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 .
Response to Arguments
Applicant’s arguments, see Applicants Remarks pages 6-12, filed 04/08/26, with respect to the rejection(s) of claim(s) 1-20 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Marwah et al US 20200394463.
Regarding claim 1, 11 and 16, Applicant states that Li fails to generating, by the second pretrained machine learning model, a classification of the target feature using the plurality of embedding vectors, wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors corresponding to each frame of the plurality of frames in the medical video (Applicants Remarks pages 7-8). Examiner agrees with Applicant. Li teaches the determination model that is trained, as a classification model, using machine learning algorithm that receives medical image (paragraph 0061). Tajbakhsh teaches of using CNN to analyze image patches of colonoscopy videos , which are medical videos (column 9, lines 40-60). Li analyzes medical images and assigns them a difficulty level using a determination model which uses machine learning aglorighm (paragraph 0061). Therefore in combination, the CNN of Tajbaksh would be able to look at segmented frames of Tajbaksh and label them by difficulty of the colonoscopy videos using the determination model of Li. Neither Tajbaksh or Li teach wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors corresponding to each frame of the plurality of frames in the medical video (Applicants Remarks pages 8). Marwah et al teaches processor may fetch, decode, and execute the instructions 604 to train a machine learning model. machine learning model may learn first embedded vectors and second embedded vectors. The processor may execute the instructions to learn the first embedded vectors and the second embedded vectors jointly. (paragraph 0078). It is well know that machine learning models can be trained to perform any process as taught. Therefore, the CNN of Tajbakshs would be able jointly analyze the embedding vectors, using the processor per Marwah et al, of colonoscopy videos that are labled by difficulty, per Li. This would suggest all of the features of each independent claims 1, 11 and 16.
Regarding claim 4, Applicant states that Tajbakshs fails to teach of classification is selected from: positive, negative and uncertain because Examiner indicated that Tajbakshs fails to teach generating... a classification or teaches classification is selected from positive, negative and uncertain (Applicants Remarks page 9). Examiner agrees with Applicant. Although Examiner indicated that Tajbakshs fails to teach generating... a classification, Examiner did indicate in the rejection of claim 1, that Tajbakhsh et al teaches train the CNNs, a polyp detection method using a CVCColonDB database was applied, similar to previous work done by the inventors. All the generated polyp candidates were grouped into true and false detections according to the available ground truth for the training videos (column 9, lines 56-65). Examiner agrees that Tajbakshs does not teach classification is selected from positive, negative and uncertain
Regarding claim 5, Applicant states that Tajbakshs fails to teach of generating... a classification or the classification comprises a textual representation (Applicants Remarks page 10). Examiner disagrees with Applicant. Although Examiner indicated that Tajbakshs fails to teach generating... a classification, Examiner did indicate in the rejection of claim 1, that Tajbakhsh et al teaches train the CNNs, a polyp detection method using a CVCColonDB database was applied, similar to previous work done by the inventors. All the generated polyp candidates were grouped into true and false detections according to the available ground truth for the training videos (column 9, lines 56-65). Tajbakshs can disclose textual representation of a classification or description. Tajbakshs teaches of generating a report which indicates confidence scores, which is displayed (column 8, lines 38-45), which would read on textual representation.
Regarding claim 6, Applicant states that prior art of record fails to teach that first pretrained machine learning model and the second pretrained machine learning model are jointly trained because two machine learning modelc old be trained separaetely (Applicants Remarks page 11). Examiner disagrees with Applicant. Tajbakshs teaches that the CNNs are trained on color patches, temporal patches and shape in context patches (column 10, lines 42-50). Since multiple CNNs are trained with the same characteristics, then they would all be trained together or jointly. This would read on training first and second pretrained machine learning model jointly since the CNNs are trained at the same time with the same object characteristics FROC
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.
Claim(s) 1-8 and 10-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tajbakhsh et al US 10055843 in view of Li et al US 20210201701 further in view of Marwah et al US 20200394463.
