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
Claims 1-13 and 15-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A system, comprising: an input for accepting a dataset including at least two sets of data in a dataset domain; and one or more processors configured to: derive at least two principal components from the dataset using principal component analysis, the at least two principal components being orthogonal to one another, map the dataset to a principal component domain derived from the at least two principal components, 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.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional element”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (machine).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
For example, the steps of “derive at least two principal components from the dataset using principal component analysis, the at least two principal components being orthogonal to one another, map the dataset to a principal component domain derived from the at least two principal components, 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” are treated as belonging to the mathematical concept grouping.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that
integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: A system, comprising: an input for accepting a dataset including at least two sets of data in a dataset domain; and one or more processors configured to....
Claim 9: A method, comprising: accepting a dataset including at least two sets of data in a dataset domain
Claim 16: 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 above additional elements in Claim 1 such as a system, comprising: an input for accepting a dataset including at least two sets of data in a dataset domain is generically recited and are not meaningful and one or more processors configured is an example of generic computer equipment (components) that is generally recited and, therefore, is not qualified as a particular machine. They do not represent a particular machine and/or eligible transformation, they do not indicate a practical application. The additional elements in Claim 16 such as a non-transitory storage medium storing a program is an example of generic computer equipment (components) that is generally recited and, therefore, is not qualified as a particular machine.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record including references in the submitted IDS (6/19/2023) by the Applicant (Corazza and Pierce).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-8, 10-15, and 17-20 provide additional features/steps which are either part of an expanded abstract idea of the independent claims (additionally comprising mathematical/mental (Claims 2-8, 10-15, and 17-20) or adding additional elements/steps that are not meaningful as they are recited in generality and/or not qualified as particular machine/ and/or eligible transformation and, therefore, do not reflect a practical application as well as not qualified for “significantly more” based on prior art of record.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable Corazza et al. (US20130235045), hereinafter referred to as ‘Corazza’ and in further view of Pierce et al. (US20200278456), hereinafter referred to as ‘Pierce’.
Regarding Claim 1, Corazza discloses a system, comprising: an input for accepting a dataset including at least two sets of data in a dataset domain (Patterns within computer generated 3D content can be found utilizing Principal Components Analysis (PCA). PCA is a process that utilizes an orthogonal transformation to convert a dataset of values into a set of values of linearly uncorrelated variables called principal components [0005], see also [0017] processor accepts data and is an input); and one or more processors configured to (One embodiment includes a video camera, a processor [0006]): derive at least two principal components from the dataset using principal component analysis, the at least two principal components being orthogonal to one another (PCA is a process that utilizes an orthogonal transformation to convert a dataset of values into a set of values of linearly uncorrelated variables called principal components. A set of values expressed in terms of the principal components can be referred to as a feature vector [0005]), map the dataset to a principal component domain derived from the at least two principal components (In certain embodiments, character facial animation is generated by mapping changes in a feature vector in a PCA space of human faces and facial expressions derived from the images of human facial expressions to a feature vector in a PCA space for the facial expressions of a 3D character [0061]), generate additional data in the principal component domain (In many embodiments, an animation trigger can cause an additional animation to be concatenated to an existing character animation to generate a new sequence of animations. An additional animation can be concatenated by generating a transition between a motion and the additional motion by modifying and/or adding additional motion data to generate a smooth animation sequence [0076]).
However, Corazza does not explicitly disclose remap the additional data in the principal component domain back to the dataset domain as a newly generated dataset.
Nevertheless, Pierce discloses generate additional data in the principal component domain (The calibration data events are then projected onto the data subspace orthogonal to the most significant principal component vector, resulting in one less dimension spanned by the transformed data. The principal components of the projected data are then computed, in a third application of PCA [0047]), and remap the additional data in the principal component domain back to the dataset domain as a newly generated dataset (The data that passes the initial filtering are then transformed into the new principal component coordinates and a third round of density-based scatter rejection is performed. The thresholds previously defined are again used along that dimension, resulting in a bounding box [0047]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to generate additional data in the principal component domain to provide new variables as linear combinations or mixtures of the initial variables and improve accuracy of the principal component analysis.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to remap the additional data in the principal component domain back to the dataset domain as a newly generated dataset to interpret the data with physical or real-world variables and to effectively filtering out noise and ensure the new data is realistic.
