DETAILED ACTION
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5 are rejected under 35 U.S.C. 101 because the claims are directed toward an abstract idea without significantly more.
Claim 1, is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) and does not include additional elements that amount significantly more than the judicial exception.
Step 1
Claim 1 is directed toward a “device”, which is a machine and thus falls within a statutory category under the most recent guidelines of 35 U.S.C. 101.
Step 2A, Prong 1
Claim 1 recites instructions for “separating a predetermined number of possibility signals from a mixed signal as possibilities of a target signal”; “encoding related information of the target signal into a first feature vector and encoding the predetermined number of possibility signals into the predetermined number of second feature vectors”; “deriving a similarity between the first feature vector and the second feature vector for each of the possibility signals”, and “selecting a possibility signal of the possibility signals having the highest similarity as the target signal from the predetermined number of possibility signals.”
These limitations collectively recite are purely mathematical in nature and as such considered to be an abstract idea of merely organizing information through mathematical correlations (see Digitech Image tech., v. Electronics for imaging, Inc., 758 F. 3d 1344 (Fed. Cir. 2014). As characterized by the USPTO guidance and case law, such separating a predetermined number of possibility signals from a mixed signal as possibilities of a target signal; encoding related information of the target signal into a first feature vector and encoding the predetermined number of possibility signals into the predetermined number of second feature vectors; and deriving a similarity between the first feature vector and the second feature vector for each of the possibility signals, and selecting a possibility signal of the possibility signals having the highest similarity as the target signal from the predetermined number of possibility signals These steps are similar to concepts and ideas that have been identified as abstract by the courts.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claim 1 is therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. These steps are similar to concepts and ideas that have been identified as abstract by the courts.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claim 1 is therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Accordingly, claim 1 recites an abstract idea.
Step 2A, Prong 2
The claim is implemented on a “server including one or more processors and memory storing one or more programs to be executed by the one or more processors.” These are generic computer components performing their well-understood, routine, and conventional functions of storing and executing instructions, receiving requests, and sending content.
The claim does not recite any specific improvement to computer functionality (e.g., a particular translation algorithm, model architecture, data structure, memory organization, caching mechanism, latency-reduction technique, or network protocol that improves the operation of the computer or network). Nor does it effect a transformation of a physical article or use the abstract idea in any other manner that imposes a meaningful limit on the claim’s scope. Therefore, the claim does not integrate the abstract idea into a practical application under Step 2A, Prong 2.
Step 2B
Beyond the abstract idea, the additional elements are the generic “server,” “one or more processors,” and “memory” performing their conventional functions. Implementing the abstract idea on generic computer components does not amount to significantly more. Alice, 573 U.S. at 223–24).
The ordered combination of limitations mirrors the abstract idea itself performed using routine computer operations. There is no recited unconventional hardware, no technical improvement to the functioning of the computer itself, and no nonconventional arrangement of known components etc.
Accordingly, claim 1 does not include an “inventive concept” sufficient to transform the abstract idea into a patent-eligible application.
Therefore , claim 1 is directed to an abstract idea and does not recite additional elements that integrate the exception into a practical application or amount to significantly more than the exception itself. Claim 1 is therefore rejected under 35 U.S.C. § 101.
The Independent claim 4 recite(s) the steps of ““separating a predetermined number of possibility signals from a mixed signal as possibilities of a target signal”; “encoding related information of the target signal into a first feature vector and encoding the predetermined number of possibility signals into the predetermined number of second feature vectors”; “deriving a similarity between the first feature vector and the second feature vector for each of the possibility signals”, and “selecting a possibility signal of the possibility signals having the highest similarity as the target signal from the predetermined number of possibility signals.”