Regarding claim 1, Tajbakhsh et al teaches a method of classifying a target feature in a medical video by one or more computer systems (the polyp detection system 100 to detect colonic polyps during a colonoscopy procedure (column 4, lines 1-8), wherein the one or more computer systems comprises a first pretrained machine learning model and a second pretrained machine learning model (a set of CNNs (first pretrained machine learning model and second pretrained machine learning model) are applied to the corresponding image patches (embedding vector), and probabilities indicative of a maximum response for each CNN are computed, as indicated by process block 410. (column 8, lines 16-35) CNNs, a polyp detection method. (column 9, lines 56-60), the method comprising:
receiving a plurality of frames of the medical video, wherein the plurality of frames comprises the target feature (A set of 40 short colonoscopy videos were collected, of which half were positive and half were negative shots. A positive shot was defined as a sequence of frames that showed a unique polyp (target feature) from different view angles (column 9, lines 40-43);
generating, by the first pretrained machine learning model, an embedding vector for each frame of the plurality of frames, each embedding vector having a predetermined number of values (a set of CNNs (first pretrained machine learning model) are applied to the corresponding image patches (embedding vector), and probabilities indicative of a maximum response for each CNN are computed, as indicated by process block 410. That is, each patch is assigned a probabilistic score (predetermined number of values). The maximum probabilistic score for each set of patches may then be computed, and a global probabilistic score may then be computed by averaging the maximum probabilistic scores for each set of patches. In this manner, a confidence value for each polyp candidate can be generated (column 8, lines 16-35) Note: the patches are collected from frames of polyps identified (column 8, lines 64-67); and,
Although Tajbakhsh et al teaches train the CNNs, a polyp detection method using a CVCColonDB database was applied, similar to previous work done by the inventors. All the generated polyp candidates were grouped into true and false detections according to the available ground truth for the training videos (column 9, lines 56-65)
Tajbakhsh et al fails to teach generating, by the second pretrained machine learning model, a classification of the target feature using the plurality of embedding vectors,
Li et al teaches generating, by the second pretrained machine learning model, a classification of the target feature using the plurality of embedding vectors, (the second server 120 may transform the reference diagnostic result of the medical image into a vector or a sequence of vectors using a word embedding technique. The second server 120 may also extract contextual semantic feature(s) from the vector or the sequence of vectors using, for example, a recurrent neural network (RNN), a convolutional neural network (CNN), a transformer, or the like. The second server 120 may further determine the difficulty level (classification) of the medical image based on the contextual semantic feature(s). As another example, the second server 120 may obtain a difficulty level determination model. The difficulty level determination model may be a trained model (e.g., a classification model) generated using a machine learning algorithm by the second server 120. The difficulty level determination model may receive the medical image and/or the reference diagnostic result of the medical image as an input, and output the difficulty level of the medical image (paragraph 0061) Note: by combining the sequence of words using work embedding technique, they are clustered together, they are jointly analyzed.
Therefore, it would have been obvious to a person of ordinary skill in the art to modify Tajbakhsh et al to include: generating, by the second pretrained machine learning model, a classification of the target feature using the plurality of embedding vectors, wherein the second pretrained machine learning model analyzes the plurality of embedding vectors jointly.
The reason for doing so would be to accurately identify objects in an image or video.
Tajbakhsh et al in view of Li fails to teach wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors corresponding to each frame of the pluraltity of frames in the medical video
Marwah et al teaches wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors (processor may fetch, decode, and execute the instructions 604 to train a machine learning model. machine learning model may learn first embedded vectors and second embedded vectors. The processor may execute the instructions to learn the first embedded vectors and the second embedded vectors jointly. (paragraph 0078). It is well know that machine learning models can be trained to perform any process as taught. Therefore, the teaching of Marwah et al can be applied to the plurality of frames taught in Tajbakhsh et al)
Therefore, it would have been obvious to a person of ordinary skill in the art to modify Tajbakhsh et al in view of Li to include: wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors.
The reason for doing so would be to save processing time and perform analysis faster.
Regarding claim 2, Tajbakhsh et al teaches wherein the first pretrained learning model comprises a convolutional neural network (CNNs, a polyp detection method. (column 9, lines 56-60), and
Tajbakhsh et al fails to teach wherein the second pretrained machine learning model comprises a transformer.
Li et al teaches wherein the second pretrained machine learning model comprises a transformer (second server 120 may also extract contextual semantic feature(s) from the vector or the sequence of vectors using, for example, a recurrent neural network (RNN), a convolutional neural network (CNN), a transformer, or the like (paragraph 0061).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify Tajbakhsh et al to include: wherein the second pretrained machine learning model comprises a transformer.
The reason for doing so would be to accurately identify vectors in an image or video.
