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 .
Notice to Applications
This communication is in response to the Application filed on March 15, 2024.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 15, 2024 is in compliance with the provisions of 27 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached by the examiner.
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119(a)-(d).
The certified copies have been filed as Application No. 2021152459, filed on September 17, 2021.
Specification
The disclosure is objected to because of the following informalities:
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.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
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 when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitations are:
“an image acquisition unit” and “a third classification data acquisition unit” in claim 17, described in paragraphs [0252] and [0241] respectively
“an image acquisition unit”, “a first classification data acquisition unit”, “a determination unit”, “a division line creation unit”, and “a three-dimensional image creation unit” in claims 18 and 20, described in paragraphs [0252], [0253], [0254], and [0007] respectively
“a first recording unit”, “a determination unit”, “a second classification data creation unit”, and “a second recording unit” in claim 19, described in paragraphs [0253] and [0254]
Regarding the claim limitation above, 112(f) is invoked because “unit” is a non-functional generic placeholder expressed merely by the function it performs. Although claims 17-20 are drafted as apparatus claims, the term “unit” is Applicant’s claim term preceding a functional limitation of “acquisition/determination/creation/recording”. Because Applicant fails to recite sufficiently definite structure for the term “unit”, the claimed limitation is akin to a generic term.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 9-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 9-10 and 12 recite the limitations “R-T format image” and “R-T length”. It is unclear to the Examiner how to interpret “R-T” because no definition or interpretation has been disclosed. If “R-T” is an abbreviation form, Applicant is respectfully suggested to include its unabbreviated form – followed by its abbreviated form “R-T”. If “R-T” is not an abbreviation form, the Examiner kindly asks Applicant to amend claims 9-10, and 12 to reflect the intended definition and meaning of the limitations, “R-T format image” and “R-T length”. Claims 11 and 13 inherit this indefiniteness in view of their dependency on claims 9 and 12 respectfully.
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 1-6, 8, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable of Richter et al., US 20110033098 A1, (hereinafter “Richter”) in view Celi at al., US 20150213629 A1, (hereinafter “Celi”).
Regarding claim 1, Richter teaches a learning model generation method comprising:
acquiring a two-dimensional image acquired with an image-acquiring catheter ([0077] “In another embodiment, the system for generating stabilized images comprises a catheter having a transmitter for transmitting a plurality of ultrasonic signals and a receiver for receiving a plurality of reflected ultrasonic signals,”);
acquiring first classification data in which respective pixels constituting the two-dimensional image are classified into a plurality of regions including a living tissue region, a lumen region into which the image-acquiring catheter is inserted, and an extra-luminal region outside the living tissue region ([0011] “Morphological features of blood vessels can be divided into three categories: the lumen, where blood flows; the vessel layers, i.e., the tissue inside the blood vessel; and the exterior, i.e., tissue outside the vessel. In IVUS, the blood presents as rapidly changing speculars.”) ([0046] “The next step in the detection process is to classify a pixel as "lumen" or "non-lumen", thus defining the lumen area and edges.”);
determining, in the two-dimensional image, whether the lumen region reaches an edge of the two-dimensional image ([0038] “The method of stabilizing an image in accordance with the invention involves analyzing and stabilizing ultrasonic images, which as a preliminary matter includes detecting the edges of the lumen.”);
([0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise.”);
creating second classification data in which a probability of being the lumen region and a probability of being the extra-luminal region are allocated for each of small regions constituting the lumen region in the first classification data, on a basis of ([0046] “The classification model yields a probability for the pixel being of "lumen" type. A threshold probability is then set, above which probability the model classifies the pixel as "lumen".”) ([0011] “Morphological features of blood vessels can be divided into three categories: the lumen, where blood flows; the vessel layers, i.e., the tissue inside the blood vessel; and the exterior, i.e., tissue outside the vessel. In IVUS, the blood presents as rapidly changing speculars.”) ([0046] “The next step in the detection process is to classify a pixel as "lumen" or "non-lumen", thus defining the lumen area and edges.”); and
associating the two-dimensional image with the second classification data, and recording the two-dimensional image associated with the second classification data in the training database ([0046] “The classification model yields a probability for the pixel being of "lumen" type. A threshold probability is then set, above which probability the model classifies the pixel as "lumen".”) ([0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise.”); and
generating a learning model that outputs third classification data by machine learning using training data recorded in the training database when the two-dimensional image is input, respective pixels constituting the two-dimensional image being classified into a plurality of regions including the living tissue region, the lumen region, and the extra-lumen region in the third classification data ([0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise.”) ([0052] “Any of the above-described prediction models may be used in accordance with the invention to construct a classification model to classify pixels as "lumen or "non-lumen", using input parameters as outlined below. Input parameters to be used for the model will describe a single pixel.”).
