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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05 February 2026 has been entered. Claims 1 - 4 , 7 - 11, and 14 are pending.
Claim Objections
Claims 1 and 8 are objected to because of the following informalities: regarding the term “predict a value for the fluid defined by one or more of hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose using the deployed data model, wherein the predicted value is the value for the fluid defined by the one or more of hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose”. It is suggested to amend the term to “predict a predicted value for the fluid defined by one or more of one or more of hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose using the deployed data model” to reduce redundancy and improve readability of the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 – 4, 7 – 11, and 14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 (lines 19 - 20) and Claim 8 (lines 15 - 16) each recite the term “probability factors tuned for minimal bias, variance, and noise”. There is no disclosure in Applicant’s disclosure of how the probability factors themselves are tuned for minimal bias, variance, and noise. Looking to Applicant’s specification, the “probability factors” are discussed in the context that there is a “data model having probability factors using existing calibrated data” at [Page 2, lines 26 – 27], [Page 3, Lines 7 – 10], and [Page 6, Lines 4 – 6 and Lines 19 - 22]. There is no clear connection between the probability factors and any tuning, as Applicant’s specification at [Page 9, Lines 23 – 24] discusses that “The data model is further optimized for minimal bias, variance and noise in the output of the calculation.” There is no particular disclosure that this is optimization accomplished by probability factors, nor particulars about how the probability factors are “tuned”. Therefore, adequate disclosure is needed.
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 , 7 - 11, and 14 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 1 (lines 20 - 23) and Claim 8 (lines 16 - 19) each recite the term “wherein the data model is trained on datasets correlating the electrical signals to clinically measured values of hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose”. As recited, the data model is trained on the data signals, which appear to be the same data signals that are converted into electrical signals by the linear image sensor, as previously-recited in the claim. It is unclear if it is truly intended that the data model is trained with the same particular data signals that are then predicted, or if it is intended training subset of data signals gathered by the measurement apparatus portion, and then additional signals that are involved in the prediction. It is unclear the metes and bounds of how many of the same signals are required to be used for training to obtain a trained model, and if those same signals are then predicted by the model. For the purposes of examination, the term “wherein the data model is trained on datasets correlating the electrical signals to clinically measured values of hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose” is deemed to claim “wherein the data model is trained on datasets correlating a training subset of the electrical signals to clinically measured values of hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose”. Claims 2 – 4, 7, 9 – 11, and 14 are similarly rejected due to their dependence on Claims 1 and 8.
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.
Claims 1, 3, 7 - 8, 10, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kulcke (US Patent Application Publication US 2013/0066172 A1), in view of Hong (US 2021/0282649 A1), further in view of Fortmann-Roe (“Understanding the Bias-Variance Tradeoff”, Ref U on PTO-892)
Regarding Claim 1, Kulcke discloses
For Claim 1:
An apparatus for determining characteristics of a fluid ([0001] “The invention relates to a device…for identifying and monitoring contents or properties of a measurement medium, in particular physiological blood values”), the apparatus comprising:
a visible light producing LED array ([[0100] “This light is preferably generated by an LED or a combination of LEDs.”)(Examiner notes that a “combination of LEDs” is a multitude of LEDs, or an array.; Fig 2, “light source” 20 (a) and (b)) configured to emit light and produce a light beam for irradiating an object ([0170] “the illumination apparatuses 20 serve to illuminate a measurement region 3 to be examined,”; Fig 2, light beam shown as a dotted line in the figure);
an optical system comprising (Fig 2, a system is shown, including optical hardware (such as lenses) and light paths.):
a grating (Fig 2, “diffraction grating” 9) configured to receive irradiated from the object light (Fig 2, dotted line of light intersecting “diffraction grating” 9) through a collimator (Fig 2, “second imaging optical unit 8”; [0118] “The second imaging optical unit then images the slit-shaped aperture to e.g. infinity, and so it serves to collimate the light strip that has passed through the gap.”) and disperse the light into wavelengths ([0182] “components of the light at different wavelengths are imaged on the individual pixels as a result of the light being spread.”);
a focusing lens (Fig 2, “imaging optical unit 6”; [0171] “The imaging optical unit 6 serves as entry objective lens for the spectrometer unit 22”)(Examiner notes that an objective lens is a focusing lens.); and
a linear image sensor (Fig 2, “image sensor 12”) arranged at a focal plane of the focusing lens (Fig 2, “image sensor 12” located beyond the “imaging optical unit 6” in the light path that has passed through the lens) and configured to convert the light by the grating and focused by the focusing lens, into electrical signals ([0183] “The photoelectric signals are already amplified and digitized in the sensor.);
a microcontroller ([0183] “microprocessor 26”) connected to the linear image sensor ([0183] “These signals are then transmitted in parallel or in series to a microprocessor 26 via a connection line 25.”) and configured to process the electrical signals ([0183] “The microprocessor 26 firstly brings about a conversion of the signals) and communicate the electrical signals ([0186] “The microprocessor 26 moreover via a communication line 29 assumes the communication with a main processor 30 of the system. Here, the image data is transmitted to the main processor 30 and the main processor provides the sensor system 33 with the parameterization data. “)(Examiner notes that the electrical signals are communicated to the main processor for further processing).
