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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on September 12, 2024 and March 5, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 10-11, 13-14 and 16-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Publication No. 2017/0238798 to Isogai et al. (hereinafter Isogai).
Regarding independent claim 1, Isogai discloses a method (paragraph 0006, “In the ophthalmological field, a method of applying the optical coherence tomography device performing an A-scan operation at a high speed has not been examined sufficiently yet.”) comprising:
capturing optical coherence tomography (OCT) data from an object (paragraph 0009, “According to an aspect of the present disclosure, there is provided an optical coherence tomography device including an OCT optical system that irradiates a tissue of the subject's eye with measurement light from a light source, and detects interference between reference light and the measurement light reflected from the tissue by using a detector”);
generating a first plurality of structural OCT images from a first location of the object based on the captured OCT data (paragraph 0009, “a processor, in which the processor performs a generation process of acquiring A-scan data based on a signal output from the detector in a cycle of 300 kilohertz or more and generating three-dimensional OCT data at any time on the basis of the acquired A-scan data, and performs an analysis process on each piece of the three-dimensional OCT data generated at any time through the generation process, so as to output a real-time analysis result of the three-dimensional OCT data which is generated at any time;” paragraph 0086, “The three-dimensional motion contrast data obtained in the above-described way indicates, for example, a three-dimensional structure of a blood vessel. The control unit 70 may sequentially generate motion contrast images on the basis of three-dimensional motion contrast data, and may sequentially display the motion contrast images on the monitor 75. A graphics indicating a three-dimensional structure of a blood vessel may be displayed to overlap a three-dimensional OCT image (an image indicating a three-dimensional reflection intensity distribution as described above) by the control unit 70. Consequently, the three-dimensional structure of a blood vessel may be displayed on the monitor 75 in real time.”);
extracting flow information from individual ones of the first plurality of structural OCT images (paragraph 0059, “ For example, a flow image is assumed to be one kind of motion contrast. The flow image is an image obtained by detecting a motion of, for example, a fluid. It can be said that a blood vessel angiographic image (OCT angiography) in which a blood vessel position obtained by detecting a motion of blood is imaged is one kind of the motion contrast.” Paragraph 0088, “In the analysis process on three-dimensional OCT data, an analysis process on pulsation of a blood flow may be performed. For example, the control unit 70 may further process a three-dimensional motion contrast image so as to acquire information regarding pulsation of a blood flow as a result of the analysis process. As specific examples, the information regarding pulsation of a blood flow may be information indicating any one of a direction of a blood flow, a velocity of a blood flow, a flow rate of a blood flow, pressure caused by a blood flow, and a pulse, and may be information indicating any one thereof in a time series.”); and
generating a first time-series flow profile of the first location of the object (paragraph 0059, “ For example, a flow image is assumed to be one kind of motion contrast. The flow image is an image obtained by detecting a motion of, for example, a fluid. It can be said that a blood vessel angiographic image (OCT angiography) in which a blood vessel position obtained by detecting a motion of blood is imaged is one kind of the motion contrast.” Paragraph 0088, “In the analysis process on three-dimensional OCT data, an analysis process on pulsation of a blood flow may be performed. For example, the control unit 70 may further process a three-dimensional motion contrast image so as to acquire information regarding pulsation of a blood flow as a result of the analysis process. As specific examples, the information regarding pulsation of a blood flow may be information indicating any one of a direction of a blood flow, a velocity of a blood flow, a flow rate of a blood flow, pressure caused by a blood flow, and a pulse, and may be information indicating any one thereof in a time series.”),
wherein the first flow profile is a relationship between the extracted flow information and a timing of the captured OCT data from which the corresponding individual one of the first plurality of structural OCT images was generated (paragraph 0059, “ For example, a flow image is assumed to be one kind of motion contrast. The flow image is an image obtained by detecting a motion of, for example, a fluid. It can be said that a blood vessel angiographic image (OCT angiography) in which a blood vessel position obtained by detecting a motion of blood is imaged is one kind of the motion contrast.” Paragraph 0088, “In the analysis process on three-dimensional OCT data, an analysis process on pulsation of a blood flow may be performed. For example, the control unit 70 may further process a three-dimensional motion contrast image so as to acquire information regarding pulsation of a blood flow as a result of the analysis process. As specific examples, the information regarding pulsation of a blood flow may be information indicating any one of a direction of a blood flow, a velocity of a blood flow, a flow rate of a blood flow, pressure caused by a blood flow, and a pulse, and may be information indicating any one thereof in a time series.”).
