DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or
under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on January 08, 2024 were considered by the examiner.
Drawings
The drawings are objected to because Figs. 4A, 19C, 27-28, and 34A-35B contain photographs; however, photographs are not the only practicable medium to show the depicted elements, see 37 C.F.R. 1.84(b)(1).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
The use of the term “Microsoft” in ¶[00291], which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore, the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
The specification is objected to because the term is not capitalized and does not include a proper symbol. In ¶[00291], “Microsoft” should be “MICROSOFT®”. Appropriate correction is required.
Claim Objections
Claims 2-3, 7, 10-12, and 15 are objected to because of the following informalities:
in claim 3, line 2: “the OCT is” should be “the OCT sensor is”;
in claim 7, line 5: “training” should be inserted before “image”;
in claim 10, line 1: “system” should be “method”;
in claim 11, line 1: “system” should be “method”;
in claim 12, line 1: “system” should be “method”;
in claim 12, line 1: “the” should be inserted before “at least one”;
in claim 15, line 5: “training” should be inserted before “image”;
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites “a plurality of noninvasive sensors” in line 3, but it is not clear if this recitation is the same as, related to, or different from the recitation “a sensor” in line 1. The indefinite article “a” and “plurality” suggest that they are different, but the similar phraseology suggests that they are the same. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). The relationship between these recitations should be made clear. Appropriate correction is required.
Claim 2 recites “a patient” in line 4, but it is not clear if this recitation is the same as, related to, or different from the recitation “a patient” in line 2. The indefinite article “a” suggests that they are different, but the similar phraseology suggests that they are the same. If the recitations are the same, the present recitation should be “the patient”. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). The relationship between these recitations should be made clear. For the purposes of examination, these recitations are being interpreted as the same. Appropriate correction is required.
Claim 2 recites “a tissue site” in line 8, but it is not clear if this recitation is the same as, related to, or different from the recitation “a tissue site” in lines 1-2. The indefinite article “a” suggests that they are different, but the similar phraseology suggests that they are the same. If the recitations are the same, the present recitation should be “the tissue site”. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). The relationship between these recitations should be made clear. For the purposes of examination, these recitations are being interpreted as the same. Appropriate correction is required.
Claims 3-8 are rejected by virtue of their dependence from claim 2.
Claim 6 recites “a tissue sample” in line 2, but it is not clear if this recitation is the same as, related to, or different from the recitation “a tissue site” in claim 2, lines 1-2. The indefinite article “a” suggests that they are different, but the similar phraseology and context of the claim suggest that they are the same. If the recitations are the same, the present recitation should be “the tissue site”. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). The relationship between these recitations should be made clear. The recitation of “the tissue site” in lines 3-4 further adds to the confusion. For the purposes of examination, these recitations are being interpreted as the same. Appropriate correction is required.
Claim 6 recites “a patient” in line 2, but it is not clear if this recitation is the same as, related to, or different from the recitation “a patient” in claim 2, line 2. The indefinite article “a” suggests that they are different, but the similar phraseology suggests that they are the same. If the recitations are the same, the present recitation should be “the patient”. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). The relationship between these recitations should be made clear. For the purposes of examination, these recitations are being interpreted as the same. Appropriate correction is required.
Claim 7 recites “wherein the neural network is configured to: for each training image of the plurality of training images: extract a plurality of one dimensional slices of the training image at different pixel locations within the image; process the plurality of one dimensional slices of the training images through a plurality of convolution layers or pooling layers; and output a weight that the surface of the one or more sensor heads is in contact with the tissue site of the patient in the one or more training images based on the processed plurality of one dimensional slices of the training images” in lines 2-10, which is generally unclear. Claim 7 recites “the training image” in line 4 and “the image” in line 5; “the training images” in line 6 and in line 10; and “the one or more training images” in line 9. There is confusion as to which steps are performed on each image, or on multiple of the training images. For example, “a plurality of one dimensional slices” is recited for a singular training image in line 4, but for multiple training images in line 6. It is not clear whether a weight is output with respect to each image, or some combination of multiple images. It is not clear how the output weight is related to the likelihood score as recited in claim 2, line 13. These inconsistencies render claim 7 indefinite. Appropriate correction is required.