Regarding Claim 2, Corazza and Pierce disclose the claimed invention discussed in claim 1.
Corazza discloses the additional data generated in the principal component domain (as discussed above).
However, Corazza does 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, Pierce discloses the additional data generated in the principal component domain is generated from data having a standard distribution in the principal component domain (The Gaussian ML position estimator requires a lookup table of the mean and variance or standard deviation of the PMT output signal for every (x,y,z) photon position within the crystal (segmented into discrete (x,y,z)-bins). The mean and variance are typically obtained by scanning a calibration beam of 511 keV photons at grid locations across the entrance face of the scintillation crystal, and recording the photosensor output [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to the additional data generated in the principal component domain is generated from data having a standard distribution in the principal component domain to increase the effectiveness of the principal component analysis and maximize variance in the original data.
Regarding Claim 3, Corazza and Pierce disclose the claimed invention discussed in claim 1.
Corazza discloses in which the additional data generated in the principal component domain (as discussed above).
However, Corazza 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, Pierce 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 Corazza in view of Pierce to incorporate the additional data generated in the principal component domain is generated from data having a non-standard distribution in the principal component domain 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 4, Corazza and Pierce disclose the claimed invention discussed in claim 1.
However, Corazza does not explicitly disclose further comprising a signal generator.
Nevertheless, Pierce discloses further comprising a signal generator (The scanner 92 includes a ring of detector modules that detect the 511 keV photons. Front-end electronics process the signals generated by the detector modules [0006]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to incorporate a signal generator to provide signals to increase the amount of data to improve accuracy of statistical analysis.
Regarding Claim 5, Corazza and Pierce disclose the claimed invention discussed in claim 4.
However, Corazza does not explicitly disclose in which the signal generator is configured to generate a signal from the newly generated dataset.
Nevertheless, Pierce discloses in which the signal generator is configured to generate a signal from the newly generated dataset (The Gaussian ML position estimator requires a lookup table of the mean and variance or standard deviation of the PMT output signal for every (x,y,z) photon position within the crystal (segmented into discrete (x,y,z)-bins). The mean and variance are typically obtained by scanning a calibration beam of 511 keV photons at grid locations across the entrance face of the scintillation crystal, and recording the photosensor output [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to incorporate remap the additional data in the principal component domain back to the dataset domain as a newly generated dataset to provide signals to increase the amount of data to improve accuracy of statistical analysis.
Regarding Claim 6, Corazza and Pierce disclose the claimed invention discussed in claim 5.
Corazza discloses the dataset (The 3D images contain more information concerning the geometry of the faces in the training dataset [0064]).
However, Corazza does not explicitly disclose in which the dataset including at least two sets of data was generated from an original signal received at the input.
Nevertheless, Pierce discloses in which the dataset including at least two sets of data was generated from an original signal received at the input (A dataset was collected by scanning a collimated beam across the detector input face. Taking the principal components of this dataset, each photon interaction output signal could be described as a weighted sum of only the most significant principal components [0012]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to incorporate in which the dataset including at least two sets of data was generated from an original signal received at the input to manipulate operation information to interpret component loading data in a productive way.
Regarding Claim 7, Corazza and Pierce disclose the claimed invention discussed in claim 6.
Corazza discloses further comprising a measurement unit (Another further embodiment includes a processor, a network interface, and storage containing an animated message application, and a 3D character model. In addition, the animated message application configures the processor to: receive a sequence of video frames from a remote device via a network interface; detect a human face within a sequence of video frames; track changes in human facial expression of a human face detected within a sequence of video frames; map tracked changes in human facial expression to motion data [0028]).
However, Corazza does not explicitly disclose further comprising a measurement unit configured to measure a signal received at the input.