Richter does not specifically disclose:
when it is determined that the lumen region does not reach an edge of the two-dimensional image,
when it is determined that the lumen region reaches an edge of the two-dimensional image,
creating a division line that divides the lumen region into a first region into which the image-acquiring catheter is inserted and a second region reaching an edge of the two-dimensional image;
However, Celi teaches:
when it is determined that the lumen region does not reach an edge of the two-dimensional image ([0136] “The step of determining whether the lumen-delimiting edge is continuous is carried out manually, with an operator visualizing the pre-processed OCT image and, if the lumen is determined to be closed, instructing the processing unit, e.g. as is usual through the input interface, to proceed with the lumen segmentation step 32.”),
when it is determined that the lumen region reaches an edge of the two-dimensional image ([0137] “In a different embodiment, the step of determining whether the lumen-delimiting edge is continuous is automatically carried out by the system, for example by using an algorithm for automatically finding a radial light intensity discontinuity region in an image portion comprising the vessel wall and having a light intensity above a given threshold value.” wherein determining whether a lumen region reaches an edge is determining whether the lumen-delimiting edge is continuous),
creating a division line that divides the lumen region into a first region into which the image-acquiring catheter is inserted and a second region reaching an edge of the two-dimensional image ([0020] “automatically finding an outer edge of the vessel wall and defining a first region of interest ROI.sub.1 between the inner edge and the outer edge of the vessel wall; [0021] automatically segmenting a fibrous plaque within the first region of interest ROI.sub.1, thereby defining a second region of interest ROI.sub.2 that contains the fibrous plaque,” wherein the division line is the segmentation line) ([0102] “For vessel wall analysis, a catheter is introduced into the lumen to the segment to be analyzed.” wherein the catheter is introduced into the first region);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the learning generation model of Richter with the lumen image processing method of Celi to improve the accuracy and quality of the catheter acquired lumen images.
Regarding claim 2, Richter in view of Celi teaches the learning model generation method according to claim 1, further comprising:
determining whether the lumen region reaches an edge of the two-dimensional image on a basis of the first classification data (Richter - [0038] “The method of stabilizing an image in accordance with the invention involves analyzing and stabilizing ultrasonic images, which as a preliminary matter includes detecting the edges of the lumen.”) (Richter - [0011] “Morphological features of blood vessels can be divided into three categories: the lumen, where blood flows; the vessel layers, i.e., the tissue inside the blood vessel; and the exterior, i.e., tissue outside the vessel. In IVUS, the blood presents as rapidly changing speculars.”) (Richter - [0046] “The next step in the detection process is to classify a pixel as "lumen" or "non-lumen", thus defining the lumen area and edges.”).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 3, Richter in view of Celi teaches the learning model generation method according to claim 1, further comprising:
determining whether the lumen region reaches an edge of the two-dimensional image on a basis of the two-dimensional image (Richter - [0038] “The method of stabilizing an image in accordance with the invention involves analyzing and stabilizing ultrasonic images, which as a preliminary matter includes detecting the edges of the lumen.”).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 4, Richter in view of Celi teaches the learning model generation method according to claim 1, further comprising:
determining whether the lumen region reaches an edge of the two-dimensional image on a basis of an output of a reach determination model that outputs whether the lumen region reaches an edge of the two-dimensional image when the two-dimensional image is input to the reach determination model (Richter - [0038] “The method of stabilizing an image in accordance with the invention involves analyzing and stabilizing ultrasonic images, which as a preliminary matter includes detecting the edges of the lumen.”) (Celi - [0137] “In a different embodiment, the step of determining whether the lumen-delimiting edge is continuous is automatically carried out by the system, for example by using an algorithm for automatically finding a radial light intensity discontinuity region in an image portion comprising the vessel wall and having a light intensity above a given threshold value.” wherein determining whether a lumen region reaches an edge is determining whether the lumen-delimiting edge is continuous).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 5, Richter in view of Celi teaches the learning model generation method according to claim 1, further comprising:
