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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim(s) 1-9, 11 & 13-19 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim(s) 1-13 & 16-19 of U.S. Patent No. 12,217445 B2.
In order to make a double patenting determination, it first must be determined whether there are any differences between the rejected claims and the patented claims and, if so, whether those differences render the claims patentably distinct.
Rejected Claim 1: A system [A system] (Line 1 of patented Claim 1), comprising:
a probe configured to be positioned on an external surface of a patient's body and configured to:
[probe configured to be positioned on an external surface of a patient's body, and configured to] (Line 2-3 of patented Claim 1)
transmit ultrasound signals to a target of interest; and
[transmit ultrasound signals to a target of interest] (Line 4 of patented Claim 1)
receive echo information associated with the transmitted ultrasound signals; and
[receive echo information associated with the transmitted ultrasound signals; and] (Line 7-8 of Patented Claim 1)
at least one processing device configured to:
[at least one processing device configured to:] (Line 8-9 of Patented Claim 1)
process the received echo information using a machine learning algorithm to generate probability information associated with the target of interest;
[process the received echo information using a machine learning algorithm to generate probability information associated with the target of interest] (Line 10-12 of Patented Claim 1)
process the probability information generated by the machine learning algorithm to identify the target of interest,
[process the probability information generated by the machine learning algorithm to identify the target of interest] (Line 17-19 of Patented Claim 1)
wherein when processing the probability information, the at least one processing device is configured to binarize the probability information to identify whether pixels or portions of an image associated with the processed echo information are within the target of interest; and
[wherein when processing the probability information, the at least one processing device is configured to binarize the probability information to identify portions of the image within the target of interest] (Line 19-23 of Patented Claim 1)
output image information corresponding to the target of interest based on the binarized probability information.
[output image information corresponding to the target of interest based on the processed binarized probability information] (Line 28-30 of Patented Claim 1).
The difference between Rejected Claim 1 and Patented Claim 1 of the patent lies in the fact that the patent claim includes many more elements in some respects and adds obvious features in other respects. For example, the Patented Claims included specific details to the patient’s bladder or prostate and is thus, in that respect, more specific. In regard to those respects, the invention of Rejected Claim is in effect a “species” of the “generic” invention of Patented Claims 1. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). Since Rejected Claim 1 is anticipated by Patented Claim 1, Rejected Claim 1 is not patentably distinct from Patented Claim 1.
A similar analysis applies to the following:
Rejected Claim 2 and Patented Claim 2
Rejected Claim 3 and Patented Claim 3
Rejected Claim 4 and Patented Claim 4
Rejected Claim 5 and Patented Claim 5
Rejected Claim 6 and Patented Claim 6
Rejected Claim 7-8 and Patented Claim 1, 7 & 13
Rejected Claim 9 and Patented Claim 8
Rejected Claim 11 and Patented Claim 9
Rejected Claim 13 and Patented Claim 10
Rejected Claim 14 and Patented Claim 11
Rejected Claim 15 and Patented Claim 12
Rejected independent method Claim 16 and Patented method Claim 16
Rejected Claim 17 and Patented Claim 17
Rejected Claim 18 and Patented Claim 18
Rejected Claim 19 and Patented Claim 19
Thus, Rejected Claims 1-9, 11 & 13-19 is anticipated by Patented Claims 1-13 & 16-19, it is not patentably distinct from Patented Claims 1-13 & 16-19.
Claim(s) 10 & 12 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim(s) 1 of U.S. Patent No. 11,160,492 in view of Perrey et al. (U.S. Patent Application 2018/0240551 A1).
Rejected Claim 10: Patented Claims fail to teach selecting the machine learning algorithm for use. However, Perrey teaches wherein the machine learning algorithm is selected for use from a plurality of machine learning algorithms based on the received information [select at least a first model from the plurality of models based on the anatomical structure of interest] (Abstract) in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Patented Claims to include the selection of machine learning algorithms as taught by Perrey in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
Rejected Claim 12: Patented Claims fail to teach selecting the machine learning algorithm for use. However, Perrey teaches wherein the machine learning algorithm is selected for use from a plurality of machine learning algorithms [select at least a first model from the plurality of models based on the anatomical structure of interest] (Abstract) based on the automatically [automatically selected] (Para 0013) determined information in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Patented Claims to include the selection of machine learning algorithms as taught by Perrey in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
The difference between Rejected Claim 10 & 12 and Patented Claim 1 of the patent lies in the fact that the patent claim includes many more elements in some respects and adds obvious features in other respects. For example, the Patented Claims included specific details to the patient’s bladder and prostates and is thus, in that respect, more specific. In regard to those respects, the invention of Rejected Claims is in effect a “species” of the “generic” invention of Patented Claims 1. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). However, with respect to the added features (the machine learning selection), the added feature is merely an obvious element absent from the patent claims. The Patented Claims are analogous to a primary reference which has every element except that which has been added in the application claim (the machine learning selection). The secondary reference of Perrey supplied the missing element and provided above is the rationale explaining why it would have been obvious to combine the Patented Claims with the secondary reference, Perrey. Thus, the Rejected Claims 20-23 are rejected under an obviousness-type double patenting rejection.
