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 .
Priority
Receipt is acknowledged that application is a National Stage application of PCT/US2022/076333. Priority to PRO 63/243,410 with a priority date of 09/13/2021 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDS dated 01/27/2025 and 06/23/2025 have been considered and placed in the application file.
Claim Objections
Claim 9 objected to because of the following informalities: Claim 9 is currently dependent on Claim 10, examiner is assuming this is a mistake and Claim 9 is meant to depend on Claim 1, if this is not the case, please correct. Appropriate correction is required.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the
plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
Claim 2 recite “or ” then listing “a set of pixels or a set of voxels”. Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim 4 recite “or ” then listing “a mean or a median”. Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim 16 recite “or ” then listing “pixels or voxels”. Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim 18 recite “or ” then listing “the average or the median”. Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim 20 recite “or ” then listing “an organ or a radioactive seed”. Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 18 is 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 pre-AIA the applicant regards as the invention. The Examiner strongly suggested that appropriate corrections be made to clarify the claim scope.
With respect to Claim 18, the claim recites the following, each of which renders the claim indefinite:
“ the average” and “the median” on line 2 (unclear antecedent basis as there is no average or median in claim 15).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 5-10, 13-14 are rejected under 35 U.S.C. 103 as unpatentable over Bazgir et al (WO Patent Publication 2021/030629 A1 hereafter referred to as Bazgir) in view of Lampotang et al. (WO Patent Publication 2020/186198 A1 hereafter referred to as Lampotang).
Regarding Claim 1, Bazgir teaches a method for identifying an object (Bazgir ¶0003, ¶0041, ¶0045, ¶0048 and Fig 5B discloses object detection) in a magnetic resonance (MR) image of a subject (Bazgir ¶0046, ¶0047, ¶0060-¶0061 and Figure 4, 405 discloses the images being MRI images), the method comprising:
(a) receiving an MR image (Bazgir ¶0046, ¶0047, ¶0060-¶0061 and Figure 4, 405 discloses the images being MRI images) that includes a set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels), each comprising one or more image unit values pertaining to a region of the subject (Bazgir ¶0053 discloses pixels of zero and nonzero intensity to indicate a region of interest or a background), wherein the image unit values form a first distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram which visualizes the distribution of the pixel values) to ;
(b) determining a variation measure (Bazgir ¶0092, ¶0131 discloses assessing variations of pixel or voxel intensities) of the image unit values in the first distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram which visualizes the distribution of the pixel values);
of the image unit values (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) in the first distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram which visualizes the distribution of the pixel values);
(d) identifying a first set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) having an image unit value (Bazgir ¶0053 discloses pixels of zero and nonzero intensity to indicate a region of interest or a background) that exceeds a threshold (Bazgir ¶0066 , ¶0100 disclose the pixel intensity value being higher or lower to a baseline relating to the brightness and resolution of the image which allows for the contrast and differentiation in the image) corresponding to the variation measure (Bazgir ¶0092, ¶0131 discloses assessing variations of pixel or voxel intensities);
the image unit values (Bazgir ¶0053 discloses pixels of zero and nonzero intensity to indicate a region of interest or a background) of the first set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels);
(f) generating a second distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram based on a second set of images which visualizes the distribution of the pixel values)
the second distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram based on a second set of images which visualizes the distribution of the pixel values) to obtain new image unit values (Bazgir ¶0054 discloses a binary mask where the pixel value is set according to the background or region of interest) for the first set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels);
(h) adding (Bazgir ¶0015-0016 discloses clustering the pixels into object classes) the new image unit values (Bazgir ¶0054 discloses a binary mask where the pixel value is set according to the background or region of interest) to the first set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) to obtain a new MR image (Bazgir ¶0096 discloses matching training images with reference images to create a new images); and
(i) processing (Bazgir Fig 1 object detection includes map processing) the new MR image (Bazgir ¶0096 discloses matching training images with reference images to create a new images) to identify a location of one or more objects within the subject (Bazgir ¶0003, 0018 discloses identifying the location of one or more objects).