Regarding claim 3, Tajbakhsh et al in view of Li et al teaches wherein the classification comprises a score, wherein the score is in a range of 0 to 1 (Tajbakhsh et al: each of the trained CNNs significantly improved performance compared to the previous method (p<0:0001, JAFROC test), the best result was obtained using score fusion framework, with the number of false positives being reduced by a factor of 50 at 50% sensitivity and by a factor of 22 at 60% sensitivity. The score fusion approach generated 0.002 false positives per frame at 50% sensitivity, a significant performance improvement compared to a previous technique, which produced 0.15 false positives per frame at the same sensitivity. (column 10, lines 53-65) Note: the score is based on positive or false positive classification.
Regarding claim 5, Tajbakhsh et al in view of Li et al teaches wherein the classification comprises a textual representation (Tajbakhsh et al: A report is then generated, at process block 414. As described, the report may provide audio and/or visual information. For instance, raw or processed optical images may be displayed, along with indicators and locations for identified objects, such as polyps, vessels, lumens, specular reflections, and so forth. The report may also indicate the probabilities or confidence scores for identified objects, including colonic polyps (column 8, lines 38-45) Note: the polyps are identified and classified using CNNs and the result of the classification is generated in the report with text describing the identified object ).
Regarding claim 6, Tajbakhsh et al in view of Li et al teaches wherein the first pretrained machine learning model and the second pretrained machine learning model are jointly trained (Tajbakhsh et al: each of the trained CNNs significantly improved performance (column 10, lines 53-65).
Regarding claim 7, Tajbakhsh et al in view of Li et al teaches wherein the first pretrained machine learning model and the second pretrained machine learning model are trained separately (Li et al: The first server(s) 110 may be configured to obtain and/or generate training material used in medical diagnosis training. The training material may relate to one or more medical images of one or more subjects (paragraph 0053). The difficulty level of a medical image may be obtained from a first server that collects the medical image and/or determined by the second server (paragraph 0061) Note: the first server and second server operate as machine learning models. The first server uses training material and the second server comprises RNN, CNN, etc (paragraph 0061)
Regarding claim 8, Tajbakhsh et al in view of Li et al teaches wherein the medical video is collected during a colonoscopy procedure using an endoscope and wherein the target feature is a polyp (Tajbakhsh et al : the polyp detection system 100 may generally include a colonoscopy device 102. the colonoscopy device 102 and controller 104 may be utilized to detect colonic polyps during a colonoscopy procedure (column 4, lines 1-8). the colonoscopy device 102 may include an endoscope (not shown in FIG. 1) configured to acquire optical image data, either continuously or intermittently, from a patient's colon, and relay the optical image data to the controller 104 for processing and analysis (column 4, lines 9-11) .
Regarding claim 10, Tajbakhsh et al in view of Li et al wherein the second pretrained machine learning model analyzes the plurality of embedding vectors without classifying each embedding vector individually (Li et al: the second server 120 may transform the reference diagnostic result of the medical image into a vector or a sequence of vectors using a word embedding technique. The second server 120 may also extract contextual semantic feature(s) from the vector or the sequence of vectors using, for example, a recurrent neural network (RNN), a convolutional neural network (CNN), along short term memory (LSTM) model, a transformer, or the like (paragraph 0061).
Regarding claim 11, Tajbakhsh et al teaches a system for classifying a target feature in a medical video (the polyp detection system 100 to detect colonic polyps during a colonoscopy procedure (column 4, lines 1-8) comprising:
an input interface configured to receive a medical video (the polyp detection system 100 may generally include a colonoscopy device 102. the colonoscopy device 102 and controller 104 may be utilized to detect colonic polyps during a colonoscopy procedure (column 4, lines 1-8). data received by the input elements includes optical image data, or video data obtained during a colonoscopy. (column 5, lines 31-34);
a memory configured to store a plurality of processor-executable instructions (a data storage or memory (column 5, lines 47-50), the memory including:
an embedder based on a first pretrained machine learning model (a set of CNNs (first pretrained machine learning model/embedder) are applied to the corresponding image patches , and probabilities indicative of a maximum response for each CNN are computed, as indicated by process block 410. (column 8, lines 16-35); and,
a processor configured to execute the plurality of processor-executable instruction to perform operations including:
receiving a plurality of frames of the medical video, wherein the plurality of frames comprises the target feature (A set of 40 short colonoscopy videos were collected, of which half were positive and half were negative shots. A positive shot was defined as a sequence of frames that showed a unique polyp (target feature) from different view angles (column 9, lines 40-43);
generating, with the embedder, an embedding vector for each frame of the plurality of frames, each embedding vector having a predetermined number of values (a set of CNNs (first pretrained machine learning model) are applied to the corresponding image patches (embedding vector), and probabilities indicative of a maximum response for each CNN are computed, as indicated by process block 410. That is, each patch is assigned a probabilistic score (predetermined number of values). The maximum probabilistic score for each set of patches may then be computed, and a global probabilistic score may then be computed by averaging the maximum probabilistic scores for each set of patches. In this manner, a confidence value for each polyp candidate can be generated (column 8, lines 16-35); and,
Although Tajbakhsh et al teaches train the CNNs, a polyp detection method using a CVCColonDB database was applied, similar to previous work done by the inventors. All the generated polyp candidates were grouped into true and false detections according to the available ground truth for the training videos (column 9, lines 56-65)
Tajbakhsh et al fails to teach a classifier based on a second pretrained machine learning model; and,
generating, with the classifier, a classification of the target feature using the plurality of embedding vectors, wherein the classifier analyzes the plurality of embedding vectors jointly.
