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 .
Election/Restrictions
Applicant's election with traverse of Group III (claims 1-4, 15, and 22) in the reply filed on May 27, 2026 is acknowledged. The traversal is on the ground(s) that all pending claims are linked by a general inventive concept and that Newberry (U.S. Patent Appl. Publ. No. 2018/0214088 A1) does not disclose determining whether the subject is in a physiological state of interest “without direct reference to analytes of the subject.” (p.3 of the Response).
This is not found persuasive because NEWBERRY clearly discloses determining whether the subject is in a physiological state of interest without direct reference to analytes of the subject. Examiner is interpreting “without direct reference to analytes” to include using a trained machine learning model to process the measured spectrum in which the model is trained with reference spectra representative of the physiological state of interest without having to sample and analyze blood. Examiner’s interpretation is consistent with the specification. (MPEP 2111: “[T]he meaning given to a claim term must be consistent with the ordinary and customary meaning of the term (unless the term has been given a special definition in the specification), and must be consistent with the use of the claim term in the specification and drawings.”). For example, Applicant’s disclosure emphasizes that determination can be made without drawing blood by using a model trained on reference spectra that correspond to the physiological state of interest. “In at least some example embodiments, determining state of interest may be performed without direct reference to analytes of a subject and, consequently, without sampling blood of the subject. Instead, one or more reference spectra are empirically determined to correspond to a particular reference state of interest of a subject, and one or more measured spectra are compared to those one or more reference spectra…The relationship between the reference spectra and the state of interest is established without having to sample and analyze blood.” ([0211]).
Like Applicant’s disclosure, NEWBERRY trains a machine-learning model using a spectrum of wavelengths of PPG data that is representative of the physiological state. (see, e.g., [0231]). Without drawing/sampling blood and without direct reference to the analytes, the trained model receives spectral data and outputs the physiological state. More specifically, NEWBERRY discloses a “neural network processing device 2100” that is “pre-configured with weights, parameters or other learning vectors 2106 derived from a training set.” ([0231]). The “input vector” to the pre-configured model can be “one or more other PPG signals at other wavelengths, such as at 880 nm, 660 nm, 468 nm, 440 m, 550 nm, 530 nm, 592 nm or in a range of +/−20 nm from these wavelengths.” ([0232]). The output of the model may include “health data, such as one or more of: liver enzyme level, blood alcohol level, ethanol, digestive indicator to measure digestive responses, concussion, PTSD, cholesterol levels, creatinine level, electrolytes, etc.” NOTE: Concussions and PTSD are not associated with particular analytes. Furthermore, NEWBERRY clearly discloses that a “spectral response” can comprise a wide range of wavelengths can be analyzed. ([0070]).
The requirement is still deemed proper and is therefore made FINAL.
Claim Objections
Claim 4 is objected to because of the following informalities:
Claim 4 depends from claim 1, which recites “measuring a spectrum of the light after the light has one or both of passed through and been reflected by the body part….” To be consistent with claim 1, claim 4 should be amended to recite “wherein the spectrum is measured on the light that has been passed through the body part and that has been reflected by the body part.”
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 1-4, 15, and 22 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.
Claims 1 and 22 recite “…determining whether the subject is in a physiological state of interest without direct reference to analytes of the subject, wherein the determining comprises using a trained machine learning model....”
It is unclear what is meant by “without direct reference to analytes of the subject….” With respect to Applicant’s arguments for traversing the Restriction Requirement, Applicant appears to imply that “analytes” means known analytes that have known relationship with respect to the physiological state of interest. However, Applicant’s disclosure acknowledges that the spectral patterns that are ultimately analyzed are caused by known analytes and unknown analytes, which are referred to “ghost analytes,” and that the spectral profile is determined by a combination of known analytes and unknown analytes. (see, e.g., [0144]: “Ghost analytes may include a plurality of analytes that can be used, along with other known analytes, to obtain a fingerprint or biochemical profile that may be used to define the status of a physiological condition as described herein.”). Accordingly, the spectral profile (i.e., shape or pattern) for a range of wavelengths is determined by the known and unknown (ghost) analytes. Applicant’s trained model is trained using spectral profiles determined by known and unknown analytes. (see, e.g., [0211]). Thus, it would be impossible to determine the physiological state without direct reference to analytes.
