DETAIL OFFICE ACTIONS
The United States Patent & Trademark Office appreciates the response filed for the current application that is submitted on 01/15/2026. The United States Patent & Trademark Office reviewed the following documents submitted and has made the following comments below.
Amendment
Applicant submitted amendments on 01/15/2026. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Information Disclosure Statement
The IDS(s) dated 09/20/2023, 10/16/2023, and 10/31/2025 have been previously considered and remain placed in the application file.
Priority
Acknowledgment is made that this application is a CON of application no. PCT/JP2022/010152 filed on 03/09/2022, which further claims the benefit of Foreign Priority from Application No JAPAN 2021-052012 filed on 03/25/2021. Claims 1-22 have been afforded the benefit of this filing date.
Overview
Claims 1-22 are pending in this application and have been considered below.
Claims 2 and 12 are cancelled.
Claims 1, 3-11 and 13-22 are pending.
Claims 21-22 are objected to.
Applicant Arguments:
In regards to the argument on Argument 1, Applicant/s state/s “the amended claims do not recite a judicial exception under Step 2A Prong One. Even if they did, the claims integrate the exception into a practical application under Step 2A Prong Two by providing a specific improvement to medical imaging technology and utilizing a particular machine. Therefore, the claims are eligible under 35 U.S.C. 101. The Applicant respectfully requests withdrawal of the rejection” therefore, the rejection of 35 U.S.C. 101 should be withdrawn (See Remarks, page 12, paragraph 4).
In regards to the argument on Argument 2, Applicant/s state/s “Atsushi does not teach the feature to "determine a plaque burden based on an area ratio of the segmented lumen to a blood vessel area defined by the segmented adventitia." Atsushi focuses on calculating a simple diameter length using optical geometry. It does not disclose identifying and segmenting the specific tissue layers of the "adventitia" and "lumen" to calculate a specific numerical "area ratio" between them, nor does it define "plaque burden" by such a ratio.," therefore, the rejection of 35 U.S.C. 103 should be withdrawn (See Remarks, page 13, paragraph 5).
In regards to the argument on Argument 3, Applicant/s state/s “Gopinath does not teach the feature to "determine a plaque burden based on an area ratio of the segmented lumen to a blood vessel area defined by the segmented adventitia" and "determine a size of a stent ... based on a distribution of the calculated plaque burden." Gopinath teaches determining stent size based on diameter measurements (EEL diameter or lumen diameter) or calcium angle/thickness. Gopinath does not disclose the specific algorithmic step of calculating "plaque burden" as the specific area ratio between the segmented lumen and the segmented adventitia (blood vessel area). Furthermore, Gopinath does not teach determining the stent size based on the distribution of this specific calculated area ratio along the vessel.” therefore, the rejection of 35 U.S.C. 103 should be withdrawn (See Remarks, page 13, paragraph 8).
In regards to the argument on Argument 4, Applicant/s state/s “neither Atsushi nor Gopinath teaches the feature to "determine a plaque burden based on an area ratio of the segmented lumen to a blood vessel area defined by the segmented adventitia" and use the "distribution" of this specific metric to determine stent size, the claimed subject matter is not obvious from the combination of the prior art references." therefore, the rejection of 35 U.S.C. 103 should be withdrawn (See Remarks, page 14, paragraph 1).
In regards to the argument on Argument 5, Applicant/s state/s “new claim 21, the claim recites the feature wherein the image processing apparatus is configured to "input a plurality of consecutive frame images acquired over a predetermined unit time as a single set into the machine learning model to simultaneously detect a plaque." The cited references, including Gopinath, do not teach or suggest this feature.” therefore, the rejection of 35 U.S.C. 103 should not be applied to the new claim (See Remarks, page 14, paragraph 3).
In regards to the argument on Argument 6, Applicant/s state/s “The cited references do not teach or suggest a single catheter having both ultrasound and optical sensors separated by a known axial distance, nor do they teach correcting a shift based on that specific distance." The cited references, including Gopinath, do not teach or suggest this feature.” therefore, the rejection of 35 U.S.C. 103 should not be applied to the new claim (See Remarks, page 14, paragraph 4).
Examiner’s Responses:
In response to Argument 1, Applicant’s arguments, see Remarks, filed 01/15/2026, with
respect to the rejection(s) of claims 1-20 under 35 U.S.C. 101 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn due to the amendment.
In response to Argument 2, Applicant’s arguments, see Remarks, filed 01/15/2026, with respect to the rejection(s) of claims 1, 11 and 20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn due to the amendment. However, upon further consideration, a new ground(s) of rejection is made for Claims 1, 11, and 20 and its dependent claims under 35 U.S.C. 103 in view of Atsushi et al (JP2020185082A (using IP.com as a translation) hereafter referred to as Atsushi) in view of Schmitt et al (WO Patent Publication WO 2014/092755 Al hereafter referred to as Schmitt).
The Examiner finds that Atsushi teaches on the amended claim language “determine a size of a stent to be implanted into the blood vessel” in Claims 1, 11, and 20 with the amendment changing the scope of the “determine a plaque burden”.