Nevertheless, Pierce discloses further comprising a measurement unit configured to measure a signal received at the input (A system and method are disclosed for calibrating a detector module 210, for example, a PET detector module, having a scintillation crystal 212 and an array of photosensors 220 (see FIG. 4). As described in more detail below, the method includes simulating the detector module 210 to generate a simulation dataset [0035]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to incorporate a measurement unit configured to measure a signal received at the input to identify qualitative information. Analyze, and measure components of a signal.
Regarding Claim 8, Corazza and Pierce disclose the claimed invention discussed in claim 5.
However, Corazza does not explicitly disclose in which the system further includes a signal validator structured to ensure the generated signal conforms to one or more signal definitions.
Nevertheless, Pierce discloses the system further includes a signal validator structured to ensure the generated signal conforms to one or more signal definitions (The 16 most significant principal components from this dataset were tested via virtual multiplexing to determine which combination of output channels performed the best (visual inspection of the half-max contours, full-width at half-maximum (FWHM) of the half-max contours, minimal positioning bias, performance at the edge of the crystal, and agreement with the Lorentzian-fit depth estimation were all considered in this choice) [0037]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to incorporate a signal validator structured to ensure the generated signal conforms to one or more signal definitions to verify the reliability and completeness of information supporting a signal and improve accuracy.
Regarding Claim 9, Corazza discloses a method, comprising: accepting a dataset including at least two sets of data in a dataset domain (Patterns within computer generated 3D content can be found utilizing Principal Components Analysis (PCA). PCA is a process that utilizes an orthogonal transformation to convert a dataset of values into a set of values of linearly uncorrelated variables called principal components [0005], see also [0017] processor accepts data); deriving at least two principal components from the dataset using principal component analysis, the at least two principal components being orthogonal to one another (PCA is a process that utilizes an orthogonal transformation to convert a dataset of values into a set of values of linearly uncorrelated variables called principal components. A set of values expressed in terms of the principal components can be referred to as a feature vector [0005]), mapping the dataset to a principal component domain derived from the at least two principal components (In certain embodiments, character facial animation is generated by mapping changes in a feature vector in a PCA space of human faces and facial expressions derived from the images of human facial expressions to a feature vector in a PCA space for the facial expressions of a 3D character [0061]), generating additional data in the principal component domain (In many embodiments, an animation trigger can cause an additional animation to be concatenated to an existing character animation to generate a new sequence of animations. An additional animation can be concatenated by generating a transition between a motion and the additional motion by modifying and/or adding additional motion data to generate a smooth animation sequence [0076]).
However, Corazza does not explicitly disclose generating additional data in the principal component domain; and remapping the additional data in the principal component domain back to the dataset domain as a newly generated dataset.
Nevertheless, Pierce discloses generating additional data in the principal component domain (The calibration data events are then projected onto the data subspace orthogonal to the most significant principal component vector, resulting in one less dimension spanned by the transformed data. The principal components of the projected data are then computed, in a third application of PCA [0047]), and remapping the additional data in the principal component domain back to the dataset domain as a newly generated dataset (The data that passes the initial filtering are then transformed into the new principal component coordinates and a third round of density-based scatter rejection is performed. The thresholds previously defined are again used along that dimension, resulting in a bounding box [0047]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce generating additional data in the principal component domain to provide new variables as linear combinations or mixtures of the initial variables and improve accuracy of the principal component analysis.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce remapping the additional data in the principal component domain back to the dataset domain as a newly generated dataset to interpret the data with physical or real-world variables and to effectively filtering out noise and ensure the new data is realistic.
Regarding Claim 10, Corazza and Pierce disclose the claimed invention discussed in claim 9.
Corazza discloses the additional data generated in the principal component domain (as discussed above).
However, Corazza does 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, Pierce discloses the additional data generated in the principal component domain is generated from data having a standard distribution in the principal component domain (The Gaussian ML position estimator requires a lookup table of the mean and variance or standard deviation of the PMT output signal for every (x,y,z) photon position within the crystal (segmented into discrete (x,y,z)-bins). The mean and variance are typically obtained by scanning a calibration beam of 511 keV photons at grid locations across the entrance face of the scintillation crystal, and recording the photosensor output [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to the additional data generated in the principal component domain is generated from data having a standard distribution in the principal component domain to increase the effectiveness of the principal component analysis and maximize variance in the original data.