These limitations collectively recite are purely mathematical in nature and as such considered to be an abstract idea of merely organizing information through mathematical correlations (see Digitech Image tech., v. Electronics for imaging, Inc., 758 F. 3d 1344 (Fed. Cir. 2014). As characterized by the USPTO guidance and case law, such separating a predetermined number of possibility signals from a mixed signal as possibilities of a target signal; encoding related information of the target signal into a first feature vector and encoding the predetermined number of possibility signals into the predetermined number of second feature vectors; and deriving a similarity between the first feature vector and the second feature vector for each of the possibility signals, and selecting a possibility signal of the possibility signals having the highest similarity as the target signal from the predetermined number of possibility signals These steps are similar to concepts and ideas that have been identified as abstract by the courts.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claim 4 is therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. These steps are similar to concepts and ideas that have been identified as abstract by the courts.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claim 4 is therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Accordingly, claim 4 recites an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to integration of the abstract idea into a practical application, the additional element of using a generic computing device the determining and data gathering steps amount to no more than mere instructions to apply the exception using a generic computer. The current specification on paragraph 0126, clearly specifies that “… The computer-readable non-transitory recording medium is, for example, a portable medium such as a flexible disk, a magneto-optical disk, a read only memory (ROM), or a compact disc read only memory (CD-ROM), or a non-transitory recording medium such as a storage device such as a hard disk built in a computer system. The communication unit 114 performs a predetermined communication process. The communication unit 114 may acquire data and a program.” The additional elements have been considered both individually and as an ordered combination in the significantly more consideration. The inclusion of the computer or memory and controller to perform the selecting and generating steps amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computing device cannot provide an inventive concept. Therefore, claim 4 as drafted is not patent eligible. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Independent claim 4 is therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. Claim 5 is directed toward a non-transitory computer readable medium with instructions to implement the method of claim 4 and is rejected under similar rationale.
All dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. §101 because any additional recited limitations fail to establish that the claims are not directed to an abstract idea for the same reasons already recited for the independent claims.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chang et al., (US 11,915,690 B1).
As per claims 1, 4 and 5 Chang et al., teach a signal filtering device/method/non-transitory computer readable medium with instructions to implement said method, comprising:
a processor (0155); and
a storage medium having computer program instructions stored thereon, when executed by the processor (0155), perform to:
separate a predetermined number of possibility signals from a mixed signal as possibilities of a target signal (Col.3, lines 43-47, “… neural beamformers also output individual audio signals that are then sent to separate speech processing components for ultimate downstream processing by specialized ASR or other components, forming a cascading chain of processing component”);
encode related information of the target signal into a first feature vector and encodes the predetermined number of possibility signals into the predetermined number of second feature vectors (Col.8, lines 7-23, “… processed with respect to the positional encoding 213. This positional encoding 213 provides some relative positional data to capture the relationship between frames within the audio data. The positional encoding data 213 may correspond to one or more matrices (e.g., sinusoidal matrices) that encode the positional embedding vector to a particular frame. For example, the device 110 may determine a value for each audio frame where the value of that audio frame is determined based on the length of the entire utterance (e.g., the number of audio frames for the entire utterance). Thus, during processing for audio data for frame t, the value of positional encoding data 213 used for that processing (e.g., for combining with that frame's worth of audio data) will be the encoding value for that frame t. The value for that frame t will be consistent across all channels C relative to the particular channel's data for frame t.”) ; and
derive a similarity between the first feature vector and the second feature vector for each of the possibility signals, and selects a possibility signal of the possibility signals having the highest similarity as the target signal from the predetermined number of possibility signals (Col.9, lines 7-18, “… function will transform the input of {circumflex over (X)}.sub.i to compute layer similarity. Based on the similarity, the function will weight the transform inputs to encode which frame of the input audio data {circumflex over (X)}.sub.i may be more important within the ith channel and to more highly weight (within transform V.sub.i.sup.CS) corresponding metrics. Thus the above represents a weighted sum operation and the softmax operation takes the similarity of the acoustic frames in the i-th channel and determines (potentially optimal) weights to obtain H.sub.i.sup.CS which represents a desirable weighted representation of the input signal {circumflex over (X)}.sub.i.”).
