DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed March 20, 2026.
Claims 1-2 and 4-22 are pending.
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 .
Response to Amendment(s)
Claims 1-2 and 4-22 are pending. Objections have been overcome.
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 1-2 and 4-22 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1, 15, 16, and 17:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent claim 1 falls within the statutory category of method
Independent claim 15 falls within a statutory category of machine
Independent claims 16 and 17 fall within the statutory category of article of manufacture
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 15, 16, and 17 claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in the independent claims 1, 15, 16, and 17 (claim 1 being representative) are:
A method for providing an observable indicating a medical diagnosis, the method comprising:
obtaining a medical image data series of a patient, wherein the medical image data series has a number of medical image datasets, which have each been recorded over a first period of time at different points in time;
extracting a first time series from the medical image data series;
determining first correlation information based on the first time series and a second time series, wherein the first correlation information specifies an amount of temporal correlation between the first time series and the second time series
determining the observable based on the first correlation information;
and providing the observable.
The abstract idea is “mental process” by making an observation or judgement when reviewing times series data to provide an observable (see MPEP § 2106.04(a)(2))
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claims 1, 15, 16, and 17 judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional elements below:
a computer
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, a computer, is recited as executing the abstract idea. The additional elements, (a), is merely a general purpose computer tool to “apply” the abstract idea (see instant application para. [00268])
Independent claim 15 recites the additional elements below not already recited in the independent claim 1:
A system
An interface
A computing device
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a), (b), and (c), are recited as “apply-it” or an equivalent to gather data
Independent claim 16 recites the additional elements below not already recited in the independent claim 1:
A non-transitory computer program product comprising a program that is loadable into a memory of a programmable processing unit
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a), is recited as executing the abstract idea as “apply-it” or an equivalent to execute the abstract idea
Independent claim 17 recites the additional elements below not already recited in the independent claim 1:
A non-transitory computer-readable medium storing readable and executable program sections that, when executed by a computing device of a system
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a), is recited as executing the abstract idea as “apply-it” or an equivalent to execute the abstract idea
Accordingly, independent claims 1, 15, 16, and 17 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as analyzed above in step 2A prong 2, are merely applying the abstract idea and therefore, do not amount to significantly more. The claims are patent ineligible.
Dependent Claims 2 and 4-14, 18, 19, and 20-22
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Dependent claims 2and 4-14, 18, 19, and 20-22 fall within the statutory category of method.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 2 and 4-14, 18, 19, and 20-22 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claims 1, 15, 16, and 17 abstract idea by (1) further limiting the determination of the observable , (2) further limiting the measurement variables , (3) further limiting the types of data, and (4) further limiting the analysis to a medical diagnosis and a treatment option. Therefore, the dependent claims inherit the same abstract idea of “mental process” by making an observation or judgement when reviewing times series data to provide an observable (see MPEP § 2106.04(a)(2))
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 2 and 4-14, 18, 19, and 20-22 this judicial exception is not integrated into a practical application.
The dependent claims recite the below additional elements not already recited in the independent claims:
A display
An electronic medical record
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional elements, (a) and (b), are recited as “apply-it” or an equivalent to communicate data
Accordingly, dependent claims 2 and 4-14, 18, 19, and 20-22 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that amount to significantly more for the same reasons given in Prong 2. The claims are patent ineligible.
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.
Claims 1, 2, 4, 6, 7, 8, 10, 11, 14, 15, 16, 17, 19, 20, 21, and 22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by KANO et. al (hereinafter KANO) (US20170071479A1)
As per claim 1, KANO teaches:
A computer-implemented method for providing an observable indicating a medical diagnosis, the computer-implemented method comprising: ([0038] discloses, “A blood vessel analyzing apparatus, a blood vessel analyzing method, and a storage medium according to the first embodiment are applicable to a computer apparatus used for analyzing a blood vessel region included in images generated by an image diagnosis apparatus. In the first embodiment, an example will be explained in which medical images are used as the images. The computer apparatus may be incorporated in the medical image diagnosis apparatus or may be configured with a workstation or the like that is separate from the medical image diagnosis apparatus. In the following sections, a medical image diagnosis apparatus that has incorporated therein the computer apparatus to which the blood vessel analyzing apparatus and the blood vessel analyzing method according to the first embodiment are applied will be explained in detail, with reference to the accompanying drawings.”)
obtaining a medical image data series of a patient, wherein the medical image data series has a number of medical image datasets, which have each been recorded over a first period of time at different points in time; ([0034] discloses, “The processing circuitry is configured to obtain images in a time series including images of a blood vessel of a Subject and correlation information indicating a correlational relationship between physical indices of the blood vessel and function indices of the blood vessel related to vascular hemodynamics.” And see [0048] discloses, “The data acquiring device 15 is configured to read the electrical signals from the X-ray detector 13 and to convert the read electrical signals into digital data. A set of digital data corresponding to one view is called a raw data set. Raw data sets that are in time series and are related to a plurality of scanning times are transferred to the console 20 by a non-contact data transfer device (not illustrated).)
extracting a first time series from the medical image data series; (see e.g. [0064] disclose, “The CT images in the time series are represented by data expressing a three-dimensional spatial distribution of the CT values in the time series. For example, the CT images in the time series include approximately twenty images per heartbeat, i.e., CT images corresponding to approximately twenty cardiac phases.”)
determining first correlation information based on the first time series and a second time series, wherein the first correlation information specifies an amount of temporal correlation between the first time series and the second time series; ( see e.g. [0088] discloses, “The blood vessel cross-sectional shape change indices of the aorta are used as blood vessel cross-sectional shape change indices of the entrance of a coronary artery (i.e., a Surrounding region of the coronary artery starting part). The coronary artery entrance is such an end of the coronary artery that is positioned on the upstream side in terms of the blood flow direction. The blood vessel cross sectional shape change indices of the aorta may be, for example, a temporal change ratio or a change amount in an average of the cross-sectional areas of a plurality of cross sectional planes that are measured in positions away from the coronary artery starting part by short distances (e.g., a number of centimeters approximately) on the upstream side in terms of the blood flow direction; coefficients indicating changes thereof from either a blood vessel expansion time or a maximum flow amount time to a contraction time (e.g., cardiac phases of 70 to 100%); or a dispersion in the cross-sectional areas of a single cross-sectional plane. Instead of the cross-sectional areas, it is also acceptable to use a temporal change ratio or a change amount related to changes in the Volume of the vascular lumen, or coefficients indicating changes thereof from either a blood vessel expan sion time or a maximum flow amount time to a contraction time (e.g., cardiac phases of 70 to 100%), in consideration of changes in the cross-sectional area in the central line direction. The change amounts may be expressed by using indices related to concentration or dispersion of a contrast agent within the blood vessel.” And see e.g. [0406] discloses, “In this situation, the third display information 330 includes cross-sectional images each indicating a cross sectional plane at a measuring point designated by the operator. For example, the display controlling unit 227g causes the display unit 31 to display, as the cross-sectional images, a cross-sectional image in a first temporal phase indicating a cross-sectional plane at a measuring point designated by the operator and at least one other cross sectional image in a second temporal phase indicating a cross-sectional plane in Such a position that anatomically corresponds to that of the cross-sectional plane in the first temporal phase. For example, as illustrated in the upper section of FIG. 21, the display controlling unit 227g realizes the display of a cross-sectional image 331a indicating the cross-sectional plane at a measuring point designated by the operator and cross-sectional images 331b to 331e in other cardiac phases each indicating a cross-sectional plane in Such a position that anatomically corresponds to the cross sectional plane of the cross-sectional image 331a.” and see [0408] / examiner notes the temporal correlation is happening over multiple time series of data aligning to cardiac phases of heartbeats which are a continuous sequence of events which occur during a heartbeat)
