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 .
Election/Restrictions
Applicant’s election without traverse of Group 1 (claims 1 – 24 and 40 – 44) in the reply filed on May 29, 2026 is acknowledged.
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.
Claim 43 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 43 claimed computer program product. It can be interpreted as software per se. It fails to fall within a statutory category of invention. It is not a process occurring as a result of executing the software, a machine programmed to operate in accordance with the software nor a manufacture structurally and functionally interconnected with the software in a manner which enables the software to act as a computer component and realize its functionality. It is also clearly not directed to a composition of matter. Therefore, it is non-statutory under 35 U.S.C. 101.
Claims 1 – 3 and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Regarding claim 1:
Step 1:
Claim 1 is directed towards a process, machine, manufacture or composition of matter which is/are statutory subject matter.
Step 2A:
Prong 1:
Claim 1 is directed an idea for analyzing blood flow in a subject which is an abstract idea.
Consideration of the claimed elements:
Regarding claim 1:
The claims in the instant application include:
generating image data of an area of skin of the subject, the image data reproducible as one or more images of the area of skin of the subject, one or more videos of the area of skin of the subject, or both;
analyzing at least a portion of the image data to determine a concentration of one or more chromophores within the area of skin of the subject; and
determining, based at least in part on the concentration of the one or more chromophores, a value of at least one metric associated with blood flow of the subject.
Regarding “analyzing at least a portion of the image data to determine a concentration of one or more chromophores within the area of skin of the subject”, it can be interpreted as a human can observe/analyze a portion of the image data to determine a concentration of one or more chromophores within the area of skin of the subject (e.g. based on color etc.). The claimed limitation can be broadly read as a concept performed by a human mind, thus categorized as mental processes.
Regarding “determining, based at least in part on the concentration of the one or more chromophores, a value of at least one metric associated with blood flow of the subject,” it can be interpreted as the human can determining, based at his/her experience or a mapping chart, a value of at least one metric associated with blood flow of the subject. The claimed limitation can be broadly read as a concept performed by a human mind, thus categorized as mental processes.
Prong 2:
The claims include additional elements of
generating image data of an area of skin of the subject, the image data reproducible as one or more images of the area of skin of the subject, one or more videos of the area of skin of the subject, or both.
Regarding “generating image data of an area of skin of the subject, the image data reproducible as one or more images of the area of skin of the subject, one or more videos of the area of skin of the subject, or both”, it is considered as data gathering of adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g.
Moreover, the claim limitations that are not indicative of integration into a practical application.
Thus, the recited generic additional element (e.g., data gathering) perform no more than their basic computer function. Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a computational algorithm, a generic memory and processor are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Accordingly, the claims are directed to an idea of itself, and therefore not patent eligible.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception such as improvements to another technology or technical field, or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Moreover, the claim language that may be separate from the abstract idea (i.e., additional elements) include data gathering.
The additional element (e.g., data gathering) simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (WURC) - see MPEP 2106.05(d) and 2106.07(a)III.
Thus, the recited generic additional elements (e.g., data gathering) perform no more than their basic computer function. Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a generic memory are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Consequently, the identified additional elements taken into consideration individually or in combination fails to amount of significantly more than the abstract idea above.
Regarding claims 2 – 3 and 24, the rejection is based on the same rationale described for claim 1 because the claims include/inherit the same/similar type of problematic limitation(s) as claim 1, wherein limitations regarding additional aspect for process; "includes ... ", and “is …”, is/are of sufficient breadth that it would be substantially directed to or reasonably interpreted as a part of the “mental processes” as the abstract idea (similar to claim as stated above). It is noted that further additional limitation is merely generic/conventional computer component/steps to implement the abstract idea, which is, individually or in combination, not sufficient to amount to significantly more than the judicial exception. Therefore, the claimed invention as a whole is directed to an ineligible subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 – 19, 21, 23 – 24 and 40 – 44 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lee et al. (US Patent Application Publication 2021/0085227, IDS), hereinafter referred as Lee.
Regarding claim 1, Lee discloses a method for analyzing blood flow in a subject ([0016], determining at least one of systolic uptake, peak systolic pressure, systolic decline, dicrotic notch, and diastolic runoff of the blood flow data signal), the method comprising:
generating image data of an area of skin of the subject ([0022, 0059], transdermal optical imaging 'TOI' module to receive a captured image sequence of light re-emitted from the skin of one or more humans), the image data reproducible as one or more images of the area of skin of the subject, one or more videos of the area of skin of the subject, or both ([0060], the TOI module 110, via the image processing unit 104, obtains each captured image in a video stream);
analyzing at least a portion of the image data to determine a concentration of one or more chromophores within the area of skin of the subject ([0060], the processor unit performs operations upon the image to generate a corresponding optimized hemoglobin (chromophore) concentration 'HG' image of the subject); and
determining, based at least in part on the concentration of the one or more chromophores, a value of at least one metric associated with blood flow of the subject ([0062 - 0063], determining blood pressure based on the concentration of hemoglobin).