As per claim 2, Chang et al., teach the signal filtering device according to claim 1, wherein the computer program instructions further perform to derive an inner product of the first feature vector and the second feature vector as the similarity (Col.9, lines 7-18, “… function will transform the input of {circumflex over (X)}.sub.i to compute layer similarity. Based on the similarity, the function will weight the transform inputs to encode which frame of the input audio data {circumflex over (X)}.sub.i may be more important within the ith channel and to more highly weight (within transform V.sub.i.sup.CS) corresponding metrics. Thus the above represents a weighted sum operation and the softmax operation takes the similarity of the acoustic frames in the i-th channel and determines (potentially optimal) weights to obtain H.sub.i.sup.CS which represents a desirable weighted representation of the input signal {circumflex over (X)}.sub.i.”).
As per claim 3, Chang et al., teach the signal filtering device according to claim 1, wherein the predetermined number of possibility signals are voice signals associated with the predetermined number of sound sources, and the computer program instructions further perform to separate the possibility signals in the mixed signal for each of the sound sources (Col.5, line 50 – Col.6, line 6, “… the individual audio data channels of the C audio data channels may also correspond to different types of data as they need not necessarily be similar types of data. For example, one audio data channel C.sub.1 may represent beamformed data (resulting from processing multiple microphones worth of data) corresponding to a first direction relative to the device, another audio data channel C.sub.2 may represent beamformed data (resulting from processing multiple microphones worth of data) corresponding to a different second direction relative to the device, another audio data channel C.sub.3 may represent feature data corresponding to a specific microphone M.sub.1, another audio data channel C.sub.4 may represent feature data corresponding to a different specific microphone M.sub.2, another audio data channel C.sub.5 may represent raw audio data from a still different specific microphone M.sub.3, another audio data channel C.sub.6 may represent raw audio data from the first microphone M.sub.1, and so forth. As can be appreciated, the specific types of audio data may depend on device configuration, operation of the preprocessing component 135 and configuration of the multi-channel transformer/transducer acoustic model 140. Thus the multi-channel transformer/transducer acoustic model 140 may be configured to operate on a variety of different types of input data.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form PTO-892.
Wingate et al., (US 2017/0178664 A1) teach Blind source separation (BSS) techniques aim to separate a sound generated by a particular source of interest from a mixture of different sounds. Various BSS techniques disclosed herein are based on recognition that providing additional information that is considered within iterations of a nonnegative tensor factorization (NTF) model improves accuracy and efficiency of source separation. Examples of such information include direction estimates or neural network models trained to recognize a particular sound of interest. Furthermore, identifying and processing incremental changes to an NTF model, rather than re-processing the entire model each time data changes, provides an efficient and fast manner for performing source separation on large sets of quickly changing data. Carrying out at least parts of BSS techniques in a cloud allows flexible utilization of local and remote sources.
Short et al., (US 9,495,975 B2) teach a method includes receiving an input signal comprising an original domain signal and creating a first window data set and a second window data set from the signal, wherein an initiation of the second window data set is offset from an initiation of the first window data set, converting the first window data set and the second window data set to a frequency domain and storing the resulting data as data in a second domain different from the original domain, performing complex spectral phase evolution (CSPE) on the second domain data to estimate component frequencies of the first and second window data sets, using the component frequencies estimated in the CSPE, sampling a set of second-domain high resolution windows to select a mathematical representation comprising a second-domain high resolution window that fits at least one of the amplitude, phase, amplitude modulation and frequency modulation of a component of an underlying signal wherein the component comprises at least one oscillator peak, generating an output signal from the mathematical representation of the original signal as at least one of: an audio file; one or more audio signal components; and one or more speech vectors and outputting the output signal to an external system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VIJAY B CHAWAN whose telephone number is (571)272-7601. The examiner can normally be reached 7-5 Monday thru Thursday.
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/VIJAY B CHAWAN/Primary Examiner, Art Unit 2658