determining in the second classification data, a probability that each of the small regions is the lumen region and a probability that each of the small regions is the extra-luminal region on a basis of lengths of connecting lines connecting the small regions and the division line (Richter - [0046] “The classification model yields a probability for the pixel being of "lumen" type. A threshold probability is then set, above which probability the model classifies the pixel as "lumen".”) (Richter - [0011] “Morphological features of blood vessels can be divided into three categories: the lumen, where blood flows; the vessel layers, i.e., the tissue inside the blood vessel; and the exterior, i.e., tissue outside the vessel. In IVUS, the blood presents as rapidly changing speculars.”) (Celi - [0047] “Preferably, the step of automatically finding an outer edge of the vessel wall and defining a first region of interest ROI.sub.1 comprises: [0048] selecting a preset wall thickness value; [0049] drawing a plurality of radial lines originating from the centroid C.sub.L of the lumen and extending through the vessel wall, the plurality of radial lines having a radial distribution extending along a circumference;”).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 6, Richter in view of Celi teaches the learning model generation method according to claim 5, wherein the connecting lines pass only through a region classified as the lumen region in the first classification data (Celi - [0047] “Preferably, the step of automatically finding an outer edge of the vessel wall and defining a first region of interest ROI.sub.1 comprises: [0048] selecting a preset wall thickness value; [0049] drawing a plurality of radial lines originating from the centroid C.sub.L of the lumen and extending through the vessel wall, the plurality of radial lines having a radial distribution extending along a circumference;”).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 8, Richter in view of Celi teaches the learning model generation method according to claim 1, further comprising:
determining each of the small regions in the second classification data to be the lumen region when the small region is closer to the image-acquiring catheter than the division line, and to be the extra-luminal region when the small region is farther from the image-acquiring catheter than the division line ([0019] “if the lumen is determined not to be closed, sequentially performing an automatic lumen closing procedure on the pre-processed image, and a second automatic lumen segmentation procedure, to define a perimeter line of the lumen, corresponding to the inner edge of the vessel wall, and determine a centroid C.sub.L of the lumen area; [0020] automatically finding an outer edge of the vessel wall and defining a first region of interest ROI.sub.1 between the inner edge and the outer edge of the vessel wall; [0021] automatically segmenting a fibrous plaque within the first region of interest ROI.sub.1, thereby defining a second region of interest ROI.sub.2 that contains the fibrous plaque,” wherein the lumen region is the first region and the extra-luminal region is the second region).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 14, Richter in view of Celi teaches the learning model generation method according to claim 1, wherein the machine learning includes:
acquiring a set of training data from the training database (Richter - [0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise.”);
inputting a two-dimensional image included in the training data to a learning model being trained, to acquire third classification data that is output (Richter - [0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise.”) (Richter - [0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise.”) (Richter - [0052] “Any of the above-described prediction models may be used in accordance with the invention to construct a classification model to classify pixels as "lumen or "non-lumen", using input parameters as outlined below. Input parameters to be used for the model will describe a single pixel.”); and
repeating a process of adjusting a parameter of the learning model being trained, to reduce a difference between classification data recorded in the training data and the third classification data (Richter - [0052] “Input parameters to be used for the model will describe a single pixel. These parameters may be determined, for example, by computing sum, average, maximum, minimum, or other mathematical computation on the gray level values of the neighboring pixels.”) (Richter - [0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise. Then, the marked set of images is used to create the model (training set) and to test it (test set). An accepted proportion of training sets and test sets may be, for example, 75% training and 25% for the test set.”).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 15, Richter in view of Celi teaches the learning model generation method according to claim 14, wherein