Claim(s) 20-23 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim(s) 20-23 of U.S. Patent No. 11,160,492 in view of Perrey et al. (U.S. Patent Application 2018/0240551 A1).
Rejected Claim 20: A system, comprising: [A system, comprising:] (Line 1 of Patented Claim 20)
a memory; and [a memory; and] (Line 2 of Patented Claim 20)
at least one processing device configured to:
[at least one processing device configured to:] (Line 3 of Patented Claim 20)
receive first image information corresponding to a scanned target of interest;
[receive…image information corresponding to a target of interest] (Line 4-7 of Patented Claim 20)
process the first image information using the machine learning algorithm to generate probability information associated with the scanned target of interest;
[process the received image information using a machine learning algorithm to generate probability information associated with the target of interest] (Line 9-11 of Patented Claim 20)
binarize the probability information; and
[binarize the probability information] (Line 6 of Patented Claim 20)
output second image information corresponding to the scanned target of interest based on the binarized probability information
[output second image information corresponding to the target of interest based on the processed binarized probability information] (Line 22-24 of Patented Claim 20)
Patented Claim 20 fails to teach selecting the machine learning algorithm for use. However, Perrey teaches wherein the machine learning algorithm is selected for use from a plurality of machine learning algorithms based on information associated with a patient [select at least a first model from the plurality of models based on the anatomical structure of interest] (Abstract) in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Patented Claim 20 to include the selection of machine learning algorithms as taught by Perrey in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
The difference between Rejected Claim 20 and Patented Claim 20 of the patent lies in the fact that the Patented Claim includes many more elements in some respects and adds obvious features in other respects. For example, the Patented Claim included specific details to the patient’s bladder and prostate and is thus, in that respect, more specific. In regard to those respects, the invention of Rejected Claim is in effect a “species” of the “generic” invention of Patented Claim 20. It has been held that the generic invention is “anticipated” by the “species”. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993). However, with respect to the added features (the machine learning selection), the added feature is merely an obvious element absent from the patent claims. The Patented Claim is analogous to a primary reference which has every element except that which has been added in the application claim (the machine learning selection). The secondary reference of Perrey supplied the missing element and provided above is the rationale explaining why it would have been obvious to combine the Patented Claim with the secondary reference, Perrey. Thus, the Rejected Claim is rejected under an obviousness-type double patenting rejection.
A similar analysis applies to the following:
Rejected Claim 21 and Patented Claim 21
Rejected Claim 22 and Patented Claim 22
Rejected Claim 23 and Patented Claim 23
Thus, the Rejected Claims 20-23 are rejected under an obviousness-type double patenting rejection.
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(s) 4-5 & 8 is/are 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.
Regarding Claim 4-5
Claim 4 & 5 recite, “image information” and “output image information” respectively. The claim terminology usage is inconsistent and confusing. Claim 1 recites, “output image information”. It is clear from the context of Claim 1 that “output” is a verb and the image information is being outputted. Claim 4 correctly refers to “image information”. However, Claim 5 which depends from Claim 4, recites, “output image information”. The claim term, “output” within Claim 5 appears to be an adjective and not a verb of outputting. Appropriate correction is required.
Regarding Claim 8
Claim 8 recites receiving gender information, information about being a child or patient data. Claim 8 then recites processing the image data based on the received information. The claim term patient data is missing reference to “information”. The term could then be interpreted as not being part of the “received information”. It is recommended to change “information” to --data-- or added --information-- between “data” and “associated”. Appropriate correction is required.
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.
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.
Claim(s) 1-2, 4-5, 7, 11 & 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajguru et al. (U.S. Patent Application 2014/0350404 A1) and further in view of Dube et al. (U.S. Patent Application 2010/0166276 A1).
Claim 1: Rajguru teaches –
A system [IVUS imaging system] (Figure 1, Element 100), comprising:
a probe [ultrasound transducer] [IVUS catheter] (Figure 1, Element 112, 114 & 116) configured to:
transmit ultrasound signals [the transducer is pulsed 256 times while rotating around 360 degrees] (Para 0046) to a target of interest [spatiotemporal feature] (Para 0060 and 0014-0017); and
Examiner’s Note: The Examiner contends that transmitting ultrasound signals occurs in the prior art when the transducer is pulsed.
