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 § 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.
Claims 1-3, 7-11, 13-16, 18, and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable Walker et al. (US20090304246), hereinafter referred to as ‘Walker’.
Regarding Claim 1, Walker discloses a waveform signal generation device, comprising: an input for accepting a dataset including at least two sets of data in a dataset domain (The present invention provides methods, systems and computer readable media for extracting information pertaining to at least one moving target, including: inputting to a principal components processor, a set of signal data comprising signal data corresponding to at least one waveform acquired from the at least one moving target, forming a complex representation of the set of signal data [0021]); and one or more processors configured to: derive at least two principal components from the dataset using principal component analysis (The present invention provides methods, systems and computer readable media for extracting information pertaining to at least one moving target, including: inputting to a principal components processor, a set of signal data comprising signal data corresponding to at least one waveform acquired from the at least one moving target, forming a complex representation of the set of signal data, calculating, using a principal components processor, at least one complex principal component of the complex representation; automatically selecting at least one of the at least one complex principal components; and applying each of the at least one automatically selected complex principal component to extract information about the at least one moving target [0021]), the at least two principal components being orthogonal to one another (Principal component filtering (PCF) applies principal component analysis (PCA) to filter signal data. Mathematically, principal component analysis (PCA) is defined as an orthogonal linear transformation of the data onto a new coordinate system such that the projection of the data onto the first coordinate (called the first principal component) has the largest variance. Likewise, the second principal component (PC), which is orthonormal to the first PC, has a projection with the second largest variance, and this trend persists for all new PC coordinates [0129]), map the dataset to a principal component domain derived from the at least two principal components (In order to filter the undesirable source signals using PC filtering (PCF), the input data matrix is mapped onto a new signal subspace that spans only the PC coordinates of interest [0129]), generate additional data in the principal component domain, and remap the additional data in the principal component domain back to the dataset domain as a newly generated dataset (After identifying the PC coordinates of interest, a signal subspace projection operator is formed by matrix multiplication of the PC coordinates of interest with their conjugate transpose. Next the input data matrix is projected, i.e., remap, onto the projection operator resulting in a filtered output of reduced rank [0129]); and a signal generator structured to produce an artificial waveform signal from the newly generated dataset (Processing then continues as described above with regard to FIG. 3. Once results are displayed at 322, processing returns to 302 if the user still desires to image the target and a new input matrix is obtained at 302, i.e., a signal generator structured to produce an artificial waveform signal, [0149]).
Regarding Claim 2, Walker discloses the claimed invention discussed in claim 1.
Walker discloses the additional data generated in the principal component domain (as discussed above) is generated from data having a standard distribution in the principal component domain (FIGS. 17A-17F show simulation results similar to FIG. 11A-11C depicting RMS error (FIGS. 17A and 17D), standard deviation at acquisition 400 (FIGS. 17B and 17E) and the average decorrelation between consecutive echoes when SNR is varied from 5 to 40 dB (FIGS. 17C and 17F) [0083]).
Regarding Claim 3, Walker discloses the claimed invention discussed in claim 1.
Walker discloses in which the additional data generated in the principal component domain (as discussed above).
However, Walker does not explicitly disclose in which the additional data generated in the principal component domain is generated from data having a non-standard distribution in the principal component domain.
Nevertheless, discloses in which the additional data generated in the principal component domain is generated from data having a non-standard distribution in the principal component domain (Principal component analysis (“PCA”) is a statistical method for converting potentially correlated data into a set of linearly uncorrelated variables referred to as principal components. The first, or primary principal component has the largest possible variance, the second principal component has the largest variance possible under the constraint that it is orthogonal to the primary principal component. [0033]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Walker with the teachings of to reduce the number of variables in a dataset while retaining as much information as possible and provide a simplified representation of complex data.
Regarding Claim 7, Walker discloses the claimed invention discussed in claim 6.
Walker discloses further comprising a measurement unit configured to measure a signal received at the input (In at least one embodiment, the signal data comprises signal data from echoes reflected off the moving target from a plurality of different depths measured along a beam axis along which the waves are emitted from a transducer [0029]).
Regarding Claim 8, Walker discloses the claimed invention discussed in claim 1.
