DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP 23152170.9, filed on 01/18/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/28/2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
FIG. 1: Although this figure includes the reference characters “PAT”, “MD”, “p(α)”, these reference characters do not appear in the specification.
FIG. 2: Although this figure includes the reference characters “OF” and “IF” these reference characters do not appear in the specification.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
[Page 2, Lines 13-15]: As written it reads “In embodiments, the said medical quantity of interest includes any one of more of: volumetric flow, propagation time of contrast agent through at least a section of said conduit, liquid-contrast agent mixing behavior, and transluminal attenuation gradient”. However, to correct the typo, the underlined “of” should be changed to “or”.
[Page 2, Line 34]: As written it reads “In embodiments, the spectral projection imagery includes a times series of frames”. However, to correct the typo, the examiner believes that “times” should be “timed”.
[Page 3, Lines 7-9]: As written it reads “The second imaging apparatus need to be configured to spectral imaging, but may well be so in some embodiments, if required”. The examiner believes that “need” should be either “needs” or “need not”.
[Page 3, Lines 25-28]: As written it reads “It is proposed herein to combine the spectral (such as dual energy) X-ray projection image data with additionally sourced 3D lumen information in respect of the conduit, such as by using intravascular ultrasound (IVUS) data, OTC data, or other additional non-ionizing imaging modality to enable accurate dilution quantification and bolus propagation tacking, etc.”. However, this is the first indication of the term “OTC” therefore, the term should be spelled out to provide clarity. The examiner believes that “OTC” is a typo which should be “OCT”. If true, the examiner would recommend, amending all instances of the term “OTC” to “OCT”. Furthermore, the examiner believes “tacking” is a typo which should be “tracking”.
[Page 4, Lines 23-34]: As written it reads “As used herein, the said conduit is preferable and moistly a part of vasculature (an artery or vein) but in some applications the conduit may a part of contrast agent delivery equipment, such as portion of a catheter or tube through whish contrast agent is released into the vasculature or other body part of interest”. However, to correct the typos “moistly” should be “mostly”, and “whish” should be “which”. Furthermore, to be grammatically correct “may a part” should be “may be a part” and “such as portion” should be “such as a portion”.
[Page 4, Lines 32-33]: As written it reads “Thus, if the only the initial concentration is required, no second imaging apparatus is needed. “user” relates to a person […]”. However, to be grammatically correct “the only” should be “only”. Additionally, the examiner believes the sentence beginning with “user” should recite “The term “user” relates to a person […]”.
[Page 4, Line 37-Page 5, Line 1]: As written it reads “Other ML operate direct on training data, not necessarily using such as model”. However, to be grammatically correct “direct” should be “directly” and “as” should be “as a”.
[Page 7, Line 1]: As written it reads “period of time contrast agent accumulates at the region of interest such as in or around the stricture”. However, to correct the typo “stricture” should be “structure”.
[Page 8, Lines 4-5]: As written it reads “However, preferably, the spectral imagery include a contrast-only used, The monochromatic image […]”. However, to be grammatically correct “include” should be “includes”. Furthermore, this sentence should end with a period “.”.
[Page 8, Lines 27-30]: As written it reads “In spite of the multi-directional imaging capability of the preferably rotational imaging apparatus IA, processing of projection imagery solely in projection domain is mainly envisaged herein in embodiments, in addition to sourced 3D information that is generally computationally cheaper to procure than tomographic reconstructions as will be explored on more detail below”. However, to correct the typo, “on” should be “in”.
[Page 9, Lines 17-18]: As written it reads “In particular, it is amount of contrast agent in the lumen L that is of main interest”. However, to be grammatically correct “it is amount” should be “it is the amount”.
[Page 10, Lines 3-5]: As written it reads “The system may further facilitate determining one more blood flow related diagnostic quantities based on said contrast agent concentration”. However, to be grammatically correct “one more” should be “one or more”.
[Page 10, Lines 13-14]: As written it reads “The specifically, contrast in the spectral X-ray imagery may be modulated by the mass of the contrast agent present in the ROI”. However, to be grammatically correct the underlined “The” should be deleted and “specifically” should be capitalized.
[Page 11, Lines 32-34]: As written it reads “For example, the control signal m- m-OUT, may be provided as a feedback system to control stetting of one or moth of the imagers IA2 IA2, of the measurement device (such as in-situ pressure measurement device), etc.”. However, to be grammatically correct “stetting” should be “setting” and the underlined instance of “IA2” should be “IA1”.