receive echo information associated with the transmitted ultrasound signals [pulse energy that is received by the transducer as the backscatter or reflected signal] (Para 0046); and
at least one processing device [processor] (Figure 1, Element 150) configured to:
process the received echo information (Figure 7, Element 356) using a machine learning algorithm [non-linear classifier] (Figure 7, Element 366) to generate probability information [direct indicator of the likelihood that a particular pixel is a blood region] (Para 0061 and Figure 7, Element 366) associated with the target of interest [region of interest in a vessel or tubular structure] (Figure 7, Element 350 and Para 0060);
Examiner’s Note: The claim term is being used is machine learning algorithm. Based on the Specification of the Applicant in Para 0036, that the machine learning algorithm is CNN (convolutional neural network). Additionally, the Specification of the Applicant in Para 0036 discloses neural networks as the machine learning algorithm with spatial information (Para 0033). The Specification is clear that classifiers without spatial information are different from a machine learning algorithm regards that machine learning algorithms use spatial information (Para 0033). It is acknowledged that the prior art uses the term classifier. However, the Specification of the Applicant explains that one difference between machine learning algorithms and classifiers is the use of spatial information (Para 0033). Thus, even though the prior art describes, non-linear classifier, it reads on the claim as it 1) has spatiotemporal features (Para 0060 of Rajguru; spatial information) and 2) discloses a neural network (Para 0057 of Rajguru; artificial neural network). The disclosures of the Applicant and Rajguru make it clear that the differences lie in the nomenclature and not in substance between the use of classifier in Rajguru and machine learning algorithm with the Applicant.
process the probability information generated by the machine learning algorithm to identify the target of interest [the Blood Likelihood Map may be used to differentially process grayscale IVUS image and then presented to the user (as well as structural borders such as, by way of non-limiting example, a lumen border)] (Para 0064 and Figure 7, Element 378); and
output image information [presented to the user] corresponding to the target of interest based on the probability information (Para 0064; Figure 1, Element 120 and Figure 7, Element 378).
Examiner’s Note: It is understood that the monitor of Figure 1, Element 120 is used to present the processed data to the user.
Rajguru fails to teach the binarizing probability information. However, Dube teaches –
wherein when processing the probability information, the at least one processing device [processor] (Para 0026-0027) is configured to
binarize the probability information to identify whether pixels or portions of an image associated with the processed echo information [binarized at probability threshold value higher than 0.9] (Para 0057) are within the target of interest [probabilistic segmentation mask is indicative of a likelihood that the corresponding voxel belongs to a pre-determined class…based on at least one feature] (Para 0036) in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Rajguru with the binarizing of probability information as taught by Dube in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
Rajguru teaches IVUS [intravascular ultrasound (IVUS) images] (Para 0001). The embodiment relied on in the rejection above is based on the IVUS embodiment. However, Rajguru teaches that non-invasive embodiments are known within the art [In other (e.g., non-invasive) ultrasound imaging applications, Doppler ultrasound methods are used to measure blood and tissue velocity] (Para 0010). Doppler ultrasound imaging probes are obviously probes used in non-invasive embodiments be positioned on an external surface of a patient's body. Thus, Rajguru teaches obviously a non-invasive embodiment where a probe is configured to be positioned on an external surface of a patient's body (Para 0010). Or in other words, that the IVUS embodiment can be modified into an external Doppler probe embodiment in order to be non-invasive (Para 0010).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invasive IVUS embodiment of Rajguru with the non-invasive embodiment of Rajguru in order to be non-invasive (Para 0010).
Claim 2/1: Rajquru teaches wherein the at least one processing device [processor] (Para 0026-0027) is further configured to:
output (Figure 1, Element 120) the at least one of the volume-related measurement or the size-related measurement [IVUS system is used to measure the lumen diameter or cross-sectional area of the vessel] (Para 0003) [then presented to the user (as well as structural borders such as, by way of non-limiting example, a lumen border)] (Para 0064)
Rajguru fails to specifically teach estimate, based on the binarized probability information, at least one of a volume-related measurement or a size-related measurement associated with the target of interest within one embodiment.
estimate at least one of a volume-related measurement or a size-related measurement associated with the target of interest (Para 0003); and
The Examiner finds that the prior art included each element claimed, although not necessarily within a single embodiment within the prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single embodiment of the prior art reference. Rajguru teaches the diameter measurement being performed within the prior art (Para 0003). The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known methods, and that in combination, each element merely performs the same function as it does separately. The background of Rajguru discloses the diameter measurement of the vessel in the combination performs the same function of diameter measurement regardless of the type of processing done to produce the IVUS image. The final measurements of Rajguru would perform the same function regardless the IVUS image processing. The user being presented data would be on the display of Rajguru (Para 0064). Thus, Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the output of the value from Rajguru with the display of Rajguru in order to determine the need for treatment (Para 0003)
Rajguru fails to teach the binarizing probability information. However, Dube teaches –
wherein when processing the probability information, the at least one processing device [processor] (Para 0026-0027) is configured to estimate, based on binarized probability information [binarized at probability threshold value higher than 0.9] (Para 0057) associated with the target of interest [probabilistic segmentation mask is indicative of a likelihood that the corresponding voxel belongs to a pre-determined class…based on at least one feature] (Para 0036) in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Rajguru with the binarizing of probability information as taught by Dube in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
Claim 4/1: Ragjuru teaches further comprising a display configured to receive the image information (Figure 1, Element 120), display the image information [presented to the user] (Para 0064) and display the value [IVUS system is used to measure the lumen diameter or cross-sectional area of the vessel] (Para 0003) [then presented to the user (as well as structural borders such as, by way of non-limiting example, a lumen border)] (Para 0064 and See Claim 1 regarding the claim analysis of the outputting the value).