Walker discloses in which the system further includes a signal validator structured to ensure the generated signal conforms to one or more signal definitions (he objective or these simulations was to assess which signal separation technique, frequency-based bandpass filtering or regression-based complex PCF, provide the best means for reducing the decorrelation and noise constraints on TDE performance. In FIGS. 11A-11C, the effects of PCF for reduced echo decorrelation and noise were examined against the alternative frequency-based filtering approach for varied levels of additive noise [0196]).
Regarding Claim 9, Walker discloses a method of generating a waveform signal, comprising: accepting a dataset including at least two sets of data in a dataset domain (The present invention provides methods, systems and computer readable media for extracting information pertaining to at least one moving target, including: inputting to a principal components processor, a set of signal data comprising signal data corresponding to at least one waveform acquired from the at least one moving target, forming a complex representation of the set of signal data [0021]); deriving at least two principal components from the dataset using principal component analysis (The present invention provides methods, systems and computer readable media for extracting information pertaining to at least one moving target, including: inputting to a principal components processor, a set of signal data comprising signal data corresponding to at least one waveform acquired from the at least one moving target, forming a complex representation of the set of signal data, calculating, using a principal components processor, at least one complex principal component of the complex representation; automatically selecting at least one of the at least one complex principal components; and applying each of the at least one automatically selected complex principal component to extract information about the at least one moving target [0021]), the at least two principal components being orthogonal to one another (Principal component filtering (PCF) applies principal component analysis (PCA) to filter signal data. Mathematically, principal component analysis (PCA) is defined as an orthogonal linear transformation of the data onto a new coordinate system such that the projection of the data onto the first coordinate (called the first principal component) has the largest variance. Likewise, the second principal component (PC), which is orthonormal to the first PC, has a projection with the second largest variance, and this trend persists for all new PC coordinates [0129]), mapping the dataset to a principal component domain derived from the at least two principal components (In order to filter the undesirable source signals using PC filtering (PCF), the input data matrix is mapped onto a new signal subspace that spans only the PC coordinates of interest [0129]), generating additional data in the principal component domain; remapping the additional data in the principal component domain back to the dataset domain as a newly generated dataset (After identifying the PC coordinates of interest, a signal subspace projection operator is formed by matrix multiplication of the PC coordinates of interest with their conjugate transpose. Next the input data matrix is projected onto the projection operator resulting in a filtered output of reduced rank [0129]); and generating the waveform signal from the newly generated dataset (Processing then continues as described above with regard to FIG. 3. Once results are displayed at 322, i.e., a signal generator structured to produce an artificial waveform signal, processing returns to 302 if the user still desires to image the target and a new input matrix is obtained at 302 [0149]).
Regarding Claim 10, Walker discloses the claimed invention discussed in claim 9.
Walker discloses the additional data generated in the principal component domain (as discussed above) is generated from data having a standard distribution in the principal component domain (FIGS. 17A-17F show simulation results similar to FIG. 11A-11C depicting RMS error (FIGS. 17A and 17D), standard deviation at acquisition 400 (FIGS. 17B and 17E) and the average decorrelation between consecutive echoes when SNR is varied from 5 to 40 dB (FIGS. 17C and 17F) [0083]).
Regarding Claim 14, Walker discloses the claimed invention discussed in claim 9. Walker further discloses accepting an input signal; performing one or more measurements on the input signal; and generating the dataset including at least two sets of data from the one or more measurements of the input signal (In at least one embodiment, the signal data comprises signal data from echoes reflected off the moving target from a plurality of different depths measured along a beam axis along which the waves are emitted from a transducer, and wherein the calculating principal components and the calculating time delay estimation values are performed for the signal data at a first depth of the plurality of depths [0029]).
Regarding Claim 15, Walker discloses the claimed invention discussed in claim 9. Walker further discloses validating the generated waveform signal against one or more signal definitions (In at least one embodiment, the window length of the window 610 used to separate signal components using PCDE may be selected by a user through controller 722. Other user selectable parameters include, but are not limited to: how many PCs to retain, i.e., validate, or reject (although the default is to automatically calculate and use only the first PC) [0167]).
