2, signal separation processing is applied to the combined (mixed) audio signal (which may have been processed based on, for example, processing operation such as those described in relation to stage 210) at stage 220. In some embodiments, speech separation processing is based on a deep neural network (DNN) approach. After obtaining the mixed/combined audio signal, a spectrogram of the mixture is obtained. This spectrogram is then fed to each of several DNNs, each trained to separate a specific speaker from a mixture (DNN Spk1 to DNN SpkN) ….”] for each of the multiple receiving channels, combining output components of the multiple encoders associated with respective ones of the multiple receiving channels; and [ Mesgarani, Figure 2, 230 Speaker Selection combines the outputs of the DNN encoders of stage 220 at stage 230 as shown on Figure 2.] deriving estimated separation functions based on the combined output components for each of the multiple receiving channels, each of the derived estimated separation functions configured to separate the combined output components for each of the multiple receiving channels into separated sound components associated with groups of the multiple sound sources . [ Megarani, Figure 2, “speaker selection 230” separates the speakers by use of correlation functions which teach the “estimated separation functions” of the Claim: “[0086] … At the same time, a user may be attending to one of the speakers (in this case, Spk1). A spectrogram of this speaker is reconstructed from the neural recordings of the user. This reconstruction is then compared with the outputs of each of the DNNs using a normalized correlation analysis (as shown at stage 230) in order to select the appropriate spectrogram, which is then converted into an acoustic waveform and added to the mixture so as to amplify the attended speak er (and/or attenuate the signals corresponding to the non-attended speakers).” Figure 3, 320. ] Mesgarani: Pu and Mesgarani pertain to use of EEG signal for Auditory Attention Decoding (AAD) and it would have been obvious to use the separation arrangement of Mesgarani which uses a separate DDN encoder for each speaker in place of single encoder/STFT of Pu for a finere-grained analysis. This combination falls under combining prior art elements according to known methods to yield predictable results or simple substitution of one known element for another to obtain predictable results. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Regarding Claim 2 , Pu teaches: 2. The method of claim 1, wherein the multiple receiving channels comprise a first and second binaural receiving channels . [Pu uses a microphone array which is binaural. “[0003] On the one hand, application of the spatially diverse binaural beam forming technology (provided by a microphone array) in the hearing system is a prior art which can significantly improve user's semantic comprehension of a target talker in a noisy environment with a plurality of talkers. …” “[007] … Specifically, by establishing an inherent association between EEG-assisted AA decoding and binaural beam forming from the perspective of signal processing, a joint algorithm for AA decoding and adaptive binaural beam forming in an ear-mounted hearing system is proposed. ….”] Regarding Claim 3 , Pu teaches: 3. The method of claim 1, wherein determining the separation filter comprises: determining based on the neural signals an estimate of an attended sound signal corresponding to the one or more target sound sources the person is attentive to; and [Pu, Figure 2A, the EEG signal is input to the “Electroencephalogram-assisted beam former and identifies the target/attended sounds: “[0023] … The beam former according to embodiments of the present application is designed to establish an inherent association between EEG-assisted AA decoding and binaural beam forming from the perspective of signal processing, improve the speeches of attended talkers in a noisy environment with a plurality of talkers and reduce other impacts. In order to effectively use the AA information contained in EEG signals , …..” AA=Auditory Attention.] generating the separation filter based, at least in part, on the determined estimate of the attended sound signal . [Pu, Figure 1, 108: “[0024] … The processing circuit 104 comprises an adaptive filter to reduce noise and sound feedback. In the embodiment shown, the adaptive filter comprises an electroencephalogram-assisted beam former 108 ….” The “adaptive filter” of Pu teaches the “separation filter” of the Claim because it separates the speech of the attended talker from the rest.] Regarding Claim 4 , Pu teaches: 4. The method of claim 3, wherein determining the estimate of the attended sound signal comprises: determining, using a learning process, an estimated target envelope for the one or more target sound sources the person is attentive to, [ Pu, “[0024] … The linear transformation coefficient reconstructs the envelope of speech sources a ccording to the EEG signals to obtain a reconstructed envelope of the speech sources, and the linear transformation module 110 is further configured to transfer the reconstructed envelope to the electroencephalogram-assisted beam former 108 . In various embodiments, when the hearing system 100 is implemented, the processing circuit 104 receives microphone signals, and the e lectroencephalogram-assisted beam former 108 uses the microphone signals from the hearing system and the reconstructed envelope to provide adaptive binaural beam forming .” “[0029] In an embodiment of the present disclosure, the linear transformation module 110 is configured to obtain an optimized linear transformation coefficient, thereby reconstructing the envelope of the speech sources