DETAILED ACTION
Summary
Claims 1-10, 13-14, and 17-18 are pending in the application. Claim 18 is rejected under 35 USC 112(b). Claims 1-10, 13-14, and 17-18 are rejected under 35 USC 103.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/29/2026 has been entered.
Claim Objections
Claim 6 objected to because of the following informalities:
Claim 6 recites “moving tissue” in line 4. It should recite “the moving tissue”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 18 recites “the at least two classes” in line 11. It is not clear if this is referring to a specific subset of the at least three classes previously set forth, or if this is referring to any two of the at least three classes. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 18 recites “the classes” in line 12. It is not clear if this is referring to the at least three classes, or the at least two classes. Clarification is required. For the purposes of examination, the former definition will be used.
All claims dependent from the above claims rejected under 35 USC 112(b) are also rejected, as the limitations of the dependent claims fail to cure the deficiencies identified above.
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 nonobviousness.
Claims 1-2, 4, and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Koh et al. (U.S PGPub 2014/0316274 A1) in view of Freiburger et al. (U.S PGPub 2019/0261952 A1), Yang (U.S PGPub 2021/0088639 A1), and Guracar et al. (U.S Patent 6,309,357 B1).
Regarding Claim 1, Koh teaches a method for adaptive clutter filtering in color imaging by an ultrasound scanner (Abstract), the method comprising:
scanning, by the ultrasound scanner (Fig. 1, 110) [0033], a patient (Fig. 6, S605-S610) [0085];
discriminating a first region from a second region (Fig. 6, S615-S620) [0085] by first and second types of signals represented in scan data from the scanning [0043]+[0045] (the different doppler signals are the first and second types of signals);
applying a first wall filter for the first region of the first type of signal (Fig. 6, S630) [0086]-[0087] and a second wall filter for the second region of the second type of signal (Fig. 6, S630) [0086]-[0087], the first wall filter different than the second wall filter [0064]-[0065]+[0068] (one of ordinary skill would recognize the clutter filter of Koh is a wall filter, as it removes clutter generated by reflections from the vessel wall [0051]); and
color imaging, by the ultrasound scanner, using estimates resulting from the applying of the first and second wall filters [0074].
Koh fails to explicitly teach the discriminating occurs using a machine-learned mode or wherein the discriminating comprises inputting versions to the machine-learned model.
Freiburger teaches a system for color flow imaging optimization (Abstract). This system uses machine learning to segment the target [0031]. This system inputs velocity, variance and/or power to the machine learned model to segment the target [0031].
It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the method of segmenting the image of Koh with a machine learning method, as taught by Freiburger, as the substitution for one known method for segmenting an image with another yields predictable results to one of ordinary skill in the art. One of ordinary skill would have been able to carry out such a substitution, and the results of using machine learning to discriminate regions in the image are reasonably predictable.
The combination fails to explicitly teach the first type of signal being from moving tissue or fluid, the second type of signal being from flash, clutter, or background noise such that the discriminating discriminates the moving tissue or the fluid from the flash, the clutter, or the background noise. The first wall filter for the first region being for the moving tissue or fluid, and the second wall filter for the second region being for the flash, the clutter, or the background noise.
Yang teaches a system for color flow imaging (Abstract). This system uses a neural network [0032] to discriminate the flow data and flash artifacts (Fig. 5, 515) [0041]-[0042]. This system then uses different clutter (i.e. wall) filters based on the flash strength (including whether the flash is not present, which would result in only a flow signal) [0044]-[0045].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system to discriminate the flow signal from the flash signal using a machine learning system, and then adjust the filter based on the signal, as taught by Yang, because this allows for strong flash artifacts to be dynamically suppressed, thereby increasing the quality of the image, as recognized by Yang [0003].
The combination fails to explicitly teach estimating two or more versions of velocity, variance, and/or power from the scan data, and inputting the at least two versions to the machine-learned model.
Guracar teaches a system for flow imaging (Abstract). This system uses multiple clutter filters (Fig. 1, 110+120) (Col 3, lines 18-35) which are used to determine different versions of the velocity, variance, and/or power (Col 4, lines 32-46).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system to use multiple versions, as taught by Guracar, because using multiple versions of the parameters allows for an improved sensitivity and calculation of the flow rate, as well as allowing the benefits of multiple filters to be obtained in the calculation, as recognized by Guracar (Col 7, lines 58-67)+(Col 8, lines 1-6). One of ordinary skill would recognize that, in the combination, the multiple versions of the parameters generated by Guracar would be input to the machine-learned model as taught by Freiburger.