Claim 8 recites “a nonlinear transformation” in line 4 and in line 8; “a transformation” in line 12; and “a nonlinear function” in line 16, but the relationship among these recitations is not clear. The similar phraseology suggests that they are the same and/or related, and the indefinite article “a” suggests that they are different. The relationship among these recitations should be made clear. Appropriate correction is required.
Claim 8 recites the limitation “the output of the fully connected layer” in lines 16-17. There is insufficient antecedent basis for this limitation in the claim. Amending the recitation to “an output of the fully connected layer” would overcome this rejection”. The claim is being read as such for the purposes of examination. Appropriate correction is required.
Claim 8 recites the limitation “the output of the third activation layer” in lines 18-19. There is insufficient antecedent basis for this limitation in the claim. Amending the recitation to “an output of the third activation layer” would overcome this rejection”. The claim is being read as such for the purposes of examination. Appropriate correction is required.
Claim 8 recites “a dropout layer configured to apply a drop a percentage of the output of the third activation layer” in lines 18-19, which is grammatically awkward and unclear. It is not clear what the dropout layer is supposed to do within the context of claim 8. This confusion renders claim 8 indefinite. For the purposes of examination, this recitation is not being given patentable weight. Appropriate correction is required.
Claim 9 recites “a plurality of noninvasive sensors” in lines 3-4, but it is not clear if this recitation is the same as, related to, or different from the recitation “a sensor” in line 1. The indefinite article “a” and “plurality” suggest that they are different, but the similar phraseology suggests that they are the same. There is further confusion between “at least one” in line 3 and “a sensor” in line 1. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). The relationship between these recitations should be made clear. Appropriate correction is required.
Claim 9 recites the limitation “the one or more sensor heads” in line 9. There is insufficient antecedent basis for this limitation in the claim. There is further confusion between this recitation and “a sensor” in line 1 and “at least one of a plurality of noninvasive sensors” in lines 3-4. Appropriate correction is required.
Claims 10-16 are rejected by virtue of their dependence from claim 9.
Claim 14 recites “a tissue sample” in line 2, but it is not clear if this recitation is the same as, related to, or different from the recitation “a tissue site” in claim 9, lines 1-2. The indefinite article “a” suggests that they are different, but the similar phraseology and context of the claim suggest that they are the same. If the recitations are the same, the present recitation should be “the tissue site”. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). The relationship between these recitations should be made clear. For the purposes of examination, these recitations are being interpreted as the same. Appropriate correction is required.
Claim 14 recites “a patient” in line 3, but it is not clear if this recitation is the same as, related to, or different from the recitation “a patient” in claim 9, line 2. The indefinite article “a” suggests that they are different, but the similar phraseology suggests that they are the same. If the recitations are the same, the present recitation should be “the patient”. If the recitations are different, the relationship between these recitations should be made clear and they should be clearly distinguished from each other (e.g., when multiple elements have similar or the same labels, distinct identifiers such as “first” and “second” should be used to clearly differentiate the elements). The relationship between these recitations should be made clear. For the purposes of examination, these recitations are being interpreted as the same. Appropriate correction is required.
Claim 15 recites “wherein the neural network is configured to: for each training image of the plurality of training images: extract a plurality of one dimensional slices of the training image at different pixel locations within the image; process the plurality of one dimensional slices of the training images through a plurality of convolution layers or pooling layers; and output a weight that the surface of the one or more sensor heads is in contact with the tissue site of the patient in the one or more training images based on the processed plurality of one dimensional slices of the training images” in lines 2-10, which is generally unclear. Claim 15 recites “the training image” in line 4 and “the image” in line 5; “the training images” in line 6 and in line 10; and “the one or more training images” in line 9. There is confusion as to which steps are performed on each image, or on multiple of the training images. For example, “a plurality of one dimensional slices” is recited for a singular training image in line 4, but for multiple training images in line 6. It is not clear whether a weight is output with respect to each image, or some combination of multiple images. It is not clear how the output weight is related to the likelihood score as recited in claim 9, lines 8-9. These inconsistencies render claim 15 indefinite. Appropriate correction is required.
Claim 16 recites “a nonlinear transformation” in line 5 and in line 8; “a transformation” in line 12; and “a nonlinear function” in line 16, but the relationship among these recitations is not clear. The similar phraseology suggests that they are the same and/or related, and the indefinite article “a” suggests that they are different. The relationship among these recitations should be made clear. Appropriate correction is required.