Li et al teaches a classifier based on a second pretrained machine learning model (second server 120 may also extract contextual semantic feature(s) from the vector or the sequence of vectors using, for example, a recurrent neural network (RNN), a convolutional neural network (CNN), a transformer, or the like (paragraph 0061); and,
generating, with the classifier, a classification of the target feature using the plurality of embedding vectors, wherein the classifier analyzes the plurality of embedding vectors jointly (the second server 120 may transform the reference diagnostic result of the medical image into a vector or a sequence of vectors using a word embedding technique. The second server 120 may also extract contextual semantic feature(s) from the vector or the sequence of vectors using, for example, a recurrent neural network (RNN), a convolutional neural network (CNN), a transformer, or the like. The second server 120 may further determine the difficulty level (classification) of the medical image based on the contextual semantic feature(s). As another example, the second server 120 may obtain a difficulty level determination model. The difficulty level determination model may be a trained model (e.g., a classification model) generated using a machine learning algorithm by the second server 120. The difficulty level determination model may receive the medical image and/or the reference diagnostic result of the medical image as an input, and output the difficulty level of the medical image (paragraph 0061).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify Tajbakhsh et al to include: a classifier based on a second pretrained machine learning model; and, generating, with the classifier, a classification of the target feature using the plurality of embedding vectors, wherein the classifier analyzes the plurality of embedding vectors jointly.
The reason for doing so would be to accurately identify objects in an image or video.
Tajbakhsh et al in view of Li fails to teach wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors corresponding to each frame of the pluraltity of frames in the medical video
Marwah et al teaches wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors (processor may fetch, decode, and execute the instructions 604 to train a machine learning model. machine learning model may learn first embedded vectors and second embedded vectors. The processor may execute the instructions to learn the first embedded vectors and the second embedded vectors jointly. (paragraph 0078). It is well know that machine learning models can be trained to perform any process as taught. Therefore, the teaching of Marwah et al can be applied to the plurality of frames taught in Tajbakhsh et al)
Therefore, it would have been obvious to a person of ordinary skill in the art to modify Tajbakhsh et al in view of Li to include: wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors.
The reason for doing so would be to save processing time and perform analysis faster.
Regarding claim 12, Tajbakhsh et al teaches wherein the first pretrained learning model comprises a convolutional neural network (CNNs, a polyp detection method. (column 9, lines 56-60), and
Tajbakhsh et al fails to teach wherein the second pretrained machine learning model comprises a transformer.
Li et al teaches wherein the second pretrained machine learning model comprises a transformer (second server 120 may also extract contextual semantic feature(s) from the vector or the sequence of vectors using, for example, a recurrent neural network (RNN), a convolutional neural network (CNN), a transformer, or the like (paragraph 0061).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify Tajbakhsh et al to include: wherein the second pretrained machine learning model comprises a transformer.
The reason for doing so would be to accurately identify vectors in an image or video.
Regarding claim 13, Tajbakhsh et al in view of Li et al teaches wherein the classification comprises a score, wherein the score is in a range of 0 to 1 (Tajbakhsh et al: each of the trained CNNs significantly improved performance compared to the previous method (p<0:0001, JAFROC test), the best result was obtained using score fusion framework, with the number of false positives being reduced by a factor of 50 at 50% sensitivity and by a factor of 22 at 60% sensitivity. The score fusion approach generated 0.002 false positives per frame at 50% sensitivity, a significant performance improvement compared to a previous technique, which produced 0.15 false positives per frame at the same sensitivity. (column 10, lines 53-65) Note: the score is based on positive or false positive classification.