Regarding Claim 2, Koh teaches the invention substantially as claimed. Koh further teaches wherein applying comprises applying to the scan data [0066], and wherein color imaging comprises color imaging from estimates of the scan data for the first region as filtered by the first wall filter [0071]+[0074].
Koh fails to explicitly teach wherein color imaging comprises color imaging from estimates of the scan data for the second region as filtered by the second wall filter.
Freiburger teaches a system for color flow imaging (Abstract). This system uses wall filtered data [0018] from the tissue region [0029]+[0034] in the color flow imaging [0070]-[0071].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system to use the estimate of the scan data from the second region in the color imaging, as taught by Freiburger, because this allows the used to see the entire image while minimizing the influence of the tissue, thereby allowing the user to see imaging data from regions other than the flow regions and increasing the user’s field of view, a recognized by Freiburger [0084]+[0086].
Regarding Claim 4, the combination of references teaches the invention substantially as claimed. Koh further teaches wherein discriminating comprises segmenting the first region from the second region sample location-by-sample location [0085] (the plurality of regions are locations, and as such the system is segmenting the image location by location).
Regarding Claim 13, the combination of references teaches the invention substantially as claimed. Koh further teaches wherein color imaging comprises color imaging for one of various imaging applications [0074], wherein discriminating comprises discriminating for any of the various imaging applications [0006]+[0051] (imaging the heart is a different application then imaging the blood vessel wall).
Koh fails to explicitly teach the discriminating occurs using a machine-learned model.
Freiburger teaches a system for color flow imaging optimization (Abstract). This system uses machine learning to segment the target [0031].
It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the method of segmenting the image of Koh with a machine learning method, as taught by Freiburger, as the substitution for one known method for segmenting an image with another yields predictable results to one of ordinary skill in the art. One of ordinary skill would have been able to carry out such a substitution, and the results of using machine learning to discriminate regions in the image are reasonably predictable.
Regarding Claim 14, Koh teaches a method for adaptive clutter filtering in color imaging by an ultrasound scanner (Abstract), the method comprising:
generating a discrimination map discriminating sample locations into multiple categories (Fig. 6, S615-S620) [0085];
adapting clutter filtering based on the discrimination map (Fig. 6, S630) [0086]-[0087]; and
color flow imaging using the clutter filtering as adapted [0074].
Koh fails to explicitly teach the generate the discrimination map by an artificial intelligence in response to input of the estimates.
Freiburger teaches a system for color flow imaging optimization (Abstract). This system uses machine learning (an artificial intelligence) to segment the target (and there generate a discrimination map) [0031]. This system inputs velocity, variance and/or power to the artificial intelligence to segment the target [0031].
It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the method of segmenting the image of Koh with a machine learning method, as taught by Freiburger, as the substitution for one known method for segmenting an image with another yields predictable results to one of ordinary skill in the art. One of ordinary skill would have been able to carry out such a substitution, and the results of using machine learning to discriminate regions in the image are reasonably predictable.
The combination fails to explicitly teach the categories distinguishing the sample locations for flow from the sample locations for flash, clutter, and/or background noise, or such that the sample locations for the flow are clutter filtered differently than the sample locations for the flash, the clutter, and/or the background noise.
Yang teaches a system for color flow imaging (Abstract). This system uses a neural network [0032] to discriminate the flow data and flash artifacts (Fig. 5, 515) [0041]-[0042]. This system then uses different clutter (i.e. wall) filters based on the flash strength (including whether the flash is not present, which would result in only a flow signal) [0044]-[0045].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system to discriminate the flow signal from the flash signal using a machine learning system, and then adjust the filter based on the signal, as taught by Yang, because this allows for strong flash artifacts to be dynamically suppressed, thereby increasing the quality of the image, as recognized by Yang [0003].
The combination fails to explicitly teach estimating at least two versions of velocity, variance, and/or power from scan data, where each of the at least two versions corresponds to a different preliminary wall filtering of the scan data.