Bazgir does not explicitly disclose (c) determining a centroid measure, (e) discarding,
centered at the centroid measure, (g) randomly sampling.
Lampotang is in the same field of medical image analysis for use in diagnosis and surgery involving the prostate. Further, Lampotang teaches (c) determining a centroid measure (Lampotang ¶0057, ¶0058, ¶0072, ¶0087 discloses the center of the image being measured in millimeters), (e) discarding (Lampotang Figure 17 discloses removing portions of the images and then adding it back in), centered at the centroid measure (Lampotang ¶0057, ¶0058, ¶0072, ¶0087 discloses the center of the image being measured in millimeters);
(g) randomly sampling (Lampotang ¶0049 discloses random sampling).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Bazgir by incorporating the centroid measure and sampling technique to determine the best method and placing of radioactive seeds as taught by Lampotang to make an invention that can automatically determine the most effective location and dosing for the treatment for the patient; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to decrease false negatives because treatment is only performed upon a positive diagnosis via prostate biopsy, false negatives unnecessarily delay treatment which, in turn, may give time for localized prostate cancer to metastasize and spread to nerve bundles that affect potency and bones, thereby reducing options, complicating treatment, and affecting quality of life of patients and survivors. (Lampotang, ¶0007).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 2, Bazgir in view of Lampotang teaches the method of claim 1, wherein the set of image units is a set of pixels or a set of voxels (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels). See rationale for Claim 1, its parent claim.
Regarding Claim 5, Bazgir in view of Lampotang teaches the method of claim 1, wherein the second distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram based on a second set of images which visualizes the distribution of the pixel values) is a random distribution (Bazgir ¶0089 discloses probability distributions parametrized with the initial estimate of the parameters for each cluster). See rationale for Claim 1, its parent claim.
Regarding Claim 6, Bazgir in view of Lampotang teaches the method of claim 1, wherein the second distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram based on a second set of images which visualizes the distribution of the pixel values) does not exceed the threshold (Bazgir ¶0066 , ¶0100 disclose the pixel intensity value being higher or lower to a baseline relating to the brightness and resolution of the image which allows for the contrast and differentiation in the image). See rationale for Claim 1, its parent claim.
Regarding Claim 7, Bazgir in view of Lampotang teaches the method of claim 1, wherein the one or more objects (Bazgir ¶0003, 0018 discloses identifying the location of one or more objects) comprise one or more radioactive seeds (Bazgir ¶0018, ¶0109 discloses seed location). See rationale for Claim 1, its parent claim.
Regarding Claim 8, Bazgir in view of Lampotang teaches the method of claim 7, further comprising calculating radiation values (Bazgir ¶0116 discloses calculating dosage of a drug for a patient) for a subset (Bazgir ¶0078, ¶0080 discloses splitting the images into subsets) of the set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels). See rationale for Claim 1, its parent claim.
Regarding Claim 9, Bazgir in view of Lampotang teaches the method of claim 10, further comprising:
implanting additional radioactive seeds into the subject (Lampotang ¶0002, ¶0096, discloses placing radioactive seeds). See rationale for Claim 1, its parent claim.
Regarding Claim 10, Bazgir in view of Lampotang teaches the method of claim 1, wherein the one or more objects (Bazgir ¶0003, 0018 discloses identifying the location of one or more objects) comprise an organ (Bazgir ¶0046, ¶0047, discloses the object being an organ). See rationale for Claim 1, its parent claim.
Regarding Claim 13, Bazgir in view of Lampotang teaches the method of claim 1, wherein:
the MR image (Bazgir ¶0046, ¶0047, ¶0060-¶0061 and Figure 4, 405 discloses the images being MRI images) includes one or more additional sets (Bazgir ¶0112, discloses additional images and sets of images used) of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels), and
the set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels)and the one or more additional sets of image units (Bazgir ¶0112, discloses additional images and sets of images used) are non- overlapping (Bazgir ¶0067 discloses the first images being different from the second image),
the method further comprises:
repeating (Bazgir ¶0084 discloses iterative operations) steps (b) to (g) (Bazgir in view of Lampotang see claim 1 above) for each of the one or more additional sets of image units (Bazgir ¶0112, discloses additional images and sets of images used) , and adding (Bazgir ¶0015-0016 discloses clustering the pixels into object classes) the respective new image unit values(Bazgir ¶0054 discloses a binary mask where the pixel value is set according to the background or region of interest) for each of the one or more additional sets of image units (Bazgir ¶0112, discloses additional images and sets of images used) to obtain the new MR images (Bazgir ¶0096 discloses matching training images with reference images to create a new images). See rationale for Claim 1, its parent claim.