determining the observable based on the first correlation information; and providing the observable. ([0113] discloses, “Next, details of the blood vessel analyzing process performed by the medical image diagnosis apparatus according to the first embodiment will be explained.” And see [0114] discloses, “First, a flow in the blood vessel analyzing process will be explained by using an example in which the first identifying unit 66 identifies such a first function index from the correlation information that corresponds to the first physical index equal to the second physical index calculated by the first calculating unit 67, as the second function index of the identification target region.” And see [0115] discloses, “FIG. 6 is a flowchart of a flow in the blood vessel analyzing process performed by the image processing apparatus 27. I0116 First, the obtaining unit 69 obtains CT images in a time series (step S100).” And see [0117] discloses, “After that, the first setting unit 51 sets an identification target region in the CT images in the time series obtained at step S100 (step S101).” And see [0118] discloses, “Subsequently, the first calculating unit 67 calculates the second physical index of the identification target region set at step S101, on the basis of the CT images in the time series obtained at step S100 (step S102).” And see [0121] discloses, “As explained above, the blood vessel analyzing apparatus 50 according to the first embodiment includes the storage unit 65, the obtaining unit 69, the first setting unit 51, the first calculating unit 67, and the first identifying unit 66. The storage unit 65 has stored therein, in advance, the correlation information indicating the correlation between the first physical indices related to the stenosis in the blood vessel and the first function indices of the blood vessel.” And see e.g. [0186]/ examiner notes the observable is considered the measure thereof of stenotic behavior or other temporal correlation behavior of blood vessel as disclosed in light of definition in [0020] of the instant application)
As per claim 2, KANO further teaches:
The computer-implemented method as claimed in claim 1 further comprising: obtaining the second time series, the second time series being of a measurement variable associated with the patient, said second time series covering a second period of time, which at least partly overlaps with the first period of time; ([0399] discloses, FIG. 19 is a drawing of another example of the first display information displayed by the display controlling unit 227g according to the fifth embodiment. For example, the display controlling unit 227g causes the display unit 31 to display first display information 410 illustrated in FIG. 19.” And see [0400] discloses, “In this situation, for example, when a plurality of measuring points “10 to “80 have been set as illustrated in the legend on the right side of FIG. 19, the display control ling unit 227g causes the display unit 31 to display a plurality of change curves 411 related to the measuring points. In that situation also, as illustrated in the upper section of FIG. 19, for example, the display controlling unit 227g causes the display unit 31 to display information 412 indicating an electrocardiographic waveform in Such a man ner that the cardiac phases thereof are aligned with the time axis related to the change curves of the cross-sectional areas. Further, the display controlling unit 227g realizes the display of the change curves by using the mutually-different types of lines.” And see [0401] discloses, “FIGS. 18 and 19 illustrate the examples in which the change curves each indicating the chronological change in either the cross-sectional area or the unit volume of the blood vessel are displayed as the information indicating the chronological changes in the blood vessel morphological indices; however, possible embodiments are not limited to these examples. For instance, the display controlling unit 227g may realize a display of a change curve indicating a chronological change in the FFR value. Alternatively, for example, the display controlling unit 227g may realize a display to indicate changes in the FFR value in response to changes of a position (X) that is set on the central line of the coronary artery.” And see [0406]-[0408] / examiner notes the times series data is shown for more than one series of timings that span and measure more than one observable displayed)
As per claim 4, KANO further teaches:
The computer-implemented method as claimed in claim 2, wherein the measurement variable of the second time series is not based on a medical imaging. ([0396] discloses, “Further, the first display information 310 includes information indicating an electrocardiographic waveform of the subject. The display controlling unit 227g causes the display unit 31 to further display the information indicating the electrocardiographic waveform of the Subject, so as to be kept in correspondence with the information indicating the chronological change in the blood vessel morphology indices. For example, as illustrated in the upper section of FIG. 18, the display controlling unit 227g causes the display unit 31 to display information 312 indicating the electrocardiographic waveform in Such a manner that the cardiac phases are aligned with the time axis related to the change curves of the cross-sectional areas. In that situation, for example, information indicating electrocardiographic signals of the Subject that were actually measured when the CT images in the time series were taken is stored in the storage unit 65 while being kept in correspondence with each of the CT images. Further, for example, when no electrocardiographic signals of the Subject were actually measured, the display controlling unit 227g may realize a display of a schematic diagram of an electrocardiographic waveform so as to be kept in correspondence with the information indicating the chronological change in the blood vessel morphology indices, as the indices, as the information indicating an electrocardiographic waveform.” / examiner notes the ECG is considered second time series observable that does not rely on medical imaging.)
As per claim 6 and 19, KANO further teaches:
The computer-implemented method as claimed in claim 1, wherein the determining the observable comprises: obtaining an individual event that lies within the first period of time, the individual event being associated with the patient; ([0066] discloses, “Function indices are indices related to vascular hemodynamics. For example, a function index may be an index indicating functions of a blood vessel related to Stenosis. Specific examples of the function indices include a Fractional Flow Reserve (FFR) value, a dynamic index for the inside of a blood vessel, a blood flow amount index, FFR difference, and stenosis ratio. / examiner notes the event is interpreted as a state of health of the patient as defined in instant application specification [00206])
determining second correlation information based on the first time series and the individual event, wherein the second correlation information specifies an amount of a temporal correlation between the first time series and the individual event; and establishing the observable based on the second correlation information. ([0268] discloses, “When the initial shape is assumed to correspond to a stress-free state (e.g., the state in which the blood vessel contracts the most), by setting the moduli of elasticity of the blood vessel wall and the plaque to certain values, it is possible to obtain a relational expression between a temporal change amount in the observed value of the blood vessel cross-sectional shape change index Such as an average radius of the vascular lumen and a change amount in the internal pressure. Observed values of the blood vessel cross sectional shape change index can be measured from the CT images in the time series. The temporal change in the internal pressure distribution of the blood vessel is determined so as to match the temporal change amount in the observed value of the blood vessel cross-sectional shape change index. By performing a fluid analysis of the blood under the condition of the internal pressure distribution, it is possible to measure a prediction value of the blood flow amount index. If the prediction value of the blood flow amount index does not match the observed value, the image processing apparatus 27A further performs the same analysis after changing the modulus of elasticity of either the blood vessel wall or the plaque that was initially determined.” And see [0269] discloses, “By repeatedly performing this process, the image processing apparatus 27A is able to determine the latent variable indicating the moduli of elasticity of the blood vessel wall and the plaque, the internal pressure distribution, a pressure boundary condition for the fluid analysis, or the like that matches the observed value of the blood vessel cross-sectional shape change index and the observed value of the blood flow amount index. In order to implement this determination method more efficiently and stably, it is also acceptable to use a statistical identifying method based on a hierarchical Bayesian model and a Markov chain Monte Carlo method.” And see [0270] discloses, “AS explained above, the image processing apparatus 27A included in the blood vessel analyzing apparatus 50A according to the second embodiment includes the storage unit 65A, the obtaining unit 69, the first setting unit 51, the first calculating unit 67, and the first identifying unit 66A. The storage unit 65A has stored therein, in advance, the correlation information indicating the correlation between the first physical indices related to the stenosis in the blood vessel and the first function indices of the blood vessel. The obtaining unit 69 is configured to obtain the images in the time series related to the blood vessel of the subject. The first setting unit 51 is configured to set the identification target region for the second function index, in the blood vessel region included in the images. The first calculating unit 67 is configured to calculate the second physical index of the identification target region, on the basis of the medical images. The first identifying unit 66A is configured to identify the second function index of the identification target region, on the basis of the correlation information and the calculated second physical index.”)