Regarding dependent claim 10, the rejection of claim 1 is incorporated herein. Additionally, Isogai further discloses further comprising:
displaying the first flow profile as a time-series graph (paragraph 0089, “ For example, a process result may be displayed in numerical values, may be displayed as a graph (for example, a trend graph) indicating a change over time of numerical values, and may be displayed in other aspects. As mentioned above, in the present embodiment, information regarding a blood flow useful for diagnosis can be obtained in real time in a wide range in which raster scanning is performed.”).
Regarding dependent claim 11, the rejection of claim 1 is incorporated herein. Additionally, Isogai further discloses further comprising:
extracting the flow information from a plurality of regions of the individual one of the first plurality of structural OCT images (paragraph 0097, “In this Example, the control unit 70 may calculate an absolute velocity of a blood flow at each location of a blood vessel in real time. For example, an absolute velocity of a blood flow at each location may be calculated whenever three-dimensional OCT data based on new raster scanning (for convenience, first raster scanning) is obtained,”);
generating a flow map of the extracted flow information over the plurality of regions (paragraph 0098, “A distribution of the absolute velocities at the respective locations obtained in the above-described way may be displayed as, for example, a color map on the monitor 75.”); and
displaying the flow map (paragraph 0098, “A distribution of the absolute velocities at the respective locations obtained in the above-described way may be displayed as, for example, a color map on the monitor 75.”).
Regarding dependent claim 13, the rejection of claim 1 is incorporated herein. Additionally, Isogai further discloses wherein the first location is a cross-sectional location and wherein the OCT data is captured from the first cross-sectional location (paragraph 0102, “acquire information regarding pulsation of a blood flow with respect to the cross-section” paragraph 0032, “The optical scanner 108 is used to scan the fundus Ef with the measurement light from the light source 102. The optical scanner 108 scans the fundus Ef with the measurement light in xy directions (crossing directions). In the present embodiment, the optical scanner 108 performs raster scanning with the measurement light on the fundus. In the present embodiment, raster scanning as exemplified in FIG. 2 is periodically performed in a predetermined region (a position and an area are constant) of the fundus.”) and from a second cross-sectional location a known distance from the first cross-sectional location (paragraph 0102, “. For example, the control unit 70 controls the optical scanner 108 so as to repeatedly acquire two-dimensional OCT data regarding the same cross-section, and can thus acquire information regarding pulsation of a blood flow with respect to the cross-section. In this case, repeated scanning on the cross-section may be performed between raster scanning operations for acquiring three-dimensional OCT data. Consequently, it is possible to obtain information regarding pulsation of a blood flow in real time at the substantially same time with three-dimensional OCT data. Each scanning line may be repeatedly scanned for a predetermined number of times in raster scanning of one cycle, and thus a plurality of pieces of two-dimensional OCT data in which a scanning interval for a scanning line is short may be obtained with respect to the same cross-section.” paragraph 0032, “The optical scanner 108 is used to scan the fundus Ef with the measurement light from the light source 102. The optical scanner 108 scans the fundus Ef with the measurement light in xy directions (crossing directions). In the present embodiment, the optical scanner 108 performs raster scanning with the measurement light on the fundus. In the present embodiment, raster scanning as exemplified in FIG. 2 is periodically performed in a predetermined region (a position and an area are constant) of the fundus.” Paragraph 0112, “The analysis process is based on three-dimensional OCT data including the fundus in a data acquisition range being acquired by the OCT 1 as an analysis target. A separation location of layers forming the fundus may be analyzed on the basis of three-dimensional OCT data generated at any time, and information (referred to as separation location information) indicating at least one of the presence or absence of a separation location of layers and a position of the separation location as a result thereof (analysis result). Obtaining the separation location information in real time is useful for, for example, vitreous body surgery. Information indicating a position of a separation location is information for specifying the separation location in either or both of the xy directions and the depth direction”), the method further comprising:
