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 .
Notice to Applicants
This communication is in response to the action filed on10/04/2024.
Claims 1-18 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 10/04/2024 has been considered.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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 1-6, 9-15, and 17-18 are rejected under 35 § U.S.C. 102(a)(1) as being anticipated by Biomedical Signal Processing and Control to SAGHEER, et al. (hereinafter “SAGHEER”).
As per claim 1, SAGHEER discloses a noise reduction processing method for reducing noise in a target signal (a system to perform a method which would produce a low noise/noise reduced signal wherein the signal is a medical imaging signal; fig 1; sections 2-3.2; sections 4-4.4), the method comprising: a step of estimating a noise signal representing an intensity distribution of the noise in the target signal (the method of operation including an estimation step where the computing system performs calculations in order to fill out table 1 which includes values for the amount/intensity of noise present in the acquired medical image (target signal); fig 1; sections 2-3.2; sections 4-4.4; table 1); a step of calculating a similarity index between a predetermined signal that is acquired at a first signal point and a signal that is acquired at a second signal point in proximity to the first signal point based on the target signal and the noise signal (the computing system further performs steps of calculating a similarity index, Structural similarity index “SSIM” shows how much the denoised images matches with the original image based on tested reference points and equation number 6; section 3.1-3.3; section 4.1.1); a step of calculating a filter coefficient of a noise reduction filter based on the similarity index (the computing system further performs the operations of calculating a standard deviation filter SRAD using the SSIM index value and the SRAD filter is used to reduce noise as seen in table 2 and fig 3; section 3.5-4.1.1; section 4.2.1; table 2 and fig 3); and a step of acquiring a low-noise signal by applying the noise reduction filter to the target signal based on the filter coefficient (from the target image/signal which is a medical image acquiring a noise removed medical image after performing the previous computing operations/steps based on the applied SRAD filter calculated based on the similarity index; sections 3-3.3; sections 4.1.1-4.2.1), wherein in the step of estimating the noise signal, a low-frequency signal is acquired by applying a first filter including a first filter window to the target signal (the SRAD filter is applied to the medical image/target signal to arrive at the noise removed signal; sections 3-3.3; sections 4.1.1-4.2.1; tables 1 and 2), a high-frequency signal is acquired based on the target signal and the low-frequency signal (based on the iterated/looped filter being a hybrid filter the original target signal/images is broken up into a high frequency portion/signal and a lower frequency portion/signal; figs 1 and 5; section 2; section 3.2.1; sections 4.1.1-5; tables 1-2 and 4-6), and the noise signal is estimated by applying a second filter including a second filter window independent of the first filter window to the high-frequency signal (various estimated noise signal of various levels (which can be seen as values in the tables provide for each respective imaging modality) are applied to the filters which is a spatial domain FROST filter acting as a second filter type and the variation in the SSIM is observed in relation to the change in noise signal intensity; figs 1 and 5; section 2; section 3.2.1; sections 4.3.1-4.4.2; tables 1-2 and 4-6).
As per claim 2, SAGHEER discloses the noise reduction processing method according to claim 1, wherein the first filter includes a smoothing filter (the computing system and method of operation include a smoothing filter; table 1; section 3.4-3.5); the second filter includes a standard deviation filter (the computing system and method of operation include a SRAD standard deviation filter; section 3.5-4.1.1; section 4.2.1; table 2 and fig 3); and in the step of estimating the noise signal, the noise signal is estimated by applying the standard deviation filter, which is different from the smoothing filter, to the high-frequency signal (the smoothing filter and the SRAD filter are separate filters and the SRAD filter is used to separate the target image/signal into the low frequency and high frequency components; section 3.4-3.5; 4.1.1; section 4.2.1; tables 1-2 and fig 3).