to obtain a reconstructed envelope of the speech source s. … Further, using the electroencephalogram signals in the beam former design includes without limitation the linear transformation method disclosed by the embodiment of the present invention, and also includes other methods, such as non-linear transformation, or a method using a neural network . …” “[0057] … A cross validation is reserved among these time slots to train and optimize the linear transformation coefficient {gi*(τ)} and evaluate beam forming performance….”] the estimated target envelope being combined with the output components of the multiple encoders . [Pu, “[0063] … The AA information contained in the EEG signals is encoded through the obtained optimized linear transformation coefficient {g.i*(τ)} according to the envelope ŝa(t). In some aspects, the value of Δρ reflects a confidence in the possibility that the speech sources are attended speech sources….” Note that g is obtained by training and optimization of a linear transformation process. ] Regarding Claim 5 , Pu does not mention the particular type of the EEG used. Mesgarani teaches: 5. The method of claim 1, wherein obtaining the neural signals for the person comprises measuring the neural signals according to one or more of: i nvasive intracranial electroencephalography (iEEG) recordings, non-invasive electroencephalography (EEG) recordings , functional near-infrared spectroscopy (fNIRS) recordings, or recordings captured with subdural or brain-implanted electrodes . [Mesgarani:”[0078] … In some embodiments, procedures to decode which speaker a person is attending to are implemented by monitoring their neural activity via both invasive and non-invasive electrophysiological recordings . In such embodiments, attention-aware brain-computer interfaces (BCIs) may be used to control smart hearable devices capable of selectively processing (e.g., amplifying) one speaker and/or suppressing other speakers in crowded environments.” “[0128] … In some embodiments, neural data may be obtained also through non-invasive neural recordings.” “[0113] With continued reference to FIG. 3, the procedure 300 also includes obtaining 330, by the device, neural signals for the person, with the neural signals being indicative of one or more of the multiple sound sources the person is attentive to. Obtaining the neural signals for the person may include obtaining one or more of, for example, electrocorticography (ECoG) signals for the person, neural measurements via a non-invasive scalp, and/or in-ear EEG recordings .” “[0121] … These results show that electrodes placed in STG are important for successfully decoding attention (an important finding for non-invasive AAD research where source localization methods can be used to target specific brain regions).” Pu and Mesgarani pertain to use of EEG signal for Auditory Attention Decoding (AAD) and it would have been obvious to use one of the EEG arrangements of Mesgarani in the system of the Pu which does not specify the EEG electrode arrangement. This combination falls under combining prior art elements according to known methods to yield predictable results or simple substitution of one known element for another to obtain predictable results. See MPEP 2141, KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Regarding Claim 6 , Pu does not teach the use of TCNs. Mesgarani teaches: 6. The method of claim 1, wherein deriving the estimated separation functions comprises: processing the combined output components for each of the multiple receiving channels with respective one or more temporal convolutional network (TCN) blocks to estimate multiplicative functions that are applied to the output components of the multiple encoders associated with respective ones of the multiple receiving channels . [Mesgarani, Figure 2, 220, the DNNs of Mesgarani are part of its TasNet system which is based on TCNs: “[0177] … Convolutional Encoder for a Time-Domain Audio Separation Network”: “[0179] The convolution TasNet systems, methods, and other implementations described herein implement a deep learning autoencoder framework for time-domain speech separation. TasNet uses a convolutional encoder to create a representation of the signal that is optimized for extracting individual speakers. Speaker extraction is achieved by applying a weighting function (mask) to the encoder output….” “[0180] The convolutional TasNet implementations described herein use a stacked dilated 1-D convolutional networks. This approach is motivated by the success of temporal convolutional network (TCN) models which allow parallel processing on consecutive frames or segments to speed up the separation process. This approach also reduces the model size. To further decrease the number of parameters and the computational complexity of the system, the original convolution operation is replaced with depth-wise separable convolution. Such a configuration provides high separation accuracy. The separation accuracy of Conv-TasNet surpasses the performance of ideal time-frequency masks, including the ideal binary mask (IBM), ideal ratio mask (IRM), and Winener filter-like mask (WFM). Each layer in a TCN contains a 1-D convolution block with increasing dilation factors….”] Rationale for combination as provided for Claim 1. The separation filter and its separation functions were brought in from Mesgarani and the details of this system go back to Mesgarani. Regarding Claim 7 , Pu teaches generating a combination of separated sounds for output in binaural format which can be a linear or non-linear combination. “[0007] … Specifically, by establishing an inherent association between EEG-assisted AA decoding and binaural beam forming from the perspective of signal processing, a joint