Regarding Claim 14, Bazgir in view of Lampotang teaches the method of claim 1, wherein processing the new MR image (Bazgir ¶0096 discloses matching training images with reference images to create a new images) comprises determining coordinates of a bounding box (Bazgir ¶0057 discloses bounding boxes being defined by coordinates) around the one or more objects (Bazgir ¶0070- ¶0071 discloses boundary boxes around each object). See rationale for Claim 1, its parent claim.
Claims 3-4 are rejected under 35 U.S.C. 103 as unpatentable over Bazgir in view of Lampotang in further view of Sanders (Sanders, Jeremiah Wayne. Automated assessment of image quality and dose attributes in clinical CT images. MS thesis. Duke University, 2016. Hereafter referred to as Sanders).
Regarding Claim 3, Bazgir in view of Lampotang teaches the method of claim 1, wherein determining the variation measure (Bazgir ¶0092, ¶0131 discloses assessing variations of pixel or voxel intensities) of the image unit values in the first distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram which visualizes the distribution of the pixel values) of the image unit values (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) in the first distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram which visualizes the distribution of the pixel values).
Bazgir in view of Lampotang does not explicitly disclose comprises determining the standard deviation.
Sanders is in the same field of medical image analysis for use in diagnosis and treatment. Further, Sanders teaches comprises determining the standard deviation (Sanders Pg 14 ¶01, and Pg 8 ¶01 and Figure 6 disclose calculating the standard deviation).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Bazgir in view of Lampotang by incorporating the use of standard deviation and mean or medium into the analysis on the image values as taught by Sanders to make an invention that can automatically determine the most effective location and dosing for the treatment for the patient; thus one of ordinary skilled in the art would be motivated to combine the references since there exists the need to strike a balance between diagnostic benefit and radiation dose by making sure there is an understanding and characterization of image quality and radiation dose.(Sanders, Abstract).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 4, Bazgir in view of Lampotang teaches the method of claim 1, wherein determining the centroid measure (Lampotang ¶0057, ¶0058, ¶0072, ¶0087 discloses the center of the image being measured in millimeters) of the image unit values (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) in the first distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram which visualizes the distribution of the pixel values) of the image unit values (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) in the first distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram which visualizes the distribution of the pixel values).
Bazgir in view of Lampotang does not explicitly disclose comprises determining a mean or a median.
Sanders is in the same field of medical image analysis for use in diagnosis and treatment. Further, Sanders teaches comprises determining a mean or a median (Sanders Pg 8 ¶01 and Figure 6 disclose calculating the mean).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Bazgir in view of Lampotang by incorporating the use of standard deviation and mean or medium into the analysis on the image values as taught by Sanders to make an invention that can automatically determine the most effective location and dosing for the treatment for the patient; thus one of ordinary skilled in the art would be motivated to combine the references since there exists the need to strike a balance between diagnostic benefit and radiation dose by making sure there is an understanding and characterization of image quality and radiation dose.(Sanders, Abstract).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 12 is rejected under 35 U.S.C. 103 as unpatentable over Bazgir in view of Lampotang in further view of Morard et al (EP Patent Publication EP 3 855 391 A1 hereafter referred to as Morard).
Regarding Claim 12, Bazgir in view of Lampotang teaches the method of claim 1, wherein processing the new MR image (Bazgir ¶0096 discloses matching training images with reference images to create a new images).
Bazgir in view of Lampotang does not explicitly disclose occurs after radiosurgery on the subject.
