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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: Determine attention point and state of lesion based on partially selected data after Fourier transformation of endoscopic image data.
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.
Claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 4, 5, 6, 7, 8, 9, 1, 10, 11, and 12, respectively, of copending Application No. 18/396,864 in view of US 20120316421 A1 (Kumar). Copending Application independent claims 1, 11, and 12 do not explicitly recite, “state of a lesion”. Kumar teaches these limitations as is mapped below in the prior art rejection. One of ordinary skill in the art would have recognized the advantage of determining the state of a lesion as for accurate diagnosis, effective treatment planning, monitoring disease progression, and for prognosis. This is a provisional nonstatutory double patenting rejection.
Claims 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11, respectively, of copending Application No. 18/288,689 in view of Kumar. Copending Application independent claims 1, 10, and 11 do not recite, “determine a state of a lesion based on the attention point”. Kumar teaches these limitations as is mapped below in the prior art rejection. One of ordinary skill in the art would have recognized the advantage of determining the state of a lesion as for accurate diagnosis, effective treatment planning, monitoring disease progression, and for prognosis. This is a provisional nonstatutory 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.
Claims 9 and 10 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. There is uncertain antecedent basis of “the determination” in claims 9 and 10, which both depend from claim 1. Claim 1 recites “determine” in 2 instances for “determine an attention point” and “determine a state of a lesion”. One of ordinary skill in the art cannot know which instance of “determine” in claim 1 is the antecedent basis for “the determination” in claims 9 and 10. Thus, the metes and bounds of the claims cannot be ascertained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental process and recited math abstract idea(s) without significantly more.
Claim(s) 1 recite(s):
“acquire data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by an endoscope”, which is recited math of performing a Fourier Transform on a data array from an endoscope;
“select partial data that is a part of the data”, which can be reasonably be interpreted as a human observer viewing the displayed Fourier transformed data and mentally selecting a portion of the displayed data via visual perception;
“determine an attention point to be (the following is recited as intended use and is not required) noticed in the examination target based on the partial data”, which can be reasonably be interpreted as a human observer viewing the displayed Fourier transformed data and mentally determining an attention point according to the differential frequency domain depiction of the frequency components – via visual perception.
“determine a state of a lesion based on the attention point”, which can be reasonably be interpreted as a human observer viewing the displayed Fourier transformed data and mentally determining a state of a lesion based on the mentally determined attention point. The human observer can use knowledge that the texture of a lesion is different than that of the rest of the image such that the corresponding location in the frequency domain can be visually perceived.
This judicial exception is not integrated into a practical application because additional elements of: “An image processing device comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to” are generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements of: “An image processing device comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to” are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Depending claims do not remedy these deficiencies:
Claims 2 and 3 further recite limitations that are reasonably interpreted as mental process abstract ideas involving the human observer visually perceiving aspects of the displayed Fourier transformed image data.
Claims 4 and 10 further recite limitations that are reasonably interpreted as mental process abstract ideas involving the human observer visually perceiving aspects of the displayed Fourier transformed image data. Claims 4 and 10 also further recite limitations that are additional elements pertaining to machine learning that are generically recited and are well-understood, routine, conventional.
Claims 5 and 6 further recite limitations that are recited math abstract ideas pertaining to Fourier transformation. Claims 5 and 6 also recite additional elements pertaining to generating partial data from a selected range in the frequency domain, after Fourier transformation, which can be interpreted as filtering in the frequency domain. This claim language is generically recited and is well-understood, routine, conventional.
Claims 7 and 8 further recite limitations that are recited math abstract ideas pertaining to Fourier transformation.
Claim 9 further recites additional element that is generically recited insignificant extra-solution activity of data outputting.
As per claim(s) 11, arguments made in rejecting claim(s) 1 are analogous. Claim 11 further recites, “executed by a computer”, which are additional elements that are a generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
As per claim(s) 12, arguments made in rejecting claim(s) 1 are analogous. Claim 12 further recites, “A non-transitory computer readable storage medium storing a program executed by a computer, the program causing the computer to”, which are additional elements that are a generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 4, 5, 9, 11, and 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 20120316421 A1 (Kumar).