Specifically, Atsushi teaches determining the size of the stent to be implanted into the blood vessel in Pg 9 ¶08, Pg 11 ¶03, and Pg 3 ¶06, based on an image obtained by a catheter inserted into a blood vessel in Pg 5 ¶04, and Pg 5 ¶08. Applicant argues that “Atsushi does not teach the feature to "determine a plaque burden based on an area ratio of the segmented lumen to a blood vessel area defined by the segmented adventitia." Atsushi focuses on calculating a simple diameter length using optical geometry. It does not disclose identifying and segmenting the specific tissue layers of the "adventitia" and "lumen" to calculate a specific numerical "area ratio" between them, nor does it define "plaque burden" by such a ratio.” as recited in claim 1, 11, and 20. However, the Examiner interprets that Atsushi teaches the main concept of using image processing on images of a blood vessel to determine a correct stent size, the additional details of the function and characteristics of the main concepts as stated above by the applicant in the amendments is taught by Schmitt in the details of the rejection below. Applicant also argues that Atsushi does not teach “identifying”, however the examiner finds that “identifying” is not present in the current limitations of the claim. The opinion in In re Hiniker Co., 47 USPQ2d 1523 (Fed. Cir. 1998) stated "...the name of the game is the claim. See Giles Sutherland Rich, Extent of Protection and Interpretation of Claims--American Perspectives , 21 Int'l Rev. Indus. Prop.& Copyright L. 497, 499 (1990) (“The U.S. is strictly an examination country and the main purpose of the examination, to which every application is subjected, is to try to make sure that what each claim defines is patentable. To coin a phrase, the name of the game is the claim.”)." The Examiner will maintain prior art Atsushi and details of the rejection are below.
In response to Argument 3, Applicant’s arguments, see Remarks, filed 01/15/2026, with respect to claims 1, 11, and 20 have been considered but are moot in view of new ground(s) of rejection caused by the amendments. However, upon further consideration, a new ground(s) of rejection is made for Claims 3-10, 13-19 under 35 U.S.C. 103 in view of Atsushi et al (JP2020185082A (using IP.com as a translation) hereafter referred to as Atsushi) in view of Schmitt et al (WO Patent Publication WO 2014/092755 Al hereafter referred to as Schmitt) in further view of Gopinath et al. (US Patent Publication US 2020/0294659 A1 hereafter referred to as Gopinath).
The Examiner finds that Gopinath teaches on the amended claim language “determine a diameter of the stent” in claims 3-10, 13-19 with the amendment changing the scope of the “plaque burden”.
Specifically, Gopinath teaches determining the diameter of the stent in ¶0014 and ¶0126, and using measurements to determine the plaque burden to be used to determine the stent size in ¶0150. Applicant argues that “Gopinath does not teach the feature to "determine a plaque burden based on an area ratio of the segmented lumen to a blood vessel area defined by the segmented adventitia" and "determine a size of a stent ... based on a distribution of the calculated plaque burden." Gopinath teaches determining stent size based on diameter measurements (EEL diameter or lumen diameter) or calcium angle/thickness. Gopinath does not disclose the specific algorithmic step of calculating "plaque burden" as the specific area ratio between the segmented lumen and the segmented adventitia (blood vessel area). Furthermore, Gopinath does not teach determining the stent size based on the distribution of this specific calculated area ratio along the vessel.” as recited in claim 1. However, the Examiner interprets that Atsushi teaches the main concept of using image processing on images of a blood vessel to determine a correct stent size, the additional details of the function and characteristics of the main concepts as stated above by the applicant in the amendments is taught by Schmitt in the details of the rejection below. The Examiner will maintain prior art Gopinath to teach the calculation and determining the diameter of the stent in the dependent claims and details of the rejection are below.
In response to Argument 4, Applicant’s arguments, see Remarks, filed 01/15/2026, with respect to claims 1, 11, and 20 have been considered but are moot in view of new ground(s) of rejection caused by the amendments. However, upon further consideration, a new ground(s) of rejection is made for Claims 1, 11, and 20 and its dependent claims under 35 U.S.C. 103 in view of Atsushi et al (JP2020185082A (using IP.com as a translation) hereafter referred to as Atsushi) in view of Schmitt et al (WO Patent Publication WO 2014/092755 Al hereafter referred to as Schmitt).
In response to Argument 5, Applicant’s arguments, see Remarks, filed 01/15/2026, with respect to claim 21 have been fully considered and are persuasive. Therefore, the claim is objected to based on its dependency on claim 1.
In response to Argument 6, Applicant’s arguments, see Remarks, filed 01/15/2026, with respect to claim 22 have been fully considered and are persuasive. Therefore, the claim is objected to based on its dependency on claim 1.
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, 11, and 20 are rejected under 35 U.S.C. 103 as unpatentable over Atsushi et al (JP2020185082A (using IP.com as a translation) hereafter referred to as Atsushi) in view of Schmitt et al (WO Patent Publication WO 2014/092755 Al hereafter referred to as Schmitt).