As per claim 7 and 20, KANO further teaches:
The computer-implemented method as claimed in claim 1, further comprising: obtaining context information concerning a clinical picture of the patient, wherein the extracting includes selecting an image data-based measurement variable from among a number of different image data-based measurement variables based on the context information to obtain a selected image data-based measurement variable, and the first time series is a time series of the selected image data-based measurement variable. ([0268] discloses, “When the initial shape is assumed to correspond to a stress-free state (e.g., the state in which the blood vessel contracts the most), by setting the moduli of elasticity of the blood vessel wall and the plaque to certain values, it is possible to obtain a relational expression between a temporal change amount in the observed value of the blood vessel cross-sectional shape change index Such as an average radius of the vascular lumen and a change amount in the internal pressure. Observed values of the blood vessel cross sectional shape change index can be measured from the CT images in the time series. The temporal change in the internal pressure distribution of the blood vessel is determined so as to match the temporal change amount in the observed value of the blood vessel cross-sectional shape change index. By performing a fluid analysis of the blood under the condition of the internal pressure distribution, it is possible to measure a prediction value of the blood flow amount index. If the prediction value of the blood flow amount index does not match the observed value, the image processing apparatus 27A further performs the same analysis after changing the modulus of elasticity of either the blood vessel wall or the plaque that was initially determined.” And see [0269] discloses, “By repeatedly performing this process, the image processing apparatus 27A is able to determine the latent variable indicating the moduli of elasticity of the blood vessel wall and the plaque, the internal pressure distribution, a pressure boundary condition for the fluid analysis, or the like that matches the observed value of the blood vessel cross-sectional shape change index and the observed value of the blood flow amount index. In order to implement this determination method more efficiently and stably, it is also acceptable to use a statistical identifying method based on a hierarchical Bayesian model and a Markov chain Monte Carlo method.” And see [0270] discloses, “AS explained above, the image processing apparatus 27A included in the blood vessel analyzing apparatus 50A according to the second embodiment includes the storage unit 65A, the obtaining unit 69, the first setting unit 51, the first calculating unit 67, and the first identifying unit 66A. The storage unit 65A has stored therein, in advance, the correlation information indicating the correlation between the first physical indices related to the stenosis in the blood vessel and the first function indices of the blood vessel. The obtaining unit 69 is configured to obtain the images in the time series related to the blood vessel of the subject. The first setting unit 51 is configured to set the identification target region for the second function index, in the blood vessel region included in the images. The first calculating unit 67 is configured to calculate the second physical index of the identification target region, on the basis of the medical images. The first identifying unit 66A is configured to identify the second function index of the identification target region, on the basis of the correlation information and the calculated second physical index.” / examiner notes that the context information is interpreted as the analysis of a CT image of the heart area for a condition and the measured variables are for example pressure in the time series data as defined in instant application 0070 and 0071)
As per claim 8, KANO further teaches:
The computer-implemented method as claimed in claim 1, wherein each medical image dataset includes a recording of an entire area of a body of the patient or a recording of the entirety of the body of the patient. ([0046] discloses, “FIG. 1 is a schematic hardware diagram of a medical image diagnosis apparatus (an X-ray computed tomography apparatus) according to the first embodiment. As illustrated in FIG. 1, the X-ray computed tomography apparatus includes a CT gantry 10 and a console 20. The CT gantry 10 is configured, under control of a gantry controlling unit 23 included in the console 20, to perform a scan on an imaging target site of a subject while using X-rays. The imaging target site may be the heart, for example.” And see [0047] discloses, “The CT gantry 10 includes an X-ray tube 11, an X-ray detector 13, and a data acquiring device 15. The X-ray tube 11 and the X-ray detector 13 are installed with the CT gantry 10 so as to be rotatable on a rotation axis Z. The X-ray tube 11 is configured to radiate the X-rays onto the subject into whom a contrast agent has been injected. The X-ray detector 13 is configured to detect X-rays that were generated by the X-ray tube 11 and have passed through the Subject and to generate electrical signals corresponding to the intensities of the detected X-rays.”)
As per claim 10, KANO further teaches:
The computer-implemented method as claimed in claim 1, wherein the providing comprises: comparing the observable with an observable pattern; and displaying the first time series or the observable based on the comparing . ([0305] discloses, “Alternatively, the index indicating the stenosis estimated by the first identifying unit 156 may express a change in the flow amount or a change in the pressure between an expansion time and a contraction time, a pressure loss between before and after the Stenosis, a pressure loss between an aorta part and a coronary artery part, a flow amount ratio between coronary arteries (between a coronary artery having a steno sis and a coronary artery having no Stenosis), or the like. In this situation, the first identifying unit 156 may be configured so as to judge the state of each of the blood vessels, by judging, for example, whether or not the index exceeds a predetermined threshold value.” See figs. 18-22)
As per claim 11, KANO further teaches:
The computer-implemented method as claimed in claim 1, further comprising: determining the medical diagnosis based on the observable; and providing the medical diagnosis. ([0307] discloses, “For example, after a blood vessel stenosis analysis has been performed by the medical image diagnosis apparatus, the display unit 31 displays a designation input Screen in which an overall image of the heart and the blood vessels is displayed as a reference image as illustrated in FIG. 23 (explained later), and further displays a cursor on the designation input screen. The cursor moves in response to an operation by the user that is input via the input unit 29 and, for example, specifies a position in a coronary artery serving as an analysis target. When the position in the coronary artery has been specified via the input unit 29, the display unit 31 displays any of the analysis results explained later with reference to FIGS. 11 to 13, in response to an instruction received via the input unit 29.” And see [0429] discloses, “For example, as illustrated in FIG. 23, the display controlling unit 227g may display, as a reference image, an image 440 represented by a volume rendering image indicating an overall image 441 of the heart of the subject and an overall image 442 of the blood vessels supplying blood to the heart, in which the display colors of pixels 443 included in the overall image 441 of the heart are varied in accordance with numerical values of perfusion values”)
As per claim 14, KANO further teaches:
The computer-implemented method as claimed in claim 1, further comprising: determining a treatment option based on the observable; and providing the treatment option. (see [0104] and see [0105] discloses, “Examples of therapies based on these observations of MC abnormalities include , for Type 1 abnormality ; anti biotics , anti - inflammatories , blood transfusions , fluid therapy ; for Type 2 abnormality : a reduction in the type or amount of fluid being administered , the administration of blood or RBC enhancing therapy ( iron or EPO ) ; for Type 3 abnormality : a reduction in the vasopressor agent , administration of a vasodilatory agent ,treatment of a tamponade by relieving an obstruction ; for Type 4 abnormality : a resolution of the capillary leak removal of excessive fluid by diuretic therapy and or haemodialysis.”)