generating a second plurality of structural OCT images from the second cross-sectional location of the object (paragraph 0112, “The analysis process is based on three-dimensional OCT data including the fundus in a data acquisition range being acquired by the OCT 1 as an analysis target. A separation location of layers forming the fundus may be analyzed on the basis of three-dimensional OCT data generated at any time, and information (referred to as separation location information) indicating at least one of the presence or absence of a separation location of layers and a position of the separation location as a result thereof (analysis result). Obtaining the separation location information in real time is useful for, for example, vitreous body surgery. Information indicating a position of a separation location is information for specifying the separation location in either or both of the xy directions and the depth direction”);
generating a second time-series flow profile of the second cross-sectional location of the object (paragraph 0097, “In this Example, the control unit 70 may calculate an absolute velocity of a blood flow at each location of a blood vessel in real time. For example, an absolute velocity of a blood flow at each location may be calculated whenever three-dimensional OCT data based on new raster scanning (for convenience, first raster scanning) is obtained”);
determining a time difference between the first flow profile and the second flow profile (paragraph 0089, “In this case, at least the graphics obtained by visualizing three-dimensional OCT data and the process result corresponding to the graphics are displayed on the monitor 75. For example, a process result may be displayed in numerical values, may be displayed as a graph (for example, a trend graph) indicating a change over time of numerical values, and may be displayed in other aspects. ”);
and determining a flow velocity of the object based on the known distance and the determined time difference (paragraph 0091, “In the present embodiment, an absolute velocity of a blood flow may be obtained by using a three-dimensional structure of a blood vessel obtained on the basis of a three-dimensional motion contrast image and three-dimensional OCT data obtained through a plurality of raster scanning operations.;” paragraph 0097, “In this Example, the control unit 70 may calculate an absolute velocity of a blood flow at each location of a blood vessel in real time. For example, an absolute velocity of a blood flow at each location may be calculated whenever three-dimensional OCT data based on new raster scanning (for convenience, first raster scanning) is obtained, o”).
Regarding dependent claim 14, the rejection of claim 13 is incorporated herein. Additionally, Isogai further discloses wherein the OCT data is alternately captured between the first cross-sectional location and the second cross-sectional location (paragraph 0062, “For example, in the present embodiment, a real-time tomographic image of a certain section may be displayed on the monitor 75. A position of a section may be defined in advance. A position of a section may be a position selected by the control unit 70 from an acquisition range of three-dimensional OCT data on the basis of a signal from the operation unit 74 (selection process). The signal from the operation unit 74 may be a signal for designating a section desired by an examiner.”).
Regarding dependent claim 16, the rejection of claim 1 is incorporated herein. Additionally, Isogai further discloses further comprising:
applying a stimulus to the object (paragraph 0106, “Each device may be controlled so that pressure or laser light is applied while three-dimensional OCT data is continuously acquired in the OCT 1”); and
determining a change to the first flow profile in response to application of the stimulus (paragraph 0106, “ A thickness at each position may be obtained through the analysis process, and, as an analysis result, a map which indicates a two-dimensional distribution of thicknesses of a tissue of the subject's eye in real time may be obtained. The map may be displayed on the monitor 75. The real-time thickness map may be used, for example, in a case where the examiner observes, in real time, a change of a thickness due to predetermined work of applying pressure to the subject's eye E, or surgery of influencing a thickness of a tissue, such as refraction correction surgery or cataract surgery. The surgery mentioned here may be surgery using an ophthalmologic laser surgery device, and, in this case, the OCT 1 may be provided with an ophthalmotonometer (for example, a tonometer) which applies pressure to the subject's eye E, or an ophthalmologic laser surgery device. Each device may be controlled so that pressure or laser light is applied while three-dimensional OCT data is continuously acquired in the OCT 1. The control unit 70 may output a thickness analysis result to the ophthalmologic laser surgery device in order to control irradiation with laser light in the ophthalmologic laser surgery device.”).