Guracar teaches a system for flow imaging (Abstract). This system uses multiple different clutter (wall) filters (Fig. 1, 110+120) (Col 3, lines 18-35) to determine at least two versions of the velocity, variance, and/or power (Col 4, lines 32-46).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system to use multiple versions, as taught by Guracar, because using multiple versions of the parameters allows for an improved sensitivity and calculation of the flow rate, as well as allowing the benefits of multiple filters to be obtained in the calculation, as recognized by Guracar (Col 7, lines 58-67)+(Col 8, lines 1-6). One of ordinary skill would recognize that, in the combination, the multiple versions of the parameters generated by Guracar would be input to the machine-learned model as taught by Freiburger.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Koh in view of Freiburger, Yang, and Guracar as applied to claim 1 above, and further in view of Kim (U.S PGPub 2022/0211352 A1).
Regarding Claim 3, the combination of references teaches the invention substantially as claimed. Koh further teaches wherein color imaging comprises color flow imaging [0074] where the first and second wall filter with different frequency responses [0065]+[0068].
Koh fails to explicitly teach the filter is a high pass filter.
Kim teaches a system for color imaging system (Abstract). This system uses a high pass filter as the wall filter [0024].
It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the filter of the combined system to be a high pass filter, as the substitution for one known clutter filter with another yields predictable results to one of ordinary skill in the art. One of ordinary skill would have been able to carry out such a substitution, and the results of using a high pass filter as a wall filter are reasonably predictable. One of ordinary skill would recognize that, in the combination, the different high pass filters of Kim would have different cutoff frequencies, as taught by Koh.
Claims 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Koh in view of Freiburger, Yang, and Guracar as applied to claim 1 above, and further in view of Mo et al. (U.S PGPub 2002/0169378 A1).
Regarding Claim 5, the combination of references teaches the invent substantially as claimed. Koh further teaches wherein discriminating comprises discriminating by the first type of signal, the second type of signal [0043]+[0045].
The combination fails to explicitly teach a third type of signal, the third type of signal being at a third region, wherein applying comprises applying a third wall filter for the third region, the third wall filter different than the first and second wall filters, and wherein color imaging comprises color imaging using estimates resulting from applying of the third wall filter.
Mo teachers a system for adaptive clutter filtering (Abstract). This system looks at each pixel, which would be a signal type representing a third region (i.e. the pixel) [0012]+[0016]. This system applies a different wall filter to each of the pixels (acoustic point) (i.e. regions including a third region) [0081]+[0099]. The filters for each of the pixels can be different from one another (which would make the third wall filter different from the first and second wall filters) [0032]. This filtered data is then used to generate a color image [0078].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system to have a third type of signal with a third wall filter, as taught by Mo, because this better suppresses artifacts in the image without eliminating low velocity signals, as recognized by Mo [0032].
Regarding Claim 7, the combination of references teaches the invention substantially as claimed. Koh further teaches wherein the first wall filter has a lowest cutoff frequency relative to the first, second, and third wall filters, the second wall filter has a highest cutoff frequency relative to the first, second, and third wall filters, and the third wall filter has a cutoff frequency between the highest and lowest cutoff frequencies relative to the first, second, and third wall filters (Fig. 6, S625) [0086].
Once of ordinary skill would recognize that as each region has a different cutoff frequency, and the combination contains at least three regions, one region must have a highest cutoff frequency, one region must have a lowest cutoff frequency, and one region would have a cutoff frequency between the two.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Koh in view of Freiburger, Yang, Guracar, and Mo as applied to claim 5 above, and further in view of Lee et al. (U.S Patent 9,261,485 B2).
Regarding Claim 6, the combination of references fails to explicitly teach wherein the first type of signal comprises flow signal from the fluid or the moving tissue, the second type of signal comprises the flash and/or the clutter, and the third type of signal comprises the background noise, and wherein color imaging comprises color imaging of the fluid or moving tissue.
Lee teaches a method for color Doppler imaging (Abstract). This system separates the Doppler signal into flow signal from fluid or moving tissue signal from tissue (Fig. 4, 410), the second type of signal comprises flash and/or clutter (Fig. 4, 420), and the third type of signal comprises background noise (Fig. 4, 430) (Col 4, lines 54-65), and wherein color imaging comprises color imaging of the fluid or moving tissue (Col 5, lines 39-44).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system so the signals are flow, clutter, and noise signals, as taught by Lee, because this allows the system to more accurately obtain an remove noise, thereby resulting in higher quality images, as recognized by Lee (Col 1, lines 51-62).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Koh in view of Freiburger, Yang, and Guracar as applied to claim 1 above, and further in view of Bakircioglu et al. (U.S PGPub 2005/0131300 A1)
Regarding Claim 8, the combination of references teaches the invention substantially as claimed. The combination fails to explicitly teach setting a threshold for the estimates for the first region differently than a threshold for the estimates for the second region, wherein the estimates for color imaging result from thresholding using the thresholds for the first and second regions.