Regarding Claim 16, Walker discloses a non-transitory computer-readable storage medium storing one or more instructions, which, when executed by one or more processors of a computing device, cause the computing device to: accept a dataset including at least two sets of data in a dataset domain (The present invention provides methods, systems and computer readable media for extracting information pertaining to at least one moving target, including: inputting to a principal components processor, a set of signal data comprising signal data corresponding to at least one waveform acquired from the at least one moving target, forming a complex representation of the set of signal data [0021]); derive at least two principal components from the dataset using principal component analysis (The present invention provides methods, systems and computer readable media for extracting information pertaining to at least one moving target, including: inputting to a principal components processor, a set of signal data comprising signal data corresponding to at least one waveform acquired from the at least one moving target, forming a complex representation of the set of signal data, calculating, using a principal components processor, at least one complex principal component of the complex representation; automatically selecting at least one of the at least one complex principal components; and applying each of the at least one automatically selected complex principal component to extract information about the at least one moving target [0021]), the at least two principal components being orthogonal to one another (Principal component filtering (PCF) applies principal component analysis (PCA) to filter signal data. Mathematically, principal component analysis (PCA) is defined as an orthogonal linear transformation of the data onto a new coordinate system such that the projection of the data onto the first coordinate (called the first principal component) has the largest variance. Likewise, the second principal component (PC), which is orthonormal to the first PC, has a projection with the second largest variance, and this trend persists for all new PC coordinates [0129]), map the dataset to a principal component domain derived from the at least two principal components (In order to filter the undesirable source signals using PC filtering (PCF), the input data matrix is mapped onto a new signal subspace that spans only the PC coordinates of interest [0129]), generate additional data in the principal component domain; and remap the additional data in the principal component domain back to the dataset domain as a newly generated dataset (After identifying the PC coordinates of interest, a signal subspace projection operator is formed by matrix multiplication of the PC coordinates of interest with their conjugate transpose. Next the input data matrix is projected onto the projection operator resulting in a filtered output of reduced rank [0129]); and produce a waveform signal from the newly generated dataset (Processing then continues as described above with regard to FIG. 3. Once results are displayed at 322, i.e., a signal generator structured to produce an artificial waveform signal, processing returns to 302 if the user still desires to image the target and a new input matrix is obtained at 302 [0149]).
Regarding Claim 21, Walker discloses the claimed invention discussed in claim 1. Walker discloses a memory for storing the artificial waveform signal (Processing then continues as described above with regard to FIG. 3. Once results are displayed at 322, processing returns to 302 if the user still desires to image the target and a new input matrix is obtained at 302, i.e., a signal generator structured to produce an artificial waveform signal, [0149]).
Regarding Claim 22, Walker discloses the claimed invention discussed in claim 1. Walker discloses an output through which the artificial waveform signal is transmitted outside the device (Processing then continues as described above with regard to FIG. 3. Once results are displayed at 322, i.e., a signal generator structured to produce an artificial waveform signal, processing returns to 302 if the user still desires to image the target and a new input matrix is obtained at 302 [0149]).
Regarding Claim 23, Walker discloses the claimed invention discussed in claim 9. Walker discloses storing the waveform signal (as discussed above).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Walker, and further in view of Korzinov et al. (US20160100803) hereinafter referred to as ‘Korzinov’.
Regarding Claim 6,Walker discloses the claimed invention discussed in claim 1.
Walker discloses an original signal received at the input (as discussed above).
However, Walker does not explicitly disclose the dataset including at least two sets of data was generated from an original signal received at the input.
Nevertheless, Korzinov discloses the dataset including at least two sets of data was generated from an original signal received at the input (receiving a first dataset based on a first set of bioelectrical signals detected from the first sensor channel S110; receiving a second dataset based on a second set of bioelectrical signals detected from the second sensor channel S120, wherein the first dataset and the second dataset comprise a local noise component and a heart signal component [0023]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Walker with the teachings of Korzinov to obtain the largest and next largest amount of variance and improve analysis accuracy.
Claims 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Walker, and further in view of Thiesson et al. (US20090259679) hereinafter referred to as ‘Thiesson’.
Regarding Claim 11, Walker discloses the claimed invention discussed in claim 9.
Walker discloses the additional data generated in the principal component domain (as discussed above).
However, Walker does not explicitly disclose the additional data generated in the principal component domain is generated from data having a non-standard distribution in the principal component domain.
Nevertheless, Thiesson discloses generating from data having a non-standard distribution in the principal component domain (PCA can also model data distributions from the exponential family other than Gaussian. It should be appreciated that for modeling count data, it can be useful to assume a Poisson distribution instead of a Gaussian distribution [0044]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Walker with the teachings of Thiesson to address real-world data which violates multivariate normality assumption and improve analysis accuracy.