Claim 16 recites the limitation “the output of the fully connected layer” in line 16. There is insufficient antecedent basis for this limitation in the claim. Amending the recitation to “an output of the fully connected layer” would overcome this rejection”. The claim is being read as such for the purposes of examination. Appropriate correction is required.
Claim 16 recites the limitation “the output of the third activation layer” in lines 17. There is insufficient antecedent basis for this limitation in the claim. Amending the recitation to “an output of the third activation layer” would overcome this rejection”. The claim is being read as such for the purposes of examination. Appropriate correction is required.
Claim 16 recites “applying a drop a percentage of the output of the third activation layer” in line 17, which is grammatically awkward and unclear. It is not clear what the drop a percentage is supposed to do within the context of claim 16. This confusion renders claim 16 indefinite. For the purposes of examination, this recitation is not being given patentable weight. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 2-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards abstract ideas without significantly more.
Claim 2 interpretation: Under the broadest reasonable interpretation (BRI), the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111. Based on the specification, the recitation “processing the image to determine a likelihood score that the surface of the one or more sensor heads is in contact with the tissue site of the patient” (see specification ¶[00285]-[00291] and ¶[00374]-[00375]) is being interpreted as mathematical calculations/evaluations and/or judgements. The recitation is computer-implemented, as indicated in the specification (see specification ¶[0015] and ¶[00373]), and in the claim line 9.
Claim 9 interpretation: Under the broadest reasonable interpretation (BRI), the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111. Based on the specification, the recitation “processing the image to determine a likelihood score that the surface of the one or more sensor heads is in contact with the tissue site of the patient” (see specification ¶[00285]-[00291] and ¶[00374]-[00375]) is being interpreted as mathematical calculations/evaluations and/or judgements (i.e., one of ordinary skill in the art could look at the OCT image and indicate a likelihood value). The recitation is computer-implemented, as indicated in the specification (see specification ¶[0015] and ¶[00373]), and in the claim line 9.
Step 1: This part of eligibility analysis evaluates whether the claim falls within any statutory category. MPEP 2106.03. Claim 2 recites a system, which is directed towards a machine/manufacture (a statutory category of invention). Claim 9 recites a method, which is directed towards a process (a statutory category of invention). Step 1: YES.
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04(a)(2)(III). The courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper” to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). The “mental processes” abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgements, and opinions. As discussed in the claim interpretation section, the limitations include, under the BRI, an evaluation and/or judgement. Accordingly, the limitations as seen in claims 2 and 9 recite judicial exceptions (abstract ideas that fall within the mental process grouping). No limitations are provided that would force the complexity of any of the identified evaluation steps to be non-performable by pen-and-paper practice.
Furthermore, as explained in MPEP 2106.04(a)(2)(I). The courts consider mathematical calculations, when the claim is given its BRI in light of the specification, as falling within the “mathematical concept” grouping of abstract ideas. A claim does not have to recite “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using a mathematical method, or “performing” a mathematical operation, may also be considered a mathematical calculation when the BRI of the claim in light of the specification encompasses a mathematical calculation. As discussed in the claim interpretation section, the limitations include, under the BRI, a mathematical calculation/evaluation. Accordingly, the limitations as seen in claims 2 and 9 recite judicial exceptions (abstract ideas that fall within the mathematical calculations grouping of mathematical concepts).
Alternatively or additionally, these steps describe the concept of using implicit mathematical formulas (i.e., calculations to determine a likelihood score) to derive a conclusion based on input of data, which corresponds to concepts identified as abstract ideas by the courts (Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)). The concept of the recited limitations identified as mathematical concepts above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
In particular, claim 1 recites the following elements, which are part of the abstract idea (i.e., the algorithm):
detecting an air gap between a surface of a sensor and a tissue site of a patient,
obtain physiological data associated with a patient;
receive an image of the tissue site of the patient;
process the image to determine a likelihood score that the surface of the one or more sensor heads is in contact with the tissue site of the patient; and
cause at least one of the plurality of noninvasive sensors to obtain physiological data associated with the patient based on the likelihood score.
Furthermore, claim 9 recites the following elements, which are part of the abstract idea (i.e., the algorithm):
a method of detecting an air gap between a surface of a sensor and a tissue site of a patient, the method comprising:
generating an image of the tissue site,
receiving the image of the tissue site of the patient;
processing the image to determine a likelihood score that the surface of the one or more sensor heads is in contact with the tissue site of the patient; and
causing at least one of the plurality of noninvasive sensors to obtain physiological data associated with the patient based on the likelihood score.