Based on Applicant’s disclosure (e.g., [0144] and [0211]), Examiner is interpreting the relevant portions of claims 1 and 22 as follows: “…determining whether the subject is in a physiological state of interest without separately quantifying known analytes of the subject that have a known relationship with respect to the physiological state of interest, wherein the determining comprises using a trained machine learning model....”
Claims 2-4 and 15 depend from claim 1. Based on their dependency, claims 2-4 and 15 are also rejected under Section 112(b) for being indefinite.
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 1-4, 15, and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
determining whether the subject is in a physiological state of interest without direct reference to analytes of the subject, wherein the determining comprises using a trained machine learning model to process the measured spectrum and wherein the trained machine learning model is trained with reference spectra representative of the physiological state of interest.
Independent claims 1 and 22, as drafted and under their broadest reasonable interpretation, recite a mathematical concept and/or mental process. (MPEP 2106.04(a)(2)(I)). The claims recite a mental process because the trained machine-learning model replicates a doctor’s analysis of clinical data by evaluating and providing a judgment/opinion as to a physiological state of interest. (see Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437, Federal Circuit, decided on 18 April 2025: “[C]laims that do no more than apply established methods of machine learning to a new data environment” are not patent eligible.”). The processes of a trained machine-learning model also involve mathematical concepts, such as pre-processing the data for the trained model (i.e. identifying relevant wavelength range or ranges) and performing mathematical operations (i.e., convolutions, downsampling, upsampling, and determining probabilities) within the trained model. As such, the claimed invention recites both a mental process and a mathematical concept (i.e., an abstract idea).
Examiner also notes that claims 1 and 22 are conceptually similar to those in Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016). The claims in Electric Power Group were found to be patent ineligible because, like the claims in this case, they essentially recited collecting information, analyzing that information, and presenting results of that analysis. “[W]e have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas…we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category…[and] we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more…, is abstract as an ancillary part of such collection and analysis.” Electric Power Group, 830 F.3d 1353-1354.
Once it is established that the claims recite a judicial exception (i.e., an abstract idea), the next question to consider is whether the claims integrate the judicial exception into a practical application. A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. (MPEP 2106.04(d)).
Additional elements should be considered to determine if they integrate the judicial exception into a practical application. Here, the additional elements include: (a) directing light at a body part of a subject such that the light passes through or is reflected by blood and interstitial fluid of the body part, wherein the light incident on the body part comprises a range of wavelengths from at least one of the near infrared and visible spectra and (b) measuring a spectrum of the light after the light has one or both of passed through and been reflected by the body part, wherein the spectrum comprises the range of wavelengths. Claim 22 also recites non-transitory computer readable medium having stored thereon computer program code that is executable by a processor and that, when executed by the processor, causes the processor to perform the method of claim 1.
In this case, the judicial exception is not integrated into a practical application. The additional element/steps of (a) and (b) are insignificant extra-solution activity that are necessary to perform for the judicial exception. (MPEP 2106.04(d), I). It is necessary to collect or gather the data for analysis. Moreover, (a) and (b) merely generally link the use of a judicial exception to a particular technological environment or field of use (i.e., analysis of spectral data). With respect to claim 22, the additional element of a non-transitory computer readable medium merely uses a computer element as a tool to perform an abstract idea.
If the claims recite a judicial exception and do not integrate that exception into a practical application, as is the case here, the next question is whether the claims include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims do not. A shared quality of the additional elements/steps (1) and (2) are that they do not recite any meaningful limitation that transforms the judicial exception into a patent-eligible application. (MPEP 2106.05(II)). Moreover, each of (a) and (b) is a well-understood, routine, conventional activity/element for spectral analysis. (MPEP 2106.05(A); see, e.g., Section 102 rejection based on KURATSUNE below). Storing a computer program on a computer-readable medium is also a well-understood, routine, conventional activity/element.
Accordingly, claims 1 and 22 do not recent patent-eligible subject matter.
Dependent claims 2-4 and 15 also fail to recite patent-eligible subject matter. For example, claims 2-4 each recite a well-understood, routine, conventional activity/element for spectral analysis. (MPEP 2106.05(A); see, e.g., Section 102 rejection based on KURATSUNE below). Adjusting specificity and sensitivity targets is also a well-understood, routine, conventional activity/element in clinical data analysis. (MPEP 2106.05(A); see, e.g., Section 102 rejection based on KURATSUNE below).