Regarding dependent claim 17, the rejection of claim 16 is incorporated herein. Additionally, Isogai further discloses wherein the applied stimulus is pressure (paragraph 0106, “Each device may be controlled so that pressure or laser light is applied while three-dimensional OCT data is continuously acquired in the OCT 1”).
Regarding dependent claim 18, the rejection of claim 1 is incorporated herein. Additionally, Isogai further discloses wherein the flow information is extracted from a region of interest identified in one of the first plurality of structural OCT images, and which is registered to the other first plurality of structural OCT images (paragraph 0035, “In the present embodiment, the two scanners 108 a and 108 b periodically perform raster scanning with the measurement light in a region (a region having a constant area) of the fundus. ”).
Regarding dependent claim 19, the rejection of claim 18 is incorporated herein. Additionally, Isogai further discloses wherein the region of interest corresponds to an area of blood flow, and is automatically identified (paragraph 0108, “The control unit 70 may analyze a coagulation spot formed on the subject's eye by photocoagulation laser light on the basis of three-dimensional OCT data generated at any time, and may output at least one of information regarding a size of the coagulation spot and information regarding a position of the coagulation spot in the subject's eye E as a real-time analysis result. The coagulation spot is displayed as a region having luminance which is different from luminance of a peripheral tissue in a graphics (which may be a three-dimensional image, and may be a two-dimensional image indicating any section) obtained by visualizing three-dimensional OCT data acquired in real time, and can thus be detected through image processing on the graphics. At least one of size information of the detected coagulation spot and position information of the detected coagulation spot in the subject's eye E may be specified through image processing on the graphics. T”).
Regarding dependent claim 20, the rejection of claim 1 is incorporated herein. Additionally, Isogai further discloses wherein the object is an eye (abstract, “An optical coherence tomography device includes an OCT optical system that irradiates a tissue of the subject's eye ”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Isogai as applied to claim 1 above, and further in view of U.S. Patent No. 6,173,197 to Boggett et al. (hereinafter Boggett).
Regarding dependent claim 2, the rejection of claim 1 is incorporated herein. Additionally, Isogai fails to explicitly disclose wherein extracting flow information comprises applying a high frequency filter to frequency information of the individual one of the first plurality of structural OCT images, thereby producing high frequency information, and
wherein the flow information corresponds to the high frequency information.
However, Boggett discloses wherein extracting flow information comprises applying a high frequency filter to frequency information of the individual one of the first plurality of structural OCT images, thereby producing high frequency information, and wherein the flow information corresponds to the high frequency information (column 5, line 13, “Also, different frequency ranges of the Doppler signal can be analysed separately by choosing the lower and upper limits of frequency components. For example, if it is known that blood flow signal for a particular application is toward high frequency band, low frequency components can be ignored by increasing the lower limit n1 to produce less noise flow output. ”).
Isogai is directed toward, “an analysis process on each piece of the three-dimensional OCT data generated at any time through the generation process, so as to output a real-time analysis result of the three-dimensional OCT data which is generated at any time (abstract).” Boggett is directed toward, “An apparatus for measuring microvascular blood flow in tissue (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Isogai and Boggett are directed toward similar methods of endeavor of medical image analysis. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would be easily aware when analyzing image data, there is a desire to limit extraneous data so that a reviewer can focus on only data of interest. As seen in Boggett, removing low frequency data is permissible being that blood flow signal for a specified application is in the high frequency realm. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Boggett in order to ensure only relevant data is considered.