As per claim 3, SAGHEER discloses the noise reduction processing method according to claim 2, wherein the target signal is a two- or higher-dimensional image signal (as shown in fig 1 all of the medical imaging modalities used are 2-dimensional or greater in imaging dimensionality; figure 1; section 2); the noise reduction processing method further comprises acquiring, based on a noise distribution of one of noise caused by an acquisition principle in acquisition of the image signal and noise caused by an apparatus that acquires the image signal, a noise model of the noise distribution (the computing system is adapted to apply learning model algorithms in order to adaptively adjust noise removal filters acting substantially as an adjustable noise removal model; figure 1; section 3-3.3); and in the step of estimating the noise signal, the noise signal is estimated by multiplying the high-frequency signal by the noise model following to the applying the second filter to the high-frequency signal (the computing system is adapted to determine noise values for medical images and remove the noise values using adjustable filters and perform multiplication to the similarity index SSIM in order to arrive at the SRAD filter which is used to remove/reduce noise in the target image/signal; figs 1 and 5; section 2; section 3.2.1; sections 4.2 and 4.3.1-4.4.2; tables 1-2 and 4-6).
As per claim 4, SAGHEER discloses the noise reduction processing method according to claim 3, wherein a series of processing including the step of estimating the noise signal, the step of calculating the similarity index, the step of calculating the filter coefficient and the step of acquiring the low-noise signal is repeatedly executed by using the low-noise signal as the target signal (the medical image noise reduction/removal process is performed by the computing system iteratively/repeated on the noise reduced image/signal until all noise is removed; section 3.2-3.2.1, and 3.4; section 4.5; table 1).
As per claim 5, SAGHEER discloses the noise reduction processing method according to claim 1, wherein in the step of acquiring the low-noise signal, the low-noise signal is acquired by using the noise signal together with the filter coefficient (the computing system further performs the operations of calculating a standard deviation filter SRAD using the SSIM index value and the SRAD filter is used to reduce noise as seen in table 2 and fig 3 and as the signal is iteratively repeated through the process the further the noise will be reduced to arrive at a signal with substantially eliminated/low noise; section 3.5-4.1.1; section 4.2.1; table 2 and fig 3).
As per claim 6, SAGHEER discloses the noise reduction processing method according to claim 1, wherein in the step of calculating the filter coefficient, the filter coefficient is calculated by using the target signal together with the similarity index (the computing system further performs the operations of calculating a standard deviation filter SRAD using the SSIM index value and the SRAD filter is used to reduce noise as seen in table 2 and fig 3; section 3.5-4.1.1; section 4.2.1; table 2 and fig 3).
As per claim 9, SAGHEER discloses the noise reduction processing method according to claim 1, wherein processing in at least one of estimating the noise signal in the step of estimating the noise signal, calculating the similarity index in the step of calculating the similarity index, calculating the filter coefficient in the step of calculating the filter coefficient, and acquiring the low-noise signal in the step of acquiring the low-noise signal is executed based on a reference dataset (the computing system receives the input images/target signals from a variety of databases containing data sets of various medical images of varying medical imaging types; figs 6-8, 10; sections 4.3-4.4.2).
As per claim 10, SAGHEER discloses the noise reduction processing method according to claim 1 further comprising a step of acquiring a low-noise composite signal by combining the target signal and the low-noise signal with weights being applied to the target signal and the low-noise signal for adjustment of noise reduction effect (using adaptive filters the computing system is adapted to change the filter weights based on the imaging type/modality providing the ability to reduce noise in many image types, the adaptive filters are based on the idea of assigning weighting coefficients for pixels in a given window whose characteristics are based on statistical properties; section 3-3.1).