As per claim 1, Kumar teaches an image processing device comprising:
at least one memory configured to store instructions; and at least one processor configured to execute the instructions to (Kumar: paras 15, 187-190, 196; Figs. 15, 16):
acquire data obtained by applying Fourier transform to an endoscopic image of an examination target photographed by an endoscope (Kumar:
para 52: “In an embodiment MPEG-7 Homogeneous Texture Descriptor (HTD), and Haralick statistics may be used. HTD may use a bank of Gabor filters containing 30 filters, for example, which may divide the frequency space into 30 channels (6 sections in the angular direction.times.5 sections in the radial direction), for example.”;
para 76: “The homogeneous texture descriptor is one of three texture descriptors in the MPEG-7 standard. It may provide a "quantitative characterization of texture for similarity-based image-to-image matching." The HTD may be computed by applying Gabor filters of different scale and orientation to an image. For reasons of efficiency, the computation may be performed in frequency space: both the image and the filters may be transformed using the Fourier transform. The Gabor filters may be chosen in such a way to divide the frequency space into 30 channels, for example, the angular direction being divided into six equal sections of 30 degrees, while the radial direction is divided into five sections on an octave scale.”;
Fig. 14 (shown below): mainly 1410-1450;
: the image is transformed into the frequency domain using the Fourier Transform before the frequency transformed filter is applied to it);
select partial data that is a part of the data (Kumar:
para 49: “he application of machine learning algorithms to image data may involve the following steps: (1) feature extraction, (2) dimensionality reduction, (3) training, and (4) validation.”;
paras 52, 76 (excerpted above);
para 61: “An imager is shown in 1420 that takes a still image or video from anatomy 1410 through imaging tools such as 110, 120, and 130. Such imaging tools include for example, a wireless capsule endoscopy device, a flexible endoscope, a flexible borescope, a video borescope, a rigid borescope, a pipe borescope, a GRIN lens endoscope, contact hysteroscope, and/or a fibroscope.”;
para 63: “Once in feature extraction 1450, feature vectors and localized descriptors may include generic descriptors such as…homogeneous texture descriptor)”;
para 72: “homogeneous texture (HTD)”;
para 77: “The mean response and the response deviation may be calculated for each channel (each Gabor filter) in the frequency space, and these values form the features of the HTD. In addition, the HTD may also calculate the mean and deviation of the whole image in image space.”;
Fig. 14 (shown below): mainly 1450;
: the Gabor filter is applied to the Fourier transformed frequency-domain of the image in order to select homogeneous texture descriptor (HTD)features);
determine an attention point to be (the following is recited as intended use and is not required) noticed in the examination target based on the partial data (Kumar:
para 42: “The method may include receiving endoscopic images and processing each of the endoscopic images to determine whether an attribute of interest is present in each image that satisfies a predetermined criterion. The method may also classify the endoscopic images into a set of images that contain at least one attribute of interest and a remainder set of images which do not contain an attribute of interest.”;
para 49: “automated detection of lesions and classification are performed using machine learning algorithms, Traditional classification and regression techniques may be utilized as well as rank learning or Ordinal regression.”;
para 56: “In one embodiment of the invention, support vector machines (SVM) are used to classify CE images into those containing lesions, normal tissue, and food, bile, stool, air bubbles, etc…
HTD feature vectors may be in one embodiment of the invention and used directly as feature vectors for binary classification (e.g., for example, lesion/nonlesion).”;
Para 57: “given a region of interest (ROI), the system determines whether or not a match is found by automatic registration to another frame is truly another instance of the selected ROI. The embodiment may use the following. Using a general discriminative learning model, an ROI pair may be associated with a set of metrics (e.g., but not limited to, pixel, patch, and histogram based statistics) and train a classifier that may discriminate misregistrations from correct registrations using, for example, adaboost…
the trained classifier may be applied to determine if any of the matches are correct. The correct matches are then ranked using ordinal regression to determine the best match. Experiments have shown that the meta-matching method outperforms any single matching method.”;
para 58 (see below): depth, presence/absence;
para 67: “From 1450, the data may flow to classification 1460. Once in classification 1460…
Classification 1460 may include supervised machine learning and/or unsupervised machine learning. Classification 1460 may also include statistical measures, machine learning algorithms, traditional classification techniques, regression techniques, feature vectors, localized descriptors, MPEG-7 visual descriptors, edge features, color histograms, image statistics, gradient statistics, Haralick texture features, dominant color descriptors, edge histogram descriptors, homogeneous texture descriptors,”;