Claim 5/4/1: Rajguru teaches wherein the display is further configured to simultaneously display B-mode image data corresponding to the received echo information (Figure 8A, Element 430) and the image information corresponding to the target of interest (Figure 8A, Element 430).
Claim 7/1: Rajguru teaches wherein the target of interest comprises an organ [IVUS system is used to measure the lumen diameter or cross-sectional area of the vessel] (Para 0003).
Claim 11/1: Rajguru fails to teach automatically determine clinical information of the and process the determined clinical information. However, Dube teaches wherein the at least one processing device is further configured to automatically determine clinical information of the subject [probabilistic segmentation mask may also be based on a distance feature. The distance feature assigns the probability of a voxel being part of a tumor class based on its distance from the seed point] (Para 0051), and process the received echo information based on the automatically determined information (Figure 8a-8c and Para 0055) in order to enhance the desired structures and not include other undesired structures (Para 0055).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps of Rajguru to include the steps of the automatically determining clinical information of Dube in order to enhance the desired structures and not include other undesired structures (Para 0055).
Claim 15/1: Rajguru teaches wherein the probe is configured to transmit the received echo information to the at least one processing device via a wireless interface [individual component parts of the IVUS imaging system 100 may be electrically and/or wirelessly connected to facilitate the transfer of power and/or data] (Para 0040).
Claim 16: Rajguru teaches –
A method (Figure 7), comprising:
transmitting [the transducer is pulsed 256 times while rotating around 360 degrees] (Para 0046), via an ultrasound probe [ultrasound transducer] [IVUS catheter] (Figure 1, Element 112, 114 & 116), ultrasound signals to a target of interest [spatiotemporal feature] (Para 0060 and 0014-0017);
Examiner’s Note: The Examiner contends that transmitting ultrasound signals occurs in the prior art when the transducer is pulsed.
receiving, via the ultrasound probe [ultrasound transducer] [IVUS catheter] (Figure 1, Element 112, 114 & 116), echo information associated with the transmitted echo information associated with the transmitted ultrasound signals [pulse energy that is received by the transducer as the backscatter or reflected signal] (Para 0046); and
processing the received echo information (Figure 7, Element 356) using a machine learning algorithm [non-linear classifier] (Figure 7, Element 366) to generate probability information [direct indicator of the likelihood that a particular pixel is a blood region] (Para 0061 and Figure 7, Element 366) associated with the target of interest [region of interest in a vessel or tubular structure] (Figure 7, Element 350 and Para 0060);
Examiner’s Note: The claim term is being used is machine learning algorithm. Based on the Specification of the Applicant in Para 0036, that the machine learning algorithm is CNN (convolutional neural network). Additionally, the Specification of the Applicant in Para 0036 discloses neural networks as the machine learning algorithm with spatial information (Para 0033). The Specification is clear that classifiers without spatial information are different from a machine learning algorithm regards that machine learning algorithms use spatial information (Para 0033). It is acknowledged that the prior art uses the term classifier. However, the Specification of the Applicant explains that one difference between machine learning algorithms and classifiers is the use of spatial information (Para 0033). Thus, even though the prior art describes, non-linear classifier, it reads on the claim as it 1) has spatiotemporal features (Para 0060 of Rajguru; spatial information) and 2) discloses a neural network (Para 0057 of Rajguru; artificial neural network). The disclosures of the Applicant and Rajguru make it clear that the differences lie in the nomenclature and not in substance between the use of classifier in Rajguru and machine learning algorithm with the Applicant.
processing the probability information generated by the machine learning algorithm to identify the target of interest [the Blood Likelihood Map may be used to differentially process grayscale IVUS image and then presented to the user (as well as structural borders such as, by way of non-limiting example, a lumen border)] (Para 0064 and Figure 7, Element 378),
outputting image information [presented to the user] corresponding to the target of interest based on the binarized probability information (Para 0064; Figure 1, Element 120 and Figure 7, Element 378)
Rajguru fails to teach the identifying based on probability information. However, Dube teaches –
wherein processing the probability information comprises binarizing the probability information to identify whether pixels or portions of an image associated with the processed echo information [binarized at probability threshold value higher than 0.9] (Para 0057) are within the target of interest [probabilistic segmentation mask is indicative of a likelihood that the corresponding voxel belongs to a pre-determined class…based on at least one feature] (Para 0036) in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
Examiner’s Note: The threshold applied to the probability information acts as a classifier.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Rajguru with the binarizing of probability information as taught by Dube in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
Rajguru teaches IVUS [intravascular ultrasound (IVUS) images] (Para 0001). The embodiment relied on in the rejection above is based on the IVUS embodiment. However, Rajguru teaches that non-invasive embodiments are known within the art [In other (e.g., non-invasive) ultrasound imaging applications, Doppler ultrasound methods are used to measure blood and tissue velocity] (Para 0010). Doppler ultrasound imaging probes are obviously probes used in non-invasive embodiments be positioned on an external surface of a patient's body. Thus, Rajguru teaches obviously a non-invasive embodiment where a probe is configured to be positioned on an external surface of a patient's body (Para 0010). Or in other words, that the IVUS embodiment can be modified into an external Doppler probe embodiment in order to be non-invasive (Para 0010).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invasive IVUS embodiment of Rajguru with the non-invasive embodiment of Rajguru in order to be non-invasive (Para 0010).