Accordingly, claims 1-4, 15, and 22 are rejected for lacking patent-eligible 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.
(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 1-4 and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Appl. Publ. No. 2008/0198378 A1 (hereinafter “KURATSUNE”).
KURATSUNE discloses a method and device of quantitatively or qualitatively examining and diagnosing chronic fatigue syndrome (CFS) by analyzing absorbance at all measurement wavelengths or at specific wavelengths in the absorption spectral data by using an analytical model prepared beforehand. KURATSUNE discloses that the known CFS diagnostic method depends on clinical findings and opinions of doctors. CFS is typically diagnosed based on clinical symptoms and not based on, for example, identifying or measuring a particular analyte in the blood. (see, e.g., [0002]-[0010]). KURATSUNE suggests that a “more objective CFS diagnostic method is needed.” ([0011]). Example 3 describes obtaining “spectral data…from the tip of a finger in a non-invasive manner….” ([0109]).
With respect to claim 1 (and in light of the Section 112(b) rejection), KURATSUNE discloses:
A method comprising:
(a) directing light at a body part of a subject (see, e.g., Example 3 at [0109] and also [0048]: “Examples of the sample to be used can include urine, another biological fluid, and a part of a living body such as an ear, or a fingertip of a hand or foot. Thus the present invention realizes non-invasive examination without damaging a living body.”) such that the light passes through or is reflected by blood and interstitial fluid of the body part (see, e.g., Example 3 at [0109] and also [0057]: “…detecting reflected light, transmitted light, or transmitted and reflected light to obtain absorption spectral data”), wherein the light incident on the body part comprises a range of wavelengths from at least one of the near infrared and visible spectra (see, e.g., [0058]: “The wavelength of light with which the sample is irradiated is in the range of 400 nm to 2500 nm or in part of the range (for example, 600 to 1000 nm). This wavelength range can be set as one wavelength region or as a plurality of regions….” NOTE: The example range of 600 to 1000 nm includes near-infrared and visible light.);
(b) measuring a spectrum of the light after the light has one or both of passed through and been reflected by the body part (see discussion of (a) above), wherein the spectrum comprises the range of wavelengths (see, e.g., [0057]: “…analyzing absorbance at all measurement wavelengths or at specific wavelengths in the absorption spectral data by using an analytical model prepared beforehand.”); and
(c) determining whether the subject is in a physiological state of interest without direct reference to analytes of the subject (the physiological state of interest in KURATSUNE is CFS and KURATSUNE teaches that “[e]xamination and judgment can be carried out with the analytical model by using the raw absorption spectral data without further processing.” [0060]), wherein the determining comprises using a trained machine learning model to process the measured spectrum and wherein the trained machine learning model is trained with
PNG
media_image1.png
419
604
media_image1.png
Greyscale
reference spectra representative of the physiological state of interest. (see, e.g., Figure 1 showing preparation of a model, [0063], and [0064]: “Examples of the multivariate analysis include a principal component analysis (PCA), a soft independent modeling of class analogy (SIMCA) method, and a k nearest neighbors (KNN) method for class discrimination. In the SIMCA method, the respective principal components of a plurality of groups (classes) are analyzed, and the principal component model of each class is prepared.” NOTE: Soft independent modeling of class analogy (SIMCA) is a machine learning model and was used to create the analytical model. ([0088]-[0089]). KURATSUNE discloses that the trained machine learning model was trained with reference spectra ([0089]), which was then used to process masked or unknown measured spectrum. ([0095]).
NOTE: As described above, KURATSUNE discloses determining whether the subject is in a physiological state of interest (i.e., CFS) without separately quantifying known analytes of the subject that have a known relationship with respect to the physiological state of interest. Instead, KURATSUNE uses multivariate analysis of a range of wavelengths. “This makes it possible to prepare the analytical model that is used for estimating the degree of fatigue from the absorption spectra at all the measurement wavelengths….” ([0063]).