Bakircioglu teaches a method for optimizing ultrasonic imaging (Abstract). This system setting a threshold for the estimates for the first region differently than a threshold for the estimates for the second region [0046] (the system sets the threshold as a function of spatial location, which suggests the different regions would have different thresholds), wherein the estimates for color imaging result from thresholding using the thresholds for the first and second regions [0038].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system to use different thresholds for the different regions, as taught by Bakircioglu, because this optimizes the flow imaging, thereby reducing artifacts in the image, as recognized by Bakircioglu [0002].
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Koh in view of Freiburger, Yang, and Guracar as applied to claim 1 above, and further in view of Yoo (U.S PGPub 2021/0224991 A1).
Regarding Claim 9, the combination of references teaches the invention substantially as claimed. The combination fails to explicitly teach wherein the machine-learned model comprises a semantic segmentation deep network.
Yoo teaches an ultrasound system for semantic segmentation in ultrasound images (Abstract). This system uses a semantic segmentation deep network to segment the images [0007]+[0012].
It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the machine learning model of the combination with a semantic segmentation, as taught by Yoo, as the substitution for one known machine learning segmentation method with another yields predictable results to one of ordinary skill in the art. One of ordinary skill would have been able to carry out such a substitution, and the results of using a semantic segmentation model are reasonably predictable.
Regarding Claim 10, the combination of references teaches the invention substantially as claimed. Koh fails to explicitly teach wherein the semantic segmentation deep network comprises an image-to-image neural network.
Yoo further teaches w the semantic segmentation deep network comprises an image-to-image neural network [0016] (as the neural network is performed on images and was taught using other images, it is considered an image to image neural network.
It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the machine learning model of the combination with an image to image neural network, as taught by Yoo, as the substitution for one known machine learning segmentation method with another yields predictable results to one of ordinary skill in the art. One of ordinary skill would have been able to carry out such a substitution, and the results of using an image to image neural network are reasonably predictable.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Koh in view of Freiburger, Yang, and Guracar as applied to claim 14 above, and further in view of Lee and Kim.
Regarding Claim 17, the combination of references teaches the invention substantially as claimed. Koh further teaches selecting different frequency responses [0042].
The combination fails to explicitly teach generating the discrimination map comprises distinguishing between the sample locations with of the flow, the sample locations with the clutter and/or the flash and the sample locations with the background noise
Lee teaches a method for color Doppler imaging (Abstract). This system separates the Doppler signal into flow signal from fluid or moving tissue signal from tissue (Fig. 4, 410), the second type of signal comprises flash and/or clutter (Fig. 4, 420), and the third type of signal comprises background noise (Fig. 4, 430) (Col 4, lines 54-65), and wherein color imaging comprises color imaging of the fluid or moving tissue (Col 5, lines 39-44).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system so the signals are flow, clutter, and noise signals, as taught by Lee, because this allows the system to more accurately obtain an remove noise, thereby resulting in higher quality images, as recognized by Lee (Col 1, lines 51-62).
The combination fails to explicitly teach high pass frequency response.
Kim teaches a system for color imaging system (Abstract). This system uses a high pass filter as the wall filter [0024].
It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the filter of the combined system to be a high pass filter, as the substitution for one known clutter filter with another yields predictable results to one of ordinary skill in the art. One of ordinary skill would have been able to carry out such a substitution, and the results of using a high pass filter as a wall filter are reasonably predictable. One of ordinary skill would recognize that, in the combination, the different high pass filters of Kim would have different cutoff frequencies, as taught by Koh. One of ordinary skill would recognize that, as Koh changes the filter for the different spatial areas, in the combined system the high pass filters of Kim would be changed for the different areas (and therefore have different frequency responses).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Koh in view of Freiburger, Yang, Guracar, and Lee.