algorithm for AA decoding and adaptive binaural beam forming in an ear-mounted hearing system is proposed ….” “[0009] … a device for constructing an optimization model and solving the optimization model to obtain a beam-forming weight coefficient for performing linear or non-linear combination on the plurality of input signals, …” Pu includes decoding but does not mention a decoder expressly. Mesgarani teaches: 7. The method of claim 1, further comprising: reconstructing the separated sound components, using linear decoders, into binaural signals associated with selected one or more of the groups of the multiple sound sources . [Mesgarani, Figure 17, “Decoder 1730” is a “linear decoder.” “[0181] With reference to FIG. 17, a block diagram of an example convolutional TasNet system 1700 to separate a combined speech signal(s) corresponding to multiple speakers is shown. The fully-convolutional time-domain audio separation network (Conv-TasNet) includes three processing stages, namely, and encoder 1710 (which may be configured to perform some pre-processing, such as dividing the combined signal into separate segments and/or normalize those segments), a separation unit 1720, and a decoder 1730 . First, the encoder 1710 is used to transform short segments of the mixture waveform into their corresponding representations in an intermediate feature space. This representation is then used to estimate a multiplicative function (mask) for each source and for each encoder output at each time step. The source waveforms are then reconstructed by transforming the masked encoder features using a linear decoder module (e.g., of the decoder 1730). ] Rationale as provided for Claim 1. Pu and Mesgarani are quite close in subject matter and Pu, being based on a journal article, does not expressly include some implementation details which Mesgarani more explicitly expresses. Further, the encoder-decoder structure is express and clear in Mesgarani but not in Pu. Claim 8 is a system claim with limitations corresponding to the limitations of Claim 1 and is rejected under similar rationale. Additionally: 8. A system comprising: at least one microphone to obtain a combined sound signal for signals combined from multiple sound sources in an area in which a person is located; [Pu, Figure 1, microphone 102 which may be a microphone array. “[0024] … The microphone 102 shown in FIG. 1 represents M microphones, all receiving an input sound and generating an electrical signal representing the input sound….”] one or more neural sensors to obtain neural signals for the person, the neural signals being indicative of one or more target sound sources, from the multiple sound sources, the person is attentive to; and [Pu, Figure 1, EEG. “[0024] … Further, FIG. 1 also shows a linear transformation module 110, which is configured to receive EEG signals from the head of the user of the hearing system through a sensor on the head of the user and perform linear transformation on the received EEG signals to obtain an optimized linear transformation coefficient….”] a controller in communication with the at least one microphone and the one or more neural sensors, [Pu, Figure 1, 104: “[0068] It should be understood that the ear-mounted hearing system cited by the present application comprises a processor, which can be a DSP, a microprocessor, a microcontroller or any other digital logic. The signal processing cited by the present application can be implemented using the processor. In various embodiments, the processing circuit 104 can be implemented on such processor ….”] the controller configured to: … Claim 9 is a system claim with limitations corresponding to the limitations of Claim 2 and is rejected under similar rationale. Claim 10 is a system claim with limitations corresponding to the limitations of Claim 3 and is rejected under similar rationale. Claim 11 is a system claim with limitations corresponding to the limitations of Claim 4 and is rejected under similar rationale. Claim 12 is a system claim with limitations corresponding to the limitations of Claim 5 and is rejected under similar rationale. Claim 13 is a system claim with limitations corresponding to the limitations of Claim 6 and is rejected under similar rationale. Claim 14 is a system claim with limitations corresponding to the limitations of Claim 7 and is rejected under similar rationale. Claim 15 is a computer program product system claim with limitations corresponding to the limitations of method Claim 1 and is rejected under similar rationale. Additionally: 15. Non-transitory computer readable media comprising computer instructions executable on a processor-based device to: [Pu, “[0012] According to a further embodiment of the present invention, a computer-readable medium including instructions is disclosed . When being executed by a processor, the instructions can cause the processor to implement the beam forming method according to the present invention.”] … Claim 16 is a computer program product system claim with limitations corresponding to the limitations of method Claim 2 and is rejected under similar rationale. Claim 17 is a computer program product system claim with limitations corresponding to the limitations of method Claim 3 and is rejected under similar rationale. Claim 18 is a computer program product system claim with limitations corresponding to the limitations of method Claim 4 and is rejected under similar rationale. Claim 19 is a computer program product system claim with limitations corresponding to the limitations of method Claim 5 and is rejected under similar rationale. Claim 20 is a computer program product system claim with limitations corresponding to the limitations of method Claim 7 and is rejected under similar rationale. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Claim 5: Lunner (U.S. 20200005770): Lunner is directed to a hearing aid that uses a non-invasive EEG device for EEG dependent sound processing but lists the types of EEG that may be used: “[0065] An EEG signal is a measurement of electrical currents that flow during synaptic excitations in the cerebral cortex. These electrical currents generate an electric field over the user's head that can be measured using EEG systems. EEG signals are recorded using multiple-electrodes placed either inside the brain (electrocorticogram), over the cortex under the skull (intracranial signals), or certain locations over the scalp of the user. Scalp EEG is non-invasive and is the type of EEG signal used in the present invention ….”] Claim 6: Calle (US 20180358003): Calle is directed to multi-channel audio processing and teaches the use of TCNs: “[0076] … In one configuration, scalable multi-channel localization may be performed using 3-4 microphones, up to 8 microphones .” “[0085] In one configuration, a CNN may be jointly trained on multi-channel microphone data to learn sound sources from different directions. The CNN may be trained to pick up voice instead of other interfering sounds.: “[0069] In one configuration, speech output may be reconstructed on the back end (e.g., the receiving end) of the voice communication. In one configuration, an over-sampled generative temporal convolutional auto-encoder network may be used for voice reconstruction. In one configuration, temporal network may be substituted with clockwork network (or recurrent neural network (RNN)) to handle voice aging and temporal effects of different voices. In one configuration, multiple neural networks may be jointly learned from speech data with unsupervised learning. For example, a high fidelity speech model for multiple voices (e.g., voice biometrics) may be learned to increase speech quality, a deep learning based voice discriminator and a voice activity detector may be learned to detect and discriminate a voice signal (e.g., in low signal-to-noise ratio (SNR), a directional beam former function may be learned to localize each voice of a plurality of multiple voices , a neural network may be trained to recover the accurate speech signal output by reducing room echo and channel problems (e.g., transcoding problems).” “[0074] In one configuration, raw speech from the transmit side may be detected and captured, and cleaner high fidelity speech output may be reconstructed (either optimized for human listening fidelity, or optimized for speech recognition fidelity). In one configuration, an over-sampling technique may be used to increase the spatial diversity of multiple microphones. In one configuration, a generative temporal convolutional auto-encoder neural network may be used to learn and then generate high fidelity voice …..” Claim 7: Vilkamo (U.S. 20120314876): Vilkamo, all of the sound rendering of Vilkamo is binaural. “[0002] … method for extracting a direct/ambience signal from a downmix signal and spatial parametric information. Further embodiments of the present invention relate to a utilization of direct-/ambience separation for enhancing binaural reproduction of audio signals . Yet further embodiments relate to binaural reproduction of multi-channel sound, where multi-channel audio means audio having two or more channels . ….” Abstract. Figure 8 shows the reconstruction of the separated channels (Ch1 … ChM) into either a direct signal or an ambient signal portion by a “decoder 820” which is performing a linear combination which is similar to the decoder shown in Figure 1 as well. “[0073] FIG. 8 shows a block diagram of an encoder/decoder system 800 according to further embodiments of the present invention. On the decoder side of the encoder/decoder system 800, an embodiment of the decoder 820 is shown, which may correspond to the apparatus 100 of FIG. 1. ….” “[0081] Regardless of the downmix- and the multichannel signal configuration, each output of the decoded signal is a linear combination of the downmix signals plus a linear combination of a decorrelated version of each of them .” Note Figure 14 of the instant Application: [0048] FIG. 14 is a schematic diagram of an example architecture for a multi-channel (e.g., binaural) speech separation network. [0117] FIG. 14 is a schematic diagram of an example architecture 1400 of a multi-channel (e.g., binaural) speech separation network. The architecture 1400 includes a feature extraction section 1410 that includes multiple encoders (in the example of FIG. 14 , two encoders 1412 and 1414 are depicted) which are shared by the mixture signals from both channels, and the encoder outputs for each channel are combined (e.g., concatenated, integrated, or fused in some manner), to thus preserve spatial cues, and passed to a mask estimation network. As noted, in some embodiment, hint information derived from the listener's neural signals (with such signals being indicative of the speaker(s) the listener's is attending to) may also be combined (e.g., concatenated, or integrated in some other manner) with the encoders' output and passed to a separator section 1420 (also referred to as a mask estimation network). Spectral-temporal and spatial filtering are performed by applying the masks to the corresponding encoder outputs (e.g., deriving and applying multiplicative functions derived for each group of sources from the multiple sound sources constituting the combined signal; for instance, multiplicative functions can be determined, per each of the receiving channels, for each speaker contributing to the combined signal), and the resultant outputs from the application of the multiplicative functions are summed up (e.g., on both left and right paths). Finally, the binaural separated speech is reconstructed by one or more linear decoders in a speech reconstruction section 1430 . For an N-channel input, N encoders were applied to each of them, and the encoder outputs are summed to create a single representation. [0118] When the architecture 1400 is used to perform the separation filter determination operation of, for example, the procedure 400 previously described, the combined sound signal may include in such embodiments components corresponding to multiple receiving channels (e.g., a first and second receiving channels, which may correspond to a left and a right binaural channels), and determining the separation filter may include applying multiple encoders (e.g., temporal-domain encoders) to the sound components corresponding to the multiple receiving channels, with each of the encoders applied to each of the sound components, and, for each of the multiple receiving channels, combining output components of the multiple encoders associated with respective ones of the multiple receiving channels. In such embodiments, the procedure 400 may also include deriving estimated separation functions based on the combined output components for each of the multiple receiving channels, with each of the derived estimated separation functions configured to separate the combined output components for each of the multiple receiving channels into separated sound components associated with groups (e.g., each group comprising one or more speakers) of the multiple sound sources. [0123] … As noted above in relation to the architecture 1400 of FIG. 14 , the MIMO TasNet contains three steps: (1) spectral and spatial feature extraction, (2) estimation of multiplicative functions, which is similar to 2-D time-frequency masks, and (3) speech reconstruction. … [0109] … The present proposed approach is based on a time-domain audio separation network (TasNet), which is a single-channel time-domain speech separation system that can be implemented in real-time. Further details about example implementation of a single channel TasNet frameworks are provided in U.S. Ser. No. 16/169,194, entitled “Systems and methods for speech separation and neural decoding of attentional selection in multi-speaker environments,” the content of which is hereby incorporated by reference in its entirety. The proposed approach is a multi-input-multi-output (MIMO) end-to-end extension of the single-channel TasNet approach , in which the MIMO TasNet approach takes binaural mixed audio as input and simultaneously separates target speakers in both channels. Experimental results show that the proposed end-to-end MIMO system is able to significantly improve the separation performance and keep the perceived location of the modified sources intact in various acoustic scenes. Claim comparison against the parent: Instant Application US 11,875,813 1. A method for speech separation comprising: obtaining, by a device, a combined sound signal for signals combined from multiple sound sources in an area in which a person is located; 1. A method for speech separation comprising: obtaining, by a device, a combined sound signal for signals combined from multiple sound sources in an area in which a person is located; obtaining, by the device, neural signals for the person, the neural signals being indicative of one or more target sound sources, from the multiple sound sources, the person is attentive to; obtaining, by the device, neural signals for the person, the neural signals being indicative of one or more target sound sources, from the multiple sound sources, the person is attentive to; determining a separation filter based, at least in part, on the neural signals obtained for the person; and determining a separation filter based, at least in part, on the neural signals obtained for the person; and applying, by the device, the separation filter to a representation of the combined sound signal to derive a resultant separated signal representation associated with sound from the one or more target sound sources the person is attentive to; applying, by the device, the separation filter to a representation of the combined sound signal to derive a resultant separated signal representation associated with sound from the one or more target sound sources the person is attentive to; wherein the combined sound signal comprises sound components corresponding to multiple receiving channels, and wherein determining the separation filter comprises: wherein determining the separation filter comprises deriving, using a trained learning model, a time-frequency mask that is applied to a time-frequency representation of the combined sound signal, including applying multiple encoders to the sound components corresponding to the multiple receiving channels, with each of the encoders applied to each of the sound components; for each of the multiple receiving channels, combining output components of the multiple encoders associated with respective ones of the multiple receiving channels; and deriving estimated separation functions based on the combined output components for each of the multiple receiving channels, each of the derived estimated separation functions configured to separate the combined output components for each of the multiple receiving channels into separated sound components associated with groups of the multiple sound sources. deriving the time-frequency mask based on a representation of an estimated target envelope for the one or more target sound sources the person is attentive to, determined based on the neural signals obtained for the person, and based on a representation for the combined sound signal. 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