2. The method of claim 1, wherein the light incident on the body part comprises a range of wavelengths from both of the near infrared and visible spectra (see, e.g., [0058]: “The wavelength of light with which the sample is irradiated is in the range of 400 nm to 2500 nm or in part of the range (for example, 600 to 1000 nm). This wavelength range can be set as one wavelength region or as a plurality of regions….” NOTE: The example range of 600 to 1000 nm includes visible light and near-infrared.)
3. The method of claim 1, wherein the spectrum is measured on the light that has passed through the body part (see, e.g., [0015]: “Thus sample data can be obtained immediately by detecting, for example, transmitted light from a sample, determining the absorbance data of the sample, and subjecting this data to a multivariate analysis.”).
4. The method of claim 1, wherein the spectrum is measured on the light that has been through the body part and that has been reflected by the body part (see, e.g., [0057]: “…detecting reflected light, transmitted light, or transmitted and reflected light to obtain absorption spectral data”.
With respect to claim 22 (and in light of the Section 112(b) rejection),
A non-transitory computer readable medium having stored thereon computer program code that is executable by a processor (see, e.g., claim 10 and [0081] and [0067]: “The program of the present invention can be provided as a recording medium in which the program can be read with a computer.”) and that, when executed by the processor, causes the processor to perform a method comprising:
(a) directing light at a body part of a subject (see, e.g., Example 3 at [0109] and also [0048]: “Examples of the sample to be used can include urine, another biological fluid, and a part of a living body such as an ear, or a fingertip of a hand or foot. Thus the present invention realizes non-invasive examination without damaging a living body.”) such that the light passes through or is reflected by blood and interstitial fluid of the body part (see, e.g., Example 3 at [0109] and also [0057]: “…detecting reflected light, transmitted light, or transmitted and reflected light to obtain absorption spectral data”), wherein the light incident on the body part comprises a range of wavelengths from at least one of the near infrared and visible spectra (see, e.g., [0058]: “The wavelength of light with which the sample is irradiated is in the range of 400 nm to 2500 nm or in part of the range (for example, 600 to 1000 nm). This wavelength range can be set as one wavelength region or as a plurality of regions….” NOTE: The example range of 600 to 1000 nm includes near-infrared and visible light.);
(b) measuring a spectrum of the light after the light has one or both of passed through and been reflected by the body part (see discussion of (a) above), wherein the spectrum comprises the range of wavelengths (see, e.g., [0057]: “…analyzing absorbance at all measurement wavelengths or at specific wavelengths in the absorption spectral data by using an analytical model prepared beforehand.”); and
(c) determining whether the subject is in a physiological state of interest without direct reference to analytes of the subject (the physiological state of interest in KURATSUNE is CFS and does not examine any particular analyte for diagnosis. KURATSUNE also teaches that “[e]xamination and judgment can be carried out with the analytical model by using the raw absorption spectral data without further processing.” [0060]), wherein the determining comprises using a trained machine learning model to process the measured spectrum and wherein the trained machine learning model is trained with reference spectra representative of the physiological state of interest. (see, e.g., Figure 1 showing preparation of a model, [0063], and [0064]: “Examples of the multivariate analysis include a principal component analysis (PCA), a soft independent modeling of class analogy (SIMCA) method, and a k nearest neighbors (KNN) method for class discrimination. In the SIMCA method, the respective principal components of a plurality of groups (classes) are analyzed, and the principal component model of each class is prepared.” NOTE: Soft independent modeling of class analogy (SIMCA) is a machine learning model that processes the measured spectrum and was used when training the model. ([0088]-[0089]). KURATSUNE discloses that the trained machine learning model was trained with reference spectra ([0089]), which was then used to process masked or unknown measured spectrum. ([0095]).
NOTE: As described above, KURATSUNE discloses determining whether the subject is in a physiological state of interest (i.e., CFS) without separately quantifying known analytes of the subject that have a known relationship with respect to the physiological state of interest. Instead, KURATSUNE uses multivariate analysis of a range of wavelengths. “This makes it possible to prepare the analytical model that is used for estimating the degree of fatigue from the absorption spectra at all the measurement wavelengths….” ([0063]).
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 15 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Appl. Publ. No. 2008/0198378 A1 (hereinafter “KURATSUNE”) and Stone, Nicholas, et al. “Raman spectroscopy for identification of epithelial cancers.” Faraday discussions 126 (2004): 141-157. (hereinafter “STONE”).