Claim 17/16: Rajguru teaches further comprising outputting. Rajguru fails to specifically teach where estimating, based on the binarized probability information, a diameter associated with the target of interest within one embodiment.
Rajguru teaches outputting [display monitor] (Figure 1, Element 120) and diameter [IVUS system is used to measure the lumen diameter or cross-sectional area of the vessel] (Para 0003).
The Examiner finds that the prior art included each element claimed, although not necessarily within a single embodiment within the prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single embodiment of the prior art reference. Rajguru teaches the diameter measurement being performed within the prior art (Para 0003). The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known methods, and that in combination, each element merely performs the same function as it does separately. The background of Rajguru discloses the diameter measurement of the vessel in the combination performs the same function of diameter measurement regardless of the type of processing done to produce the IVUS image. The final measurements of Rajguru would perform the same function regardless the IVUS image processing. The user being presented data would be on the display of Rajguru (Para 0064). Thus, Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the output of the value from Rajguru with the display of Rajguru in order to determine the need for treatment (Para 0003), Thus, Rajguru obviously teaches estimating, based on the binarized probability information, at least one of a volume, length, height, width, depth, diameter or area associated with the target of interest; and outputting the at least one of the volume, length, height, width, depth, diameter or area to a display.
Rajguru fails to teach the binarizing probability information. However, Dube teaches –
wherein when processing the probability information, the at least one processing device [processor] (Para 0026-0027) is configured to binarize the probability information to identify pixel or portions of the image [binarized at probability threshold value higher than 0.9] (Para 0057) within the target of interest [probabilistic segmentation mask is indicative of a likelihood that the corresponding voxel belongs to a pre-determined class…based on at least one feature] (Para 0036) in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Rajguru with the binarizing of probability information as taught by Dube in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
Claim 18/16: Rajguru teaches further comprising simultaneously displaying B-mode image data corresponding to the received echo information (Figure 8A, Element 430) and the image information corresponding to the target of interest (Figure 8A, Element 430).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajguru et al. (U.S. Patent Application 2014/0350404 A1) and Dube et al. (U.S. Patent Application 2010/0166276 A1) and further in view of Cho (U.S. Patent Application 2015/0230773 A1).
Claim 3/1: Rajguru teaches wherein the machine learning algorithm comprises an artificial neural network (ANN) (Para 0057). Rajguru teaches that ANN is not a limiting example but fails to teach a convolutional neural network algorithm (CNN). Dube also fails to teach CNN. However, Cho teaches a teach a convolutional neural network algorithm [Convolutional Neural Network (CNN)] (Para 0063) in order to detect anatomical objects from an image (Para 0063).
The Examiner finds that the prior art contained a machine learning algorithm which differed from the claimed device by the substitution of ANN with another machine learning algorithm CNN. The Examiner finds that the substituted components and their functions were known in the art. Rajguru discloses that ANN is a non-limiting example (Para 0057). The Examiner finds that one of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable. Cho additionally discloses another different machine learning algorithm such as convolutional deep belief network (CDBN) (Para 0063). The Examiner contends that the prior art of both Rajguru and Cho recognize the obviousness of using other machine learning algorithms.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the ANN of Rajguru with the CNN as taught by Cho in order to detect anatomical objects from an image (Para 0063).
Claim(s) 6 & 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajguru et al. (U.S. Patent Application 2014/0350404 A1) and further in view of Dube et al. (U.S. Patent Application 2010/0166276 A1) and further in view of Verathon (Verathon (2016, February 23). BLADDERSCAN BVM 9500: Operations & Maintenance Manual. Retrieved March 21, 2021, from https://www.verathon.com/wp-content/uploads/product_docs/0900-1596-xx-60.pdf; previously enclosed in parent application).
Claim 6/1: Rajguru and Dube fail to teach aiming instructions. However, Verathon teaches wherein the at least one processing device is further configured to generate aiming instructions for directing the probe to the target of interest (Product Description) in order to guide the operator into optimal probe placement (Product Description).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps of Rajguru and Dube to include the aiming instructions as taught by Verathon in order to guide the operator into optimal probe placement (Product Description).
Claim 8/1: Rajguru and Dube teach various anatomy but fail to teach specific the bladder. However, Verathon teaches wherein the target of interest comprises a bladder (Title) in order to provide a non-invasive measurements of the bladder volume, thickness and weight (Product Description).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the target of interest of Rajguru and Dube to include the bladder as taught by Verathon in order to provide a non-invasive measurements of the bladder volume, thickness and weight (Product Description).
Claim(s) 9 & 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajguru et al. (U.S. Patent Application 2014/0350404 A1) and further in view of Dube et al. (U.S. Patent Application 2010/0166276 A1) and further in view of Ishizu et al. (U.S. Patent Application 2015/0070385 A1).