Fig. 14 (shown below): mainly 1460;
Para 80: “support vector machines (SVM) may be used to classify CE images into lesion (L), normal tissue, and extraneous matter (food, bile, stool, air bubbles, etc).”;
Para 99: “FIG. 5, 510 shows a typical Crohn's disease lesion with the lesion highlighted.”;
Figs. 4 and 5 (shown below);
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determine a state of a lesion based on the attention point (Kumar:
para 58: “a severity assessment is accomplished through the following. A semi-automatic framework to assess the severity of Crohn's lesions may be used…
The severity rank may be based on pairwise comparisons among representative images. Classification and ranking, have been formulated as problems of learning a map from a set of features to a discrete set of label, for example, for face detection [3], object recognition [4], and scene classification…
ranking may be treated as a regression problem to find a ranking function between a set of input features and a continuous range of ranks or ssessment. Assuming a known relationship (e.g. global severity rating mild<moderate<severe) on a set of Images I, a real-valued ranking function R may be computed such that I.sub.xI.sub.y.di-elect cons.PR(I.sub.x)<R(I.sub.y). The ranking function may be based on empirical statistics of the training set. A preference pair x, y.di-elect cons. P, where P is the transitive closure of P, may be thought of as a pair of training examples for a binary classifier”
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Para 58 (shown above): severity, global rating, LesionID, ROI, Ulcer;
Para 68: “From 1460, the data may flow to severity assessment 1470. A severity of a located lesion or other attribute of interest may be calculated using a severity scale (e.g., but not limited to global severity rating shown in table I, mild, moderate, severe). The extracted features may be processed to extract feature vectors summarizing appearance, shape, and size of the attribute of interest. Additionally overall lesion severity may be more effectively computed from component indications (e.g., for example, level of inflammation, lesion size, etc.) than directly from image feature descriptions. This may be accomplished through a logistic regression (LR) that performs severity classification from attribute of interest component classifications To compute overall severity, LR, Generalized Linear Models as well as support vector regression (SVR) may be used. The assessment may include calculating a score, a rank, a structured assessment comprising of one or more categories, a structured assessment on a Likert scale, and/or a relationship with one or more other images (where the relationship may be less severe or more severe).”;
Para 69: “The score may include a Lewis score, a Crohn's Disease Endoscopy index of Severity, a Simple Endoscopic Score for Crohn's Disease, a Crohn's Disease Activity Index, or another rubric based on image appearance attributes. The appearance attributes may include lesion exudates, inflammation, color, and/or texture.”;
Fig. 14 (shown below): mainly 1470;
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Para 80: “support vector machines (SVM) may be used to classify CE images into lesion (L), normal tissue, and extraneous matter (food, bile, stool, air bubbles, etc). FIG. 4 depicts example normal tissue 410; air bubbles 420; floating matter, bile, food, and stool 430; abnormalities such as bleeding, polyps, non-Chrohn's lesions, darkening old blood 440; and rated lesions from severe, moderate, to mild 450. In addition to lesions other attributes of interest may include blood, bleeding, inflammation, mucosal inflammation, submucosal inflammation, discoloration, an erosion, an ulcer, stenosis, a stricture, a fistulae, a perforation, an erythema, edema, or a boundary organ”;
Para 91: “Learning ranking functions may require manually assigning a consistent ranking scale to a set of training data. Although the scale may be arbitrary, what is of interest is the consistent ordering of the sequence of images; a numerical scale is only one of the possible means of representing this ordering. Ordinal regression tries to learn a ranking function from a training set of partial order relationships. The learned global ranking function then seeks to respect these partial orderings while assigning a fixed rank score to each individual image or object…
In one embodiment of the invention selective sampling techniques and SVMs with user provided sparse partial ordering in combination with image feature vectors automatically generated from a training set of images may be used.”;
Para 99: “lesions as well as data for other classes for interest may be selected and assigned a global ranking (e.g., for example, mild, moderate, or severe) based upon the size, and severity of lesion and any surrounding inflammation, for example. Lesions may be ranked into three categories: mild, moderate or severe disease.”
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).
As per claim 2, Kumar teaches the image processing device according to claim 1, wherein the state of the lesion includes at least one of (as per Superguide guidelines on claim interpretation, both listed items are required due to the use of the “and” conjunction) a name of the lesion and a degree of the lesion (Kumar : See arguments and citations offered in rejecting claim 1 above; paras 58, 68, 69, 80, 91, 99 and Fig. 4, 14 (all referenced above);
: lesion state constitutes name/type and severity).