Step 2A Prong One: YES.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the judicial exceptions into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exceptions, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exceptions into a practical application. The claims recite additional elements related to a generic computer (i.e., the one or more hardware processors). The system/method are merely instructions to implement an abstract idea on a generic computer or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f).
Claims 2 and 9 further recite an additional element of noninvasive sensors. The noninvasive sensors do not qualify as integration into a practical application because this limitation is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a high level of generality – see MPEP 2106.04(d) and MPEP 2106.05(g) using a generic component (i.e., the noninvasive sensors are claimed generically).
The claims recite additional elements relating to one or more sensor heads, a frame, and a surface. Van Dorpe et al. (US Patent Application Publication 2017/0100064) teaches devices and methods for non-invasive measuring of analytes, such as in skin tissue, with a first optical unit including a Raman spectrometer, and a second optical unit, including an OCT spectrometer or IR spectrometer (see abstract and Fig. 5), in which the materials in the sensor head are commercially available and “off-the-shelf” thereby decreasing cost (see ¶[0099]-[0100] and ¶[0102]; Figs. 4-5). Therefore, the known and conventional, commercially available components, cannot be seen as integration into a practical application.
Step 2A Prong Two: NO.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05. As explained with Step 2A Prong Two, the claims recite additional elements directed towards a generic computer (i.e., the one or more hardware processors). The system/method utilizing a generic computer do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Claims 2 and 9 further recite additional elements relating to one or more sensor heads, a frame, a surface, and noninvasive sensors. These elements do not qualify as significantly more because this is simply appending well-understood, routine, conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception. For example, Van Dorpe et al. (US Patent Application Publication 2017/0100064) teaches devices and methods for non-invasive measuring of analytes, such as in skin tissue, with a first optical unit including a Raman spectrometer, and a second optical unit, including an OCT spectrometer or IR spectrometer (see abstract and Fig. 5), in which the materials in the sensor head are commercially available and “off-the-shelf” thereby decreasing cost (see ¶[0099]-[0100] and ¶[0102]; Figs. 4-5). Therefore, the known and conventional, commercially available components, cannot be seen as significantly more.
The claims also recite a classifier trained by a neural network which is merely insignificant extrasolution activity to the judicial exception, e.g., mere data manipulation using a well-known element (i.e., the classifier trained by the neural network) claimed generically. Machine learning, when claimed generically, is well-known, routine, and conventional in the art. For example, see Hu et al. (“Intelligent Sensor Networks The Integration of Sensor Networks, Signal Processing and Machine Learning”, CRC Press, 2012/10/23) teaches machine learning may utilize data from wireless sensor networks in supervised and unsupervised fashion (see pg. 3-7), and may be utilized for predictions (see pg. 153-180). See Huang et al. (“Kernal Based Algorithms for Mining Huge Data Sets Supervised, Semi-supervised, and Unsupervised Learning”, Springer, 2006) teaches about supervised learning, including for predicting labels of unseen data (see pg. 1-3). See Mitchell (“The Discipline of Machine Learning”, Machine Learning Department, Carnegie Mellon, July 2006) teaches about the state of machine learning, and including the usage in biological settings (see pg. 1-7). Therefore, the classifier trained by the neural network cannot be seen as significantly more.
Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no
indication that the combination of elements improves the functioning of a computer, for
example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is
no indication that the combination of elements includes a particular solution to a computer-based
problem or a particular way to achieve a desired computer-based outcome. Rather, the collective
functions of the claimed invention merely provide conventional computer implementation, i.e.,
the computer is simply a tool to perform the process. Step 2B: NO.
Claims 2 and 9 are not eligible.