Regarding Claim 1, Atsushi teaches a medical system (Atsushi Pg 2 ¶03, Fig 1 discloses a blood vessel diameter measuring system) comprising:
a catheter that includes a sensor (Atsushi Pg 5 ¶04, Pg 5 ¶08 discloses an angioscopic catheter with a vascular endoscope camera with an image sensor attached to the end) and can be inserted into a blood vessel (Atsushi Pg 5 ¶04 discloses the catheter being inserted and retreated in a blood vessel, which is a form of luminal organ);
a display apparatus (Atsushi Pg 5 ¶01, discloses a 3D display on a monitor); and
an image processing apparatus (Atsushi Pg 6 ¶03 discloses an image processing unit) configured to:
generate an image of the luminal organ (Atsushi Pg 6 ¶01 discloses generating an image of the inner wall of the blood vessel) based on a signal output from the sensor of the catheter (Atsushi Pg 6 ¶01 discloses the signal output being the captured image signal),
input the generated image (Atsushi Pg 6 ¶01 discloses generating an image of the inner wall of the blood vessel) to a machine learning model (Atsushi Pg 6 ¶05 discloses the image being input into a machine learning model that uses deep learning using said image to set the scale for the image),
determine a size of a stent to be implanted into the blood vessel (Atsushi Pg 9 ¶08, Pg 11 ¶03, Pg 3 ¶06 discloses that the processor can determine the size of the stent to be inserted into the blood vessel based on the blood vessel diameter),
cause the display apparatus (Atsushi Pg 5 ¶01, discloses a 3D display on a monitor) to display information indicating the determined size of the stent (Atsushi Pg 11 ¶02, Pg 3 ¶05 discloses the diameter of the blood vessel being displayed allowing the size of the stent to be selected).
Atsushi does not explicitly disclose trained to segment an adventitia and a lumen of the blood vessel and acquire an output indicating the segmented adventitia and lumen
determine a plaque burden based on an area ratio of the segmented lumen to a blood vessel area based on defined by the segmented adventitia
based on a distribution of the calculated plaque burden along the blood vessel.
Schmitt is in the same field of blood vessel medical image analysis. Further, Schmitt teaches trained to segment an adventitia (Schmitt ¶0066 discloses a training process that identifies the range of parameters that highlight the specular characteristics, especially the angular and size-intensity variation of the OA (outer adventitia) region ¶0071 and Fig 10b disclose the segmentation of the outer adventitia area) and a lumen of the blood vessel (Schmitt ¶0083, discloses lumen segments used to determine lumen diameter) and acquire an output indicating the segmented adventitia (Schmitt ¶0071 and Fig 10b disclose the segmentation of the outer adventitia area indicating a normality )and lumen (Schmitt ¶0083, discloses lumen segments used to determine lumen diameter as output),
determine a plaque burden (Schmitt ¶0037, ¶0056 discloses determining a plaque burden) based on an area ratio of the segmented lumen (Schmitt ¶0074, ¶0079 discloses calculating the current lumen ratio) to a blood vessel area (Schmitt ¶0054 discloses the area of the cross section of the vessel based on previous calculations using segments) based on defined by the segmented adventitia (Schmitt ¶0071 and Fig 10b disclose the segmentation of the outer adventitia area),
based on a distribution of the calculated plaque burden along the blood vessel (Schmitt ¶0037, ¶0056, ¶0084, and Fig 3 and Fig 4 disclose calculating the plaque burdens and the difference in area between the segments of the distal and proximal end of the blood vessel).
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 Atsushi by using the segmentation of the different parts of the blood vessel to calculate the plaque burden on the blood vessel and the distribution of the burden along the blood vessel as taught by Schmitt, to make an invention that can automatically be inserted into the blood vessel and determine the best size location for the stent based on the plaque in the vessels; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need for a simple and fast method for applying intravascular imaging information to properly size and deploy stents to yield the best possible restoration of the normal vessel contours. (Schmitt ¶0003).
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 11, Atsushi teaches a method for processing a medical image (Atsushi Pg 1 ¶01, ¶04 Pg 3 ¶07 discloses a method for measuring the blood vessel diameter in the image of the blood vessel) of a blood vessel (Atsushi Pg 5 ¶04 discloses the catheter being inserted and retreated in a blood vessel, which is a form of luminal organ), comprising:
generating an image of the blood vessel (Atsushi Pg 6 ¶01 discloses generating an image of the inner wall of the blood vessel) based on a signal output from a sensor of a catheter(Atsushi Pg 6 ¶01 discloses the signal output being the captured image signal) inserted into the blood vessel (Atsushi Pg 5 ¶04 discloses the catheter being inserted and retreated in a blood vessel, which is a form of luminal organ);
inputting the generated image (Atsushi Pg 6 ¶01 discloses generating an image of the inner wall of the blood vessel)
determining a size of a stent to be implanted into the blood vessel (Atsushi Pg 9 ¶08, Pg 11 ¶03, Pg 3 ¶06 discloses that the processor can determine the size of the stent to be inserted into the blood vessel based on the blood vessel diameter)
and
displaying information indicating the determined size of the stent (Atsushi Pg 11 ¶02, Pg 3 ¶05 discloses the diameter of the blood vessel being displayed allowing the size of the stent to be selected).
Atsushi does not explicitly disclose to a machine learning model trained to segment an adventitia and a lumen of the blood vessel and acquiring an output indicating the segmented adventitia and lumen,
determining a plaque burden based on an area ratio of the segmented lumen to a blood vessel area based on defined by the segmented adventitia,
determining a plaque burden based on an area ratio of the segmented lumen to a blood vessel area based on defined by the segmented adventitia,
based on a distribution of the calculated plague burden along the blood vessel.