Claim 9/1: Rajguru teaches the at least one processing device (Figure 1, Element 150). Rajguru and Dube fails to teach the details of receive information. However, Ishizu teaches wherein the at least one processing device is further configured to receive at least one of age information of the patient [An calculation based on the age] (Para 0086), and process the received echo information based on the received information [unit 510 acquires, from the data server 560, the three-dimensional image data of each subject…information of the age] (Page 0077) in order to provide a technique for deriving support information being used without using many pieces of measurement information for a reduction in load by a computer (Para 0008 & 0005)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps of Rajguru and Dube to include the details of the received information as taught by Ishizu in order to provide a technique for deriving support information being used without using many pieces of measurement information for a reduction in load by a computer (Para 0008 & 0005).
Claim 19/16: Rajguru and Dube fails to teach the details of receive information. However, Ishizu teaches a method further comprising receiving, prior to transmitting the ultrasound signals to the target of interest (Figure 6, S6005 & S6010), information comprising at least one of age information for the subject [An calculation based on the age] (Para 0086) and processing the received echo information based on the received information [unit 510 acquires, from the data server 560, the three-dimensional image data of each subject…information of the age] (Page 0077)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps of Rajguru and Dube to include the details of the received information as taught by Ishizu in order to provide a technique for deriving support information being used without using many pieces of measurement information for a reduction in load by a computer (Para 0008 & 0005).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajguru et al. (U.S. Patent Application 2014/0350404 A1); Dube et al. (U.S. Patent Application 2010/0166276 A1) and Ishizu et al. (U.S. Patent Application 2015/0070385 A1) as applied to Claim 1 & 9 above, and further in view of Perrey et al. (U.S. Patent Application 2018/0240551 A1).
Claim 10/9/1: Rajguru, Dube and Ishizu fails to teach selecting the machine learning algorithm for use. However, Perrey teaches wherein the machine learning algorithm is selected for use from a plurality of machine learning algorithms based on the received information [select at least a first model from the plurality of models based on the anatomical structure of interest] (Abstract) in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rajguru, Dube and Ishizu to include the selection of machine learning algorithms as taught by Perrey in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajguru et al. (U.S. Patent Application 2014/0350404 A1) and Dube et al. (U.S. Patent Application 2010/0166276 A1) as applied to Claim 1 & 11 above, and further in view of Perrey et al. (U.S. Patent Application 2018/0240551 A1).
Claim 12/11/1: Rajguru and Dube fails to teach selecting the machine learning algorithm for use. However, Perrey teaches wherein the machine learning algorithm is selected for use from a plurality of machine learning algorithms [select at least a first model from the plurality of models based on the anatomical structure of interest] (Abstract) based on the automatically [automatically selected] (Para 0013) determined information in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rajguru and Dube to include the selection of machine learning algorithms as taught by Perrey in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajguru et al. (U.S. Patent Application 2014/0350404 A1) and Dube et al. (U.S. Patent Application 2010/0166276 A1) and further in view of Yang (U.S. Patent Application 2009/0264757 A1).
Claim 13/1: Rajguru teaches wherein when processing the received echo information (Figure 7, Element 356), the at least one processing device [processor] (Figure 1, Element 150) is configured to determine values for each pixel of the image (Para 0064; Figure 1, Element 120 and Figure 7, Element 378), process pixels associated with the output image data (Figure 7, Element 350), determine values for each processed pixel (Figure 7, Element 367).
Rajguru and Dube fail to teach to identify a peak value. However, Yang teaches identify a peak value [gradient is maximum] (Para 0105 and Figure 5, Element 72 & 100 and Figure 6, Element 92), and fill in an area around a point associated with the peak value to identify a portion of the target of interest (Para 0089 & 0110 and Figure 5, Element 104 & 188; Figure 7, Element 126 and Figure 27) in order to find the centroid of the object of interest (Para 0105) in order to identify the front and back of the anatomical objects walls (Para 0089) in order to optimize measurements (Abstract).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rajguru and Dube to include the additional steps of identifying a peak as taught by Yang in order to find the centroid of the object of interest (Para 0105) in order to identify the front and back of the anatomical object’s walls (Para 0089) in order to optimize measurements (Abstract).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajguru et al. (U.S. Patent Application 2014/0350404 A1) and further in view of Dube et al. (U.S. Patent Application 2010/0166276 A1) and further in view of Carneiro et al. (U.S. Patent Application 2009/0093717 A1).
Claim 14/1: Rajguru teaches wherein when processing the received echo information (Para 0046), the at least one processing device (Figure 1, Element 150) is configured to identify transmitted ultrasound signals (Figure 7, Element 356, 366 & 350) and generate the probability information (Figure 7, Element 356, 366 & 350).
Rajguru and Dube fail to teach higher order harmonic information with respect to a frequency. However, Carneiro teaches higher order harmonic information with respect to a frequency associated with the transmitted ultrasound signals (Para 0022) as one of many known types of ultrasound imaging (Para 0022) that can be processed for machine learning and/or learnt algorithms (Para 0029).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the probability information generation of Rajguru and Dube to include the harmonic information as taught by Carneiro as one of many known types of ultrasound imaging (Para 0022) that can be processed for machine learning and/or learnt algorithms (Para 0029).