Claims 3-8 and 10-16 depend from claims 2 and 9, respectively, merely further define the abstract ideas of claims 2 and 9. The claims recite no additional element that integrates the judicial exceptions into a practical application. The device/method are merely instructions to implement an abstract idea on a generic computer or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). The claims recite no additional element that adds an inventive concept to the claim and/or amounts to significantly more than the recited exception. The method/devices utilizing a generic computer do not qualify as significantly more because these limitations are simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Claims 3-6 and 10-14 recite further elements relating to the Raman spectrometer and OCT sensors. Such elements cannot be seen as integration into a practical application or significantly more because they are simply appending well-understood, routine, conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception. For example, Van Dorpe et al. (US Patent Application Publication 2017/0100064) teaches devices and methods for non-invasive measuring of analytes, such as in skin tissue, with a first optical unit including a Raman spectrometer, and a second optical unit, including an OCT spectrometer or IR spectrometer (see abstract and Fig. 5), and a laser light source (see ¶[0110], ¶[0119], and ¶[0137]), in which the materials in the sensor head are commercially available and “off-the-shelf” thereby decreasing cost (see ¶[0099]-[0100] and ¶[0102]; Figs. 4-5). In addition, Lui et al. (US Patent Application Publication 2015/0011893) teaches a Raman spectrometer system capable of discriminating between different tissue pathologies via ranges in a Raman spectrum with improved sensitivity and specificity (see abstract and Figs. 1-2), in which the tissue is skin (see ¶[0036]), in which the ranges may be utilized for reference molecules in skin (see ¶[0073] and ¶[0123]; Figs. 4A-4D), and the ranges are captured in at least 11 sub-ranges in the Raman spectrum (see ¶[0040]-[0054]) captured via spectrometer 38 which is implemented with an NIR-optimized CCD array with holographic grating, both commercial components (see ¶[0106] and Fig. 2), with light source 32, such as implemented via a laser (see ¶[0102] and Fig. 2). Furthermore, Zhou (US Patent Application Publication 2020/0166328) teaches about integrated photonic chips and related systems and methods for OCT scanning (see abstract), in which a commercially available laser light source is utilized (see ¶[0043]), at various wavelengths, such as a range of 1310 ± 60 nm (see ¶[0036] and ¶[0068]). Therefore, the known and conventional, commercially available components, cannot be seen as integration into a practical application or significantly more.
Claims 7-8 and 15-20 recite further elements relating to the neural network. Even if such elements were to be considered as non-generic machine learning, those elements would still be interpreted as mathematical calculations and evaluations. Therefore, such elements cannot be seen as integration into a practical application or significantly more, as those elements are directed towards abstract ideas themselves.
Looking at the limitations of each claim as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The succeeding art rejections to the claims under 35 U.S.C. § 103 below are made with the claims as best understood and interpreted in light of the preceding rejections under 35 U.S.C. § 112 above.
Claims 2-4 and 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Van Dorpe et al. (US Patent Application Publication 2017/0100064), hereinafter Van Dorpe, and in view of Margallo Balbás (US Patent Application Publication 2017/0027639), hereinafter Margallo Balbás, and in view of Baker Jr. et al. (WIPO Publication WO 2009/088799 A1), hereinafter Baker Jr.
Regarding Claims 2 and 9, Van Dorpe teaches devices and methods for non-invasive measuring of analytes, such as in skin tissue, with a first optical unit including a Raman spectrometer, and a second optical unit, including an OCT spectrometer or IR spectrometer (see abstract and Fig. 5). Van Dorpe teaches a system/method for detecting an air gap between a surface of a sensor and a tissue site of a patient (see abstract and Figs. 4-5, see also ¶[0134], the integrated circuit for contacting the tissue), the system comprising:
a plurality of noninvasive sensors configured to obtain physiological data associated with a patient (abstract and ¶[0101]-[0102] the integrated circuit 100 including the optical unit, including the Raman spectrometer 102, OCT spectrometer 103, and the IR spectrometer 107, ¶[0080]-[0081] and ¶[0137]-[0140] the integrated circuit may comprise a plurality of optical units, each simultaneously performing Raman and OCT spectroscopy at different locations on the body; Figs. 4-5);
one or more sensor heads (¶[0080]-[0081] and ¶[0101]-[0102] the individual optical unit with Raman, OCT, and IR spectroscopy; Figs. 4-5) comprising:
a frame configured to support at least a portion of each of the plurality of noninvasive sensors (¶[0101]-[0102] the silicon substrate 101; Figs. 4-5); and
a surface configured to contact a tissue site of the patient (¶[0099]-[0103] the optical window 110, ¶[0134] the optical window can directly contact the tissue; Figs. 4-6b); and
one or more hardware processors (¶[0110]-[0117] the integrated circuit that processes the recorded image data to determine analyte concentration) configured to:
cause at least one of the plurality of noninvasive sensors to obtain physiological data associated with the patient based on tissue contact (¶[0110]-[0117], ¶[0134], and ¶[0137]-[0140] the images (i.e., the spectra) are obtained once the sensor (i.e., the optical window) is pressed against the tissue, and the analyte concentration determined from the images).