Schmitt is in the same field of blood vessel medical image analysis. Further, Schmitt teaches a machine learning model trained to segment an adventitia (Schmitt ¶0066 discloses a training process that identifies the range of parameters that highlight the specular characteristics, especially the angular and size-intensity variation of the OA (outer adventitia) region ¶0071 and Fig 10b disclose the segmentation of the outer adventitia area) and a lumen of the blood vessel (Schmitt ¶0083, discloses lumen segments used to determine lumen diameter) and acquiring an output indicating the segmented adventitia (Schmitt ¶0071 and Fig 10b disclose the segmentation of the outer adventitia area indicating a normality )and lumen (Schmitt ¶0083, discloses lumen segments used to determine lumen diameter as output),
determining a plaque burden (Schmitt ¶0037, ¶0056 discloses determining a plaque burden) based on an area ratio of the segmented lumen (Schmitt ¶0074, ¶0079 discloses calculating the current lumen ratio) to a blood vessel area (Schmitt ¶0054 discloses the area of the cross section of the vessel based on previous calculations using segments) based on defined by the segmented adventitia (Schmitt ¶0071 and Fig 10b disclose the segmentation of the outer adventitia area),
determining a plaque burden (Schmitt ¶0037, ¶0056 discloses determining a plaque burden) based on an area ratio of the segmented lumen (Schmitt ¶0074, ¶0079 discloses calculating the current lumen ratio) to a blood vessel area (Schmitt ¶0054 discloses the area of the cross section of the vessel based on previous calculations using segments) based on defined by the segmented adventitia (Schmitt ¶0071 and Fig 10b disclose the segmentation of the outer adventitia area),
based on a distribution of the calculated plague burden along the blood vessel (Schmitt ¶0037, ¶0056, ¶0084, and Fig 3 and Fig 4 disclose calculating the plaque burdens and the difference in area between the segments of the distal and proximal end of the blood vessel).
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 Atsushi by using the segmentation of the different parts of the blood vessel to calculate the plaque burden on the blood vessel and the distribution of the burden along the blood vessel as taught by Schmitt, to make an invention that can automatically be inserted into the blood vessel and determine the best size location for the stent based on the plaque in the vessels; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need for a simple and fast method for applying intravascular imaging information to properly size and deploy stents to yield the best possible restoration of the normal vessel contours. (Schmitt ¶0003).
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 20, Atsushi teaches a medical image processing apparatus (Atsushi Pg 2 ¶01, Pg 5 ¶03, Pg 6 ¶03 discloses a blood vessel diameter measuring device that uses images of the blood vessel and include processing steps) comprising:
an interface circuit connectable (Atsushi Pg 6 ¶06 discloses an electronic circuit in the form of a processor Pg 10 ¶03 discloses the PC connected to the endoscope) to a display apparatus (Atsushi Pg 5 ¶01, discloses a 3D display on a monitor) and a catheter that includes a sensor (Atsushi Pg 5 ¶04, Pg 5 ¶08 discloses an angioscopy catheter with a vascular endoscope camera with an image sensor attached to the end) and can be inserted into a blood vessel (Atsushi Pg 5 ¶04 discloses the catheter being inserted and retreated in a blood vessel, which is a form of luminal organ) ; and
a processor (Atsushi Pg 3 ¶05-¶07 discloses a processor performing many functions) configured to:
generate an image of the blood vessel (Atsushi Pg 6 ¶01 discloses generating an image of the inner wall of the blood vessel) based on a signal output from the sensor of the catheter (Atsushi Pg 6 ¶01 discloses the signal output being the captured image signal),
input the generated image (Atsushi Pg 6 ¶01 discloses generating an image of the inner wall of the blood vessel)
determine a size of a stent to be implanted into the blood vessel (Atsushi Pg 9 ¶08, Pg 11 ¶03, Pg 3 ¶06 discloses that the processor can determine the size of the stent to be inserted into the blood vessel based on the blood vessel diameter)
cause the display apparatus (Atsushi Pg 5 ¶01, discloses a 3D display on a monitor) to display information indicating the determined size of the stent (Atsushi Pg 11 ¶02, Pg 3 ¶05 discloses the diameter of the blood vessel being displayed allowing the size of the stent to be selected).
Atsushi does not explicitly disclose a machine learning model trained to segment an adventitia and a lumen of the blood vessel and acquire an output indicating the segmented adventitia and lumen,
determine a plaque burden based on an area ratio of the segmented lumen to a blood vessel area based on defined by the segmented adventitia.