[Page 12, Lines 17-18]: As written it reads “Suitable US technologies may include PMUTs or CMUTs”. However, this is the first indication of the terms “PMUTs” and “CMUTs” therefore, the terms should be spelled out to provide clarity.
[Page 12, Lines 22-23]: As written it reads “Similarly, and also invasively, using a suitable light probe, optical imagery may be used in OTC to obtain the 3D structure information”. However, the examiner believes “OTC” is a typo which should instead be “OCT”.
[Page 12, Line 31]: As written it reads “such as MRI, CT, CTA or others”. However, this is the first indication of the terms MRI and CTA, therefore, the terms should be spelled out to provide clarity.
[Page 12, Line 36-Page 13, Line 2] As written it reads “IN particular, the combination of IVUS imagery and angiographic spectral data have been found to have particular synergy in producing accurate and realistic contrast agent concentration estimates”. However, to correct the typo “IN” should be “In”.
[Page 15, Lines 26-27]: As written it reads “In a regressional setup, OL may be fully connected layer”. However to be grammatically correct, “may be fully” should be “may be a fully”.
[Page 16, Lines 22-23]: As written it reads “Training data (x,y) can be procured from existing medical data such as may be found in a medical data bases, such a PACS, HIS or other data base or storage system”. However, to correct the typos “data bases” should be “databases” and “a” (underlined above) should be “as”. Additionally, this is the first indication of the terms “PACS” and “HIS”, therefore, the terms should be spelled out to provide clarity.
[Page 18, Lines 16-19]: As written it reads “BN operators allow mitigating vanishing gradient effect, the gradual reduction of gradient magnitude in the repeated forward and backward passes experienced during gradient-based learning algorithms in the learning phase of the model M The batch normalization operators BN may be used in training, but may also be used in deployment”. However, there should be a period between “M” and “The” (underlined).
[Page 21, Lines 1-2]: As written it reads “In step 1120, contrast agent mass M1 extraction may be done by threshold-based segmentation or other”. However, the examiner believes 1120 is a typo, since none of the figures include a step 1120.
[Page 21, Line 34-Page 22, Line 1]: As written it reads “Various refinements of the method in FIG. 6 are envisaged herein which will now be explained. In step S740, for determining the blood-contrast agent mixing ratio RB1 […]”. However, step S740 is within FIG. 7. Therefore, is appears that “FIG. 6” (underlined) is a typo.
[Page 22, Lines 22-23]: As written it reads “This allows disease quantification indirectly for the microvasculature by calculating e.g., and IMR index”. However, this is the first indication of the term “IMR”. Therefore the term should be spelled out to provide clarity.
[Page 22, Lines 25-27]: As written it reads “For the volumetric flow and/or the bolus propagation time, with the IVUS pullback across a lesion ST (such as stenosis/calcified plaque/stent/stent with restenosis). the severity or flow relevance of the vessel structure CN can be assessed”. However, there should be a comma “,” between “restenosis)” and “the” (underlined).
[Page 23, Lines 9-11]: As written it reads “Examples for gradient-based optimizations may include gradient descent, stochastic gradient. conjugate gradients, Maximum likelihood methods, EM-maximization, Gauss-Newton, and others”. However, there should be a comma between “stochastic gradient” and “conjugate gradients”. Furthermore, this is the first indication of the term “EM” therefore, the term should be spelled out to provide clarity.
[Page 23, Line 33]: As written it reads “a hardwired IC chip”. However, this is the first indication of the term “IC” therefore, the term should be spelled out to provide clarity.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: the predictor component in claims 1, 6, and 10; and the flow assessment component in claim 4.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. That being said, the predictor component is described in the specification when it states “In embodiments, the predictor component or the flow assessment component is configured to compute an initial concentration of the contrast agent upon entering the conduit and/or a rate of entering into the conduit of the said contrast agent” [Page 2, Lines 23-25]; “In embodiments, the predictor component based on a machine learning model” [Page 3, Line 4]. Therefore, the examiner is interpreting the predictor component as being a processor/machine learning model used to calculate an initial concentration of a contrast agent. Thus, claims 1, 6 and 10 are not subject to further rejection under 35 U.S.C. 112(a)/112(b) with respect to the predictor component.