Van Dorpe teaches that measurements are collected once the sensor is pressed against the tissue, but does not teach a modality of determining sensor contact, specifically including to receive an image of the tissue site of the patient from at least one of the plurality of noninvasive sensors; process the image using a classifier trained by a neural network to determine a likelihood score that the surface of the one or more sensor heads is in contact with the tissue site of the patient; and cause at least one of the plurality of noninvasive sensors to obtain physiological data associated with the patient based on the likelihood score.
Margallo Balbás teaches systems and methods for performing RF ablation while monitoring via low coherence interferometry (LCI) data (see abstract and ¶[0044]-[0045]; Figs. 3A-3B), in which the LCI data is utilized to continuously confirm tissue contact, in which the data may be analyzed via a neural network, and the user notified if poor contact detected, or feedback to stabilize data acquisition (see ¶[0109]-[0110]). Note that OCT utilizes LCI images to construct the 3D OCT image (see Margallo Balbás ¶[0117]-[0118]; see also generally pg. 1-3, Wasatch Photonics, “OCT Tutorial”, Wasatch Photonics, accessed on 12/29/2025, accessed at https://wasatchphotonics.com/oct-tutorial/).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the LCI data of the OCT spectrometer (note that OCT utilizes LCI images to construct the OCT images) of Van Dorpe to determine and monitor tissue contact via a neural network as taught by Margallo Balbás because (1) it is the application of a known technique to a known device/method ready for improvement to yield predictable results and/or (2) determining and maintaining tissue contact throughout data acquisition would help to limit noise and other artifacts in the data from poor tissue contact, such as light leakage, other light signals being received, etc. Here, it is inherent that such a neural network would be trained, as otherwise, the neural network would not be able to determine proper tissue contact.
The modified Van Dorpe does not specifically teach that the classifier utilizes a likelihood score.
Baker Jr. teaches a system and method for determining whether a sensor is coupled to arterialized tissue (see abstract and ¶[0017]), in which the sensor utilizes emitted light from LED and the reflected/transmitted light detected by a photodetector to compute waveforms (see ¶[0009] and ¶[0031]-[0032]; Fig. 2), in which the waveforms may be input into a neural network, which outputs a probability that the sensor is in contact with the tissue (see abstract, ¶[0017], ¶[0035]-[0039], ¶[0053]), in which in response to the determined probability, actions include updating physiological data, holding the current data, and clearing the current data (see ¶[0038]), in which the neural network is trained using labeled data (see ¶[0036]-[0039] and ¶[0054]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the probability value as determined in Baker Jr. and actions with the neural network with input OCT values of the modified Van Dorpe, so that the neural network of the modified Van Dorpe outputs a probability of contact, rather than a binary determination, because (1) it is the application of a known technique to a known device/method ready for improvement to yield predictable results and/or (2) the probability value would provide easier control of the system than the binary determination, so that the user may adjust the action (i.e., update, hold, or clear) based on the probability value without needing to update the neural network parameters (such as in the output layer as in a binary determination), providing an easier adjustment that may be necessitated by various circumstances, such as in a patient by patient basis.
Furthermore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the labeled (i.e., supervised) training of Baker Jr. with the neural network of the modified Van Dorpe because (1) it is the application of a known technique to a known device/method ready for improvement to yield predictable results and/or (2) the neural network of the modified Van Dorpe requires training and Baker Jr. teaches one such modality of training.
Here, the neural network of the modified Van Dorpe is a classifier as it is classifying the input LCI/OCT image data into probability of touching or not. Furthermore, as the sensors (i.e., the Raman and OCT spectrometers) are measuring data from skin, and such measurement does not occur in a vacuum, if there is high probability of non-contact, that would necessarily indicate the presence of an air gap.