a remote application ([0186] “the main processor”) configured to collect the electrical signals from the microcontroller ([0186] “The microprocessor 26 moreover via a communication line 29 assumes the communication with a main processor 30 of the system. Here, the image data is transmitted to the main processor 30”) and process the electrical signals through a signal processing technique ([0186] “and the main processor provides the sensor system 33 with the parameterization data...The main processor is typically a dual-core computer, with image processing taking place in the first core and the evaluation of the data for determining the tissue and blood values taking place in the second core.”); and
a remote server ([0138] “computational evaluation can be performed in an evaluation apparatus which is separate from the device”; [0135] – [0137])
Kulcke does not specifically disclose a trained remote server configured to collect the electrical signals processed by the remote application to deploy a data model comprising an auto-generated series of if-then rules with probability factors tuned for minimal bias, variance, and noise, wherein the data model is trained on datasets correlating the electrical signals to clinically measured values of hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose, and to predict a value for the fluid defined by one or more of hemoglobin bilirubin, oxygen saturation creatinine and random blood glucose using the deployed data model, wherein the predicted value is the value for the fluid defined by the one or more of hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose.
Hong teaches a system that compiles information from a “medical data device” (that collects, manages, or generates patient diagnostic data) input and makes a prediction of patient parameters at various time periods in the future ([0172]; [Abstract]; [0026] – [0035]) using machine learning. Specifically for Claim 1, Hong teaches disclose a trained remote server ([0170] Fig. 1…system 200a…server 216 also includes a model training system 224…”; Fig. 1) configured to collect the electrical signals processed by the remote application ([0172] “system 200..includes a client 204…client 204 receives input from the input device 201…a medical data device (e.g. a small or large-format device used in a healthcare setting to collect, manage, or generate patient diagnostic data…”; Fig. 1) to deploy a data model (Fig. 1., “Patient Parameter Prediction Models 232”; [0084] “…neural net predictive experiment…”) comprising an auto-generated series of if-then rules ([0068] – [0070] “Iterative (regression) imputation…matrix factorization problem…”)(Examiner notes that the if-then rules are autogenerated by finding the solutions to the matrix factorization equations, where if are the inputs of the equations and then are the output of what the equations equal.), wherein the data model is trained on datasets correlating the electrical signals to clinically measured values of hemoglobin ([0085]; [0097]“…hemoglobin…”), bilirubin ([0085]; [0088] “…bilirubin…”), oxygen saturation ([0085]; [0102] “o2sat”), creatinine ([0085]; [0091] “creatinine”), and random blood glucose ([0085]; [0094] “glucose”), and to predict a value for the fluid defined by one or more of hemoglobin ([0084] “…predict hemoglobin…”) bilirubin, oxygen saturation creatinine and random blood glucose using the deployed data model ([0084] “…neural net predictive experiment…used a series of low and high frequency clinical data to predict hemoglobin…”), wherein the predicted value is the value for the fluid defined by the one or more of hemoglobin ([0084] “…predict hemoglobin…”), bilirubin, oxygen saturation, creatinine, and random blood glucose.
Hong provides a motivation to combine at [0004] with “It is particularly important to be able to predict the time dynamics of vitals as they possess a great deal of information on the health status of a patient,” and [0018] “Described herein are systems and methods for predicting future values of vital signs using predictive models. The future values of any given vital sign are predicted based on times series of values of multiple vital signs.” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that providing one or more vital sign measurements such as hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose to a machine learning model would be useful for obtaining predictions of future hemoglobin levels to give users time to counteract poor hemoglobin level effects. It would have been predictable to use machine learning and trained model with any electronic vital sign monitoring system that generates data and determines hemoglobin (and/or bilirubin, oxygen saturation, creatinine, and random blood glucose levels), as it would continue to function with the purpose of predicting future hemoglobin levels based on a machine learning data model.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the optical glucose sensing system a remote application to collect the signals and perform calculations disclosed by Kulcke with the machine learning network model to predict hemoglobin levels from vital sign measurement input taught by Hong, creating a single hemoglobin monitoring and prediction device that can give users reliable information in advance for more advantageous health outcomes.