Regarding dependent claim 3, the rejection of claim 2 is incorporated herein. Additionally, Boggett in the combination further discloses wherein extracting flow information further comprises:
applying a low frequency filter to the frequency information of the individual one of the first plurality of structural OCT images, thereby producing low frequency information, wherein the flow information is a ratio of the high frequency information to the low frequency information (column 3, line 40, “As shown in FIG. 1, red or near infra-red light from a low power laser (2) is directed via an optical fibre (1) to the tissue and the light Scattered back from the tissue is collected by one or more other optical fibres (1) and received by the photodetector (2). The photodetector converts the optical Signal into an electrical Signal. A bandpass filter (3) is used to remove noise outside the bandwidth and extract blood flow related AC components. A low-pass filter (4) is also connected to the output of the photodetector and is used to extract DC components proportional to the intensity of the collected light. Outputs of the bandpass (3) and low-pass filter (4) are converted into digital form by a multiplexer and A/D (5). Spectral analysis of the digitised Doppler signal, blood flow calculation and movement artefact detection and removal are performed by the powerful DSP device (6) in real-time.” Column 5, line 20, “Another example is to calculate the ratio of flow from high frequency band and low frequency band using the present invention apparatus. ”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Boggett in order to ensure only relevant data to the parameter in question is considered.
Regarding dependent claim 4, the rejection of claim 2 is incorporated herein. Additionally, Isogai in the combination further discloses wherein extracting flow information comprises:
applying two-dimensional Fourier transform to the individual one of the first plurality of structural OCT images, thereby producing the frequency information (paragraph 0051, “The A-scan data may be a complex OCT signal obtained by performing Fourier transform on a signal (OCT signal) output from the detector 120.” Paragraph 0084, “More specifically, the control unit 70 acquires a complex OCT signal by performing Fourier transform on a signal (OCT signal) output from the detector 120. For example, the complex OCT signal is stored in the memory 72 when raster scanning is performed.”).
Claim(s) 5 and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Isogai as applied to claim 1 above, and further in view of Braaf, Boy, et al. "A neural network approach to quantify blood flow from retinal OCT intensity time-series measurements." Scientific Reports 10.1 (2020): 9611. (hereinafter Braaf).
Regarding dependent claim 5, the rejection of claim 1 is incorporated herein. Additionally, Isogai fails to explicitly disclose wherein the extracted flow information is a speckle density of the individual one of the first plurality of structural OCT images.
However, Braff discloses wherein the extracted flow information is a speckle density of the individual one of the first plurality of structural OCT images (page 3, “OCT intensity images were obtained from the B-scan data by averaging the intensity information for every M-scan (see Fig. 1(b)). In the M-scan intensity images of Fig. 1(b), the time-series data obtained from static structures showed constant signals over time (denoted by *), while the speckle signals obtained from blood flow within the tubing showed rapid modulations across time (denoted by †). This clearly demonstrates the speckle intensity modulations caused by flowing blood that are quantitatively analyzed in the next sections.;” Figure 9, “Regions of flow and static tissue can be identified based on the visibility of the speckle features (B-scan view) or the temporal modulation of intensity (M-scan views).”).
Isogai is directed toward, “an analysis process on each piece of the three-dimensional OCT data generated at any time through the generation process, so as to output a real-time analysis result of the three-dimensional OCT data which is generated at any time (abstract).” Braaf is directed toward, “a robust blood flow quantification framework is presented based on optical coherence tomography (OCT) angiography imaging and deep learning (abstract).” As can be easily seen by one of ordinary sill in the art before the effective filing date of the claimed invention, Isogai and Braaf are directed toward similar methods of endeavor of OCT image analysis. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would know that speckle pattern can change or be related to the tissue and blood patterns within the field of view. Thus, in order to best understand the components within the OCT image through speckle density, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Braaf.
Regarding dependent claim 7, the rejection of claim 1 is incorporated herein. Additionally, Isogai fails to explicitly disclose wherein extracting flow information comprises:
inputting the individual one of the first plurality of structural OCT images to a machine learning system trained to output flow information based on an input structural OCT image.