Regarding Claims 3 and 10, Van Dorpe in view of Margallo Balbás and Baker Jr. teaches the system/method of claims 2 and 9 as stated above. The modified Van Dorpe further teaches the plurality of noninvasive sensors comprises an OCT sensor (see Van Dorpe abstract and ¶[0101]-[0102], the integrated circuit 100 including the optical unit, including the Raman spectrometer 102, OCT spectrometer 103, and the IR spectrometer 107, ¶[0080]-[0081] and ¶[0137]-[0140] the integrated circuit may comprise a plurality of optical units, each simultaneously performing Raman and OCT spectroscopy at different locations on the body; Figs. 4-5), and wherein the OCT is configured to generate the image of the tissue site (see Margallo Balbás ¶[0109]-[0110], the LCI data is utilized to continuously confirm tissue contact, in which the data may be analyzed via a neural network).
Regarding Claims 4 and 11, Van Dorpe in view of Margallo Balbás and Baker Jr. teaches the system/method of claims 2 and 9 as stated above. Van Dorpe further teaches the plurality of noninvasive sensors comprises a Raman spectrometer (abstract and ¶[0101]-[0102] the integrated circuit 100 including the optical unit, including the Raman spectrometer 102, OCT spectrometer 103, and the IR spectrometer 107, ¶[0080]-[0081] and ¶[0137]-[0140] the integrated circuit may comprise a plurality of optical units, each simultaneously performing Raman and OCT spectroscopy at different locations on the body; Figs. 4-5).
Regarding Claim 12, Van Dorpe in view of Margallo Balbás and Baker Jr. teaches the method of claim 9 as stated above. Van Dorpe further teaches wherein causing at least one of the plurality of noninvasive sensors to obtain physiological data associated with the patient based on the likelihood score comprises causing a Raman spectrometer to obtain spectrographic data (abstract and ¶[0101]-[0102] the integrated circuit 100 including the optical unit, including the Raman spectrometer 102, OCT spectrometer 103, and the IR spectrometer 107, ¶[0080]-[0081] and ¶[0137]-[0140] the integrated circuit may comprise a plurality of optical units, each simultaneously performing Raman and OCT spectroscopy at different locations on the body, ¶[0110]-[0117], ¶[0134], and ¶[0137]-[0140] the images (i.e., the spectra) are obtained once the sensor (i.e., the optical window) is pressed against the tissue, and the analyte concentration determined from the images; Figs. 4-5).
Claims 5-6 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Van Dorpe in view of Margallo Balbás and Baker Jr. as applied to claims 4 and 11 above, and in view of Lui et al. (US Patent Application Publication 2015/0011893), hereinafter Liu.
Regarding Claims 5 and 13, Van Dorpe in view of Margallo Balbás and Baker Jr. teaches the system/method of claims 4 and 11 as stated above. The modified Van Dorpe does not specifically teach about the Raman spectroscopy, including that the Raman spectrometer is configured to obtain spectrographic data associated with a first band of wavenumbers and a second band of wavenumbers at least 500 cm-1 away from the first band.
Liu teaches a Raman spectrometer system capable of discriminating between different tissue pathologies via ranges in a Raman spectrum with improved sensitivity and specificity (see abstract and Figs. 1-2), in which the tissue is skin (see ¶[0036]), in which the ranges may be utilized for reference molecules in skin (see ¶[0073] and ¶[0123]; Figs. 4A-4D), and the ranges are captured in at least 11 sub-ranges in the Raman spectrum (see ¶[0040]-[0054]) captured via spectrometer 38 which is implemented with an NIR-optimized CCD array with holographic grating (see ¶[0106] and Fig. 2), with light source 32, such as implemented via a laser (see ¶[0102] and Fig. 2).
Accordingly, it would have been obvious to implement the Raman spectroscopy as taught in Liu as the Raman spectroscopy in the modified Van Dorpe, for the analyte (i.e., molecule) detection because (1) it is the application of a known technique to a known device/method ready for improvement to yield predictable results and/or (2) the Raman spectroscopy of Liu provides improved sensitivity and specificity (see Liu abstract, ¶[0073], ¶[0123], and ¶[0141]).
Here, the modified Van Dorpe teaches a plurality of sub-ranges over the Raman spectrum, such as the 11 sub-ranges described in Liu ¶[0041], the sub-ranges of 500-513, 546-586, 611-675, 721-736, 760-830, 870-900, 947-1320, 1345-1420, 1434-1457, 1478-1520, and 1540-1790 cm-1. The present claim requires only two bands, with the second at least 500 cm-1 away. The modified Van Dorpe teaches several such ranges (i.e., bands), such as 500-513 cm-1 with 947-1320, 1345-1420, 1434-1457, 1478-1520, or 1540-1790 cm-1. Therefore, the modified Van Dorpe teaches the elements as required by claims 5 and 13.