As per claim 21, KANO teaches:
The computer-implemented method as claimed in claim 1, wherein each medical image dataset among the number of medical image datasets includes image data, the image data corresponding to data values represented by pixels or voxels as a function of respective positions of the pixels or voxels; ([0052] discloses, “The reconstructing device 25 generates the CT images in a time series, on the basis of the projection data sets in the time series. The CT images include pixel regions (hereinafter, “blood vessel regions”) related to blood vessels of which the contrast is enhanced by the contrast agent. The CT images may be represented by slice data expressing a two-dimensional spatial distribution of the CT values or may be represented by Volume data expressing a three-dimensional spatial distribution of the CT values. In the following explanation, the CT images are assumed to be represented by volume data. The CT images in the time series ate stored in a storage unit 33 and a storage unit 65 included in an image processing apparatus 27 (explained later).” And see [0181])
the first time series includes a series of measured values recorded at different points in time; and the extracting the first time series extracts the measured values from the image data. (e.g. [0181] discloses, “Further, the third calculating unit 53 performs the tracking process as follows: The third calculating unit 53 sets a plurality of tracked points such as a feature point, a feature shape, a representative point, a pixel, or the like in the blood vessel region, the blood, the contrast agent, or protons, according to an instruction from the user via the input unit 29 or through an image processing process. For example, the third calculating unit 53 sets a set of tracked points including a blood vessel branching part, a feature shape on the Surface, and the like. Further, by performing an interpolating process or the like, the third calculating unit 53 calculates a temporal change in displacements of a node in the blood vessel wall surface, the blood vessel wall interior, or the blood vessel lumen of the dynamic model, on the basis of displacement data of the set of tracked points obtained from the tracking process performed at different points in time (in different cardiac phases), so as to provide the calculated result as a forced displacement.”)
As per claim 22, KANO teaches:
The computer-implemented method as claimed in claim 1, wherein the first correlation information is a time series. ( see e.g. [0088] discloses, “The blood vessel cross-sectional shape change indices of the aorta are used as blood vessel cross-sectional shape change indices of the entrance of a coronary artery (i.e., a Surrounding region of the coronary artery starting part). The coronary artery entrance is such an end of the coronary artery that is positioned on the upstream side in terms of the blood flow direction. The blood vessel cross sectional shape change indices of the aorta may be, for example, a temporal change ratio or a change amount in an average of the cross-sectional areas of a plurality of cross sectional planes that are measured in positions away from the coronary artery starting part by short distances (e.g., a number of centimeters approximately) on the upstream side in terms of the blood flow direction; coefficients indicating changes thereof from either a blood vessel expansion time or a maximum flow amount time to a contraction time (e.g., cardiac phases of 70 to 100%); or a dispersion in the cross-sectional areas of a single cross-sectional plane. Instead of the cross-sectional areas, it is also acceptable to use a temporal change ratio or a change amount related to changes in the Volume of the vascular lumen, or coefficients indicating changes thereof from either a blood vessel expan sion time or a maximum flow amount time to a contraction time (e.g., cardiac phases of 70 to 100%), in consideration of changes in the cross-sectional area in the central line direction. The change amounts may be expressed by using indices related to concentration or dispersion of a contrast agent within the blood vessel.” And see e.g. [0406] discloses, “In this situation, the third display information 330 includes cross-sectional images each indicating a cross sectional plane at a measuring point designated by the operator. For example, the display controlling unit 227g causes the display unit 31 to display, as the cross-sectional images, a cross-sectional image in a first temporal phase indicating a cross-sectional plane at a measuring point designated by the operator and at least one other cross sectional image in a second temporal phase indicating a cross-sectional plane in Such a position that anatomically corresponds to that of the cross-sectional plane in the first temporal phase. For example, as illustrated in the upper section of FIG. 21, the display controlling unit 227g realizes the display of a cross-sectional image 331a indicating the cross-sectional plane at a measuring point designated by the operator and cross-sectional images 331b to 331e in other cardiac phases each indicating a cross-sectional plane in Such a position that anatomically corresponds to the cross sectional plane of the cross-sectional image 331a.” and see [0408] / examiner notes the temporal correlation is happening over multiple time series of data aligning to cardiac phases of heartbeats which are a continuous sequence of events which occur during a heartbeat)
As per claim 15 it is a system claim which repeat the same limitations of claim 1 the corresponding method claim, as a collection of elements as opposed to a series of process steps. Since the teachings of KANO disclose the underlying process steps that constitute the methods of claim 1, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claim 15 are rejected for the same reasons given above for claim 1.
As per claims 16-17 they are article of manufacture claims which repeat the same limitations of claim 1, the corresponding method claim, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of KANO disclose the underlying process steps that constitute the method of claim 1 it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claims 16-17 are rejected for the same reasons given above for 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.
Claims 5 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over KANO et. al (hereinafter KANO) (US20170071479A1)in view of Ince et. al (hereinafter Ince) (US20200310098A1)
As per claim 5, KANO does not teach:
The computer-implemented method as claimed in claim 4, wherein the measurement variable of the second time series includes one or more blood values of the patient.
However, Ince does teach:
The computer-implemented method as claimed in claim 4, wherein the measurement variable of the second time series includes one or more blood values of the patient. ([0077] discloses, “Referring now to FIG . 3 , a series of pictures 300 of white and red blood cells in microcirculation analysis is illustrated , which can be analysed using AI techniques according to examples of the invention . In a first illustration 310 , white blood cells are shown as white cells 315 in the arched capillary in the middle of the picture . In a second illustration 340 , microcircirculation vessels 345 are shown with blood flow therein , as seen in moving cells . In a third illustration 370 , flowing single red blood cells are illustrated 375 in sublingual in magnified microcirculation recorded by an HVM device . Red blood cells have a diameter of about 5 micrometers in this example . For clarity purposes only , note that the dimensions of the field of view in these illustrations are approximately 200 microns” and see [0022] discloses, “In this manner , the claimed IVM device is able to enhance MC images and recognise patterns . In doing so the claimed IVM device may be able to identify and quantify abnormal red and white blood cell flow kinetics , and / or provide distribution of blood cell velocities . In addition , the claimed IVM device may be able to calculate capillary hematocrit, discharge hematocrit and tissue red blood cell perfusion.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine KANO’s teachings in [0003] that Causes of ischemic diseases of an organ can be roughly divided into hemodynamic failure and function failure of the organ itself. For the situations where the cause is hemodynamic failure, it is in demand to provide an evaluation index that can Suggest a treatment method in a non-invasive manner preemptive care to improve action plans and provide opportunities for pre - emptive treatment, with Ince’s teachings of gathering lab and blood values of the patient as this would be predictble to gather even non-invasively for a patient when you are solving blood vessel issues such as in KANO and would improve the ability to suggest a most successful treatment method.
As per claim 18, KANO does not teach:
The computer-implemented method as claimed in claim 4, wherein the measurement variable of the second time series includes one or more laboratory values of the patient.
However, Ince does teach:
The computer-implemented method as claimed in claim 4, wherein the measurement variable of the second time series includes one or more laboratory values of the patient. ([0038] discloses, “A mechanism to identify a high blood cell concentration , where there are abnormally high amount of red and white blood cell count such as occurs in polycythemia and leukemia can be provided by the IVM device described herein , without a need for the current requirement to withdraw blood . Advantageously , a blood - less , non - invasive methodology by the IVM device enables a clinician to visualize red and white blood cells.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine KANO’s teachings with Ince’s teachings for the same reasons given for claim 5.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over KANO et. al (hereinafter KANO) (US20170071479A1)in view of Udupa et. al (hereinafter Udupa) (US20190259159A1)
As per claim 9, KANO not teach:
The computer-implemented method as claimed in claim 1, wherein a measurement variable of the first time series is selected from: a Body Composition Analysis BCA value, including at least one of a proportion of muscle or a proportion of body fat; a size of a lesion; or a tumor burden in an area of a body of the patient or in an entirety of the body of the patient.