Regarding the flow assessment component, this component is described in the specification when it states “Thus, a flow assessment component FAC may be used to compute, based on the output
one or more concentration values, related flow characteristics of the liquid comprising blood and the contrast agent” [Page 11, Lines 11-13]. Therefore, the examiner is interpreting the flow assessment component as being a processor used to compute, based on the output one or more concentration values, related flow characteristics. Thus, claims 4 is not subject to further rejection under 35 U.S.C. 112(a)/112(b) with respect to the flow assessment component.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Claim Objections
Claims 1-10, 12-13, and 15 are objected to because of the following informalities:
Regarding claim 1, the claim recites “System for image processing […] a predictor component configured to predict, when the system is in use, based on the spectral 2D projection imagery and the additional intravascular image data, at least a concentration of contrast agent in the said conduit” However, the examiner would recommend amending the claim to recite “A system for image processing […]”. The examiner further notes that the inclusion of “said” appears to be redundant and thus would recommend deleting it.
Regarding claims 2-10, the claims recite “System of claim 1” (Claims 2, 4, 6, 7, 8, 9, 10); “System of claim 2” (Claim 3); “System of claim 4” (Claim 5). However, the examiner would recommend amending these claims to recite “The system of claim 1” (Claims 2, 4, 6, 7, 8, 9, 10); “The system of claim 2” (Claim 3); “The system of claim 4” (Claim 5).
Regarding claim 5, as written it reads “wherein the said medical quantity of interest includes any one of more of: volumetric flow, propagation time of contrast agent through at least a section of said conduit, liquid -contrast agent mixing behavior, and transluminal attenuation gradient”. However, to correct the typo “one of more of” should be “one or more of”. The examiner further notes that the inclusion of “said” appears to be redundant and thus would recommend deleting it.
Regarding claim 6, as written it reads "wherein predictor component is configured to compute an initial concentration of the contrast agent upon entering the conduit and/or a rate of entering into the conduit of the said contrast agent”. However, the examiner believes “the” should be between “wherein” and “predictor”.
Regarding claim 7, as written it reads “wherein the conduit is part of a vascular, urinary or lymphatic system of mammal, such as a human patient”. However, the examiner believes the word “a” should be between “of” and “mammal”.
Regarding claim 8, as written it reads “wherein the further imaging apparatus (IA2) is any one or more of: i) an intravascular ultrasound (IVUS) imaging apparatus or ii) an optical coherence tomography (OCT) imaging apparatus”. However, the examiner believes that the Applicant intended to remove the term “(IA2)” since it was removed from claim 1. While not necessary, the Applicant may also remove the terms “(IVUS)” and “(OCT)”.
Regarding claim 10, as written it reads “the predictor component based on a machine learning model”. However, to be grammatically correct, the examiner believes it should read “wherein the predictor component is based on a machine learning model”.
Regarding claim 11, the claims recite “An imaging arrangement (MIA) comprising the system of claim 1, further including the spectral X-ray imagining apparatus and the second imaging apparatus”. However, the examiner believes the user intended to delete the term “(MIA)”. Furthermore, the examiner believes “imagining” is a typo which should read “imaging”.
Regarding claim 12, the claims recite “Method of image processing […] predicting, when the system is in use, based on the spectral projection imagery and the additional intravascular image data, at least a concentration of contrast agent in the said conduit”. However, the examiner would recommend amending the claim to recite “A method of image processing […]”. The examiner further notes that the inclusion of “said” appears to be redundant and thus would recommend deleting it.
Regarding claim 13, the claim recites “Method of training the model as per claim 10 based on training data”. However, the examiner would recommend amending the claim to recite “A method of training […]”. Furthermore, to avoid antecedent basis, the examiner would recommend amending the claim to recite: “A method of training the machine learning model as per claim 10 based on training data”.
Regarding claim 15, the claim recites “At least one computer readable medium having stored thereon the program element of claim 14, or having stored thereon the machine learning model”. However, to avoid potential antecedent basis issues, the examiner would recommend amending the claim to recite “a machine learning model”. Furthermore, the examiner would recommend amending the claim to recite “At least one non-transitory computer readable medium […]”, to avoid the claim reading on a signal per se. Furthermore, the examiner would recommend amending the phrase “the program element” to be “the computer program element”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 14-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claims 14 and 15, the claims recite: “A computer program element, which, when being executed by at least one processing unit, is adapted to cause the processing unit to perform the method as per claim 12” (Claim 14) and “At least one computer readable medium having stored thereon the program element of claim 14, or having stored thereon the machine learning model” (Claim 15).