However, Braaf discloses wherein extracting flow information comprises:
inputting the individual one of the first plurality of structural OCT images to a machine learning system trained to output flow information based on an input structural OCT image (abstract, “The presented OCT-based NN flow rate estimation framework addresses the need for a robust, deployable, and label-free quantitative retinal blood flow mapping technique.;” page 2, “we demonstrate for the first time robust blood flow rate estimation from OCT intensity time-series measurements using a neural network (NN) analysis;” page 3, “We developed a NN that takes as input a measured time-series OCT intensity dataset of a prescribed length and outputs a flow velocity likelihood curve. This NN was applied pixel-by-pixel to the stepped M-scan B-scan images to generate likelihood curves for each pixel individually without including knowledge from neighboring pixels.”).
Isogai is directed toward, “an analysis process on each piece of the three-dimensional OCT data generated at any time through the generation process, so as to output a real-time analysis result of the three-dimensional OCT data which is generated at any time (abstract).” Braaf is directed toward, “a robust blood flow quantification framework is presented based on optical coherence tomography (OCT) angiography imaging and deep learning (abstract).” As can be easily seen by one of ordinary sill in the art before the effective filing date of the claimed invention, Isogai and Braaf are directed toward similar methods of endeavor of OCT image analysis. Further, one of ordinary skill in the art would be well aware of the benefits of utilizing a neural network to perform analysis such as efficiency and accuracy. Thus, in order to analyzed medical data both efficiently and accurately, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Braaf.
Regarding dependent claim 8, the rejection of claim 1 is incorporated herein. Additionally, Isogai fails to explicitly disclose wherein extracting flow information and generating the time-series flow profile comprises:
inputting the first plurality of structural optical coherence tomography (OCT) images as a time series to a machine learning system trained to output the flow profile based on an input time series of structural OCT images.
However, Braaf discloses wherein extracting flow information and generating the time-series flow profile comprises:
inputting the first plurality of structural optical coherence tomography (OCT) images as a time series to a machine learning system trained to output the flow profile based on an input time series of structural OCT images (page 2, “we demonstrate for the first time robust blood flow rate estimation from OCT intensity time-series measurements using a neural network (NN) analysis;” page 3, “We developed a NN that takes as input a measured time-series OCT intensity dataset of a prescribed length and outputs a flow velocity likelihood curve. This NN was applied pixel-by-pixel to the stepped M-scan B-scan images to generate likelihood curves for each pixel individually without including knowledge from neighboring pixels.”).
Isogai is directed toward, “an analysis process on each piece of the three-dimensional OCT data generated at any time through the generation process, so as to output a real-time analysis result of the three-dimensional OCT data which is generated at any time (abstract).” Braaf is directed toward, “a robust blood flow quantification framework is presented based on optical coherence tomography (OCT) angiography imaging and deep learning (abstract).” As can be easily seen by one of ordinary sill in the art before the effective filing date of the claimed invention, Isogai and Braaf are directed toward similar methods of endeavor of OCT image analysis. Further, one of ordinary skill in the art would be well aware of the benefits of utilizing a neural network to perform analysis such as efficiency and accuracy. Thus, in order to analyzed medical data both efficiently and accurately, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Braaf.
Regarding dependent claim 9, the rejection of claim 1 is incorporated herein. Additionally, Isogai fails to explicitly disclose wherein the OCT data is captured for a time period comprising a plurality of cardiac cycles.
However, Braaf discloses wherein the OCT data is captured for a time period comprising a plurality of cardiac cycles (page 9, “the average flow rate across a cardiac cycle per vessel segment was calculated using both NN and Doppler methods.”).
Isogai is directed toward, “an analysis process on each piece of the three-dimensional OCT data generated at any time through the generation process, so as to output a real-time analysis result of the three-dimensional OCT data which is generated at any time (abstract).” Braaf is directed toward, “a robust blood flow quantification framework is presented based on optical coherence tomography (OCT) angiography imaging and deep learning (abstract).” As can be easily seen by one of ordinary sill in the art before the effective filing date of the claimed invention, Isogai and Braaf are directed toward similar methods of endeavor of OCT image analysis. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would easily understand acquiring data over time as related to cardiac cycles allows one to analyze the effect of the cardiac cycle on the output OCT data. Said differently, the cardiac cycle impacts blood flow throughout the body, thus when analyzing those other areas across the cardiac cycle, the impacts can be quantified. Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teaching of Braaf in order to determine if there is a relationship between the cardiac cycle and the OCT imaging output.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Isogai as applied to claim 1 above, and further in view of U.S. Patent No. 9,092,691 to Beaumont et al. (hereinafter Beaumont).