Morard is in the same field of medical image analysis for identifying anatomical structures in images. Further, Morard teaches occurs after radiosurgery on the subject (Morard ¶0080 discloses processing images before and after the surgery).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Bazgir in view of Lampotang by applying the imaging to the patient before and after surgery as taught by Morard to make an invention that can automatically determine the effectiveness of the treatment for the patient; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to determining characteristics of the anatomical structure by tracking pixel values of the segmented anatomical structure over time, and outputting the determined characteristics on a display device, thus quickly and accurately characterizing anatomical structure in a quick and accurate manner.(Morard, ¶0004).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claims 15-24 are rejected under 35 U.S.C. 103 as unpatentable over Bazgir et al (WO Patent Publication 2021/030629 A1 hereafter referred to as Bazgir) in view of Morard et al (EP Patent Publication EP 3 855 391 A1 hereafter referred to as Morard).
Regarding Claim 15, Bazgir teaches a method for identifying an object (Bazgir ¶0003, ¶0041, ¶0045, ¶0048 and Fig 5B discloses object detection) in a magnetic resonance (MR) image of a subject (Bazgir ¶0046, ¶0047, ¶0060-¶0061 and Figure 4, 405 discloses the images being MRI images), the method comprising:
(a) storing a plurality of machine learning models (Bazgir ¶0022 discloses a three dimensional neural network model ¶0077 discloses training one or more models and ¶0115 discloses storage in a storage device) trained on training MR images (Bazgir ¶0078, ¶0081, ¶ discloses MR images being used as the training dataset) to identify one or more objects within the training MR images (Bazgir ¶0087 discloses a trained object detection model), wherein each training MR image (Bazgir ¶0046, ¶0047, ¶0060-¶0061 and Figure 4, 405 discloses the images being MRI images) includes image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels), each training MR image (Bazgir ¶0078, ¶0081, ¶ discloses MR images being used as the training dataset) comprising one or more image unit values pertaining to a region of a training subject (Bazgir ¶0053 discloses pixels of zero and nonzero intensity to indicate a region of interest or a background), and wherein each of the plurality of machine learning models (Bazgir ¶0022 discloses a three dimensional neural network model ¶0077 discloses training one or more models and ¶0115 discloses storage in a storage device),
within the subject (Bazgir ¶0046 discloses identifying organs lesions or tumors);
(b) receiving (Bazgir ¶0084, ¶0085, ¶0098 discloses inputting images into the model) a test MR image (Bazgir ¶0085-¶0086, ¶0125 discloses a testing set of images) that includes a set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels), each test MR image (Bazgir ¶0085-¶0086, ¶0125 discloses a testing set of images) comprising one or more image unit values pertaining to a region of the subject (Bazgir ¶0053 discloses pixels of zero and nonzero intensity to indicate a region of interest or a background);
(c) for each of the plurality of machine learning models (Bazgir ¶0022 discloses a three dimensional neural network model ¶0077 discloses training one or more models and ¶0115 discloses storage in a storage device):
(i) generating input features (Bazgir ¶0098 discloses generating input features for a 3D U net 300) using the image unit values (Bazgir ¶0053 discloses pixels of zero and nonzero intensity to indicate a region of interest or a background) of the test MR image (Bazgir ¶0085-¶0086, ¶0125 discloses a testing set of images); and
(ii) obtaining, using the input features (Bazgir ¶0098 discloses generating input features for a 3D U net 300) and the machine learning model (Bazgir ¶0022 discloses a three dimensional neural network model ¶0077 discloses training one or more models),
for the particular object (Bazgir ¶0046 discloses identifying organs lesions or tumors);
(d) for each image unit of the set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) :
in the test MR image (Bazgir ¶0085-¶0086, ¶0125 discloses a testing set of images); and
(g) displaying (Bazgir ¶0014, ¶0015 discloses displaying on the user device).