Regarding claim 2 (depends on claim 1), Lee discloses the method wherein the one or more chromophores include hemoglobin, melanin, or both ([0060], hemoglobin).
Regarding claim 3 (depends on claim 1), Lee discloses the method wherein the area of skin of the subject includes at least a portion of a face of the subject ([0060], face).
Regarding claim 4 (depends on claim 1), Lee discloses the method wherein analyzing at least the portion of the image data includes inputting at least the portion of the image data into one or more trained machine learning algorithms, the one or more trained machine learning algorithms trained to output an indication of the concentration of the one or more chromophores within the area of the skin of the subject ([0061 – 0062, 0079 – 0082], predicting HC change).
Regarding claim 5 (depends on claim 4), Lee discloses the method wherein the one or more trained machine learning algorithms are configured to:
identify one or more landmarks within the area of skin of the subject ([0063], identify forehead, nose and cheeks);
based at least in part on the identified landmarks, divide the area of skin of the subject into a plurality of regions ([0063, 0066]); and
determine the concentration of the one or more chromophores of each of the plurality of regions based on a color value of the at least one pixel within each of the plurality of regions ([0061 – 0062, 0066]).
Regarding claim 6 (depends on claim 5), Lee discloses the method wherein the image data is representative of the area of the skin of the subject over a time period and includes a plurality of frames, each of the plurality of frames being reproducible as an image of the area of the skin at a distinct point in time within the time period (Fig. 8 802 – 806, [0107]), and wherein the one or more trained machine learning algorithms are configured to determine the concentration of the one or more chromophores of each of the plurality of regions for each of the plurality of frames within the time period ([0060 – 0063, 0107]).
Regarding claim 7 (depends on claim 6), Lee discloses the method further comprising forming a temporal chromophore signal for the area of the skin of the subject based at least in part on the concentration of the one or more chromophores of each of the plurality of regions at each of the plurality of frames within the time period ([0063], signals that change over a particular time period (for example, 10 seconds) on each of the ROIs are extracted).
Regarding claim 8 (depends on claim 7), Lee discloses the method wherein the temporal chromophore signal represents a spatial variation of the concentration of the one or more chromophores across the area of skin of the subject (Fig. 4 shows a spatial variation of the concentration of the one or more chromophores across the area of skin of the subject), and a temporal variation of the concentration of the one or more chromophores across the time period (Fig. 7 shows a temporal variation of the concentration of the one or more chromophores across the time period, [0063]).
Regarding claim 9 (depends on claim 7), Lee discloses the method wherein the one or more trained machine learning algorithms are configured to form the temporal chromophore signal ([0060 – 0063, 0065, 0107]).
Regarding claim 10 (depends on claim 7), Lee discloses the method further comprising applying one or more filtering operations to the temporal chromophore signal to remove an influence of a cardiac cycle of the subject on the temporal chromophore signal ([0012, 0014, 0021, 0027, 0064, 0088 – 0093, 0105]).
Regarding claim 11 (depends on claim 10), Lee discloses the method wherein the one or more trained machine learning algorithms are configured to apply the one or more filtering operations ([0064 – 0065]).
Regarding claim 12 (depends on claim 10), Lee discloses the method wherein the one or more filtering operations include a Butterworth filter, an elliptical filter, a band-pass filter, or any combination thereof ([0012, 0014, 0021, 0027, 0064, 0088 – 0093, 0105]).
Regarding claim 13 (depends on claim 10), Lee discloses the method wherein the one or more filtering operations are configured to filter out variations in the temporal chromophore signal having a frequency corresponding to a frequency of the cardiac cycle of the subject ([0018, 0033, 0100, 0107], heart rate).
Regarding claim 14 (depends on claim 13), Lee discloses the method wherein the frequency of the cardiac cycle of the subject is between about 0.01 Hz and about 5.0 Hz ([0089 – 0090]).
Regarding claim 15 (depends on claim 7), Lee discloses the method wherein determining the value of at least one metric associated with blood flow of the subject includes determining at least one blood pressure value based at least in part on the concentration of the one or more chromophores for each of the plurality of regions ([0062 - 0063], determining blood pressure based on the concentration of hemoglobin).
Regarding claim 16 (depends on claim 15), Lee discloses the method wherein determining the value of at least one metric associated with blood flow of the subject includes determining a time-varying blood pressure signal based at least in part on the temporal chromophore signal (Fig. 7, [0086]).
Regarding claim 17 (depends on claim 15), Lee discloses the method wherein the blood pressure of the subject is a mean arterial blood pressure, a systolic blood pressure, a diastolic blood pressure, or any combination thereof ([0023]).