Regarding claim 14, Tajbakhsh et al in view of Li et al teaches wherein the classification comprises one of: positive, negative, or uncertain (Tajbakhsh et al: For evaluation, a detection was considered as a true (false) positive if it fell inside (outside) the white region of the ground truth image. (column 9, lines 52-55).
Regarding 15, Tajbakhsh et al in view of Li et al teaches wherein the classification comprises a textual representation (Tajbakhsh et al: A report is then generated, at process block 414. As described, the report may provide audio and/or visual information. For instance, raw or processed optical images may be displayed, along with indicators and locations for identified objects, such as polyps, vessels, lumens, specular reflections, and so forth. The report may also indicate the probabilities or confidence scores for identified objects, including colonic polyps (column 8, lines 38-45) Note: the polyps are identified and classified using CNNs and the result of the classification is generated in the report with text describing the identified object ).
Regarding claim 16, Tajbakhsh et al teaches a non-transitory processor-readable storage medium storing a plurality of processor-executable instructions (the processor may read and execute software instructions from a non-transitory computer-readable medium, (column 5, lines 47-52) for classifying a target feature in a medical video ( (the polyp detection system 100 to detect colonic polyps during a colonoscopy procedure (column 4, lines 1-8), the plurality of processor executable instructions being executed by a processor to perform operations comprising:
receiving a plurality of frames of the medical video, wherein the plurality of frames comprises the target feature (A set of 40 short colonoscopy videos were collected, of which half were positive and half were negative shots. A positive shot was defined as a sequence of frames that showed a unique polyp (target feature) from different view angles (column 9, lines 40-43);
generating, by a first pretrained machine learning model, an embedding vector for each frame of the plurality of frames, each embedding vector having a predetermined number of values (a set of CNNs (first pretrained machine learning model) are applied to the corresponding image patches (embedding vector), and probabilities indicative of a maximum response for each CNN are computed, as indicated by process block 410. That is, each patch is assigned a probabilistic score (predetermined number of values). The maximum probabilistic score for each set of patches may then be computed, and a global probabilistic score may then be computed by averaging the maximum probabilistic scores for each set of patches. In this manner, a confidence value for each polyp candidate can be generated (column 8, lines 16-35); and,
Although Tajbakhsh et al teaches train the CNNs, a polyp detection method using a CVCColonDB database was applied, similar to previous work done by the inventors. All the generated polyp candidates were grouped into true and false detections according to the available ground truth for the training videos (column 9, lines 56-65)
Tajbakhsh et al fails to teach generating, by the second pretrained machine learning model, a classification of the target feature using the plurality of embedding vectors, wherein the second pretrained machine learning model analyzes the plurality of embedding vectors jointly
Li et al teaches generating, by the second pretrained machine learning model, a classification of the target feature using the plurality of embedding vectors, wherein the second pretrained machine learning model analyzes the plurality of embedding vectors jointly (the second server 120 may transform the reference diagnostic result of the medical image into a vector or a sequence of vectors using a word embedding technique. The second server 120 may also extract contextual semantic feature(s) from the vector or the sequence of vectors using, for example, a recurrent neural network (RNN), a convolutional neural network (CNN), a transformer, or the like. The second server 120 may further determine the difficulty level (classification) of the medical image based on the contextual semantic feature(s). As another example, the second server 120 may obtain a difficulty level determination model. The difficulty level determination model may be a trained model (e.g., a classification model) generated using a machine learning algorithm by the second server 120. The difficulty level determination model may receive the medical image and/or the reference diagnostic result of the medical image as an input, and output the difficulty level of the medical image (paragraph 0061).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify Tajbakhsh et al to include: generating, by the second pretrained machine learning model, a classification of the target feature using the plurality of embedding vectors, wherein the second pretrained machine learning model analyzes the plurality of embedding vectors jointly.
The reason for doing so would be to accurately identify objects in an image or video.