Bazgir does not explicitly disclose provides a probability matrix of probability values, each probability value providing a probability that a given image unit includes a particular object
a respective probability matrix
(i) identifying the corresponding probability values from each of the probability matrices; and
(ii) combining the corresponding probability values to obtain a new probability value;
(e) assembling the new probability values to obtain a new probability matrix;
(f) identifying, using the new probability matrix the particular object , the particular object.
Morard is in the same field of medical image analysis for identifying anatomical structures in images. Further, Morard teaches provides a probability matrix (Morard ¶0075, ¶0115 discloses a matrix of values corresponding to an atomical feature which are determined using probability values) of probability values (Morad ¶0115 discloses probability values in the form of percentages), each probability value providing a probability that a given image unit includes a particular object (Morad ¶0115 discloses that probability of at least 90% triggers positive identification of the corresponding feature, whereas probabilities less than 90% denotes that the corresponding feature is absent)
a respective probability matrix (Morad ¶0075, ¶0115 discloses a matrix of values corresponding to an atomical feature which are determined using probability values)
(i) identifying the corresponding probability values (Morad ¶0115 discloses that probability of at least 90% triggers positive identification of the corresponding feature, whereas probabilities less than 90% denotes that the corresponding feature is absent) from each of the probability matrices (Morad ¶0075, ¶0115 discloses a matrix of values corresponding to an atomical feature which are determined using probability values); and
(ii) combining the corresponding probability values (Morad ¶0115 discloses combining all of the different probability values in the columns) to obtain a new probability value (Morad ¶0115 Fig 11 discloses how all of the probability values are used to determine the presence of an object);
(e) assembling the new probability values (Morad ¶0115 Fig 11 discloses how all of the probability values are used to determine the presence of an object)to obtain a new probability matrix (Morad ¶0075, ¶0115 discloses a matrix of values corresponding to an atomical feature which are determined using probability values);
(f) identifying, using the new probability matrix (Morad ¶0075, ¶0115 discloses a matrix of values corresponding to an atomical feature which are determined using probability values), the particular object (Morad ¶0115 discloses that probability of at least 90% triggers positive identification of the corresponding feature, whereas probabilities less than 90% denotes that the corresponding feature is absent)
the particular object (Morad ¶0115 discloses that probability of at least 90% triggers positive identification of the corresponding feature, whereas probabilities less than 90% denotes that the corresponding feature is absent).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Bazgir by applying the probability matrix and probability values including the median and threshold for determining the presence of the anatomical structure as taught by Morard to make an invention that can automatically determine the effectiveness of the treatment for the patient; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to determining characteristics of the anatomical structure by tracking pixel values of the segmented anatomical structure over time, and outputting the determined characteristics on a display device, thus quickly and accurately characterizing anatomical structure in a quick and accurate manner.(Morard, ¶0004).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Regarding Claim 16, Bazgir in view or Morard teaches the method of claim 15, wherein the image units are pixels or voxels (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels). See Claim 15 for rationale, its parent claim.
Regarding Claim 17, Bazgir in view or Morard teaches the method of claim 15, wherein the plurality of machine learning models (Bazgir ¶0022 discloses a three dimensional neural network model ¶0077 discloses training one or more models and ¶0115 discloses storage in a storage device) comprises at least 7 machine learning models (Morad ¶0012 discloses one or more machine learning models which examiner is interpreting as 1 through an infinite number of machine learning models). See Claim 15 for rationale, its parent claim.
Regarding Claim 18, Bazgir in view or Morard teaches the method of claim 15, wherein combining the corresponding probability values (Morard ¶0115 discloses combining all of the different probability values in the columns) to obtain a new probability value (Morad ¶0115 Fig 11 discloses how all of the probability values are used to determine the presence of an object) comprises calculating the average (Bazgir ¶0062 discloses calculating an average value) or the median (Morard ¶0131 disclose calculating median) of the corresponding probability values (Morad ¶0115 discloses probability values in the form of percentages). See Claim 15 for rationale, its parent claim.