the difference is the number of pixels having different classifications in the classification data and in the third classification data, among respective pixels constituting the classification data recorded in the training data (Richter - [0052] “Input parameters to be used for the model will describe a single pixel. These parameters may be determined, for example, by computing sum, average, maximum, minimum, or other mathematical computation on the gray level values of the neighboring pixels.”) (Richter - [0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise.”) (Richter - [0052] “Any of the above-described prediction models may be used in accordance with the invention to construct a classification model to classify pixels as "lumen or "non-lumen", using input parameters as outlined below. Input parameters to be used for the model will describe a single pixel.”).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 16, Richter in view of Celi teaches the learning model generation method according to claim 15, wherein
the difference is a distance between a correct boundary line related to a predetermined region in the classification data recorded in the training data and an output boundary line related to the predetermined region in the third classification data (Richter - [0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise. Then, the marked set of images is used to create the model (training set) and to test it (test set). An accepted proportion of training sets and test sets may be, for example, 75% training and 25% for the test set.” wherein the training data is the training set and the third classification data is the test set); and
the distance is a distance in a direction away from a center of the image-acquiring catheter (Celi - [0145] “In this embodiment and referring to FIG. 6, which is described in greater detail below, from the at least one point of intersection with the region associated with the vessel wall an intersection point IP.sub.L is defined, as a point located at the minimum distance from the centroid C.sub.w of the guidewire area along the same radial line, to obtain a plurality of intersection points and the method proceeds with the steps from 66 to 68, to close the lumen and define the area and centroid of the lumen.”).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 17, Richter in view of Celi teaches an image processing apparatus comprising:
an image acquisition unit that acquires a plurality of two-dimensional images obtained in time series with an image-acquiring catheter (Richter - [0077] “In another embodiment, the system for generating stabilized images comprises a catheter having a transmitter for transmitting a plurality of ultrasonic signals and a receiver for receiving a plurality of reflected ultrasonic signals,”); and
a third classification data acquisition unit configured to sequentially input the two-dimensional images to a trained model generated by the learning model generation method according to claim 1, and to sequentially acquire the third classification data that is output (Richter - [0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise.”) (Richter - [0052] “Any of the above-described prediction models may be used in accordance with the invention to construct a classification model to classify pixels as "lumen or "non-lumen", using input parameters as outlined below. Input parameters to be used for the model will describe a single pixel.”).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 18, Richter teaches an image processing apparatus comprising:
an image acquisition unit configured to acquire a plurality of two-dimensional images obtained in time series with an image-acquiring catheter ([0077] “In another embodiment, the system for generating stabilized images comprises a catheter having a transmitter for transmitting a plurality of ultrasonic signals and a receiver for receiving a plurality of reflected ultrasonic signals,”);
a first classification data acquisition unit configured to acquire a series of first classification data in which respective pixels constituting each two-dimensional image of the plurality of two-dimensional images are classified into a plurality of regions including a living tissue region, a lumen region into which the image-acquiring catheter is inserted, and an extra-luminal region outside the living tissue region ([0011] “Morphological features of blood vessels can be divided into three categories: the lumen, where blood flows; the vessel layers, i.e., the tissue inside the blood vessel; and the exterior, i.e., tissue outside the vessel. In IVUS, the blood presents as rapidly changing speculars.”) ([0046] “The next step in the detection process is to classify a pixel as "lumen" or "non-lumen", thus defining the lumen area and edges.”);
a three-dimensional image creation unit configured to create a three-dimensional image by using the series of first classification data ([0018] “The processor may also be programmed to filter images or series of images to improve the image stabilization and remove motion interference and/or may be programmed to extract the 3D shape of the environment.”) ([0011] “Morphological features of blood vessels can be divided into three categories: the lumen, where blood flows; the vessel layers, i.e., the tissue inside the blood vessel; and the exterior, i.e., tissue outside the vessel. In IVUS, the blood presents as rapidly changing speculars.”) ([0046] “The next step in the detection process is to classify a pixel as "lumen" or "non-lumen", thus defining the lumen area and edges.”).