Schmitt is in the same field of blood vessel medical image analysis. Further, Schmitt teaches a machine learning model trained to segment an adventitia (Schmitt ¶0066 discloses a training process that identifies the range of parameters that highlight the specular characteristics, especially the angular and size-intensity variation of the OA (outer adventitia) region ¶0071 and Fig 10b disclose the segmentation of the outer adventitia area) and a lumen of the blood vessel (Schmitt ¶0083, discloses lumen segments used to determine lumen diameter) and acquire an output indicating the segmented adventitia (Schmitt ¶0071 and Fig 10b disclose the segmentation of the outer adventitia area indicating a normality )and lumen (Schmitt ¶0083, discloses lumen segments used to determine lumen diameter as output),
determine a plaque burden (Schmitt ¶0037, ¶0056 discloses determining a plaque burden) based on an area ratio of the segmented lumen (Schmitt ¶0074, ¶0079 discloses calculating the current lumen ratio) to a blood vessel area (Schmitt ¶0054 discloses the area of the cross section of the vessel based on previous calculations using segments) based on defined by the segmented adventitia (Schmitt ¶0071 and Fig 10b disclose the segmentation of the outer adventitia area);
based on a distribution of the calculated plaque burden along the blood vessel (Schmitt ¶0037, ¶0056, ¶0084, and Fig 3 and Fig 4 disclose calculating the plaque burdens and the difference in area between the segments of the distal and proximal end of the blood vessel).
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 Atsushi by using the segmentation of the different parts of the blood vessel to calculate the plaque burden on the blood vessel and the distribution of the burden along the blood vessel as taught by Schmitt, to make an invention that can automatically be inserted into the blood vessel and determine the best size location for the stent based on the plaque in the vessels; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need for a simple and fast method for applying intravascular imaging information to properly size and deploy stents to yield the best possible restoration of the normal vessel contours. (Schmitt ¶0003).
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 3-10 and 13-19 are rejected under 35 U.S.C. 103 as unpatentable over Atsushi et al in view Schmitt in further view of Gopinath et al. (US Patent Publication US 2020/0294659 A1 hereafter referred to as Gopinath).
Regarding Claim 3, Atsushi in view of Schmitt discloses the medical system according to claim 1, wherein the image processing apparatus (Atsushi Pg 6 ¶03 discloses an image processing unit) is configured to:
specify a lesion in the blood vessel (Schmitt ¶0061 discloses specifying the most severe portion of the lesion for use in choosing a stent).
Atsushi in view of Schmitt does not explicitly disclose calculate a plurality of plaque burdens along the blood vessel having a maximum plaque burden,
specify reference portions of the blood vessel located on a distal side and a proximal side with respect to the lesion
determine diameters of the specified reference portions and
determine a diameter of the stent to be displayed based on the determined diameters of the reference portions.
Gopinath is in the same field of blood vessel medical image analysis. Further, Gopinath teaches calculate a plurality of plaque burdens along the blood vessel (Gopinath ¶0150 discloses using measurements to generate ratings or scores to communicate to the user the plaque burden in a region of the blood vessel), having a maximum plaque burden (Gopinath ¶0024, ¶0097 discloses finding the maximum thickness of the plaque which is used to determine the burden),
specify reference portions of the blood vessel located on a distal side and a proximal side (Gopinath Fig 2D Y an B display the reference line used to measure the distance between the proximal and distal side of the blood vessel) with respect to the lesion (Gopinath ¶0098 discloses providing information on lesion preparation),
determine diameters (Gopinath¶0014 discloses displaying a diameter value for both a proximal and distal reference) of the specified reference portions (Gopinath ¶0014, Fig 2D Y an B display the reference line used to measure the distance between the proximal and distal side of the blood vessel), and
determine a diameter of the stent to be displayed (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel) based on the determined diameters of the reference portions (Gopinath ¶0014, Fig 2D Y an B display the reference line used to measure the distance between the proximal and distal side of the blood vessel).
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 Atsushi in view of Schmitt by using the machine learning in Atsushi to identify the region of blood vessel with plaque to be able to calculate the burden and location of the plaque to determine the correct stent size and placement as taught by Gopinath, to make an invention that can automatically be inserted into the blood vessel and determine the best location for the stent; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to address technical challenges faced when diagnosing the plaque obstruction using a machine learning system while the patient is still catheterized and prepared to receive a stent or other treatment option resulting in significant time savings and improvements in patient outcomes (Gopinath ¶0083-¶0084).
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, Atsushi in view of Schmitt in view of Gopinath discloses the medical system according to claim 3, wherein the image processing apparatus (Atsushi Pg 6 ¶03 discloses an image processing unit) is configured to:
determine a landing zone of the stent (Gopinath ¶0021, ¶0046, Fig 3A-3D discloses selecting stent based on the diameter and other characteristics of the vessel) based on locations of the reference portions (Gopinath ¶0014, Fig 2D Y an B display the reference line used to measure the distance between the proximal and distal side of the blood vessel), and
determine a length of the stent (Gopinath ¶0046 and Figs 3A-3D discloses a stent sizing workflow for detecting length) corresponding to the determined landing zone (Gopinath ¶0021, ¶0046, Fig 3A-3D discloses selecting stent based on the diameter and other characteristics of the vessel). See Claim 3 for rational, its parent claim.
Regarding Claim 5, Atsushi in view of Schmitt in view of Gopinath discloses the medical system according to claim 3, wherein the image processing apparatus (Atsushi Pg 6 ¶03 discloses an image processing unit) is configured to:
receive an input (Atsushi Pg 7 ¶08 discloses the being a tilt in the z direction) for correcting the diameters of the reference portions (Atsushi Pg 7 ¶08 discloses performing correction on the image based on the tilt of the camera and sensor), and
determine the diameter of the stent (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel) based on the corrected diameters of the reference portions (Atsushi Pg 7 ¶08 discloses the being a tilt in the z direction and the correction being applied to the diameter calculation of the vessel so that the calculated diameter is not unintentionally longer due to the ellipsis shape from a tilt). See Claim 3 for rational, its parent claim.