The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to a signal per se. To overcome this rejection, the examiner would recommend amending the claims to avoid them reading on a signal per se. For example, claim 15 can be amended to recite a “non-transitory” computer readable medium.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-5, 7-8, 11-12, and 14-15 is/are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Miller US 2014/0276085 A1 “Miller”.
Regarding claims 1 and 12, Miller teaches “System for image processing, comprising:” (Claim 1) (“FIG. 1 depicts an exemplary layout of an intravascular imaging system 101 as may be found, for example, in a catheter lab. An operator uses control station 110 and navigational device 125 to operate catheter 112 via patient interface module (PIM) 105. At a distal tip of catheter 112 is imaging tip 114. Computer device 120 works with PIM 105 to coordinate imaging operations. […] The image data is received by device 120 and interpreted to provide an image on monitor 103. System 101 is operable for use during diagnostic imaging of the peripheral and coronary vasculature of the patient. System 101 can be configured to automatically visualize boundary features, perform spectral analysis of vascular features, provide qualitative or quantitate blood flow data, or a combination thereof” [0034]; “In some embodiments, operation of system 101 employs a sterile, single use intravascular ultrasound imaging catheter 112. Catheter 112 is inserted into the coronary arteries and vessels of the peripheral vasculature under the guidance of angiographic system 107” [0035]. Therefore, since the intravascular imaging system 101 includes a computer device 120 which receives image data and interprets it such that it can be displayed on a monitor 103, the system 101 shown in FIG. 1 represents a system for image processing.).
“Method of image processing, comprising:” (Claim 12) (“The invention provides systems and methods for presenting intravascular images and angiographic images co-registered on a display” [0005]. Therefore, Miller discloses a method of image processing.);
“at least one input interface configured to receive input data comprising i) spectral projection imagery of a region of interest including a conduit for passage of a liquid, the spectral projection imagery obtainable in an imaging procedure by a spectral X-ray imaging apparatus with contrast agent present in the liquid, and ii) additional image data acquirable by a further imaging apparatus of the non-ionizing type being configured for intravascular imaging in parallel with the spectral X-ray imaging, the said additional intravascular image data representative of 3D information of the conduit” (Claim 1); “receiving input data comprising i) spectral projection imagery of a region of interest including a conduit for passage of a liquid, the spectral projection imagery obtainable in an imaging procedure by a spectral X-ray imagining apparatus with contrast agent present in the liquid, and ii) additional intervascular image data acquirable in parallel with the spectral X-ray imaging by a further imaging apparatus of the non-ionizing type, the said additional intravascular image data representative of 3D information of the conduit” (Claim 12) (See computer device 120 in [0034] above and “The invention provides methods of co-registering angiographic images with IVUS images. An angiography system may be used with an intravascular imaging system (e.g., OCT, IVUS, or optical-acoustic imaging). Angiography systems can be used to visualize the blood vessels by injecting a radio-opaque contrast agent into the blood vessel and imaging using X-ray based techniques such as fluoroscopy. […] In certain embodiments, angiography involves using a catheter to administer the x-ray contrast agent at the desired area to be visualized. The catheter is threaded into an artery, and the tip is advanced through the arterial system into the major coronary artery. X-ray images of the transient radio contrast distribution within the blood flowing within the coronary arteries allows visualization of the size of the artery openings” [0042]; “The angiography system can be used to detect a change. The angiography system can be used to detect the flush with saline (e.g., the temporary displacement of the radiopaque dye by the saline), the initial influx of radiopaque dye, or other such flushes. A processor that receives the angiography signal data can detect a brightness or contrast change” [0043]; “The angiography images will be co-registered to one or more images from an IVUS system or the like” [0044]; “IVUS uses a catheter with an ultrasound probe attached at the distal end. The proximal end of the catheter is attached to computerized ultrasound equipment. To visualize a vessel via IVUS, angiographic techniques are used and the physician positions the tip of a guide wire […] The physician steers the guide wire from outside the body, through angiography catheters and into the blood vessel branch to be imaged” [0045]; “The 3D vessel wall is established by doing an IVUS pull back” [0062]; “FIG. 11 shows a longitudinal planar view of the vessel in the right panel. Such a planar image along a vessel is sometimes referred to as an in-line digital view or image longitudinal display (ILD). The system captures a 3D data set that is used to present the image of tissue. An electronic apparatus within the system (e.g., PC, dedicated hardware, or firmware) stores the three dimensional data set in a tangible, non-transitory memory and renders a display 237 (e.g., on a screen or computer monitor) that includes a 2D image of the tissue” [0084].