Claim(s) 20-21 & 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajguru et al. (U.S. Patent Application 2014/0350404 A1) and Dube et al. (U.S. Patent Application 2010/0166276 A1) and further in view of Perrey et al. (U.S. Patent Application 2018/0240551 A1).
Claim 20: Rajguru teaches –
A system [IVUS imaging system] (Figure 1, Element 100), comprising:
a memory [memory] (Figure 1, Element 160); and
at least one processing device [processor] (Figure 1, Element 150) configured to:
receive first image information (Figure 7, Element 356) corresponding to a scanned target of interest [region of interest in a vessel or tubular structure] (Figure 7, Element 350 and Para 0060);
process the first image information using the selected machine learning algorithm [non-linear classifier] (Figure 7, Element 366; See Examiner’s Note from the rejection of Claim 1) to generate probability information [direct indicator of the likelihood that a particular pixel is a blood region] (Para 0061 and Figure 7, Element 366) associated with the scanned target of interest [region of interest in a vessel or tubular structure] (Figure 7, Element 350 and Para 0060); and
output second image information [presented to the user] corresponding to the scanned target of interest based on the probability information (Para 0064; Figure 1, Element 120 and Figure 7, Element 378).
Rajguru fails to teach the binarizing probability information. However, Dube teaches binarize [binarized at probability threshold value higher than 0.9] (Para 0057) the probability information [probabilistic segmentation mask is indicative of a likelihood that the corresponding voxel belongs to a pre-determined class…based on at least one feature] (Para 0036) in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Rajguru with the binarizing of probability information as taught by Dube in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
Rajguru and Dube fail to teach to select a machine learning algorithm from a plurality of machine learning algorithms based on information associated with a patient. However, Perrey teaches to select a machine learning algorithm from a plurality of machine learning algorithms based on information associated with a patient [select at least a first model from the plurality of models based on the anatomical structure of interest] (Abstract) in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Rajguru and Dube to include the selection of machine learning algorithms as taught by Perrey in order to reduce the time and cost required to train staff to perform the selection (Para 0002).
Claim 21/20: Rajguru teaches further comprising outputting. Rajguru fails to specifically teach where estimating, based on the binarized probability information, a diameter associated with the target of interest within one embodiment.
Rajguru teaches outputting [display monitor] (Figure 1, Element 120) and diameter [IVUS system is used to measure the lumen diameter or cross-sectional area of the vessel] (Para 0003).
The Examiner finds that the prior art included each element claimed, although not necessarily within a single embodiment within the prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single embodiment of the prior art reference. Rajguru teaches the diameter measurement being performed within the prior art (Para 0003). The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known methods, and that in combination, each element merely performs the same function as it does separately. The background of Rajguru discloses the diameter measurement of the vessel in the combination performs the same function of diameter measurement regardless of the type of processing done to produce the IVUS image. The final measurements of Rajguru would perform the same function regardless the IVUS image processing. The user being presented data would be on the display of Rajguru (Para 0064). Thus, Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the output of the value from Rajguru with the display of Rajguru in order to determine the need for treatment (Para 0003), Thus, Rajguru obviously teaches estimating, based on the binarized probability information, at least one of a volume, length, height, width, depth, diameter or area associated with the target of interest; and outputting the at least one of the volume, length, height, width, depth, diameter or area to a display.
Rajguru fails to teach the binarizing probability information. However, Dube teaches –
wherein when processing the probability information, the at least one processing device [processor] (Para 0026-0027) is configured to binarize the probability information to identify pixel or portions of the image [binarized at probability threshold value higher than 0.9] (Para 0057) within the target of interest [probabilistic segmentation mask is indicative of a likelihood that the corresponding voxel belongs to a pre-determined class…based on at least one feature] (Para 0036) in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Rajguru with the binarizing of probability information as taught by Dube in order to obtain an ideal segmentation to allow for accurate extraction of features which can then be used for more effective treatment planning and monitoring (Para 0023).
Claim 23/20: Rajguru further comprising:
a probe [ultrasound transducer] [IVUS catheter] (Figure 1, Element 112, 114 & 116) configured to:
transmit ultrasound signals [the transducer is pulsed 256 times while rotating around 360 degrees] (Para 0046) to scan the target of interest [spatiotemporal feature] (Para 0060 and 0014-0017);
Examiner’s Note: The Examiner contends that transmitting ultrasound signals occurs in the prior art when the transducer is pulsed.