However, Udupa teaches:
The computer-implemented method as claimed in claim 1, wherein a measurement variable of the first time series is selected from: a Body Composition Analysis BCA value, including at least one of a proportion of muscle or a proportion of body fat; a size of a lesion; or a tumor burden in an area of a body of the patient or in an entirety of the body of the patient. ([0054] discloses, “In one aspect of the present disclosure , a method , referred to as automatic anatomy recognition body compo sition analysis ( AAR - BCA ) , may be used to quantify at least four tissue components in body torso ( BT ) - subcutaneous adipose tissue ( SAT ) , visceral adipose tissue ( VAT ) , bone tissue , and muscle tissue from CT images of given whole body PET / CT acquisitions . AAR - BCA consists of three key steps — modeling BT with its ensemble of key objects from a population of patient images , recognition or localization of these objects in a given patient image I , and delineation and quantification of the four tissue components in I guided by the recognized objects . In the first step , from a given set of patient images and the associated delineated objects , a fuzzy anatomy model of the key object ensemble , including ana tomic organs , tissue regions , and tissue interfaces , is built where the objects are organized in a hierarchical order . The second step involves recognizing , or finding roughly the location of , each object in any given whole - body image I of a patient following the object hierarchy and guided by the built model . The third step makes use of this fuzzy local ization information of the objects and the intensity distri butions of the four tissue components , already learned and encoded in the model , to optimally delineate in a fuzzy manner and quantify these components . All parameters in the AAR - BCA method are determined from training data sets . 10055 ) In a non - limiting example , 25 low - dose CT images from different subjects were tested in a 5 - fold cross valida tion strategy for evaluating AAR - BCA with a 15 - 10 train test data division . For BT , over all objects , AAR - BCA achieves a false positive volume fraction ( FPVF ) of 3 . 6 % and false negative volume fraction ( FNVF ) of 4 . 2 % . Nota bly , SAT and bone tissue both achieve a FPVF under 4 % and a FNVF under 3 % . For muscle tissue , the FNVF of 6 . 4 % is higher than for other objects and so also for VAT ( 4 . 4 % ) . The level of accuracy for the four tissue components in indi vidual body sub - regions mostly remains at the same level as for BT . The processing time required per patient image is under a minute . [ 0056 ] Motivated by applications in cancer and other non - neoplastic systemic diseases , one non - limiting objec tive described in this disclosure is a practical method for body composition quantification which is automated , accu rate , and efficient , and works on BT in low dose CT . The proposed AAR - BCA method towards this goal can quantify four tissue components including SAT , VAT , bone tissue , and muscle tissue in the body torso with about 5 % overall error . All needed parameters can be automatically estimated from the training data sets.”
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine KANO’s teachings in [0005] that it would be motivating and beneficial to realize observable issues based on imaging data, with Udupa’s teachings of BCA values from imaging data as this would be choice data used to determine a observable for a time series which would decrease resources if found in imaging data without further steps for the patient.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over KANO et. al (hereinafter KANO) (US20170071479A1)in view of Shukla et. al (hereinafter Shukla) (US20210327594A1)
As per claim 12, KANO does not teach:
The computer-implemented method as claimed in claim 11, wherein the determining the medical diagnosis comprises: selecting a disease pattern from among a number of disease patterns based on the observable; retrieving an electronic medical record of the patient from a database; reconciling the disease pattern with entries in the electronic medical record; and determining the medical diagnosis based on the reconciling.
However, Shukla does teach:
The computer-implemented method as claimed in claim 11, wherein the determining the medical diagnosis comprises: selecting a disease pattern from among a number of disease patterns based on the observable; ([0037] discloses, “Pattern integration refers to aspects of embodiments of the present invention in which the program code develops a pattern for identifying records with a given event based on using the most distinctive features extracted during data integration . For example , the program code would develop patterns describing the most distinctive features of the given disease that the program code extracted from the patient records.” And see [0038] discloses, “Population separation refers to aspects of embodiments of the present invention where the program code utilizes the pattern to identify the event in one or more data store . For example , returning to the disease example , by analyzing data resources including records identifying large populations , the program code identifies within the resources which patient clusters match the treatment path ways exhibited by the known sufferers.” )
retrieving an electronic medical record of the patient from a database; ([0030] discloses, “For example , in a scenario where an individual visits a healthcare provider , the individual and the provider would benefit from acquiring information regarding whether the individual , as represented by an electronic medical record , has items in the record that match the data sought by one or more disease models . If this information cannot be provided within the visit , it is arguably not useful to the individual or the healthcare provider . Thus , in embodiments of the present invention , the program code analyzes an individual record and applies disease models in real - time , or close to real time.”)
reconciling the disease pattern with entries in the electronic medical record; and determining the medical diagnosis based on the reconciling. ([0041] discloses, “As will be described in more detail below , and as illustrated utilizing FIGS . 1-3 , in embodiments of the pres ent invention , one or more programs obtain ( exclusively ) machine - readable electronic medical records of individuals who were previously medically diagnosed with a disease . The one or more programs analyze ( mine ) the data utilizing both frequency ranking and by identifying mutual informa tion . Thus , the program code in some embodiments of the present invention employs an analysis that utilizes two data - ranking methods : a frequency method and a mutual information method . The program code utilizes the mutual information measure to quantify the statistical relevance of every feature in the electronic data set ( s ) of medical records to a future diagnosis of a given disease . The program code computes the relative frequency of pertinent events to rank the differentiating features based on the mutual information measure . Based on frequency ranking and mutual informa tion , the one or more programs identify distinguishing features in categories that include diagnoses , procedures , drugs , providers , and locations . Based on identifying the distinguishing features , the one or more programs generate predictors ( e.g. , an adaptive data model ) , that the one or more programs can apply to data sets where it is unknown whether the individuals represented have the given disease , and based on applying the model , the one or more programs can identify probabilities of the given disease being present among the individuals represented.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine KANO’s teachings of indications of a medical diagnosis as previously cited with Shukla’s teachings of electronic record reconciliation when compared to patient disease patterns as previously cited, the motivation being that KANO’s clear in [0003] techniques and improved diagnosis methods are needed, therefore it would be predictable to combine with Shukla based on HIPAA to chart any methods and increase successful diagnosis by identifying patterns in the data.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over KANO et. al (hereinafter KANO) (US20170071479A1)in view of ZHAO et. al (hereinafter ZHAO (CN111710413A)
As per claim13, KANO does not teach:
The computer-implemented method as claimed in claim 1, further comprising: comparing the observable with corresponding observables of a number of reference patients that are different from the patient; selecting a comparison patient from among the number of reference patients based on the comparing; and providing information about the comparison patient.
However, ZHAO does teach:
The computer-implemented method as claimed in claim 1, further comprising: comparing the observable with corresponding observables of a number of reference patients that are different from the patient; selecting a comparison patient from among the number of reference patients based on the comparing; and providing information about the comparison patient. (Page 1 para. 11 discloses, “The comparison processing unit is configured to identify whether the first patient and the second patient are similar patients at least according to the basic information and medical treatment information of the first patient and the second patient.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine KANO’s teachings of indications of a medical diagnosis as previously cited with ZHAO’s teachings of comparing observable with reference patients when as previously cited, the motivation being that KANO’s clear in [0003] techniques and improved diagnosis methods are needed, therefore it would be predictable to combine with ZHAO based to increase successful diagnosis by identifying patterns in the data to inform the modeling used in KANO.
Response to Arguments Regarding 35 U.S.C § 101 Rejections
Applicant’s arguments on pages 3-6 of remarks have been considered. Applicant argues the 35 U.S.C § 101 rejection should be withdrawn for the following reasons. Without conceding to this rejection, Applicants have amended claims 1-2, 4, 6- 13, 15-17 and 19-20, and canceled claim 3. Applicants submit that claim 1 is directed to patent eligible subject matter at least because any alleged abstract idea in claim 1 is integrated into a practical application.