Therefore, since the computer device 120 receives the angiography signal data and the IVUS image data such that angiography images can be co-registered with one or more images from the IVUS system (i.e. IVUS catheter 112), the computer device 120 includes at least one input interface configured to perform the step of receiving input data comprising i) spectral projection imagery of a region of interest including a conduit (i.e. blood vessel) for passage of a liquid, the spectral projection imagery obtainable in an imaging procedure by a spectral X-ray imaging apparatus (i.e. see [0042] and angiographic system 107 in FIG. 1) with contrast agent present in the liquid (i.e. x-ray contrast agent, see [0042]) and ii) additional intravascular image (i.e. IVUS images, see [0044], [0045]) data acquirable in parallel with the spectral X-ray imaging (i.e. angiographic images, see [0042], [0044]) by a further imaging apparatus of the non-ionizing type (i.e. IVUS), the said additional intravascular image data representative of 3D information of the conduit (i.e. 3D vessel wall obtained doing IVUS pull back, see [0062]); and
“a predictor component configured to predict, when the system is in use, based on the spectral 2D projection imagery and the additional intravascular image data, at least a concentration of contrast agent in the said conduit” (Claim 1); “predicting, when the system is in use, based on the spectral projection imagery and the additional intravascular image data, at least a concentration of contrast agent in the said conduit” (Claim 12) (See [0043] and [0044] as discussed above. Therefore, since the processor (i.e. computer device 120) receives the angiography signal data, and the additional intravascular image data (i.e. IVUS) in order to be co-register them and the processor is can detect contrast change (i.e. resulting from the influx of radiopaque dye, see [0043]).
Regarding claim 2, Miller discloses all features of the claimed invention as discussed with respect to claim 1 above, and Miller further teaches "wherein the 3D information is representative of a volume of a lumen of the conduit inside a region of interest” (See [0062] and [0084] as discussed in claim 1 above. Therefore, the 3D information is representative of a volume of a lumen of the conduit inside a region of interest.).
Regarding claim 4, Miller discloses all features of the claimed invention as discussed with respect to claim 1 above, and Miller further teaches "further comprising a flow assessment component configured to compute a medical quantity of interest based at least on the predicted concentration of contrast agent in the said conduit, the quantity of interest descriptive of one or more flow characteristics of the liquid” (“System 101 can be configured to automatically visualize boundary features, perform spectral analysis of vascular features, provide qualitative or quantitate blood flow data, or a combination thereof” [0034] and “Angiography systems can be used to visualize the blood vessels by injecting a radio-opaque contrast agent into the blood vessel and imaging using X-ray based techniques such as fluoroscopy. […] X-ray images of the transient radio contrast distribution within the blood flowing within the coronary arteries allows visualization of the size of the artery openings” [0042]. Therefore, since the system 101 provides qualitative or quantitate blood flow data, the system further comprises a flow assessment component configured to compute a medical quantity of interest (i.e. qualitative or quantitate blood flow data) based at least on the predicted concentration of contrast agent (i.e. radio-opaque contrast agent) in the said conduit (i.e. blood vessel), the quantity of interest descriptive of one or more flow characteristics of the liquid (i.e. qualitative or quantitate blood flow data).).
Regarding claim 5, Miller discloses all features of the claimed invention as discussed with respect to claim 4 above, and Miller further teaches "wherein the said medical quantity of interest includes any one of more of: volumetric flow, propagation time of contrast agent through at least a section of said conduit, liquid -contrast agent mixing behavior, and transluminal attenuation gradient” (See [0034] as discussed with respect to claim 4 above. Therefore, the said medical quantity of interest includes any one or more of volumetric flow (i.e. qualitative or quantitate blood flow data).).