As per claim 11, SAGHEER discloses a noise reduction processing apparatus for reducing noise in a target signal (a system to perform a method which would produce a low noise/noise reduced signal wherein the signal is a medical imaging signal; fig 1; sections 2-3.2; sections 4-4.4), the apparatus including a target signal acquirer for acquiring the target signal (the method of operation including an estimation step where the computing system performs calculations in order to fill out table 1 which includes values for the amount/intensity of noise present in the acquired medical image (target signal); fig 1; sections 2-3.2; sections 4-4.4; table 1); and a noise reducer for reducing noise in the target signal, wherein the noise reducer is configured to acquire a low-frequency signal by applying a first filter including a first filter window to the target signal (the computing system further performs the operations of calculating a standard deviation filter SRAD using the SSIM index value and the SRAD filter is used to reduce noise as seen in table 2 and fig 3 and is applied as a “reducer” a Rayleigh Maximum Likelihood RML filter, RML filters make use of neighboring pixels statistical characteristic within the local window to calculate the expected value needed to replace the filtered pixel where the size of the filter window will determine the amount of speckle reduced and the visual quality of the denoised image; section 3-3.2.1 and 3.5-4.1.1; section 4.2.1; table 2 and fig 3), to acquire a high-frequency signal based on the target signal and the low-frequency signal and to estimate the noise signal by applying a second filter including a second filter window independent of the first filter window to the high-frequency signal (various estimated noise signal of various levels (which can be seen as values in the tables provide for each respective imaging modality) are applied to the filters which is a spatial domain FROST filter acting as a second filter type and the variation in the SSIM is observed in relation to the change in noise signal intensity; figs 1 and 5; section 2; section 3.2.1; sections 4.3.1-4.4.2; tables 1-2 and 4-6), to calculate a similarity index between a predetermined signal that is acquired at a first signal point and a signal that is acquired at a second signal point in proximity to the first signal point based on the target signal and the noise signal (the computing system further performs steps of calculating a similarity index, Structural similarity index “SSIM” shows how much the denoised images matches with the original image based on tested reference points and equation number 6; section 3.1-3.3; section 4.1.1), to calculate a filter coefficient of a noise reduction filter based on the similarity index, and to acquire a low-noise signal by applying the noise reduction filter to the target signal based on the filter coefficient (the computing system further performs the operations of calculating a standard deviation filter SRAD using the SSIM index value and the SRAD filter is used to reduce noise as seen in table 2 and fig 3; section 3.5-4.1.1; section 4.2.1; table 2 and fig 3).
As per claim 12, SAGHEER discloses a noise reduction processing method for reducing noise in a target signal (a system to perform a method which would produce a low noise/noise reduced signal wherein the signal is a medical imaging signal; fig 1; sections 2-3.2; sections 4-4.4), the method comprising: a step of estimating a noise signal representing an intensity distribution of the noise in the target signal based on the target signal (the method of operation including an estimation step where the computing system performs calculations in order to fill out table 1 which includes values for the amount/intensity of noise present in the acquired medical image (target signal); fig 1; sections 2-3.2; sections 4-4.4; table 1); a step of calculating a similarity index between a predetermined signal that is acquired at a first signal point and a signal that is acquired at a second signal point in proximity to the first signal point based on the target signal and the noise signal (the computing system further performs steps of calculating a similarity index, Structural similarity index “SSIM” shows how much the denoised images matches with the original image based on tested reference points and equation number 6; section 3.1-3.3; section 4.1.1); a step of calculating a filter coefficient of a noise reduction filter based on the similarity index (the computing system further performs the operations of calculating a standard deviation filter SRAD using the SSIM index value and the SRAD filter is used to reduce noise as seen in table 2 and fig 3; section 3.5-4.1.1; section 4.2.1; table 2 and fig 3); a step of acquiring a low-noise signal by applying the noise reduction filter to the target signal based on the filter coefficient (from the target image/signal which is a medical image acquiring a noise removed medical image after performing the previous computing operations/steps based on the applied SRAD filter calculated based on the similarity index; sections 3-3.3; sections 4.1.1-4.2.1); and a step of acquiring a low-noise composite signal by combining the target signal and the low-noise signal for adjustment of noise reduction effect (based on the iterated/looped filter being a hybrid filter the original target signal/images is broken up into a high frequency portion/signal and a lower frequency portion/signal which has the noise reduction effects applied; figs 1 and 5; section 2; section 3.2.1; sections 4.1.1-5; tables 1-2 and 4-6.