Regarding Claim 11, Corazza and Pierce disclose the claimed invention discussed in claim 9.
Corazza discloses in which the additional data generated in the principal component domain (as discussed above).
However, Corazza 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, Pierce 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 Corazza in view of Pierce to incorporate the additional data generated in the principal component domain is generated from data having a non-standard distribution in the principal component domain 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 12, Corazza and Pierce disclose the claimed invention discussed in claim 9.
However, Corazza does not explicitly disclose generating a signal from the newly generated dataset.
Nevertheless, Pierce discloses generating a signal from the newly generated dataset (The scanner 92 includes a ring of detector modules that detect the 511 keV photons. Front-end electronics process the signals generated by the detector modules [0006]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to incorporate a signal generator to provide signals to increase the amount of data to improve accuracy of statistical analysis.
Regarding Claim 13, Corazza and Pierce disclose the claimed invention discussed in claim 9.
However, Corazza does not explicitly disclose generating the dataset including at least two sets of data from an input signal.
Nevertheless, Pierce discloses generating the dataset including at least two sets of data from an input signal (The Gaussian ML position estimator requires a lookup table of the mean and variance or standard deviation of the PMT output signal for every (x,y,z) photon position within the crystal (segmented into discrete (x,y,z)-bins). The mean and variance are typically obtained by scanning a calibration beam of 511 keV photons at grid locations across the entrance face of the scintillation crystal, and recording the photosensor output [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to generating the dataset including at least two sets of data from an input signal to provide signals to increase the amount of data to improve accuracy of statistical analysis.
Regarding Claim 14, Corazza and Pierce disclose the claimed invention discussed in claim 9.
Corazza discloses accepting an input signal (as discussed above); generating the dataset (The 3D images contain more information concerning the geometry of the faces in the training dataset [0064]).
However, Corazza does not explicitly disclose 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.
Nevertheless, Pierce discloses 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 (A dataset was collected by scanning a collimated beam across the detector input face. Taking the principal components of this dataset, each photon interaction output signal could be described as a weighted sum of only the most significant principal components [0012]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to 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 to manipulate operation information to interpret component loading data in a productive way.
Regarding Claim 15, Corazza and Pierce disclose the claimed invention discussed in claim 12.
However, Corazza does not explicitly disclose validating the generated signal against one or more signal definitions.
Nevertheless, Pierce discloses validating the generated signal against one or more signal definitions (The 16 most significant principal components from this dataset were tested via virtual multiplexing to determine which combination of output channels performed the best (visual inspection of the half-max contours, full-width at half-maximum (FWHM) of the half-max contours, minimal positioning bias, performance at the edge of the crystal, and agreement with the Lorentzian-fit depth estimation were all considered in this choice) [0037]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to validate the generated signal against one or more signal definitions to verify the reliability and completeness of information supporting a signal and improve accuracy.
Regarding Claim 16, Corazza 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 (Patterns within computer generated 3D content can be found utilizing Principal Components Analysis (PCA). PCA is a process that utilizes an orthogonal transformation to convert a dataset of values into a set of values of linearly uncorrelated variables called principal components [0005], see also [0017] processor accepts data); derive at least two principal components from the dataset using principal component analysis, the at least two principal components being orthogonal to one another (PCA is a process that utilizes an orthogonal transformation to convert a dataset of values into a set of values of linearly uncorrelated variables called principal components. A set of values expressed in terms of the principal components can be referred to as a feature vector [0005]), map the dataset to a principal component domain derived from the at least two principal components (In certain embodiments, character facial animation is generated by mapping changes in a feature vector in a PCA space of human faces and facial expressions derived from the images of human facial expressions to a feature vector in a PCA space for the facial expressions of a 3D character [0061]), generate additional data in the principal component domain (In many embodiments, an animation trigger can cause an additional animation to be concatenated to an existing character animation to generate a new sequence of animations. An additional animation can be concatenated by generating a transition between a motion and the additional motion by modifying and/or adding additional motion data to generate a smooth animation sequence [0076]).