receive echo information associated with the transmitted ultrasound signals [pulse energy that is received by the transducer as the backscatter or reflected signal] (Para 0046), and
forward the echo information (Figure 7, Element 356) to the at least one processing device [processor] (Figure 1, Element 150),
wherein the at least one processing device [processor] (Figure 1, Element 150) is further configured to:
generate, using the selected machine learning algorithm (See rejection of Claim 20 above), the image information [direct indicator of the likelihood that a particular pixel is a blood region] (Para 0061 and Figure 7, Element 366) corresponding to the scanned target of interest based on the echo information [region of interest in a vessel or tubular structure] (Figure 7, Element 350 and Para 0060)
Rajguru teaches IVUS [intravascular ultrasound (IVUS) images] (Para 0001). The embodiment relied on in the rejection above is based on the IVUS embodiment. However, Rajguru teaches that non-invasive embodiments are known within the art [In other (e.g., non-invasive) ultrasound imaging applications, Doppler ultrasound methods are used to measure blood and tissue velocity] (Para 0010). Doppler ultrasound imaging probes are obviously probes used in non-invasive embodiments be positioned on an external surface of a patient's body. Thus, Rajguru teaches obviously a non-invasive embodiment where a probe is configured to be positioned on an external surface of a patient's body (Para 0010). Or in other words, that the IVUS embodiment can be modified into an external Doppler probe embodiment in order to be non-invasive (Para 0010).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invasive IVUS embodiment of Rajguru with the non-invasive embodiment of Rajguru in order to be non-invasive (Para 0010).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajguru et al. (U.S. Patent Application 2014/0350404 A1), Dube et al. (U.S. Patent Application 2010/0166276 A1) and Perrey et al. (U.S. Patent Application 2018/0240551 A1) as applied to Claim 20 above, and further in view of Cho (U.S. Patent Application 2015/0230773 A1).
Claim 22/20: Rajguru teaches wherein the machine learning algorithm comprises an artificial neural network (ANN) (Para 0057). Rajguru teaches that ANN is not a limiting example but fails to teach a convolutional neural network algorithm (CNN). Dube also fails to teach CNN. Rajguru, Dube and Perrey teach wherein the selected machine learning algorithm comprises an algorithm [select at least a first model from the plurality of models based on the anatomical structure of interest] (Abstract of Perrey) and the memory stores instructions to execute the algorithm [memory] (Para 0015). However, Cho teaches a teach a convolutional neural network algorithm [Convolutional Neural Network (CNN)] (Para 0063) in order to detect anatomical objects from an image (Para 0063).
The Examiner finds that the prior art contained a machine learning algorithm which differed from the claimed device by the substitution of ANN with another machine learning algorithm CNN. The Examiner finds that the substituted components and their functions were known in the art. Rajguru discloses that ANN is a non-limiting example (Para 0057). The Examiner finds that one of ordinary skill in the art could have substituted one known element for another, and the results of the substitution would have been predictable. Cho additionally discloses another different machine learning algorithm such as convolutional deep belief network (CDBN) (Para 0063). The Examiner contends that the prior art of both Rajguru and Cho recognize the obviousness of using other machine learning algorithms.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the ANN of Rajguru with the CNN as taught by Cho in order to detect anatomical objects from an image (Para 0063).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Giger et al. (U.S. Patent 5,984,870) – Giger teaches a method and apparatus for the computerized automatic analysis of lesions in ultrasound images, including the computerized analysis of lesions in the breast, using gradient, gray-level, and texture based measures. Echogenicity features are developed to assess the characteristics of the lesions and in some cases give an estimate of the likelihood of malignancy or of prognosis. The output from the computerized analysis is used in making a diagnosis and/or prognosis. For example, with the analysis of the ultrasound images of the breast, the features can be used to either distinguish between malignant and benign lesions, or distinguish between (i.e., diagnosis) the types of benign lesions such as benign solid lesions (e.g., fibroadenoma), simple cysts, complex cysts, and benign cysts. The ultrasound image features can be merged with those from mammographic and/or magnetic resonance images of the same lesion for classification by means of a common artificial neural network.
Feleppa et al. (U.S. Patent 6,238,342) – Feleppa teaches clinical data, ultrasonic radio frequency (RF) backscatter spectral data and histological results of corresponding biopsy sites are stored in a database and are used to train a classifier suitable for real-time tissue classification and imaging. In clinical use, clinical data and ultrasonic RF backscatter data are applied as input variables to the trained classifier which assigns a likelihood of cancer (LOC) score to each pixel location in an ultrasound image. The LOC scores are then categorized by ranges, which can be established by user selected threshold values, to apply different colors or grey scale values distinguishing varying levels of suspicion (LOS) to each pixel position in real-time. This classification and display technique is especially valuable for guiding a person performing a needle biopsy of the prostate.
Chen et al. (U.S. Patent 10,852,379 B2) – Chen teaches artifact reduction in a magnetic resonance imaging system, deep learning trains an image-to-image neural network to generate an image with reduced artifact from input, artifacted MR data. For application, the image-to-image network may be applied in real time with a lower computational burden than typical post-processing methods. To handle a range of different imaging situations, the image-to-image network may use an auxiliary map as an input with the MR data from the patient, use sequence metadata as a controller of the encoder of the image-to-image network, and/or be trained to generate contrast invariant features in the encoder using a discriminator that receives encoder features.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HELENE C BOR whose telephone number is (571)272-2947. The examiner can normally be reached Mon - Fri 10:30 - 6:30.
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/Helene Bor/Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797
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