Regarding Claim 19, Bazgir in view or Morard teaches the method of claim 15, wherein identifying, using the new probability matrix (Morad ¶0075, ¶0115 discloses a matrix of values corresponding to an atomical feature which are determined using probability values), the particular object (Bazgir ¶0046 discloses identifying organs lesions or tumors) in the test MR image (Bazgir ¶0085-¶0086, ¶0125 discloses a testing set of images) comprises:
comparing the new probability values to a threshold value (Morad ¶0115 discloses that probability of at least 90% triggers positive identification of the corresponding feature, whereas probabilities less than 90% denotes that the corresponding feature is absent), and assigning the image unit to the particular object (Bazgir ¶0046 discloses identifying organs lesions or tumors)when the corresponding new probability value exceeds the threshold value (Morad ¶0115 discloses that probability of at least 90% triggers positive identification of the corresponding feature, whereas probabilities less than 90% denotes that the corresponding feature is absent). See Claim 15 for rationale, its parent claim.
Regarding Claim 20, Bazgir in view or Morard teaches the method of claim 15, wherein the particular object (Bazgir ¶0003, 0018 discloses identifying the location of one or more objects) is an organ (Bazgir ¶0046, ¶0047, discloses the object being an organ) or a radioactive seed (Bazgir ¶0018, ¶0109 discloses seed location). See Claim 15 for rationale, its parent claim.
Regarding Claim 21, Bazgir in view or Morard teaches the method of claim 15, wherein:
the set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) is a first set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels),
the test MR image (Bazgir ¶0085-¶0086, ¶0125 discloses a testing set of images) includes a plurality of sets of image units (Bazgir ¶0089, ¶0108 discloses a plurality of sets) of image units,
the plurality of sets of image (Bazgir ¶0089, ¶0108 discloses a plurality of sets) units comprises the first set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels),
each of the plurality of sets (Bazgir ¶0089, ¶0108 discloses a plurality of sets) of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) overlaps with at least one other set (Bazgir ¶0091 and ¶0110 discloses overlaying the images) of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels),
the method further comprising:
repeating steps (Bazgir ¶0084 discloses iterative operations) (c) to (f) (Bazgir in view of Lam see claim 1 above) for each of the plurality of the sets of image units (Bazgir ¶0089, ¶0108 discloses a plurality of sets),
determining boundaries of the particular object (Bazgir ¶0091 discloses using a bounding box around a region of interest) using the identifications of the particular object (Bazgir ¶0046 discloses identifying organs lesions or tumors) from the plurality of the sets of image units (Bazgir ¶0089, ¶0108 discloses a plurality of sets). See Claim 15 for rationale, its parent claim.
Regarding Claim 22, Bazgir in view or Morard teaches the method of claim 21, wherein:
each of the plurality of sets (Bazgir ¶0015 discloses a plurality of pixels or voxels) of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) has dimensions of nx by ny by nz image units (Bazgir ¶0125 discloses the dimensions of images being 64x64x16 or 64x64x64),
nx is at least 9 (Bazgir ¶0125 discloses the images being 64x64x16 or 64x64x64),
ny is at least 9 (Bazgir ¶0125 discloses the images being 64x64x16 or 64x64x64), and
nz is at least 9 (Bazgir ¶0125 discloses the images being 64x64x16 or 64x64x64). See Claim 15 for rationale, its parent claim.
Regarding Claim 23, Bazgir in view or Morard teaches the method of claim 21, wherein each of the plurality of sets (Bazgir ¶0015 discloses a plurality of pixels or voxels) of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) the same dimensions (Bazgir ¶0125 discloses the images being 64x64x16 or 64x64x64). See Claim 15 for rationale, its parent claim.
Regarding Claim 24, Bazgir in view or Morard teaches the method of claim 15, further comprising:
generating an entropy value (Bazgir ¶0102 discloses using an entropy loss function for the machine learning model) for the plurality of machine learning models (Bazgir ¶0022 discloses a three dimensional neural network model ¶0077 discloses training one or more models and ¶0115 discloses storage in a storage device) , and
determining a confidence interval (Bazgir ¶0127 discloses a positive predictive value) for the particular object (Bazgir ¶0046 discloses identifying organs lesions or tumors) using the entropy value (Bazgir ¶0102 discloses using an entropy loss function for the machine learning model). See Claim 15 for rationale, its parent claim.