Regarding Claim 6, Atsushi in view of Schmitt in view of Gopinath discloses the medical system according to claim 3, wherein the image processing apparatus (Atsushi Pg 6 ¶03 discloses an image processing unit) is configured to:
receive a selection of one of methods (Gopinath ¶0021, ¶0024 discloses selecting an option for the workflow of stent sizing) for calculating the diameter of the stent (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel), and
calculate the diameter of the stent (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel) using the selected method (Gopinath ¶0021, ¶0024 discloses selecting an option for the workflow of stent sizing). See Claim 3 for rational, its parent claim.
Regarding Claim 7, Atsushi in view of Schmitt in view of Gopinath discloses the medical system according to claim 3, wherein the image processing apparatus (Atsushi Pg 6 ¶03 discloses an image processing unit) is configured to:
store information (Gopinath ¶0127 discloses storing information) indicating a plurality of types of stent (Gopinath ¶0052, ¶0083, ¶0098 discloses the types of stents available for selection based on size) each associated with a diameter thereof (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel),
select one of the types of stent (Gopinath ¶0052, ¶0083, ¶0098 discloses the types of stents available for selection based on size) corresponding to the determined diameter of the stent (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel), and
cause the display apparatus (Atsushi Pg 5 ¶01, discloses a 3D display on a monitor) to display information indicating the selected one(Gopinath ¶0052, ¶0083, ¶0098 discloses the types of stents available for selection based on size) of the types of stent (Atsushi Pg 11 ¶02, Pg 3 ¶05 discloses the diameter of the blood vessel being displayed allowing the size of the stent to be selected). See Claim 3 for rational, its parent claim.
Regarding Claim 8, Atsushi in view of Schmitt in view of Gopinath discloses the medical system according to claim 7, wherein the image processing apparatus(Atsushi Pg 6 ¶03 discloses an image processing unit) is configured to:
store information (Gopinath ¶0030, ¶0127 discloses storing information) indicating an availability of each of the types of stent (Gopinath ¶0052, ¶0083, ¶0098 discloses the types of stents available for selection based on size), and select said one of the types (Gopinath ¶0021, ¶0024 discloses selecting an option for the workflow of stent sizing) of stent based on the availability (Gopinath ¶0052, ¶0083, ¶0098 discloses the types of stents available for selection based on size). See Claim 3 for rational, its parent claim.
Regarding Claim 9, Atsushi in view of Schmitt discloses the medical system according to claim 1, wherein the image processing apparatus (Atsushi Pg 6 ¶03 discloses an image processing unit) is configured to:
specify a lesion in the plaque (Schmitt ¶0061 discloses specifying the most severe portion of the lesion for use in choosing a stent).
Atsushi in view of Schmitt does not explicitly disclose calculate a plurality of plaque burdens along the blood vessel, having a plaque burden that is greater than or equal to a threshold value,
specify healthy portions of the blood vessel located on a distal side and a proximal side with respect to the lesion and having a plaque burden less than the threshold value,
determine a landing zone of the stent based on locations of the healthy portions, and
determine a length of the stent corresponding to the determined landing zone.
Gopinath is in the same field of blood vessel medical image analysis. Further, Gopinath teaches calculate a plurality of plaque burdens along the blood vessel (Gopinath ¶0150 discloses using measurements to generate ratings or scores to communicate to the user the plaque burden in a region of the blood vessel),
having a plaque burden that is greater than or equal to a threshold value (Gopinath ¶0089 discloses calcium being detected and the threshold was met or succeeded),
specify healthy portions (Gopinath ¶0007, ¶0081 discloses how the healthy positions of the vessel and differentiated from the unhealthy portions in the machine learning algorithm)of the blood vessel located on a distal side and a proximal side (Gopinath ¶0014, Fig 2D Y an B display the reference line used to measure the distance between the proximal and distal side of the blood vessel) with respect to the lesion (Gopinath ¶0098 discloses providing information on lesion preparation) and having a plaque burden less than the threshold value (Gopinath ¶0110 discloses setting threshold values to being less then to determine the significance of a feature in a frame),
determine a landing zone of the stent (Gopinath ¶0021, ¶0046, Fig 3A-3D discloses selecting stent based on the diameter and other characteristics of the vessel) based on locations of the healthy portions (Gopinath ¶0007, ¶0081 discloses how the healthy positions of the vessel and differentiated from the unhealthy portions in the machine learning algorithm), and
determine a length of the stent (Gopinath ¶0046 and Figs 3A-3D discloses a stent sizing workflow for detecting length) corresponding to the determined landing zone (Gopinath ¶0021, ¶0046, Fig 3A-3D discloses selecting stent based on the diameter and other characteristics of the vessel).