As per claim 4, Kumar teaches the image processing device according to claim 1, wherein the at least one processor is configured to execute the instructions to determine the attention point and the state of lesion, based on the partial data and a model into which the partial data is inputted, and
wherein the model is a machine learning model which learned a relation between the partial data to be inputted to the model and a determination result regarding the attention point and the state of the lesion in the endoscopic image used for generation of the partial data (Kumar: See arguments and citations offered in rejecting claim 1 above;
para 54: “One embodiment of the invention includes machine learning or training including the following. There may be two main paradigms in machine learning: supervised learning and unsupervised learning. In supervised learning, each point in the data set may be associated with a label while training. In unsupervised learning, labels are not available while training but other statistical priors such as the number of expected classes may be assumed. Supervised statistical learning algorithms include Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA)…
For unsupervised learning, common methods may include algorithms such as the k-means…
One can apply supervised learning algorithms to solve classification and regression problems. Data clustering may be a classic unsupervised learning problem. Two powerful methods for improving classifier performance include boosting and bagging…
Both may be methods of using several classifiers together to "vote" for a final decision. Combination rules include voting, decision trees, and linear and nonlinear combinations of classifier outputs. These approaches also provide the ability to control the tradeoff between precision and accuracy through changes in weights or thresholds. These methods naturally lend themselves to extension to large numbers of localized features.”;
Para 70: “Once the relevance feedback is received by 1440 the system may be trained. The training may include using artificial neural networks, support vector machines, and/or linear discriminant analysis.”;
: train model to determine attention point (e.g. location) and lesion state (e.g. name/type or degree) based on input partial data – where known attention point and lesion state would be ground truth labels).
As per claim 5, Kumar teaches the image processing device according to claim 1, wherein the at least one processor is configured to execute the instructions to acquire the data obtained by applying two dimensional Fourier transform to the endoscopic image, and wherein the at least one processor is configured to execute the instructions to generate the partial data in a selected partial range in at least one of the axes to which the Fourier transform is applied (Kumar: See arguments and citations offered in rejecting claim 1 above;
Para 52: “MPEG-7 Homogeneous Texture Descriptor (HTD), and Haralick statistics may be used. HTD may use a bank of Gabor filters containing 30 filters, for example, which may divide the frequency space into 30 channels (6 sections in the angular direction.times.5 sections in the radial direction)”;
para 76: “For reasons of efficiency, the computation may be performed in frequency space: both the image and the filters may be transformed using the Fourier transform. The Gabor filters may be chosen in such a way to divide the frequency space into 30 channels, for example, the angular direction being divided into six equal sections of 30 degrees, while the radial direction is divided into five sections on an octave scale.”;
: partial data selected from along one of axes from 2D Fourier transformation).
As per claim 9, Kumar teaches the image processing device according to claim 1, wherein the at least one processor is configured to further execute the instructions to display information regarding a result of the determination and the endoscopic image on a display device (Kumar: See arguments and citations offered in rejecting claim 1 above;
[0208] FIG. 18 depicts an illustrative screen shot of a user interface application 1800 designed to support review of imaging data. The software should have, at least, the following features: [0209] Study Review: The ability to review, store, and recall identified or de-identified studies (in randomized and blind fashion). This may be either lesion thumbnails (selected images) and associated data, or an entire CE study as a single image stream. [0210] Clinical Review: The ability to review, edit, and export identified or de-identified clinical data relevant to diagnosis. [0211] Longitudinal Review: The ability to relate studies linked together by the patient ID. [0212] Study Annotation: The ability to annotate, review, and export annotated information, including regions of interest and landmarks. [0213] Study Scoring: The ability to assign scores, using multiple alphanumeric scoring methods including the CDAI and the Lewis score, both individual lesions, and a study as appropriate. [0214] Assessment: The ability to automatically assess, and manually adjust severity of lesions, and studies using detection, classification, and severity rating methods;
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: display the determination and original endoscopic image).
As per claim(s) 11, arguments made in rejecting claim(s) 1 are analogous. Kumar also teaches an image processing method executed by a computer (Kumar: See arguments and citations offered in rejecting claim 1 above; paras 15, 187-190, 196; Figs. 15, 16).
As per claim(s) 12, arguments made in rejecting claim(s) 1 are analogous. Kumar also teaches a non-transitory computer readable storage medium storing a program executed by a computer (Kumar: See arguments and citations offered in rejecting claim 1 above; paras 15, 187-190, 196; Figs. 15, 16).