Regarding claim 7, Miller discloses all features of the claimed invention as discussed with respect to claim 1 above, and Miller further teaches "wherein the conduit is part of a vascular, urinary or lymphatic system of mammal, such as a human patient” (“In certain embodiments, systems and methods of the invention image within a lumen of tissue. Various lumen of biological structures may be imaged including, but not limited to, blood vessels, vasculature of the lymphatic and nervous systems, various structures of the gastrointestinal tract including lumen of the small intestine, large intestine, stomach, esophagus, colon, pancreatic duct, bile duct, hepatic duct, lumen of the reproductive tract including the vas deferens, vagina, uterus and fallopian tubes, structures of the urinary tract including urinary collecting ducts, renal tubules, ureter, and bladder, and structures of the head and neck and pulmonary system including sinuses, parotid, trachea, bronchi, and lungs” [0041]. As shown in FIG. 1, the intravascular imaging system 101 (i.e. along with the angiographic system 107) are used to examine a human patient. Therefore, the conduit is part of a vascular (i.e. blood vessel), urinary (i.e. urinary tract including urinary collecting ducts, renal tubules, ureter, and bladder), or lymphatic system (i.e. vasculature of the lymphatic system) of a mammal, such as a human patient.).
Regarding claim 8, Miller discloses all features of the claimed invention as discussed with respect to claim 1 above, and Miller further teaches "wherein the further imaging apparatus (IA2) is any one or more of: i) an intravascular ultrasound (IVUS) imaging apparatus or ii) an optical coherence tomography (OCT) imaging apparatus” (See [0035], [0045] and [0062] as discussed with respect to claim 1 above and “Systems and methods of the invention have application in intravascular imaging methodologies such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT) among others that produce a three-dimensional image of a vessel” [0025]. Therefore, the further imaging apparatus (IA2) is any one or more of: i) an intravascular ultrasound (IVUS) imaging apparatus or ii) an optical coherence tomography (OCT) imaging apparatus.).
Regarding claim 11, Miller discloses all features of the claimed invention as discussed with respect to claim 1 above, and Miller further teaches "An imaging arrangement (MIA) comprising the system of claim 1, further including the spectral X-ray imagining apparatus and the second imaging apparatus” (See FIG. 1. The system shown in FIG. 1 includes the intravascular imaging system 101 as well as the angiographic system 107 (i.e. the spectral X-ray imaging apparatus). Therefore, the system shown in FIG. 1 represents an imaging arrangement (MIA) comprising the system of claim 1, and further including the spectral X-ray imaging apparatus and the second imaging apparatus.).
Regarding claim 14, Miller discloses all features of the claimed invention as discussed with respect to claim 12 above, and Miller further teaches "A computer program element, which, when being executed by at least one processing unit, is adapted to cause the processing unit to perform the method as per claim 12” (“A computer system includes a processor coupled to a tangible, non-transitory memory operable to cause the system to perform the method steps” [0067]. Therefore, Miller includes a computer program element, which, when being executed by at least one processing unit (i.e. processor), is adapted to cause the processing unit to perform the method as per claim 12.).
Regarding claim 15, Miller discloses all features of the claimed invention as discussed with respect to claim 14 above, and Miller further teaches "At least one computer readable medium having stored thereon the program element of claim 14, or having stored thereon the machine learning model” (See [0067] as discussed in claim 14 above. Therefore, Miller includes at least one computer readable medium (i.e. tangible-non-transitory memory) having stored thereon the program element of claim 14.).
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.
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.
Claim(s) 3 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller US 2014/0276085 A1 “Miller” as applied to claim 2 above, and further in view of So et al. US 2021/0228171 A1 “So”.
Regarding claim 3, Miller discloses all features of the claimed invention as discussed with respect to claim 2 above, however Miller does not teach "wherein the concentration is a mass concentration representing a mass of contrast agent inside the volume of the lumen inside the region of interest”.
So is within the same field of endeavor as the claimed invention because it involves a computer implemented method for dynamic angiographic imaging involving obtaining image data comprising a plurality of images during and increase phase and decline phase of a contrast agent in a blood vessel of interest (see [Abstract]).