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 Atsushi in view of Schmitt by using the machine learning in Atsushi to identify the region of blood vessel with plaque to be able to calculate the burden and location of the plaque to determine the correct stent size and placement as taught by Gopinath, to make an invention that can automatically be inserted into the blood vessel and determine the best location for the stent; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to address technical challenges faced when diagnosing the plaque obstruction using a machine learning system while the patient is still catheterized and prepared to receive a stent or other treatment option resulting in significant time savings and improvements in patient outcomes (Gopinath ¶0083-¶0084).
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 10, Atsushi in view of Schmitt in view of Gopinath discloses the medical system according to claim 9, wherein the image processing apparatus (Atsushi Pg 6 ¶03 discloses an image processing unit) is configured to:
receive an input for correcting (Atsushi Pg 7 ¶08 discloses performing correction on the image based on the tilt of the camera and sensor) the locations of the healthy portions(Gopinath ¶0007, ¶0081 discloses how the healthy positions of the vessel and differentiated from the unhealthy portions in the machine learning algorithm) , and
determine the length of the stent (Gopinath ¶0046 and Figs 3A-3D discloses a stent sizing workflow for detecting length) based on the corrected (Atsushi Pg 7 ¶08 discloses performing correction on the image based on the tilt of the camera and sensor) locations of the healthy portions (Gopinath ¶0007, ¶0081 discloses how the healthy positions of the vessel and differentiated from the unhealthy portions in the machine learning algorithm). See rationale for Claim 9, its parent claim.
Regarding Claim 13, Atsushi in view of Schmitt discloses the method according to claim 11 (Atsushi Pg 1 ¶01, ¶04 Pg 3 ¶07 discloses a method for measuring the blood vessel diameter in the image of the blood vessel) , further comprising:
specifying a lesion in the blood vessel (Schmitt ¶0061 discloses specifying the most severe portion of the lesion for use in choosing a stent).
Atsushi in view of Schmitt does not explicitly disclose calculating a plurality of plaque burdens along the blood vessel;having a maximum plaque burden;
specifying reference portions of the blood vessel located on a distal side and a proximal side with respect to the lesion;
determining diameters of the specified reference portions; and
determining a diameter of the stent to be displayed based on the determined diameters of the reference portions.
Gopinath is in the same field of blood vessel medical image analysis. Further, Gopinath teaches calculating a plurality of plaque burdens along the blood vessel (Gopinath ¶0150 discloses using measurements to generate ratings or scores to communicate to the user the plaque burden in a region of the blood vessel);having a maximum plaque burden (Gopinath ¶0024, ¶0097 discloses finding the maximum thickness of the plaque which is used to determine the burden);
specifying reference portions of the blood vessel located on a distal side and a proximal side (Gopinath Fig 2D Y an B display the reference line used to measure the distance between the proximal and distal side of the blood vessel) with respect to the lesion (Gopinath ¶0098 discloses providing information on lesion preparation);
determining diameters (Gopinath¶0014 discloses displaying a diameter value for both a proximal and distal reference) of the specified reference portions (Gopinath ¶0014, Fig 2D Y an B display the reference line used to measure the distance between the proximal and distal side of the blood vessel); and
determining a diameter of the stent to be displayed (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel) based on the determined diameters of the reference portions (Gopinath ¶0014, Fig 2D Y an B display the reference line used to measure the distance between the proximal and distal side of the blood vessel).
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 Atsushi in view of Schmitt by using the machine learning in Atsushi to identify the region of blood vessel with plaque to be able to calculate the burden and location of the plaque to determine the correct stent size and placement as taught by Gopinath, to make an invention that can automatically be inserted into the blood vessel and determine the best location for the stent; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to address technical challenges faced when diagnosing the plaque obstruction using a machine learning system while the patient is still catheterized and prepared to receive a stent or other treatment option resulting in significant time savings and improvements in patient outcomes (Gopinath ¶0083-¶0084).
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 14, Atsushi in view of Schmitt in view of Gopinath discloses the method according to claim 13, further comprising:
determining a landing zone of the stent (Gopinath ¶0021, ¶0046, Fig 3A-3D discloses selecting stent based on the diameter and other characteristics of the vessel) based on locations of the reference portions (Gopinath ¶0014, Fig 2D Y an B display the reference line used to measure the distance between the proximal and distal side of the blood vessel); and
determining a length of the stent (Gopinath ¶0046 and Figs 3A-3D discloses a stent sizing workflow for detecting length) corresponding to the determined landing zone (Gopinath ¶0021, ¶0046, Fig 3A-3D discloses selecting stent based on the diameter and other characteristics of the vessel). See Claim 13 for rational, its parent claim.
Regarding Claim 15, Atsushi in view of Schmitt in view of Gopinath discloses the method according to claim 13, further comprising:
receiving an input (Atsushi Pg 7 ¶08 discloses the being a tilt in the z direction) for correcting the diameters of the reference portions (Atsushi Pg 7 ¶08 discloses performing correction on the image based on the tilt of the camera and sensor); and
determining the diameter of the stent (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel) based on the corrected diameters of the reference portions (Atsushi Pg 7 ¶08 discloses the being a tilt in the z direction and the correction being applied to the diameter calculation of the vessel so that the calculated diameter is not longer due to the ellipsis shape from a tilt). See Claim 13 for rational, its parent claim.