Tajbakhsh et al in view of Li fails to teach wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors corresponding to each frame of the plurality of frames in the medical video
Marwah et al teaches wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors (processor may fetch, decode, and execute the instructions 604 to train a machine learning model. machine learning model may learn first embedded vectors and second embedded vectors. The processor may execute the instructions to learn the first embedded vectors and the second embedded vectors jointly. (paragraph 0078). It is well know that machine learning models can be trained to perform any process as taught. Therefore, the teaching of Marwah et al can be applied to the plurality of frames taught in Tajbakhsh et al)
Therefore, it would have been obvious to a person of ordinary skill in the art to modify Tajbakhsh et al in view of Li to include: wherein the second pretrained machine learning model jointly analyzes the plurality of embedding vectors.
The reason for doing so would be to save processing time and perform analysis faster.
Regarding claim 17, Tajbakhsh et al teaches wherein the first pretrained learning model comprises a convolutional neural network (CNNs, a polyp detection method. (column 9, lines 56-60), and
Tajbakhsh et al fails to teach wherein the second pretrained machine learning model comprises a transformer.
Li et al teaches wherein the second pretrained machine learning model comprises a transformer (second server 120 may also extract contextual semantic feature(s) from the vector or the sequence of vectors using, for example, a recurrent neural network (RNN), a convolutional neural network (CNN), a transformer, or the like (paragraph 0061).
Therefore, it would have been obvious to a person of ordinary skill in the art to modify Tajbakhsh et al to include: wherein the second pretrained machine learning model comprises a transformer.
The reason for doing so would be to accurately identify vector in an image or video.
Regarding claim 18, Tajbakhsh et al in view of Li et al teaches wherein the classification comprises a score, wherein the score is in a range of 0 to 1 (Tajbakhsh et al: each of the trained CNNs significantly improved performance compared to the previous method (p<0:0001, JAFROC test), the best result was obtained using score fusion framework, with the number of false positives being reduced by a factor of 50 at 50% sensitivity and by a factor of 22 at 60% sensitivity. The score fusion approach generated 0.002 false positives per frame at 50% sensitivity, a significant performance improvement compared to a previous technique, which produced 0.15 false positives per frame at the same sensitivity. (column 10, lines 53-65) Note: the score is based on positive or false positive classification.
Regarding claim 19, Tajbakhsh et al in view of Li et al teaches wherein the classification comprises one of: positive, negative, or uncertain (Tajbakhsh et al: For evaluation, a detection was considered as a true (false) positive if it fell inside (outside) the white region of the ground truth image. (column 9, lines 52-55).
Regarding claim 20, Tajbakhsh et al in view of Li et al teaches wherein the classification comprises a textual representation (Tajbakhsh et al: A report is then generated, at process block 414. As described, the report may provide audio and/or visual information. For instance, raw or processed optical images may be displayed, along with indicators and locations for identified objects, such as polyps, vessels, lumens, specular reflections, and so forth. The report may also indicate the probabilities or confidence scores for identified objects, including colonic polyps (column 8, lines 38-45) Note: the polyps are identified and classified using CNNs and the result of the classification is generated in the report with text describing the identified object ).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tajbakhsh et al US 10055843 in view of Li et al US 20210201701 further in view of Veidman et al US 20200372635.
Regarding claim 9, Tajbakhsh et al in view of Li et al teaches all of the limitation of claims 1 and 8
Tajbakhsh et al in view Li et al fails to teach wherein the classification comprises one of: adenomatous and non-adenomatous.
Veidman et al teaches wherein the classification comprises one of: adenomatous and non-adenomatous (the received indication of polyp, exemplary potential slide-level type type(s) include: hyperplastic, low grade adenoma, high grade adenoma, and malignant. Optionally, the slide-level tissue type(s) include slide-level sub-tissue type(s), for example, for slide-level tissue type of Adenomatous Lesion, potential sub-tissue type(s) include tubular, tubulovillous, and villous (paragraph 0201)
Therefore, it would have been obvious to a person of ordinary skill in the art to modify Tajbakhsh et al in view of Li et al to include: wherein the classification comprises one of: adenomatous and non-adenomatous.
The reason for doing so would be to accurately identify the type of polyp.
Allowable Subject Matter
Claim 4, 14, 19 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL L BURLESON whose telephone number is (571)272-7460.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Akwasi Sarpong can be reached on 571 270-3438 The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Michael Burleson
Patent Examiner
Art Unit 2683
Michael Burleson
June 25, 2026
/MICHAEL BURLESON/
/AKWASI M SARPONG/ SPE, Art Unit 2681 6/26/2026
Prosecution Insights