Regarding dependent claim 6, the rejection of claim 1 is incorporated herein. Additionally, Isogai fails to explicitly disclose wherein extracting flow information comprises:
applying a co-occurrence matrix to the first plurality of structural OCT images; and
determining a correlation among the first plurality of structural OCT images based on the co-occurrence matrix.
However, Beaumont discloses wherein extracting flow information comprises:
applying a co-occurrence matrix to the first plurality of structural OCT images (Column 4, line 22, “An image processing apparatus for computing a QIB of disease progression from a first temporal tomographic image sequence of a first modality co-registered with a second temporal tomographic image sequence of a second modality, the apparatus comprising a first identification module for identifying at least one common ROI in each image of the first image sequence based on features extracted in the second image sequence; a second registration module for performing a registration of the common ROIs in the first sequence; a third module for computing a gray-level co-occurrence matrix for each voxel of the common ROIs in the temporal sequence of the first modality; a fourth feature extraction module for extracting a texture based feature vector from the co-occurrence matrix for each voxel the common ROIs; a fifth processing module for computing the distance between the texture-based feature vectors across the common ROIs in the temporal sequence of the first modality.); and
determining a correlation among the first plurality of structural OCT images based on the co-occurrence matrix (Column 4, line 22, “An image processing apparatus for computing a QIB of disease progression from a first temporal tomographic image sequence of a first modality co-registered with a second temporal tomographic image sequence of a second modality, the apparatus comprising a first identification module for identifying at least one common ROI in each image of the first image sequence based on features extracted in the second image sequence; a second registration module for performing a registration of the common ROIs in the first sequence; a third module for computing a gray-level co-occurrence matrix for each voxel of the common ROIs in the temporal sequence of the first modality; a fourth feature extraction module for extracting a texture based feature vector from the co-occurrence matrix for each voxel the common ROIs; a fifth processing module for computing the distance between the texture-based feature vectors across the common ROIs in the temporal sequence of the first modality.).
Isogai is directed toward, “an analysis process on each piece of the three-dimensional OCT data generated at any time through the generation process, so as to output a real-time analysis result of the three-dimensional OCT data which is generated at any time (abstract).” Beaumont is directed toward, “An image processing apparatus for computing a quantitative imaging biomarker (QIB) of disease severity from variations in texture-based features in a tomographic image (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Isogai and Beaumont are directed toward similar methods of endeavor of medical image processing. Further, one of ordinary skill in the art before the effective filing date of the claimed invention would be well aware GLCMs can reveal additional information as related to medical images. The concept of analyzing image texture through GLCMs is seen in Beaumont at column 2, line 4, “Texture analysis is also a promising field in biomedical imaging. It is found that most of the tissue types have strong multi-scale directional properties that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. (Depeursinge, 2013). Texture in medical images is defined as cellularly organized areas of pixels. Such patterns can be described by a given spatial organization of grey levels (e.g., random, periodic). Early examples of texture features are the autocorrelation function, textural edginess, measurements derived from mathematical morphology, run-length and gray-level co-occurrence matrices (GLCMs).” Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date to incorporate the teaching of Beaumont in order to ensure quantification of image texture is performed through GLCMs in order to ensure additional image properties that can lead to diagnostic values are determined.
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Isogai as applied to claim 11 above, and further in view of U.S. Publication No. 2005/0254008 to Ferguson et al. (hereinafter Ferguson).