Richter does not specifically disclose:
a determination unit configured to determine whether the lumen region reaches an edge of each two-dimensional image, in each two-dimensional image of the plurality of two-dimensional images;
a division line creation unit configured to create a division line that divides the lumen region into a first region into which the image-acquiring catheter is inserted and a second region reaching an edge of the two-dimensional image, when the determination unit determines that the lumen region reaches an edge of the two-dimensional image;
the second region has been changed to the extra-luminal region and processing the second region as the same region as the extra-luminal region.
However, Celi teaches:
a determination unit configured to determine whether the lumen region reaches an edge of each two-dimensional image, in each two-dimensional image of the plurality of two-dimensional images ([0137] “In a different embodiment, the step of determining whether the lumen-delimiting edge is continuous is automatically carried out by the system, for example by using an algorithm for automatically finding a radial light intensity discontinuity region in an image portion comprising the vessel wall and having a light intensity above a given threshold value.” wherein determining whether a lumen region reaches an edge is determining whether the lumen-delimiting edge is continuous);
a division line creation unit configured to create a division line that divides the lumen region into a first region into which the image-acquiring catheter is inserted and a second region reaching an edge of the two-dimensional image, when the determination unit determines that the lumen region reaches an edge of the two-dimensional image ([0047] “Preferably, the step of automatically finding an outer edge of the vessel wall and defining a first region of interest ROI.sub.1 comprises: [0048] selecting a preset wall thickness value; [0049] drawing a plurality of radial lines originating from the centroid C.sub.L of the lumen and extending through the vessel wall, the plurality of radial lines having a radial distribution extending along a circumference;”) ([0020] “automatically finding an outer edge of the vessel wall and defining a first region of interest ROI.sub.1 between the inner edge and the outer edge of the vessel wall; [0021] automatically segmenting a fibrous plaque within the first region of interest ROI.sub.1, thereby defining a second region of interest ROI.sub.2 that contains the fibrous plaque,” wherein a second region or the extra-luminal region is the second region of interest that contains the fibrous plaque);
the second region has been changed to the extra-luminal region and processing the second region as the same region as the extra-luminal region ([0020] “automatically finding an outer edge of the vessel wall and defining a first region of interest ROI.sub.1 between the inner edge and the outer edge of the vessel wall; [0021] automatically segmenting a fibrous plaque within the first region of interest ROI.sub.1, thereby defining a second region of interest ROI.sub.2 that contains the fibrous plaque,” wherein a second region or the extra-luminal region is the second region of interest that contains the fibrous plaque).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 19, Richter in view of Celi teaches the information processing apparatus according to claim 18, further comprising:
a first recording unit configured to associate the two-dimensional image with the first classification data and to record the two-dimensional image associated with the first classification data in a training database, when the determination unit determines that the lumen region does not reach an edge of the two-dimensional image (Richter - [0011] “Morphological features of blood vessels can be divided into three categories: the lumen, where blood flows; the vessel layers, i.e., the tissue inside the blood vessel; and the exterior, i.e., tissue outside the vessel. In IVUS, the blood presents as rapidly changing speculars.”) (Richter - [0046] “The next step in the detection process is to classify a pixel as "lumen" or "non-lumen", thus defining the lumen area and edges.”) (Richter - [0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise.”) (Celi - [0137] “In a different embodiment, the step of determining whether the lumen-delimiting edge is continuous is automatically carried out by the system, for example by using an algorithm for automatically finding a radial light intensity discontinuity region in an image portion comprising the vessel wall and having a light intensity above a given threshold value.” wherein determining whether a lumen region reaches an edge is determining whether the lumen-delimiting edge is continuous);