As per claim 13, SAGHEER discloses the noise reduction processing method according to claim 12, wherein in the step of acquiring the low-noise composite signal, a low-noise composite signal is acquired by combining the target signal and the low-noise signal with weights being applied to the target signal and the low-noise signal (using adaptive filters the computing system is adapted to change the filter weights based on the imaging type/modality providing the ability to reduce noise in many image types, the adaptive filters are based on the idea of assigning weighting coefficients for pixels in a given window whose characteristics are based on statistical properties; section 3-3.1).
As per claim 14, SAGHEER discloses the noise reduction processing method according to claim 12, wherein in the step of acquiring the low-noise composite signal, the low-noise signal is acquired by using the noise signal together with the target signal and the low-noise signal (the computing system further performs the operations of calculating a standard deviation filter SRAD using the SSIM index value and the SRAD filter is used to reduce noise as seen in table 2 and fig 3 and as the signal is iteratively repeated through the process the further the noise will be reduced to arrive at a signal with substantially eliminated/low noise; section 3.5-4.1.1; section 4.2.1; table 2 and fig 3).
As per claim 15, SAGHEER discloses the noise reduction processing method according to claim 12, wherein in the step of acquiring the low-noise composite signal, the low-noise signal is acquired by using at least one of the similarity index and the filter coefficient together with the target signal and the low-noise signal (the computing system further performs the operations of calculating a standard deviation filter SRAD using the SSIM index value and the SRAD filter is used to reduce noise as seen in table 2 and fig 3; section 3.5-4.1.1; section 4.2.1; table 2 and fig 3).
As per claim 17, SAGHEER discloses the noise reduction processing method according to claim 12, wherein processing in at least one of estimation of the noise signal in the step of estimating the noise signal, calculation of the similarity index in the step of calculating the similarity index, calculation of the filter coefficient in the step of calculating the filter coefficient, acquisition of the low-noise signal in the step of acquiring the low-noise signal, and acquisition of the low-noise composite signal in the step of acquiring the low-noise composite signal is executed based on a reference dataset (the computing system receives the input images/target signals from a variety of databases containing data sets of various medical images of varying medical imaging types; figs 6-8, 10; sections 4.3-4.4.2).
As per claim 18, SAGHEER discloses the noise reduction processing apparatus according to claim 11, wherein the noise reducer is configured to acquire a low-noise composite signal by combining the target signal and the low-noise signal for adjustment of noise reduction effect (the computing system further performs the operations of calculating a standard deviation filter SRAD using the SSIM index value and the SRAD filter is used to reduce noise as seen in table 2 and fig 3 and is applied as a “reducer” a Rayleigh Maximum Likelihood RML filter, RML filters make use of neighboring pixels statistical characteristic within the local window to calculate the expected value needed to replace the filtered pixel where the size of the filter window will determine the amount of speckle reduced and the visual quality of the denoised image; section 3-3.2.1 and 3.5-4.1.1; section 4.2.1; table 2 and fig 3).
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 7-8, and 16 are rejected under 35 § U.S.C. 103 as being obvious over Biomedical Signal Processing and Control to SAGHEER, et al. (hereinafter “SAGHEER”) in view of CT and MR Image Fusion Scheme in Nonsubsampled Contourlet Transform Domain to GANASALA et al. (hereinafter “GANASALA”).
As per claim 7, SAGHEER discloses the noise reduction processing method according to claim 1. SAGHEER fails to disclose further comprising a step of acquiring an auxiliary signal of a common target whose target signal has been acquired, the auxiliary signal being acquired by an apparatus that is different from an apparatus that captures the target signal, under an acquisition condition that is different from a condition that the target signal is acquired, or under a processing condition that is different from processing condition that the target signal is acquired, wherein processing in at least one of estimation of the noise signal in the step of estimating the noise signal, calculation of the similarity index in the step of calculating the similarity index, calculation of the filter coefficient in the step of calculating the filter coefficient, and acquisition of the low-noise signal in the step of acquiring the low-noise signal is executed by using the auxiliary signal.