Regarding Claim 18, Walker discloses the claimed invention discussed in claim 16. Walker discloses the additional data generated in the principal component domain (as discussed above).
However, Walker does not explicitly disclose the additional data generated in the principal component domain is generated from data having a non-standard distribution in the principal component domain.
Nevertheless, Thiesson discloses generating from data having a non-standard distribution in the principal component domain (PCA can also model data distributions from the exponential family other than Gaussian. It should be appreciated that for modeling count data, it can be useful to assume a Poisson distribution instead of a Gaussian distribution [0044]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Walker with the teachings of Thiesson to address real-world data which violates multivariate normality assumption and improve analysis accuracy.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Walker, and further in view of Hirai et al. (US20180128748), hereinafter referred to as ‘Hirai’.
Regarding Claim 17, Walker discloses the claimed invention discussed in claim 16.
Walker discloses the principal component domain (as discussed above).
However, Walker do not explicitly disclose the additional data generated in the principal component domain is generated from data having a standard distribution in the principal component domain.
Nevertheless, Hirai discloses the additional data generated in the principal component domain is generated from data having a standard distribution in the principal component domain (Assuming that each principal component calculated in step S804 follows normal distribution in each principal component axis, the analyzer 310 calculates normal distribution of each principal component score. The acquired distribution will be multivariate normal distribution [0066]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Walker with the teachings of Hirai to identify data within the transformed space with a normal distribution and improve accuracy of the signal.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Walker, and further in view of Nakano et al. (US9009052), hereinafter referred to as ‘Nakano’.
Regarding Claim 20, Walker discloses the claimed invention discussed in claim 16.
However, Walker does not explicitly disclose wherein execution of the one or more instructions causes the computing device to validate the generated waveform signal to ensure the generated waveform signal conforms to one or more signal definitions.
Nevertheless, Nakano discloses wherein execution of the one or more instructions causes the computing device to validate the generated waveform signal to ensure the generated waveform signal conforms to one or more signal definitions (In step ST43, the voice timbre estimating section 111 applies principal component analysis to the S L.sub.2-dimensional discrete cosine transform coefficient vectors in each of T frames in which the S audio signals i, k.sub.1-k.sub.K, and j.sub.1-j.sub.J are voiced at the same instant of time where T is the number of seconds of duration of the audio signal times (multiplied by) sampling period at a maximum, i.e., one or more signal definitions , Col. 10, Lines 62-67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Walker with the teachings of Nakano to ensure wave signals conform to specified time-domain characteristics and improve technical and safety requirements.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Walker, and further in view of Bedo et al. (US20150026134), hereinafter referred to as ‘Bedo’.
Regarding Claim 24, Walker discloses the claimed invention discussed in claim 9. Walker discloses transmitting the waveform signal from an output port (FIG. 8A schematically illustrates a beamforming instrument 800 that is provided independently of the transducers used to transmit waves and receive echoes, and which is also connectable to an external component used to perform the PCF/PCDE and to output the filtered results. Alternatively, FPGA can be programmed to perform the PCF/PCDE at the location of the instrument 800 [0169]).
However, Walker does not explicitly disclose transmitting the waveform signal from an output port.
Nevertheless, Bedo discloses output port (The processor 102 may receive data, such as data of the dataset or the stored principle components, from data memory 106 as well as from the communications port 108 and the user port 110, which is connected to a display 112 that shows a visual representation 114 of the principal components to a user 116. In one example, the processor 102 receives data from a genome sequencer or other database via communications port 108, i.e., output port, such as by using a Fibre Channel, PCI Express, Serial ATA, or InfiniBand connection. The dataset may be stored in a file, such as in PLINK format or a database, such as SQLite3 [0044]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Walker with the teachings of Bedo to provide new variables as linear combinations or mixtures of the initial variables and improve accuracy of the principal component analysis.
Response to Arguments
35 USC § 101
Applicant’s arguments, filed 04/14/2026, with respect to claims 1-24 have been fully considered and are persuasive. The rejection of claims 1-24 has been withdrawn.
35 USC § 103
Applicant’s arguments, filed, with respect to the rejection(s) of claim(s) 1-3, 6-11, 13-18, and 20-24 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Walker and Bedo.
Conclusion
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/SHARAH ZAAB/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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