a second classification data creation unit configured to create second classification data in which a probability of being the lumen region and a probability of being the extra-luminal region are allocated for each of small regions constituting the lumen region of the first classification data, on a basis of the division line and the first classification data, when the determination unit determines that the lumen region reaches an edge of the two-dimensional image (Richter - [0046] “The classification model yields a probability for the pixel being of "lumen" type. A threshold probability is then set, above which probability the model classifies the pixel as "lumen".”) (Richter - [0052] “Any of the above-described prediction models may be used in accordance with the invention to construct a classification model to classify pixels as "lumen or "non-lumen", using input parameters as outlined below. Input parameters to be used for the model will describe a single pixel.” wherein the lumen region is “lumen” and extra-luminal region is “non-lumen”) (Richter - [0011] “Morphological features of blood vessels can be divided into three categories: the lumen, where blood flows; the vessel layers, i.e., the tissue inside the blood vessel; and the exterior, i.e., tissue outside the vessel. In IVUS, the blood presents as rapidly changing speculars.”) (Richter - [0046] “The next step in the detection process is to classify a pixel as "lumen" or "non-lumen", thus defining the lumen area and edges.”) (Celi - [0020] “automatically finding an outer edge of the vessel wall and defining a first region of interest ROI.sub.1 between the inner edge and the outer edge of the vessel wall; [0021] automatically segmenting a fibrous plaque within the first region of interest ROI.sub.1, thereby defining a second region of interest ROI.sub.2 that contains the fibrous plaque,” wherein the division line is the segmentation line) (Celi - [0102] “For vessel wall analysis, a catheter is introduced into the lumen to the segment to be analyzed.” wherein the catheter is introduced into the first region) (Celi - [0137] “In a different embodiment, the step of determining whether the lumen-delimiting edge is continuous is automatically carried out by the system, for example by using an algorithm for automatically finding a radial light intensity discontinuity region in an image portion comprising the vessel wall and having a light intensity above a given threshold value.” wherein determining whether a lumen region reaches an edge is determining whether the lumen-delimiting edge is continuous); and
a second recording unit configured to associate the two-dimensional image with the second classification data, and records the two-dimensional image associated with the second classification data in the training database, when the determination unit determines that the lumen region reaches an edge of the two-dimensional image (Richter - [0046] “The classification model yields a probability for the pixel being of "lumen" type. A threshold probability is then set, above which probability the model classifies the pixel as "lumen".”) (Richter - [0047] “The classification model may be constructed in exemplary lumens, and used in similar types of lumens for analytical purposes. To construct the classification model, one first obtains a training set of images, in which the lumen area has been marked by an expert or specialist based expertise.”) (Celi - [0137] “In a different embodiment, the step of determining whether the lumen-delimiting edge is continuous is automatically carried out by the system, for example by using an algorithm for automatically finding a radial light intensity discontinuity region in an image portion comprising the vessel wall and having a light intensity above a given threshold value.” wherein determining whether a lumen region reaches an edge is determining whether the lumen-delimiting edge is continuous).
The motivation for combining Richter and Celi is the same motivation as used for claim 1.
Regarding claim 20, the claim recites similar limitations to claim 18 but in the form of a method. Therefore, claim 20 recites similar limitations to claim 18 and is rejected for similar rationale and reasoning (see the analysis for claim 18 above).
Allowable Subject Matter
Claims 7 is 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. Claims 9-13 are rejected under 35 U.S.C. 112(b) but would be allowable if amended to overcome the 112(b) rejections and rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666