However, Corazza does not explicitly disclose 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.
Nevertheless, Pierce discloses generate additional data in the principal component domain (The calibration data events are then projected onto the data subspace orthogonal to the most significant principal component vector, resulting in one less dimension spanned by the transformed data. The principal components of the projected data are then computed, in a third application of PCA [0047]); and remap the additional data in the principal component domain back to the dataset domain as a newly generated dataset (The data that passes the initial filtering are then transformed into the new principal component coordinates and a third round of density-based scatter rejection is performed. The thresholds previously defined are again used along that dimension, resulting in a bounding box [0047]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce generate additional data in the principal component domain to provide new variables as linear combinations or mixtures of the initial variables and improve accuracy of the principal component analysis.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to remap the additional data in the principal component domain back to the dataset domain as a newly generated dataset to interpret the data with physical or real-world variables and to effectively filtering out noise and ensure the new data is realistic.
Regarding Claim 17, Corazza and Pierce disclose the claimed invention discussed in claim 16.
Corazza discloses generate additional data in the principal component domain (as discussed above).
However, Corazza does 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, Pierce discloses generate additional data in the principal component domain using data having a standard distribution in the principal component domain (The Gaussian ML position estimator requires a lookup table of the mean and variance or standard deviation of the PMT output signal for every (x,y,z) photon position within the crystal (segmented into discrete (x,y,z)-bins). The mean and variance are typically obtained by scanning a calibration beam of 511 keV photons at grid locations across the entrance face of the scintillation crystal, and recording the photosensor output [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to generate additional data in the principal component domain using data having a standard distribution in the principal component domain to increase the effectiveness of the principal component analysis and maximize variance in the original data.
Regarding Claim 18, Corazza and Pierce disclose the claimed invention discussed in claim 16.
Corazza discloses in which the additional data generated in the principal component domain (as discussed above).
However, Corazza 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, Pierce 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 Corazza in view of Pierce to incorporate the additional data generated in the principal component domain is generated from data having a non-standard distribution in the principal component domain 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 19, Corazza and Pierce disclose the claimed invention discussed in claim 16.
Corazza discloses accepting an input signal (as discussed above); generating the dataset (The 3D images contain more information concerning the geometry of the faces in the training dataset [0064]).
However, Corazza does not explicitly disclose 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.
Nevertheless, Pierce discloses 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 (A dataset was collected by scanning a collimated beam across the detector input face. Taking the principal components of this dataset, each photon interaction output signal could be described as a weighted sum of only the most significant principal components [0012]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to 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 to manipulate operation information to interpret component loading data in a productive way.
Regarding Claim 20, Corazza and Pierce disclose the claimed invention discussed in claim 19.
However, Corazza does not explicitly disclose validating the generated signal against one or more signal definitions.
Nevertheless, Pierce discloses validating the generated signal to ensure the generated signal conforms to one or more signal definitions (The 16 most significant principal components from this dataset were tested via virtual multiplexing to determine which combination of output channels performed the best (visual inspection of the half-max contours, full-width at half-maximum (FWHM) of the half-max contours, minimal positioning bias, performance at the edge of the crystal, and agreement with the Lorentzian-fit depth estimation were all considered in this choice) [0037]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Corazza in view of Pierce to validate the generated signal against one or more signal definitions to verify the reliability and completeness of information supporting a signal and improve accuracy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Brian Kloer (US9396552) discloses principal analysis is performed on the composite image and those components representing a change rather than a correlation are identified.
Tomoyasu Nakano (US9009052) discloses a synthesized audio signal generating section generates a transform spectral envelop at each instant of time
Justin Bedo (US20150026134) discloses approximating multiple data vectors of a dataset which are approximated by one or more stored principal components.
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/SHARAH ZAAB/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863