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) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar as applied to claim 1 above, and further in view of US 20200294287 A1 (Schlemper).
As per claim 6, Kumar teaches the image processing device according to claim 1, wherein the at least one processor is configured to execute the instructions to acquire the data obtained by applying (Kumar: See arguments and citations offered in rejecting claim 1 above).
Kumar does not teach one-dimensional Fourier transform.
Schlemper teaches acquire the data obtained by applying one-dimensional Fourier transform to the image (Schlemper: para 84: “the sensor domain, k-space, and image domain are not the only domains on which the neural networks described herein may operate. For example, the data in a source domain (e.g., sensor domain, k-space, or image domain) may be further transformed by an invertible transformation (e.g., 1D, 2D, or # d Fourier, Wavelet, and/or short-time Fourier transformation, etc.) to a target domain, the neural network may be configured to receive as input data in the target domain, and after completing processing, the output may be transformed back to the source domain.”
Para 105: “a 1D Fourier (spectral) transform”).
Thus, it would have been obvious for one of ordinary skill in the art, prior to filing, to implement the teachings of Schlemper into Kumar since both Kumar and Schlemper suggest a practical solution and field of endeavor of medical image analysis involving Fourier Transform and machine learning in general and Schlemper additionally provides teachings that can be incorporated into Kumar in that the Fourier Transform is one-dimensional. One of ordinary skill in the art would have recognized that the 1D Fourier Transform (FT) offers advantages in simplicity, directness for sequential data, and computational efficiency for separable operations. The teachings of Schlemper can be incorporated into Kumar in that the Fourier Transform is one-dimensional. Furthermore, one of ordinary skill in the art could have combined the elements as claimed by known methods and, in combination, each component functions the same as it does separately. One of ordinary skill in the art would have recognized that the results of the combination would be predictable.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar as applied to claim 1 above, and further in view of US 20090237498 A1 (Modell).
As per claim 7, Kumar teaches the image processing device according to claim 1, wherein the at least one processor is configured to execute the instructions to acquire the data that represents (Kumar: See arguments and citations offered in rejecting claim 1 above).
Kumar does not teach an absolute value or a phase into which a complex number for each frequency is converted, the complex number for each frequency being obtained by applying the Fourier transform.
Modell teaches an absolute value or a phase into which a complex number for each frequency is converted, the complex number for each frequency being obtained by applying the Fourier transform to the endoscopic image (Modell:
Para 57: “FIGS. 9-11 illustrate examples of images formed by an optical implementation of image filtering using a Fourier transform, according to an embodiment of the invention. As described above, a honeycomb pattern in an image caused by hexagonal packing of the fibers in a fiberscope can be removed by directly transforming the image data from each frame into the complex Fourier domain (frequency and phase), multiplying the transformed image by the desired filter response”;
: obtain magnitude or phase of complex frequency domain data).
Thus, it would have been obvious for one of ordinary skill in the art, prior to filing, to implement the teachings of Modell into Kumar since both Kumar and Modell suggest a practical solution and field of endeavor of endoscopy imaging, Fourier Transformation, and frequency domain image filtering in general and Modell additionally provides teachings that can be incorporated into Kumar in that the system performs transforming the image data from each frame into the complex Fourier domain so that “a honeycomb pattern in an image caused by hexagonal packing of the fibers in a fiberscope can be removed” (Modell: para 57). The teachings of Modell can be incorporated into Kumar in that the system performs transforming the image data from each frame into the complex Fourier domain. Furthermore, one of ordinary skill in the art could have combined the elements as claimed by known methods and, in combination, each component functions the same as it does separately. One of ordinary skill in the art would have recognized that the results of the combination would be predictable.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar as applied to claim 1 above, and further in view of US 20130182261 A1 (Hirota).
As per claim 8, Kumar teaches the image processing device according to claim 1, wherein the at least one processor is configured to execute the instructions to acquire the data obtained by transform to the endoscopic image (Kumar: See arguments and citations offered in rejecting claim 1 above).
Kumar does not teach applying logarithmic conversion to a value for each frequency.