On page 4 of the Office Action, the Examiner asserts that the below-bolded features previously recited by claim 1 recite the alleged abstract idea of "making an observation or judgment when reviewing time series data to provide an observable:" A computer-implemented method for providing an observable indicating a medical diagnosis, the computer-implemented method comprising: obtaining a medical image data series of a patient, wherein the medical image data series has a number of medical image datasets, which have each been recorded over a first period of time at different points in time; extracting a first time series from the medical image data series; determining the observable based on the first time series; and providing the observable. Specifically, the Examiner asserts that the above-bolded features recite a mental process. Applicants disagree. "Mental processes" include those processes "performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule)." Gottschalk v. Benson, 409 U.S. 63, 65, 67 (1972); MPEP 2106.04(a)(2)(III). "The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another"[emphasis added]. MPEP 2 106.04(a)(2)(III)(B). Applicants submit that the human mind clearly cannot obtain a medical image data series of a patient, extract a first time series from the medical image data series, determine an observable based on the first time series; and provide the observable. And, it is unclear how the Office is interpreting claim 1 to conclude that could even arguably be the case. Because one or more of the operations of claim 1 cannot be performed entirely in the human mind, claim 1 does not recite an abstract idea. See MPEP 2106.04(a)(2)(III)(A) ("Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019)").
Examiner appreciates applicant’s arguments but respectfully does not find them persuasive. The MPEP states The Alice/Mayo two-part test is the only test that should be used to evaluate the eligibility of claims under examination. While the machine-or-transformation test is an important clue to eligibility, it should not be used as a separate test for eligibility. Instead it should be considered as part of the "integration" determination or "significantly more" determination articulated in the Alice/Mayo test. Bilski v. Kappos, 561 U.S. 593, 605, 95 USPQ2d 1001, 1007 (2010). See MPEP § 2106.04(d) for more information about evaluating whether a claim reciting a judicial exception is integrated into a practical application and MPEP § 2106.05(b) and MPEP § 2106.05(c) for more information about how the machine-or-transformation test fits into the Alice/Mayo two-part framework.
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I); (Mathematical Calculations - A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.)
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014);
a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); and
a claim to identifying head shape and applying hair designs, which is a process that can be practically performed in the human mind, In re Brown, 645 Fed. App'x 1014, 1016-17 (Fed. Cir. 2016) (non-precedential).
If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, 839 F.3d at 1139, 120 USPQ2d at 1474 (holding that claims to the mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For instance, in CyberSource, the court determined that the step of "constructing a map of credit card numbers" was a limitation that was able to be performed "by writing down a list of credit card transactions made from a particular IP address." In making this determination, the court looked to the specification, which explained that the claimed map was nothing more than a listing of several (e.g., four) credit card transactions. The court concluded that this step was able to be performed mentally with a pen and paper, and therefore, it qualified as a mental process. 654 F.3d at 1372-73, 99 USPQ2d at 1695. See also Flook, 437 U.S. at 586, 198 USPQ at 196 (claimed "computations can be made by pencil and paper calculations"); University of Florida Research Foundation, Inc. v. General Electric Co., 916 F.3d 1363, 1367, 129 USPQ2d 1409, 1411-12 (Fed. Cir. 2019) (relying on specification’s description of the claimed analysis and manipulation of data as being performed mentally "‘using pen and paper methodologies, such as flowsheets and patient charts’"); Symantec, 838 F.3d at 1318, 120 USPQ2d at 1360 (although claimed as computer-implemented, steps of screening messages can be "performed by a human, mentally or with pen and paper").
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").
In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01.
Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were "the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries." 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of "anonymous loan shopping", which was a concept that could be "performed by humans without a computer." 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
Examiners should keep in mind that both product claims (e.g., computer system, computer-readable medium, etc.) and process claims may recite mental processes. For example, in Mortgage Grader, the patentee claimed a computer-implemented system and a method for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The Federal Circuit determined that both the computer-implemented system and method claims were directed to "anonymous loan shopping", which was an abstract idea because it could be "performed by humans without a computer." 811 F.3d. at 1318, 1324-25, 117 USPQ2d at 1695, 1699-1700. See also FairWarning IP, 839 F.3d at 1092, 120 USPQ2d at 1294 (identifying both system and process claims for detecting improper access of a patient's protected health information in a health-care system computer environment as directed to abstract idea of detecting fraud); Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1345, 113 USPQ2d 1354, 1356 (Fed. Cir. 2014) (system and method claims of inputting information from a hard copy document into a computer program). Accordingly, the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim.
Examples of product claims reciting mental processes include:
An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356; A computer-implemented system for enabling anonymous loan shopping – Mortgage Grader, 811 F.3d at 1318, 117 USPQ2d at 1695; A computer readable medium containing program instructions for detecting fraud – CyberSource, 654 F.3d at 1368 n. 1, 99 USPQ2d at 1692 n.1; A post office for receiving and redistributing email messages on a computer network – Symantec, 838 F.3d at 1316, 120 USPQ2d at 1359;A self-verifying voting system – Voter Verified, 887 F.3d at 1384-85, 126 USPQ2d at 1504; A wide-area real-time performance monitoring system for monitoring and assessing dynamic stability of an electric power grid – Electric Power Group, 830 F.3d at 1351 and n.1, 119 USPQ2d at 1740 and n.1; and Computer readable storage media comprising computer instructions to implement a method for determining a price of a product offered to a purchasing organization – Versata, 793 F.3d at 1312-13, 115 USPQ2d at 1685.
Examples of process claims reciting mental process-type abstract ideas are discussed in the preceding subsections (A) through (C). See, for example, the discussion of Flook, 437 U.S. 584, 198 USPQ 193; Benson, 409 U.S. 63, 175 USPQ 673; Berkheimer, 881 F.3d 1360, 125 USPQ2d 1649; Synopsys, 839 F.3d 1138, 120 USPQ2d 1473; and Ambry Genetics, 774 F.3d 755, 113 USPQ2d 1241, supra.
Further MPEP states Examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above. Furthermore, the MPEP state in 2106.04(d), “Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical applications.”
Therefore examiner does not find applicant’s argument persuasive as the instant application claims do recite a mental process by making an observation or judgement when reviewing times series data to provide an observable. The recited claims (claim 1 as representative) is broad and although recites a general purpose computer (see instant application [00268]) this does not make it dispositive of reciting an abstract idea nor being a mental process. On the contrary, its not would a human do it mentally but could they and humans can obtain imaging data and determine a series of timed data form this imaging information determine correlations and determine based on observation and judgement an observable as for examples doctors can do this and the use of a computer to make this process more automated does not again make it dispositive of a mental process. Examiner maintains the claims are directed to an abstract idea of the enumerated grouping “mental process”.
Applicant further argues that amended claim 1 as a whole recites such an improvement to a technology or technical field, and thus, integrates the alleged exception into a practical application. Parameter values over time sequences provide decisive medical diagnostic indications (see, for example, paragraph [0003] of the specification as originally filed). Conventionally, however, doctors do not obtain quantitative measured values over time sequences for consideration during diagnosis of medical image data due to a lack of time (see, for example, paragraph [0005] of the specification as originally filed). Instead, doctors limit their analyses to image datasets obtained at different points in time alongside one another (see, for example, paragraph [0005] of the specification as originally filed). Accordingly, conventional approaches result in insufficiently accurate medical diagnoses due to the lack of decisive medical diagnostic indications provided by time sequences of parameter values (see, for example, paragraphs [0003]-[0005] of the specification as originally filed).