Regarding dependent claim 12, the rejection of claim 11 is incorporated herein. Additionally, Isogai fails to explicitly disclose further comprising:
generating the flow map for at least two of the first plurality of structural OCT images;
generating a flow video from the generated flow maps; and
displaying the flow video
However, Ferguson discloses further comprising:
generating the flow map for at least two of the first plurality of structural OCT images (paragraph 0153, “This implies that the choroidal contribution to the flow maps can be attenuated by the RPE in darkly pigmented eyes, and perhaps by the focus and depth of field of the quasi-confocal imaging system.” paragraph 0152, “FIG. 19 shows a blood flow video for a healthy 24-year old subject. The vertical line noise at the lowest frequency bin is caused by small amplitude (<1 pixel) transient tracking stability artifacts.” Paragraph 0154, “The foveal avascular region is again apparent at lower frequencies. At higher frequencies, vertical banding caused by pulsatile flow (at about 15 lines per beat) is quite strong in this subject in the choroid visible below the retinal circulation. It is interesting to observe that very little of this pronounced choroidal structure is visible in the reconstructed SLO image. Intense pulsatile flow occasionally gives arteries a beaded appearance. The perfusion of the choriocapillaris is undoubtedly contributing to the perfusion maps, but it does not overwhelm the retinal signal.”);
generating a flow video from the generated flow maps (paragraph 0152, “FIG. 19 shows a blood flow video for a healthy 24-year old subject. The vertical line noise at the lowest frequency bin is caused by small amplitude (<1 pixel) transient tracking stability artifacts.”); and
displaying the flow video (paragraph 0152, “FIG. 19 shows a blood flow video for a healthy 24-year old subject”)
Isogai is directed toward, “an analysis process on each piece of the three-dimensional OCT data generated at any time through the generation process, so as to output a real-time analysis result of the three-dimensional OCT data which is generated at any time (abstract).” Ferguson is directed toward, “By performing a slow scan with the laser line imager, frequency-resolved retinal perfusion and vascular flow images can be obtained free of eye motion artifacts (abstract).” As can be easily seen by one of ordinary skill in the art before the effective filing date of the claimed invention, Isogai and Ferguson are directed toward similar methods of endeavor of eye image analysis. Further, Ferguson allows for generation of video, and reduced artifacts. Video data is well known to be beneficial to a reviewing user for diagnosis of eye diseases. Additionally, one goal of data processing for diagnosis is to generate data with as little artifacts as possible, Having artifacts present can lead to misdiagnoses and further, inaccurate or no treatment. Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Ferguson in order to ensure the data presented for review is as clear and accurate as possible for diagnosis.
Allowable Subject Matter
Claim 15 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.
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach methods of analyzing flow data by processing OCT data.
However, none of them alone or in any combination teaches determining a time difference between two flow profiles, where the time difference is between local maxima or minima of the first and second flow profiles.
The closest prior art being Isogai discloses at paragraph 0097, “In this Example, the control unit 70 may calculate an absolute velocity of a blood flow at each location of a blood vessel in real time. For example, an absolute velocity of a blood flow at each location may be calculated whenever three-dimensional OCT data based on new raster scanning (for convenience, first raster scanning) is obtained” and paragraph 0089, “In this case, at least the graphics obtained by visualizing three-dimensional OCT data and the process result corresponding to the graphics are displayed on the monitor 75. For example, a process result may be displayed in numerical values, may be displayed as a graph (for example, a trend graph) indicating a change over time of numerical values, and may be displayed in other aspects.” Thus, Isogai does disclose generation of multiple graphs based on the varying locations.
However, Isogai fails to disclose determining a time difference between two flow profiles, where the time difference is between local maxima or minima of the first and second flow profiles.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Publication No. 2023/0091487 to Lee et al. discloses, “A system and/or method uses a trained U-Net neural network to remove flow artifacts from optical coherence tomography (OCT) angiography (OCTA) data (abstract).”
U.S. Patent No. 10,485,423 to Huang et al. discloses, “A reliable method to quantify blood flow in the various intraocular vascular beds could provide insight into the vascular component of ocular disease pathophysiology (abstract)”
U.S. Publication No. 2019/0082952 to Zhang et al. discloses, “Systems and methods to measure blood flow using optical coherence tomography (abstract)”
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/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661
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