Regarding claim 18 (depends on claim 15), Lee discloses the method wherein the one or more trained machine learning algorithms are configured to determine the at least one blood pressure value (abstract, [0065, 0082, 0102, 0107]).
Regarding claim 19 (depends on claim 15), Lee discloses the method wherein one or more additional trained machine learning algorithms are configured to determine the at least one blood pressure value ([0107], CNN for blood pressure), the one or more additional trained machine learning algorithms that generate the at least one blood pressure value being different than the one or more trained machine learning algorithms that determine the concentration of the one or more chromophores ([0065], LSTM for HC).
Regarding claim 21 (depends on claim 5), Lee discloses the method wherein the image data is generated while the area of skin of the subject is illuminated by one or more illumination sources (Fig. 3, white light (source 301), [0059]), and wherein the determination of the concentration of the one or more chromophores is based at least in part on one or more characteristics of the one or more illumination sources ([0059 – 0062]).
Regarding claim 23 (depends on claim 4), Lee discloses the method wherein the one or more trained machine learning algorithms includes one or more convolutional neural networks ([0107], CNN).
Regarding claim 24 (depends on claim 1), Lee discloses the method wherein the metric associated with blood flow of the subject is a blood pressure signal (abstract, [0061]).
Regarding claim 40, Lee discloses a method of training one or more machine learning algorithms ([0061 – 0062, 0079 – 0082]), the method comprising:
generating a plurality of measurements of a concentration of one or more chromophores in skin tissue ([0060], the processor unit performs operations upon the image to generate a corresponding optimized hemoglobin (chromophore) concentration 'HG' image of the subject), each of the plurality of chromophore concentration measurement corresponding to a respective one of a plurality of subjects ([0016], determining at least one of systolic uptake, peak systolic pressure, systolic decline, dicrotic notch, and diastolic runoff of the blood flow data signal);
forming training data by correlating each of the plurality of chromophore concentration measurements with a blood pressure measurement of the respective one of the plurality of subjects ([0061], forming HC training set); and
training one or more machine learning algorithms using the training data such that the one or more machine learning algorithms are trained ([0061 – 0062, 0079 – 0082]) to determine a measurement of blood pressure in a subject based at least in part on a measurement of the concentration of the one or more chromophores in skin tissue of the subject ([0062 - 0063], determining blood pressure based on the concentration of hemoglobin).
Regarding claim 41, Lee discloses a system for analyzing blood flow (Fig. 1), the system comprising:
a processing device including one or more processors ([0050]); and
a memory having stored thereon machine-readable instructions, wherein the processing device is coupled to the memory ([0050]), and the method of claim 1 is implemented when the machine-readable instructions in the memory are executed by at least one of the one or more processors of the processing device (see claim 1 rejection).
Regarding claim 42, Lee discloses a system for analyzing blood flow (Fig. 1), the system including a processing device having one or more processors ([0050]) configured to implement the method of claim 1 (see claim 1 rejection).
Regarding claim 43, Lee discloses a computer program product comprising instructions ([0050]) which, when executed by a computer, cause the computer to carry out the method of claim 1 (see claim 1 rejection).
Regarding claim 44 (depends on claim 43), Lee discloses the computer program product wherein the computer program product is a non-transitory computer readable medium ([0050]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Michael et al. (US Patent Application Publication 2019/0130208), hereinafter referred as Michael.
Regarding claim 20 (depends on claim 19), Lee fails to explicitly disclose the method wherein the one or more additional trained machine learning algorithms includes at least one transformer.
However, in a similar field of endeavor Michael discloses a method for image processing (abstract). In addition, Michael discloses the one or more additional trained machine learning algorithms includes at least one transformer ([0033, 0045]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lee, and the one or more additional trained machine learning algorithms includes at least one transformer. The motivation for doing this is to overcome the severe computational and memory bottlenecks.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Sadalgi et al. (US Patent Application Publication 2022/0084296), hereinafter referred as Sadalgi.
Regarding claim 22 (depends on claim 21), Lee fails to explicitly disclose the method wherein the one or more trained machine learning algorithms are configured to determine the identity of the one or more illumination sources.
However, in a similar field of endeavor Sadalgi discloses a method for image processing (abstract). In addition, Sadalgi discloses one or more trained machine learning algorithms are configured to determine the identity of the one or more illumination sources ([0212, 0213]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Lee, and one or more trained machine learning algorithms are configured to determine the identity of the one or more illumination sources. The motivation for doing this is to adapts to dynamic conditions.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QIAN YANG whose telephone number is (571)270-7239. The examiner can normally be reached on Monday-Thursday 8am-6pm.
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/QIAN YANG/
Primary Examiner, Art Unit 2677
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