So teaches "wherein the concentration is a mass concentration representing a mass of contrast agent inside the volume of the lumen inside the region of interest” (“FIG. 15 shows a schematic correlating mass of contrast agent in a vessel section to different time points of a corresponding measured time-enhancement curve” [0026]; “The dynamic CT scan includes acquiring of projection data prior to entry 54 of contrast agent at the sampling site(s), as well as acquiring projection data during an increase phase 56 of the contrast agent at the sampling site(s) and acquiring projection data during a decline phase 58 of the contrast agent at the sampling site. An increase phase refers to an increase of mass of contrast agent at the sampling site as time advances subsequent to initial entry of the contrast agent into the sampling site, while a decline phase or decrease phase refers to a decrease of mass of contrast agent at the sampling site as time advances prior to substantially complete clearance of the contrast agent from the sampling site” [0047]. As shown in FIG. 15, the mass of contrast agent present at time T = t3 is the greatest (i.e. at the peak) in the time-enhancement curve. Therefore, the concentration is a mass concentration representing a mass of contrast agent inside the volume of the lumen inside the region of interest.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Miller such that the concentration is a mass concentration representing a mass of contrast agent inside the volume of the lumen inside the region of interest as disclosed in So in order to allow a user to better understand how the mass of contrast agent within the region of interest in the lumen changes over time (see So: FIG. 15). Generating a time-enhancement curve is one of a finite number of techniques which can be used to allow a user to assess how the mass of contrast agent inside a lumen changes over time with a reasonable expectation of success. Thus, modifying the system of Miller such that the concentration is a mass concentration representing a mass of contrast agent inside the volume of the lumen inside the region of interest as disclosed in So would yield the predictable result of allowing a user to better understand how the mass of contrast agent within the region of interest in the lumen changes over time.
Regarding claim 6, Miller discloses all features of the claimed invention as discussed with respect to claim 1 above, however Miller does not teach "wherein predictor component is configured to compute an initial concentration of the contrast agent upon entering the conduit and/or a rate of entering into the conduit of the said contrast agent”.
So teaches "wherein predictor component is configured to compute an initial concentration of the contrast agent upon entering the conduit and/or a rate of entering into the conduit of the said contrast agent” (See FIG. 15 and “The maximum value (peak value) of the time-enhancement curve has a unit of HU (Hounsfield Unit) and can be converted to (mg/mL)” [0070]. In this case, as shown in FIG. 15, T = t1 represents the initial concentration of the contrast agent upon entering the vessel (i.e. conduit). In order to generate the corresponding time-enhancement curve, the predictor component must have been configured to compute an initial concentration of the contrast agent upon entering the conduit.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Miller such that the predictor component is configured to compute an initial concentration of the contrast agent upon entering the conduit as disclosed in So in order to allow a user to better understand how the concentration of the contrast agent within the conduit (i.e. vessel) changes over time (see So: FIG. 15). Generating a time-enhancement curve is one of a finite number of techniques which can be used to allow a user to assess how the concentration of contrast agent inside a lumen/vessel/conduit changes over time with a reasonable expectation of success. Thus, modifying the system of Miller such that predictor component is configured to compute an initial concentration of the contrast agent upon entering the conduit as disclosed in So would yield the predictable result of allowing a user to better understand how the concentration of the contrast agent within the conduit (i.e. vessel) changes over time (see So: FIG. 15).
Claim(s) 9-10 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Miller US 2014/0276085 A1 “Miller” as applied to claim 2 above, and further in view of Sahbaee Bagherzadeh et al. US 2020/0281543 A1 “Sahbaee Bagherzadeh”.
Regarding claim 9, Miller discloses all features of the claimed invention as discussed with respect to claim 1 above, however Miller does not teach "further comprising a spectral processor implementing a material decomposition algorithm for providing the spectral projection imagery including a contrast-only image”.
Sahbaee Bagherzadeh is within the same field of endeavor as the claimed invention because it involves utilizing material decomposition in medical imaging to output material composition contained therein (see [Abstract]).
Sahbaee Bagherzadeh teaches "further comprising a spectral processor implementing a material decomposition algorithm for providing the spectral projection imagery including a contrast-only image” (“The medical imaging system performs the acts. A medical imager, such as a CT system, performs act 10. An image processor performs act 12. The image processor uses a display screen to perform act 14” [0015]; “In act 12, an image processor generates a material decomposition for one or more locations in the scanned region of the patient” [0022]; “In act 14 of FIG. 1, the image processor generates and a display displays an image of the material decomposition for the plurality of the locations. The material decomposition image may be visualized on the CT or medical scanner or on another device, such as an imaging workstation” [0045]; “In one embodiment, the machine-learned model is used for part of material decomposition, and spectral CT (i.e., traditional or algorithm-based) material decomposition is used for another part of the material decomposition. […] The machine-learned model is trained to refine the material decomposition from the algorithm-based material decomposition results with or without input of other data (e.g., scan data). In any of these or other combinations, the resulting images are a function of output of the spectral CT material decomposition and the machine-learned model” [0050]; “FIG. 4 shows a system for material decomposition. The system implements the method of FIG. 1 or another method to output material composition for an interior region of a patient, such as shown in FIG. 3” [0051].