Regarding Claim 16, Atsushi in view of Schmitt in view of Gopinath discloses the method according to claim 13, further comprising:
receiving a selection of one of methods (Gopinath ¶0021, ¶0024 discloses selecting an option for the workflow of stent sizing) for calculating the diameter of the stent (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel), wherein the diameter of the stent is determined (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel) using the selected method (Gopinath ¶0021, ¶0024 discloses selecting an option for the workflow of stent sizing). See Claim 13 for rational, its parent claim.
Regarding Claim 17, Atsushi in view of Schmitt in view of Gopinath discloses the method according to claim 13, further comprising:
storing information (Gopinath ¶0127 discloses storing information) indicating a plurality of types of stent (Gopinath ¶0052, ¶0083, ¶0098 discloses the types of stents available for selection based on size) each associated with a diameter thereof (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel);
selecting one of the types of stent (Gopinath ¶0052, ¶0083, ¶0098 discloses the types of stents available for selection based on size) corresponding to the determined diameter of the stent (Gopinath ¶0126 discloses determining stent selection based off of the diameter of the blood vessel); and
displaying information indicating the selected one (Gopinath ¶0052, ¶0083, ¶0098 discloses the types of stents available for selection based on size) of the types of stent (Atsushi Pg 11 ¶02, Pg 3 ¶05 discloses the diameter of the blood vessel being displayed allowing the size of the stent to be selected). See Claim 13 for rational, its parent claim.
Regarding Claim 18, Atsushi in view of Schmitt in view of Gopinath discloses the method according to claim 17, further comprising:
storing information (Gopinath ¶0030, ¶0127 discloses storing information) indicating an availability of each of the types of stent (Gopinath ¶0052, ¶0083, ¶0098 discloses the types of stents available for selection based on size), wherein said one of the types (Gopinath ¶0021, ¶0024 discloses selecting an option for the workflow of stent sizing) of stent based on the availability (Gopinath ¶0052, ¶0083, ¶0098 discloses the types of stents available for selection based on size) See Claim 13 for rational, its parent claim.
Regarding Claim 19, Atsushi in view of Schmitt discloses the method according to claim 11 (Atsushi Pg 1 ¶01, ¶04 Pg 3 ¶07 discloses a method for measuring the blood vessel diameter in the image of the blood vessel), further comprising:
specifying a lesion in the blood vessel (Schmitt ¶0061 discloses specifying the most severe portion of the lesion for use in choosing a stent).
Atsushi in view of Schmitt does not explicitly disclose calculating a plurality of plaque burdens along the blood vessel;
having a plaque burden that is greater than or equal to a threshold value;
specifying healthy portions of the blood vessel located on a distal side and a proximal side with respect to the lesion and having a plaque burden less than the threshold value;
determining a landing zone of the stent based on locations of the healthy portions; and
determining a length of the stent corresponding to the determined landing zone.
Gopinath is in the same field of blood vessel medical image analysis. Further, Gopinath teaches calculating a plurality of plaque burdens along the blood vessel (Gopinath ¶0150 discloses using measurements to generate ratings or scores to communicate to the user the plaque burden in a region of the blood vessel);
having a plaque burden that is greater than or equal to a threshold value (Gopinath ¶0089 discloses calcium being detected and the threshold was met or succeeded);
specifying healthy portions (Gopinath ¶0007, ¶0081 discloses how the healthy positions of the vessel and differentiated from the unhealthy portions in the machine learning algorithm)of the blood vessel located on a distal side and a proximal side (Gopinath ¶0014, Fig 2D Y an B display the reference line used to measure the distance between the proximal and distal side of the blood vessel) with respect to the lesion (Gopinath ¶0098 discloses providing information on lesion preparation) and having a plaque burden less than the threshold value (Gopinath ¶0110 discloses setting threshold values to being less then to determine the significance of a feature in a frame);
determining a landing zone of the stent (Gopinath ¶0021, ¶0046, Fig 3A-3D discloses selecting stent based on the diameter and other characteristics of the vessel) based on locations of the healthy portions (Gopinath ¶0007, ¶0081 discloses how the healthy positions of the vessel and differentiated from the unhealthy portions in the machine learning algorithm); and
determining a length of the stent (Gopinath ¶0046 and Figs 3A-3D discloses a stent sizing workflow for detecting length) corresponding to the determined landing zone (Gopinath ¶0021, ¶0046, Fig 3A-3D discloses selecting stent based on the diameter and other characteristics of the vessel).
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 Atsushi in view of Schmitt by using the machine learning in Atsushi to identify the region of blood vessel with plaque to be able to calculate the burden and location of the plaque to determine the correct stent size and placement as taught by Gopinath, to make an invention that can automatically be inserted into the blood vessel and determine the best location for the stent; thus one of ordinary skilled in the art would be motivated to combine the references since there is a need to address technical challenges faced when diagnosing the plaque obstruction using a machine learning system while the patient is still catheterized and prepared to receive a stent or other treatment option resulting in significant time savings and improvements in patient outcomes (Gopinath ¶0083-¶0084).
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.
Allowable Subject Matter
Claims 21 and 22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL LYNN ROBERTS whose telephone number is (571)272-6413. The examiner can normally be reached Monday- Friday 7:30am- 5:00pm.
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/RACHEL L ROBERTS/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674