With respect to claim 15, KURATSUNE does not explicitly teach wherein the determining comprises receiving a sensitivity target and a specificity target, and outputting the physiological state of interest in accordance with the sensitivity and specificity targets. However, KURATSUNE does teach using a probability threshold of 0.9500, which one having ordinary skill in the art would understand is a metric used for classifying data.
STONE evaluates “the potential for Raman spectroscopy” to provide an objective and non-invasive real time probe for accurate and repeatable measurements of a tissue’s pathological state. (Abstract). “The technique measures the molecular specific inelastic scattering of laser light within tissue, thus enabling the analysis of biochemical changes that precede and accompany disease processes. Initial work has been carried out to optimise a commercially available Raman microspectrometer for tissue measurements; to target potential malignancies with a clinical need for diagnostic improvements (oesophagus, colon, breast, and prostate) and to build and test spectral libraries and prediction algorithms for tissue types and pathologies… Diagnostic predictive models have been constructed and optimised using multivariate analysis techniques. They have been tested using cross-validation or leave-one-out and demonstrated high levels of discrimination between pathology groups (greater than 90% sensitivity and specificity for all tissues).” (Abstract).
STONE specifically discusses measuring the power of the diagnostic test to correctly predict “whether a patient has a particular condition.” (p.148, top paragraph). “The most common of these are sensitivity and specificity. The sensitivity of a test is the percentage of individuals with disease who are classified as having disease. A test is sensitive to the disease if it is positive for most individuals having the disease. The specificity of a test is the percentage of individuals without the disease who are classified as not having the disease. A test is specific if it is positive for a small percentage of those without the disease.” (p.148, top paragraph).
STONE also teaches that sensitivity and specificity targets can be adjusted by adjusting misclassification costs. “It has been possible to modify the outcome measures of sensitivity and specificity by adjustment of the misclassification costs used in eqn. (4) to calculate predicted group membership. This has enabled an increased sensitivity to malignancy and hence reduced the number of false negative results. The first model has equal misclassification costs of unity, the next two have increasing misclassification costs for neoplasia achieving over 90% and 95% sensitivities respectively. These adjustments have had the effect of moving the cut-off point for predicted group membership (in Fig. 4) to the right, to include more samples in the neoplastic/malignant diseased group.” (p.148, 3rd paragraph in Oesophagus section). STONE also teaches that an “optimum model” for identifying certain cancers is likely to have “greater than 90% sensitivity and specificity for both benign and malignant conditions.” (p.149, last paragraph in Colon section and also p.150, last paragraph in Breast section).
It would have been obvious to one having ordinary skill in the art at the time of filing to adjust the probability threshold and/or class membership decisionmaking (e.g., adjustment of misclassification costs), as taught in STONE, to enable the KURATSUNE system to receive a sensitivity target and a specificity target and output the physiological state of interest in accordance with the sensitivity and specificity targets. One of ordinary skill in the art would have been motivated to configure the system to enable adjusting sensitivity and specificity targets in order to tailor the diagnostic model in KURATSUNE to avoid false-negative and false-positive results for the particular clinical use (i.e., particular physiological state of interest). There would have been a reasonable expectation of success as enabling adjustable sensitivity and specificity targets would require only routine software configuration.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20050090750-A1 describes constructing two different types of multivariate models, a quantitative model and a classification model. (see, e.g., [0042] and [0055]). For the classification model, “the goal of the training process is to create a model that correctly classifies the disease state of the measured tissue...These classes or groups might represent different grades or manifestations of a particular disease.” ([0042]). For the quantitative model, “the goal is to provide a quantitative estimate of some diabetes-induced chemical change in the system. The output of this model can be continuously variable across the relevant range of variation and is not necessarily indicative of disease status.” ([0042]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON P GROSS whose telephone number is (571)272-1386. The examiner can normally be reached Monday-Friday 9:00-5:00CT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne M. Kozak can be reached at (571) 270-5284. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JASON P GROSS/ Examiner, Art Unit 3797
/ANNE M KOZAK/ Supervisory Patent Examiner, Art Unit 3797
Prosecution Insights