However, according to some example embodiments, improved devices and methods are provided. For example, the improved devices and methods extract a time series from a medical image data series, and determine correlation information specifying an amount of temporal correlation between the first time series and a second time series (see, for example, paragraphs [0023]-[0024] of the specification as originally filed). Accordingly, the improved devices and methods enable the time series and correlation information to be available to a doctor during the limited time available for diagnosis of medical image data (see, for example, paragraphs [0005] and [0023] of the specification as originally filed). Thus, the improved devices and methods overcome the deficiencies of the conventional approaches to improve the accuracy of medical diagnoses by enabling the consideration of decisive medical diagnostic indications provided by time sequences of parameter values (see, for example, paragraphs [0003]- [0005] and [0023] of the specification as originally filed).
In amended claim 1, for example, these features are characterized by, inter alia, extracting a first time series from the medical image data series; determining first correlation information based on the first time series and a second time series, wherein the first correlation information specifies an amount of temporal correlation between the first time series and the second time series; determining the observable based on the first correlation information.
Therefore, claim 1 is directed to improved devices and methods that improve the accuracy of medical diagnoses (see, for example, paragraphs [0003]-[0005] and [0023] of the specification as originally filed). Accordingly, Applicants respectfully submit that claim 1 is subject matter eligible.
Regarding independent claim 15, this claim is separate from claim 1, wherein each claim contains its own individual limitations and should be interpreted solely based upon limitations set forth therein. However, Applicants respectfully submit that claim 15 recites features similar to the above-quoted features of claim 1 that integrate the alleged judicial exception into a practical application. Accordingly, Applicants respectfully submit that claim 15 is not directed to a judicial exception for reasons at least similar to those provided above, and thus, are subject matter eligible. Claims 2, 4-14 and 16-22 are patent eligible by virtue of their dependencies. Accordingly, Applicants request the Examiner to reconsider and withdraw the above rejection. Examiner appreciates applicants arguments but does not find them persuasive. The judicial exception (abstract idea) cannot integrate itself into a practical application but identification of any additional elements recited in the claim can be evaluated to determine if the additional elements integrate the exception into a practical application. The claims additional elements are not recited as being an improvement to a technology field or a technology confined to the general computer environment in which the claims recite. A technical problem must first be identified in instant application specification and reflected in the claims. Problems recited in the arguments are abstract problems related to clinical decision making due to a lack of time and do not improve diagnosis accuracy as a human still needs to review the data with no claimed recitation or reflection of improvement to a technology in this field. The claimed invention is using the computer as a tool and any improvement present is an improvement to the abstract idea. If applicant’s line of reasoning were correct Alice corp. would have been deemed eligible. Examiner maintains the claims are directed to an abstract idea and do not integrate into a practical application. Therefore, they also do not amount to significantly more.
Examiner maintains the 35 U.S.C § 101 rejection
Response to Arguments Regarding 35 U.S.C § 102/103 Rejections
Applicant’s arguments on pages 6-12 of remarks have been considered. Applicant argues the 35 U.S.C § 102/103 rejection should be withdrawn for the following reasons:
Independent claims 1 and 15: In order for a claim to be anticipated under 35 U.S.C. § 102, each and every element as set forth in the claim must be found in a single prior art reference. See Verdegaal Bros. v. Union Oil Co. of California, 814 F.2d 628, 631 (Fed. Cir. 1987); MPEP § 2131. Applicants respectfully submit that Kano does not meet this criterion because Kano does not disclose or suggest at least, "determining first correlation information based on the first time series and a second time series, wherein the first correlation information specifies an amount of temporal correlation between the first time series and the second time series; [and] determining the observable based on the first correlation information," as recited by amended claim 1.
On pages 12-14 of the Office Action, the Examiner cites paragraphs 0268-0270 of Kano to teach "determining correlation information based on the first time series and the second time series, wherein the correlation information specifies a measure for a temporal correlation between the first time series and the second time series; and establishing the observable based on the correlation information," as previously recited by claim 3 and similar to the above-quoted features of amended claim 1. Applicants disagree, at least in view of claim 1 as amended.
Kano describes identifying a latent variable using a formula modeling deformation of a blood vessel and a relational expression between a temporal change amount in an observed value of a blood vessel cross-sectional shape change index and a change amount in internal pressure (para. 0267-0268). The observed values of the blood vessel cross-sectional shape change index are measured from CT images in a time series (para. 0268). The temporal change in the internal pressure distribution is determined to match the temporal change amount in the observed value of the blood vessel cross-sectional shape change index (para. 0268). A prediction value of a blood flow amount index is measured based on the internal pressure distribution, and compared to an observed value (para. 0268). If the prediction value of the blood flow amount index does not match the observed value, an elasticity of the blood vessel in the formula is changed, and the analysis repeated to determine the elasticity as the latent variable (para. 0268-0269).
Kano also describes obtaining images in a time series related to a blood vessel, calculating a physical index of an identification target region based on medical images, and identifying a function index of the identification target region based on the physical index and prestored correlation information (para. 0270).
Contrary to amended claim 1, Kano does not describe or suggest at least, "determining first correlation information based on the first time series and a second time series, wherein the first correlation information specifies an amount of temporal correlation between the first time series and the second time series; [and] determining the observable based on the first correlation information." For example, Kano is silent regarding "determining first correlation information ... specif[ying] an amount of temporal correlation between the first time series and the second time series," as recited by amended claim 1. Kano merely describes determining a temporal change in the internal pressure distribution that matches the temporal change amount in the observed value of the blood vessel cross-sectional shape change index. Kano does not describe determining "an amount of temporal correlation" between the temporal change in the internal pressure distribution and the temporal change amount in the observed value of the blood vessel cross-sectional shape change index.
Also, Kano describes determining a physical index based on a function index and prestored correlation information, but does not describe determining "an amount of temporal correlation" between the physical index and the function index.
Therefore, Kano does not describe or suggest at least, "determining first correlation information based on the first time series and a second time series, wherein the first correlation information specifies an amount of temporal correlation between the first time series and the second time series; [and] determining the observable based on the first correlation information," as recited by amended claim 1.
In view of the above, Applicants respectfully submit that Kano fails to disclose or suggest each and every element of amended claim 1, and therefore an anticipatory rejection has not been established. In regard to independent claim 15, this claim is a separate claim from claim 1, wherein each claim contains its own individual limitations and should be interpreted solely based upon limitations set forth therein. However, considering claim 15 includes language at least similar to that in claim 1 not disclosed by Kano, Applicants respectfully submit that an anticipatory rejection has not been established with respect to claim 15. Since Kano fails to disclose each and every element of claims 1 and 15, it cannot provide a basis for a rejection under 35 U.S.C. § 102. Further, Applicants submit that at least the above-quoted features of claims 1 and 15 are not obvious over Kano, and the Examiner has not provided any evidence to the contrary. Thus, claims 1 and 15 are allowable over Kano. Claims 2, 4-14 and 16-22 cannot be rejected under 35 U.S.C. § 102 by virtue of their dependencies on allowable claim 1.
Accordingly, Applicants request the Examiner to reconsider and withdraw the above rejection.