Claim 25 is rejected under 35 U.S.C. 103 as unpatentable over Bazgir in view of Morard in further view of Lampotang et al. (WO Patent Publication 2020/186198 A1 hereafter referred to as Lampotang).
Regarding Claim 25, Bazgir in view of Morard teaches the method of claim 15, further comprising:
receiving an MR image (Bazgir ¶0046, ¶0047, ¶0060-¶0061 and Figure 4, 405 discloses the images being MRI images) that includes a set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels), each comprising one or more image unit values pertaining to a region of the subject (Bazgir ¶0053 discloses pixels of zero and nonzero intensity to indicate a region of interest or a background), wherein the image unit values form a first distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram which visualizes the distribution of the pixel values);
determining a variation measure (Bazgir ¶0092, ¶0131 discloses assessing variations of pixel or voxel intensities) of the image unit values in the first distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram which visualizes the distribution of the pixel values);
of the image unit values (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) in the first distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram which visualizes the distribution of the pixel values);
identifying a first set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) having an image unit value (Bazgir ¶0053 discloses pixels of zero and nonzero intensity to indicate a region of interest or a background) that exceeds a threshold (Bazgir ¶0066 , ¶0100 disclose the pixel intensity value being higher or lower to a baseline relating to the brightness and resolution of the image which allows for the contrast and differentiation in the image) corresponding to the variation measure (Bazgir ¶0092, ¶0131 discloses assessing variations of pixel or voxel intensities);
the image unit values (Bazgir ¶0053 discloses pixels of zero and nonzero intensity to indicate a region of interest or a background) of the first set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels);
generating a second distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram based on a second set of images which visualizes the distribution of the pixel values)
the second distribution (Bazgir ¶0081, ¶0003 discloses using pixel values to create a histogram based on a second set of images which visualizes the distribution of the pixel values) to obtain new image unit values (Bazgir ¶0054 discloses a binary mask where the pixel value is set according to the background or region of interest) for the first set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels);
adding (Bazgir ¶0015-0016 discloses clustering the pixels into object classes) the new image unit values (Bazgir ¶0054 discloses a binary mask where the pixel value is set according to the background or region of interest) to the first set of image units (Bazgir ¶0003, ¶0004, ¶0018 discloses sets of pixels and voxels) to obtain the test MR image (Bazgir ¶0085-¶0086, ¶0125 discloses a testing set of images).
Bazgir in view of Morard does not explicitly disclose determining a centroid measure, discarding, centered at the centroid measure randomly sampling.
Lampotang is in the same field of medical image analysis for use in diagnosis and surgery involving the prostate. Further, Lampotang teaches determining a centroid measure (Lampotang ¶0057, ¶0058, ¶0072, ¶0087 discloses the center of the image being measured in millimeters) discarding (Lampotang Figure 17 discloses removing portions of the images and then adding it back in) centered at the centroid measure (Lampotang ¶0057, ¶0058, ¶0072, ¶0087 discloses the center of the image being measured in millimeters); randomly sampling (Lampotang ¶0049 discloses random sampling).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Bazgir in view of Morard by incorporating the centroid measure and sampling technique to determine the best method and placing of radioactive seeds as taught by Lampotang to make an invention that can automatically determine the most effective location and dosing for the treatment for the patient; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to decrease false negatives because treatment is only performed upon a positive diagnosis via prostate biopsy, false negatives unnecessarily delay treatment which, in turn, may give time for localized prostate cancer to metastasize and spread to nerve bundles that affect potency and bones, thereby reducing options, complicating treatment, and affecting quality of life of patients and survivors. (Lampotang, ¶0007).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
WO WO-2021083774-A1 to Yang et al. discloses method and system for detecting the presence or absence of a target or desired object within a three-dimensional (3D) image.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL LYNN ROBERTS whose telephone number is (571)272-6413. The examiner can normally be reached Monday- Friday 7:30am- 5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Oneal Mistry can be reached on (313) 446-4912. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RACHEL L ROBERTS/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674