Hirota teaches applying logarithmic conversion to a value for each frequency, the value being obtained by applying the Fourier transform to the endoscopic image (Hirota:
Para 19: “a logarithmic transformation unit configured to logarithmically transform data that have been entirely integrated by the Fourier transformation data integrating unit.”;
Para 29: “a logarithmic transformation step of logarithmically transforming data that have been entirely integrated in a frequency direction in the Fourier transformation data integrating step.”;
Para 21: “The logarithmic transformation unit 444 carries out logarithmic transformation of the tomographic data that have been Fourier-transformed in the Fourier transformation unit 442.”;
Para 130: “the tomographic data that have been generated by the Fourier transformation unit 442 are logarithmically transformed in the logarithmic transformation unit 444.”;
Para 142: “the other piece of the tomographic data outputted from the Fourier transformation unit 450 is logarithmically transformed in the logarithmic transformation unit 444 and then inputted to the tomographic image constructing unit 446.”;
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: apply logarithmic conversion after Fourier Transform to generate (logarithmically converted) frequency-domain).
Thus, it would have been obvious for one of ordinary skill in the art, prior to filing, to implement the teachings of Hirota into Kumar since both Kumar and Hirota suggest a practical solution and field of endeavor of endoscopy, Fourier Transformation, filtering in the frequency domain in general and Hirota additionally provides teachings that can be incorporated into Kumar in that logarithmic conversion is applied after Fourier Transformation. One of ordinary skill in the art would recognize that Logarithmically transforming Fourier-transformed data (like power spectra) helps handle wide dynamic ranges, compresses large values, stabilizes variance, converts multiplicative to additive relationships, and reveals hidden periodicities, making data more amenable to analysis, visualization, and machine learning. The teachings of Hirota can be incorporated into Kumar in that logarithmic conversion is applied after Fourier Transformation. Furthermore, one of ordinary skill in the art could have combined the elements as claimed by known methods and, in combination, each component functions the same as it does separately. One of ordinary skill in the art would have recognized that the results of the combination would be predictable.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kumar as applied to claim 1 above, and further in view of US 20220138933 A1 (Wang).
As per claim 10, Kumar teaches the image processing device according to claim 1. Kumar does not teach the at least one processor is configured to further execute the instructions to determine a coping method based on information regarding a result of the determination and a model into which the information regarding the result of the determination is inputted, wherein the model is a machine learning model which learned relation between information regarding a result of the determination to be inputted to the model and the coping method according to the result of the determination.
Wang teaches these limitations (Wang:
Para 88: “overview of the AI pipeline from the level illustrated in FIG. 1 and that subsequent sections of this description will go into additional detail regarding individual stages of the AI pipeline. Each of the stages of the AI pipeline, in some illustrative embodiments, are implemented as configured and trained ML/DL computer models, such as a neural network of deep learning neural network, as represented by the symbol 103 in the various stages of the AI pipeline 100. These different ML/DL computer models are specifically configured and trained to perform the particular AI operations described herein, e.g., body part identification, liver detection, phase classification, liver minimum amount detection, liver/lesion detection, lesion segmentation, false positive remove, lesion classification, etc.”;
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: “coping method” is interpreted to be remedy or treatment;
: train a machine learning model to receive lesion determination result and output the coping method).
Thus, it would have been obvious for one of ordinary skill in the art, prior to filing, to implement the teachings of Wang into Kumar since both Kumar and Wang suggest a practical solution and field of endeavor of medical image analysis system performing lesion detection on internal organs and providing a report to clinicians in general and Wang additionally provides teachings that can be incorporated into Kumar in that treatment recommendations are reported as “for review and consideration by medical practitioners … for assisting with treatment” (Wang: para 105). The teachings of Wang can be incorporated into Kumar in that treatment recommendations are reported. Furthermore, one of ordinary skill in the art could have combined the elements as claimed by known methods and, in combination, each component functions the same as it does separately. One of ordinary skill in the art would have recognized that the results of the combination would be predictable.
Allowable Subject Matter
Claim 3 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101 set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Limitations pertaining to “select the partial data to be asymmetric with respect to at least one of a first axis and/or a second axis in a frequency domain which expresses the data by the first axis and the second axis”, in conjunction with other limitations present in independent claim 1 and dependent claim 3, distinguish over the prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Atiba Fitzpatrick whose telephone number is (571) 270-5255. The examiner can normally be reached on M-F 10:00am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on (571) 270-5183. The fax phone number for Atiba Fitzpatrick is (571) 270-6255.
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Atiba Fitzpatrick
/ATIBA O FITZPATRICK/
Primary Examiner, Art Unit 2677