GANASALA discloses further comprising a step of acquiring an auxiliary signal of a common target whose target signal has been acquired (the computing system is adapted to input an imaging signal from a plurality of potential imaging devices including CT, PET, MRI, x-ray; abstract), the auxiliary signal being acquired by an apparatus that is different from an apparatus that captures the target signal (the signal may be of many imaging types/modalities including CT, PET, MRI, x-ray etc... this requires the computing system to be adaptable to many different imaging types including a second which is different than the first; abstract; page 407, paragraphs 1-3), under an acquisition condition that is different from a condition that the target signal is acquired, or under a processing condition that is different from processing condition that the target signal is acquired (the computing system is adapted to determine and separate using a band pass filter a high frequency sub band of the respective image signal and a low frequency sub band of the respective image signal and is to apply a unique set of rules to each frequency band a set of high frequency sub-band fusion rules to the high band frequencies and a set of low frequency sub-band fusion rule to the low band frequencies; page 408, paragraphs 1-2; and pages 410-page 411, equation 6), wherein processing in at least one of estimation of the noise signal in the step of estimating the noise signal, calculation of the similarity index in the step of calculating the similarity index, calculation of the filter coefficient in the step of calculating the filter coefficient, and acquisition of the low-noise signal in the step of acquiring the low-noise signal is executed by using the auxiliary signal (wherein the chosen auxiliary signal of the chosen respective imaging modality including CT, PET, MRI, x-ray etc.. is applied to the band pass filter and the rules of GANASALA and the produced low noise low frequency image is to be processed by SAGHEER to produce the similarity index, filter coefficient values; abstract; page 407, para 1-3; page 408, paragraphs 1-2; and pages 410-page 411, equation 6).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify SAGHEER to have a signal produced from a different imaging system used as the input and having different conditions associated with that input type of GANASALA reference. The Suggestion/motivation for doing so would have been to provide the ability to make the neighboring coefficients in the composite medical fusion image belong to the same source image in order to overcome the effect due to noise and guarantee the homogeneity of the fused image as suggested by page 411, of GANASALA. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine GANASALA with SAGHEER to obtain the invention as specified in claim 7.
As per claim 8, SAGHEER in view of GANASALA discloses the noise reduction processing method according to claim 7, where modified SAGHEER further discloses and processing in at least one of estimating the noise signal in the step of estimating the noise signal (the SRAD filter is applied to the medical image/target signal to arrive at the noise removed signal; sections 3-3.3; sections 4.1.1-4.2.1; tables 1 and 2), calculating the similarity index in the step of calculating the similarity index (the computing system further performs steps of calculating a similarity index, Structural similarity index “SSIM” shows how much the denoised images matches with the original image based on tested reference points and equation number 6; section 3.1-3.3; section 4.1.1), calculating the filter coefficient in the step of calculating the filter coefficient (the computing system further performs the operations of calculating a standard deviation filter SRAD using the SSIM index value and the SRAD filter is used to reduce noise as seen in table 2 and fig 3; section 3.5-4.1.1; section 4.2.1; table 2 and fig 3), and acquiring the low-noise signal in the step of acquiring the low-noise signal is executed by using the low-noise signal as the auxiliary signal (from the target image/signal which is a medical image acquiring a noise removed medical image after performing the previous computing operations/steps based on the applied SRAD filter calculated based on the similarity index; sections 3-3.3; sections 4.1.1-4.2.1). SAGHEER fails to disclose wherein the auxiliary signal includes the low-noise signal; and processing in at least one of estimating the noise signal in the step of estimating the noise signal, calculating the similarity index in the step of calculating the similarity index, calculating the filter coefficient in the step of calculating the filter coefficient, and acquiring the low-noise signal in the step of acquiring the low-noise signal is executed by using the low-noise signal as the auxiliary signal.