Therefore, the system (i.e. see FIG. 4, image processor 42) further comprises a spectral processor implementing a material decomposition algorithm (i.e. see [0045], [0050]) for providing the spectral projection imagery including a contrast-only image (i.e. resulting images, see [0050] and step 14 in FIG. 1).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Miller such that the system further comprises a spectral processor implementing a material decomposition algorithm for providing the spectral projection imagery including a contrast-only image as disclosed in Sahbaee Bagherzadeh in order to automate the process of identifying material composition of a patient (see Sahbaee Bagherzadeh: [0003]). Utilizing a material decomposition algorithm is one of a finite number of techniques which can be used to identify the material composition present within a patient with a reasonable expectation of success. Thus, modifying the system of Miller such that the system further comprises a spectral processor implementing a material decomposition algorithm for providing the spectral projection imagery including a contrast-only image as disclosed in Sahbaee Bagherzadeh would yield the predictable result of automating the process of identifying material composition of a patient.
Regarding claim 10, Miller discloses all features of the claimed invention as discussed with respect to claim 1 above, however Miller does not teach "the predictor component based on a machine learning model”.
Sahbaee Bagherzadeh teaches "the predictor component based on a machine learning model” (“Deep learning or other machine learning is used for multi-material decomposition in medical imaging. Being trained by thousands of images and decomposed images, such as from traditional two and/or three material decomposition algorithms, the machine-learned model is not limited to use the information only from the individual voxels to extract the decomposed information. Machine learning may train on labeled data with spatial information (e.g., planar and z axis) and/or temporal information (e.g., timing relative to contrast agent injection) for each location” [0013]. Therefore, the predictor component is based on a machine learning model.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Miller such that the predictor component is based on a machine learning model as disclosed in Sahbaee Bagherzadeh in order to automate the process of identifying the material composition of a patient (see Sahbaee Bagherzadeh: [0003]). Utilizing a material decomposition algorithm/machine learning model is one of a finite number of techniques which can be used to identify the material composition present within a patient with a reasonable expectation of success. Thus, modifying the system of Miller such that the predictor component is based on a machine learning model as disclosed in Sahbaee Bagherzadeh would yield the predictable result of automating the process of identifying the material composition of a patient.
Regarding claim 13, Miller in view of Sahbaee Bagherzadeh discloses all features of the claimed invention as discussed with respect to claim 10 above, and Sahbaee Bagherzadeh further teaches "Method of training the model as per claim 10 based on training data” (See [0013] as discussed with respect to claim 10 above and “The machine-learned model may have been trained based on a database of past image acquisitions covering a wide range of scan phases (i.e., relative timing of the scan to contrast agent injection) and conditions (e.g., scan settings and/or task)” [0027]). Therefore, Sahbaee Bagherzadeh discloses a method of training the model as per claim 10 based on training data.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Miller such that the predictor component is based on a machine learning model and is subjected to a method of training as disclosed in Sahbaee Bagherzadeh in order to automate the process of identifying the material composition of a patient (see Sahbaee Bagherzadeh: [0003]). Training a machine learning model to identify the material composition(s) present within a patient is one of a finite number of techniques which can be used to effectively automate the process of identifying materials with a reasonable expectation of success. Thus, modifying the system of Miller such that the predictor component is subjected to a method of training as disclosed in Sahbaee Bagherzadeh would yield the predictable result of automating the process of identifying the material composition of a patient.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ortega et al. US 2022/0071573 A1 “Ortega” is pertinent to the applicant’s disclosure because it discloses “At step 408, material decomposition techniques are applied by the one or more processors to acquire quantitative information about the chemical composition of the patient's anatomy, or the area through which the x-ray beam has passed. One technique includes applying a material decomposition separation algorithm to acquire quantitative information about the chemical materials in said area. To perform this separation, prior acquired spectral calibration information may be compared to the presently acquired information. Another technique includes applying a deep-learning material decomposition algorithm to improve the separation between different materials. Through these techniques, a quantitative planar image of the patient's anatomy can be acquired at step 410” [0105].
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/KAITLYN E SEBASTIAN/Examiner, Art Unit 3797