Examiner appreciates applicant’s arguments but does not find them persuasive. The claims are interpreted based on the claim construction and language in light of the specification, but the specification is not read into the claims. Further, if no specific limiting definition is given for a term claimed then examiner takes the plain definition as one of ordinary skill in the art would understand. Claim 1 amended limitation argued recites, “determining first correlation information based on the first time series and a second time series, wherein the first correlation information specifies an amount of temporal correlation between the first time series and the second time series;” and Examiner cites and further expand the citation of Kano’s teachings based on the amended claim 1 to include see e.g. [0088] which discloses, “The blood vessel cross-sectional shape change indices of the aorta are used as blood vessel cross-sectional shape change indices of the entrance of a coronary artery (i.e., a Surrounding region of the coronary artery starting part). The coronary artery entrance is such an end of the coronary artery that is positioned on the upstream side in terms of the blood flow direction. The blood vessel cross sectional shape change indices of the aorta may be, for example, a temporal change ratio or a change amount in an average of the cross-sectional areas of a plurality of cross sectional planes that are measured in positions away from the coronary artery starting part by short distances (e.g., a number of centimeters approximately) on the upstream side in terms of the blood flow direction; coefficients indicating changes thereof from either a blood vessel expansion time or a maximum flow amount time to a contraction time (e.g., cardiac phases of 70 to 100%); or a dispersion in the cross-sectional areas of a single cross-sectional plane. Instead of the cross-sectional areas, it is also acceptable to use a temporal change ratio or a change amount related to changes in the Volume of the vascular lumen, or coefficients indicating changes thereof from either a blood vessel expan sion time or a maximum flow amount time to a contraction time (e.g., cardiac phases of 70 to 100%), in consideration of changes in the cross-sectional area in the central line direction. The change amounts may be expressed by using indices related to concentration or dispersion of a contrast agent within the blood vessel.” And see e.g. [0406] discloses, “In this situation, the third display information 330 includes cross-sectional images each indicating a cross sectional plane at a measuring point designated by the operator. For example, the display controlling unit 227g causes the display unit 31 to display, as the cross-sectional images, a cross-sectional image in a first temporal phase indicating a cross-sectional plane at a measuring point designated by the operator and at least one other cross sectional image in a second temporal phase indicating a cross-sectional plane in Such a position that anatomically corresponds to that of the cross-sectional plane in the first temporal phase. For example, as illustrated in the upper section of FIG. 21, the display controlling unit 227g realizes the display of a cross-sectional image 331a indicating the cross-sectional plane at a measuring point designated by the operator and cross-sectional images 331b to 331e in other cardiac phases each indicating a cross-sectional plane in Such a position that anatomically corresponds to the cross sectional plane of the cross-sectional image 331a.” and see [0408] and see [0268]-[0269]” Examiner notes the interpretation of a time series is a sequence of data points collected or recorded and an temporal correlation information is interpreted as a relationship between data points indicated by some quantitative amount which aligns with the specification of the instant application and the broad claim construction of the instant application claims. Thus examiner notes KANO clearly teaches time series imaging data in a previously cited limitation (see KANO [0064]) over the course of twenty cardiac phases and each cardiac phase can be reasonably understood by one of ordinary skill to be its on time series of data as a cardiac phase is a continuous sequence of events which occur within a heartbeat. Further KANO teaches as previously cited that the temporal correlation is happening over multiple time series of data aligning to cardiac phases of heartbeats which are a continuous sequence of events which occur during a heartbeat and are correlated with quantitative values in examples given by KANO such as temporal change ratio for example. This meets the broad level of claim construction for the instant application limitation recitation as there is no further limiting of the how or what the time series must be or present as and no further recitation on what exactly an “amount” of temporal correlation is. Examiner maintains KANO teaches this limitation to the broad level in which it is recited.
Applicant further argues, dependent claim 9. Further, regarding dependent claim 9, in addition to being patentable by virtue of its dependency from claim 1, Applicants submit that Kano does not disclose or suggest at least, "a measurement variable of the first time series is selected from: a Body Composition Analysis (BCA) value including at least one of a proportion of muscle or a proportion of body fat; a size of a lesion; or a tumor burden in an area of a body of the patient or in an entirety of the body of the patient," as recited by amended claim 9. On pages 19-20 of the Office Action, the Examiner cites paragraph 0366 of Kano to teach "a measurement variable of the first time series is selected from: an analysis value of a bodily composition, BCA value, including at least one of a proportion of muscle or a proportion of body fat, a size of a lesion, or a tumor burden in a part of the body or an area of the body of the patient or in the entire body of the patient," as previously recited by claim 9. Applicants disagree, especially in view of claim 9 as amended. Kano describes measuring points in a blood vessel region such as a lesion site where a pathological issue has occurred. Kano is silent, however, regarding "a measurement variable of the first time series [being] selected from ... a tumor burden in an area of a body of the patient or in an entirety of the body of the patient," as recited by amended claim 9. Instead, Kano merely describes measuring points in a blood vessel region. Accordingly, Kano does not disclose or suggest at least, "a measurement variable of the first time series is selected from: a Body Composition Analysis (BCA) value including at least one of a proportion of muscle or a proportion of body fat; a size of a lesion; or a tumor burden in an area of a body of the patient or in an entirety of the body of the patient," as recited by amended claim 9.
In view of the above, Applicants respectfully submit that Kano fails to disclose or suggest each and every element of amended claim 9, and therefore an anticipatory rejection has not been established. Since Kano fails to disclose each and every element of claim 9, it cannot provide a basis for a rejection under 35 U.S.C. § 102. Further, Applicants submit that at least the above-quoted features of claim 9 are not obvious over Kano, and the Examiner has not provided any evidence to the contrary. Thus, claim 9 is allowable over Kano.
Accordingly, Applicants request the Examiner to reconsider and withdraw the above rejection.
Applicant’s arguments with respect to claim 9 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant further argues claim rejections under 35 U.S.C. § 103 stating claims 5 and 18 stand rejected under 35 U.S.C. § 103 as allegedly unpatentable over Kano in view of U.S. Patent Application Publication No. 2020/0310098 ("Ince"); claim 12 stands rejected under 35 U.S.C. § 103 as allegedly unpatentable over Kano in view of U.S. Patent Application Publication No. 2021/0327594 ("Shukla"); and claim 13 stands rejected under 35 U.S.C. § 103 as allegedly unpatentable over Kano in view of Chinese Patent Publication No. CN111710413 ("Zhao").
Applicants respectfully traverse these rejections in that even assuming arguendo that Kano could be combined with Ince, Shukla and/or Zhao, which Applicants do not admit, the resultant combination fails to render claims 5, 12-13 and 18 obvious because Ince, Shukla and/or Zhao suffer from at least the same deficiencies as Kano with regard to independent claim.
Accordingly, Applicants request the Examiner to reconsider and withdraw the above rejection. Applicants add new claims 21-22. Applicants assert that these claims are allowable by virtue of their dependencies and/or for the further features recited therein. Support for claims 21-22 may be found in, for example, paragraphs [0012], [0017]- [0019] and [0210] of the specification as originally filed.
Examiner notes no specific arguments explaining how the claims avoid the references or distinguish from them is presented for arguments under 103.
Examiner maintains the 35 U.S.C 102/103 rejections.
Prior Art not cited but made of record
US20210251503A1 - GUREVICH et. al
Methods and systems for characterizing tissue of a subject include acquiring and receiving data for a plurality of time series of fluorescence images , identifying one or more attributes of the data relevant to a clinical characterization of the tissue , and categorizing the data into clusters based on the attributes such that the data in the same cluster are more similar to each other than the data in different clusters , wherein the clusters characterize the tissue . The methods and systems further include receiving data for a subject time series of fluorescence images , associating a respective clus ter with each of a plurality of subregions in the subject time series of fluorescence images , and generating a subject spatial map based on the clusters for the plurality of subre gions in the subject time series of fluorescence images . The generated spatial maps may then be used as input for tissue diagnostics using supervised machine learning.
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 Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
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/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687