GANASALA discloses wherein the auxiliary signal includes the low-noise signal (the system using/processing the auxiliary signal is to reduce the noise in a medical imaging signal and include a low noise signal which is iteratively processed to eliminate noise from the target input; page 411, paragraphs 1-2).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify SAGHEER to have *** of GANASALA reference. The Suggestion/motivation for doing so would have been to provide the ability to make the neighboring coefficients in the composite medical fusion image belong to the same source image in order to overcome the effect due to noise and guarantee the homogeneity of the fused image as suggested by page 411, of GANASALA. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine GANASALA with SAGHEER to obtain the invention as specified in claim 8.
As per claim 16, SAGHEER discloses the noise reduction processing method according to claim 12. SAGHEER fails to disclose further comprising a step of acquiring an auxiliary signal of a common target whose target signal has been acquired, the auxiliary signal being acquired by an apparatus that is different from an apparatus that captures the target signal, under an acquisition condition that is different from a condition that the target signal is acquired, or under a processing condition that is different from processing condition that the target signal is acquired, wherein processing in at least one of estimation of the noise signal in the step of estimating the noise signal, estimation of the similarity index in the step of calculating the similarity index, calculation of the filter coefficient in the step of calculating the filter coefficient, acquisition of the low-noise signal in the step of acquiring the low-noise signal, and acquisition of the low-noise composite signal in the step of acquiring the low-noise composite signal is executed by using the auxiliary signal.
GANASALA discloses further comprising a step of acquiring an auxiliary signal of a common target whose target signal has been acquired (the computing system is adapted to input an imaging signal from a plurality of potential imaging devices including CT, PET, MRI, x-ray; abstract), the auxiliary signal being acquired by an apparatus that is different from an apparatus that captures the target signal (the signal may be of many imaging types/modalities including CT, PET, MRI, x-ray etc... this requires the computing system to be adaptable to many different imaging types including a second which is different than the first; abstract; page 407, paragraphs 1-3), under an acquisition condition that is different from a condition that the target signal is acquired, or under a processing condition that is different from processing condition that the target signal is acquired (the computing system is adapted to determine and separate using a band pass filter a high frequency sub band of the respective image signal and a low frequency sub band of the respective image signal and is to apply a unique set of rules to each frequency band a set of high frequency sub-band fusion rules to the high band frequencies and a set of low frequency sub-band fusion rule to the low band frequencies; page 408, paragraphs 1-2; and pages 410-page 411, equation 6), wherein processing in at least one of estimation of the noise signal in the step of estimating the noise signal, estimation of the similarity index in the step of calculating the similarity index, calculation of the filter coefficient in the step of calculating the filter coefficient, acquisition of the low-noise signal in the step of acquiring the low-noise signal, and acquisition of the low-noise composite signal in the step of acquiring the low-noise composite signal is executed by using the auxiliary signal (wherein the chosen auxiliary signal of the chosen respective imaging modality including CT, PET, MRI, x-ray etc.. is applied to the band pass filter and the rules of GANASALA and the produced low noise low frequency image is to be processed by SAGHEER to produce the similarity index, filter coefficient values; abstract; page 407, para 1-3; page 408, paragraphs 1-2; and pages 410-page 411, equation 6).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify SAGHEER to have a signal produced from a different imaging system used as the input and having different conditions associated with that input type of GANASALA reference. The Suggestion/motivation for doing so would have been to provide the ability to make the neighboring coefficients in the composite medical fusion image belong to the same source image in order to overcome the effect due to noise and guarantee the homogeneity of the fused image as suggested by page 411, of GANASALA. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine GANASALA with SAGHEER to obtain the invention as specified in claim 16.
Conclusion
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
LI-RADS technical requirements for CT, MRI, and contrast-enhanced ultrasound
Fusion of The Multimodal Medical Images to Enhance the Quality Using Discrete Wavelet Transform
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
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/D J DHOOGE/Examiner, Art Unit 2677
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