Regarding Claim 18, Koh teaches an ultrasound system for color imaging (Abstract), the ultrasound system comprising:
a transducer and beamformer (Fig. 1, 110) [0035] for scanning a scan region (Fig. 6, S605-S610) [0085];
a programmable wall filter (Fig. 3, 415) [0042]+[0054] (choosing different clutter filters is programming the wall filter to be a certain one)
an image processor (Fig. 1, 130) configured to segment, by application of a machine-learned segmentation model, the scan region into classes (Fig. 6, S615-S620) [0085] and adapt settings of the programmable wall filter based on the at least two classes such that different locations of the scan region use different ones of the settings [0042]+[0086]-[0087]
a Doppler estimator (Fig. 1, 130) [0072] configured to estimate, from data filtered by the programmable wall filter based on the settings, color values in the scan region [0074]
a display configured to display an image using the color values [0074].
Koh fails to explicitly teach the application of a machine-learned segmentation model, or a third class for background noise.
Freiburger teaches a system for color flow imaging optimization (Abstract). This system uses machine learning to segment the target [0031]. This system determines an area which is dominated by background noise [0032]+[0043]
It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the method of segmenting the image of Koh with a machine learning method, as taught by Freiburger, as the substitution for one known method for segmenting an image with another yields predictable results to one of ordinary skill in the art.
The combination fails to explicitly teach the classes comprising a first class for flow or moving tissue, a second class for clutter or flash.
Yang teaches a system for color flow imaging (Abstract). This system uses a neural network [0032] to discriminate the flow data and flash artifacts (Fig. 5, 515) [0041]-[0042]. This system then uses different clutter (i.e. wall) filters based on the flash strength (including whether the flash is not present, which would result in only a flow signal) [0044]-[0045].
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system to discriminate the flow signal from the flash signal using a machine learning system, and then adjust the filter based on the signal, as taught by Yang, because this allows for strong flash artifacts to be dynamically suppressed, thereby increasing the quality of the image, as recognized by Yang [0003]. One of ordinary skill would recognize the combined system suggests segmenting into three classes, as Yang teaches the benefits of both flow and flash artifacts, while Freiburger teaches the benefits of segmenting background noise.
The combination fails to explicitly teach estimate at least two versions of at least one of velocity, variance, or power from scan data, where each of the at least versions corresponds to a different preliminary filtering by the programmable wall filter or another filter.
Guracar teaches a system for flow imaging (Abstract). This system uses multiple clutter filters (Fig. 1, 110+120) (Col 3, lines 18-35) which are used to determine different versions of the velocity, variance, and/or power (Col 4, lines 32-46). These filters have different frequency responses (Col 2, lines 32-44)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system to use multiple versions, as taught by Guracar, because using multiple versions of the parameters allows for an improved sensitivity and calculation of the flow rate, as well as allowing the benefits of multiple filters to be obtained in the calculation, as recognized by Guracar (Col 7, lines 58-67)+(Col 8, lines 1-6).
The combination fails to explicitly teach segmenting into three classes or a third class for background noise.
Lee teaches a method for color Doppler imaging (Abstract). This system separates the Doppler signal into flow signal from fluid or moving tissue signal from tissue (Fig. 4, 410), the second type of signal comprises flash and/or clutter (Fig. 4, 420), and the third type of signal comprises background noise (Fig. 4, 430) (Col 4, lines 54-65).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system so the classes are flow, clutter, and noise signals, as taught by Lee, because this allows the system to more accurately obtain an remove noise, thereby resulting in higher quality images, as recognized by Lee (Col 1, lines 51-62).
Response to Arguments
Applicant's arguments filed 4/7/2026 have been fully considered but they are not persuasive.
Applicant argues that the combination of Koh, Freiburger, Yang, and Guracar is not suggested by the prior art. The Examiner disagrees. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., multiple versions of velocity/variance/power are simultaneously input) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant argues nowhere does Guracar teaches taking multiple inputs and using them as simultaneous inputs to an analysis block. The Examiner disagrees. The different versions of the velocity/variance/power are input into the selector block, which performs analysis (i.e. selecting the optimal values) (Col 5, lines 6-20). The Examiner notes that none of the details of how the machine learned model uses the at least two versions for discrimination are claimed. Therefore, by the broadest reasonably interpretation, the machine learned model selecting the optimal input out of the multiple inputs, as taught by Guracar, is within the scope of the claim.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). As all the features were combined using the knowledge which was within the level of ordinary skill in the art at the time the invention was made, it is not improper hindsight reasoning.
Claim 1 remains rejected under 35 USC 103. For similar reasons, the other claims also remain rejected.
Conclusion
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/SEAN D MATTSON/Primary Examiner, Art Unit 3798
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