Regarding Claims 6 and 14, Van Dorpe in view of Margallo Balbás and Baker Jr. teaches the system/method of claims 4 and 11 as stated above. The modified Van Dorpe further teaches the Raman spectrometer comprises:
an emitter configured to emit light towards a tissue sample of a patient (see Van Dorpe ¶[0110], ¶[0119], and ¶[0137], the laser light source, Fig. 5; see Liu ¶[0102] the laser light source 32, Fig. 2);
a diffraction grating configured to diffract Raman scattered light from the tissue site of the patient towards (see Liu see ¶[0106], the NIR-optimized CCD array, with detectors capable of detecting all the sub-ranges utilized; Fig. 2) a first detector and a second detector, wherein the first detector is configured to detect Raman scattered light in the first band and the second detector is configured to detect Raman scattered light in the second band (see Liu see ¶[0106], the holographic grating for directing light towards the NIR-optimized CCD array; Fig. 2).
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Van Dorpe in view of Margallo Balbás and Baker Jr. as applied to claims 2 and 9 above, and in view of SuperDataScience Team (“The Ultimate Guide to Convolutional Neural Networks (CNN)”, SuperDataScience Team, SuperDataScience, published on 08/27/2018, accessed on 12/27/2025, accessed at https://www.superdatascience.com/blogs/the-ultimate-guide-to-convolutional-neural-networks-cnn), hereinafter SuperDataScience.
Regarding Claims 7 and 15, Van Dorpe in view of Margallo Balbás and Baker Jr. teaches the system/method of claims 2 and 9 as stated above. The modified Van Dorpe generally teaches that the neural network is trained with labeled data (see Baker Jr. ¶[0036]-[0039] and ¶[0054]), but does not teach the specifics of such training.
SuperDataScience teaches about the general structure of convolutional neural networks (CNNs) (see generally pg. 8-11), and that the known and conventional operations of convolution, pooling, and flattening may alter the input 2D/3D images into 1D outputs (see pg. 11-33), and that through training using the labeled/classified images, feature maps (i.e., the weights) may be constructed in the convolution layer (see pg. 13-18).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to train the neural network as recited in the claims, using the known and conventional CNN training implementations as taught by SuperDataScience because it is within the skill of one of ordinary skill in the art to write an algorithm (i.e., the training of the neural network) with known and conventional CNN training implementations so as to achieve the desired function (i.e., the probability of contact as taught by the modified Van Dorpe), and such training would be required for such a neural network to function.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Van Dorpe in view of Margallo Balbás and Baker Jr. as applied to claims 4 and 9 above, and in view of Alves (“Understanding ConvNets (CNN)”, Neuronio, Medium, published on 10/08/2018, accessed on 12/22/2025, accessed at https://medium.com/neuronio/understanding-convnets-cnn-712f2afe4dd3), hereinafter Alves.
Regarding Claims 8 and 16, Van Dorpe in view of Margallo Balbás and Baker Jr. teaches the system/method of claims 4 and 9 as stated above. The modified Van Dorpe teaches the usage of a neural network for determining the probability of contact, but does not teach the specifics of the neural network.
Alves teaches about the general structure utilized in convolutional neural networks (CNNs), used for classic problems such as image recognition (see pg. 1-3), including known and conventional operations including convolution layers (see pg. 3-6), activation functions for bring nonlinearity (see pg. 7), a pooling layer (see pg. 7), a fully connected layer (see pg. 8), and a dropout layer (see generally pg. 8-12, see other layers utilized in CNN as well).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to develop the neural network as recited in the claims, using the known and conventional CNN implementations as taught by Alves to solve a CNN classical problem (i.e., the image recognition, recognizing contact or non-contact) because it is within the skill of one of ordinary skill in the art to write an algorithm (i.e., the convolutional neural network) with known and conventional CNN implementations so as to achieve the desired function (i.e., the probability of contact as taught by the modified Van Dorpe), and such implementation and structure would be required for such a neural network to function.
Conclusion
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/J.D.M./ Examiner, Art Unit 3791
/JUSTIN XU/ Primary Examiner, Art Unit 3791
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