Hong does not specifically teach with probability factors tuned for minimal bias, variance, and noise. However, Hong is open to combine with a particular model that specifically teaches probability factors tuned for minimal bias, variance, and noise with [0026] – [0035] “…other forms of regression based machine learning models may be used, including: Linear Regression, Logistic Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Lasso Regression, ElasticNet Regression, Support Vector Regression...other predictive algorithms or methodologies may be utilized without departing from the scope of the disclosure.”
Fortmann-Roe teaches prediction models with probability factors and how bias, variance, and noise can be defined and accommodated in regard to prediction models such as decision trees in Random Forest modeling ([Abstract, Page 9--Section 4.2. “Bagging and Resampling”; Page 2 – 3: “Section 1.3. Mathematical Definition”]). Specifically for Claim 1, Fortmann-Roe teaches to deploy a data model comprising an auto-generated series of if-then rules ([Page 9, Section 4.2. “Bagging and Resampling”] “…Random Forests works by training numerous decision tree each based on a different resampling of the original training data…”); with probability factors tuned for minimal bias ([Page 9, Section 4.2. “Bagging and Resampling”] “…infinite number of trees could be trained without ever increasing bias…continual…decrease in the variance…”), variance ([Page 9, Section 4.2. “Bagging and Resampling”] “By creating many of these trees, in effect a "forest", and then averaging them the variance of the final model can be greatly reduced over that of a single tree…”), and noise ([Page 2 – 3, “Section 1.3. Mathematical Definition”] “That third term, irreducible error, is the noise term in the true relationship that cannot fundamentally be reduced by any model…tradeoff between minimizing the bias and minimizing the variance…”)(Examiner notes that using a Random Forest model is effectively minimizing bias, variance, and noise to the amount that it can be practically minimized. It is further noted that this term is being interpreted that the variance, bias, and noise are being minimized to the extent that is practical, such that all three are not simultaneously at their absolute minimum.)
Fortmann-Roe provides a motivation to combine at [Page 9, Section 4.2. “Bagging and Resampling”] with “By creating many of these trees, in effect a "forest", and then averaging them the variance of the final model can be greatly reduced over that of a single tree…” and “…infinite number of trees could be trained without ever increasing bias…continual…decrease in the variance…” A person having ordinary skill in the art before the effective filing date of the claimed invention would recognize that a random forest machine learning model would be useful for obtaining predictions using a machine learning model that minimizes bias, variance, and noise factors such as Random Forest in order to obtain more accurate results for users.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the optical biological parameter sensing system and machine learning-based prediction device (for hemoglobin) disclosed by Kulcke in view of Hong with the random forest machine learning model taught by Fortmann-Roe, creating a single hemoglobin monitoring and prediction device using a random forest machine learning model that minimizes bias, variance, and noise factors in order to obtain more accurate results for users.
Regarding Claim 8, Kulcke discloses A method of determining characteristics of a fluid ([0001] “The invention relates to a device and a method for identifying and monitoring contents or properties of a measurement medium, in particular physiological blood values”), the method comprising:
emitting light by a visible light producing LED array ([[0100] “This light is preferably generated by an LED or a combination of LEDs.”)(Examiner notes that a “combination of LEDs” is a multitude of LEDs, or an array.; Fig 2, “light source” 20 (a) and (b)) and producing a light beam for irradiating an object ([0170] “the illumination apparatuses 20 serve to illuminate a measurement region 3 to be examined,”; Fig 2, light beam shown as a dotted line in the figure);
receiving, from a grating (Fig 2, “diffraction grating” 9) of an optical system (Fig 2, a system is shown, including optical hardware (such as lenses and the “diffraction grating” 9) and light paths.), irradiated light from the object through a collimator (Fig 2, dotted line of light intersecting “diffraction grating” 9; (Fig 2, “second imaging optical unit 8”; [0118] “The second imaging optical unit then images the slit-shaped aperture to e.g. infinity, and so it serves to collimate the light strip that has passed through the gap.”) and dispersing the light into wavelengths ([0182] “components of the light at different wavelengths are imaged on the individual pixels as a result of the light being spread.”);
converting the light into electrical signals, by a linear image sensor (Fig 2, “image sensor 12”) arranged at a focal plane of a focusing lens (Fig 2, “image sensor 12” located beyond the “imaging optical unit 6” in the light path that has passed through the lens) in the optical system (Fig 2, a system is shown, including optical hardware (such as the “imaging optical unit 6” focusing lens) and light paths.) using the grating and focusing using the focusing lens ([0183] “The photoelectric signals are already amplified and digitized in the sensor.);
processing the electrical signals ([0183] “The microprocessor 26 firstly brings about a conversion of the signals) by a microcontroller connected to the linear image sensor ([0183] “These signals are then transmitted in parallel or in series to a microprocessor 26 via a connection line 25.”) and communicating the electrical signals ([0186] “The microprocessor 26 moreover via a communication line 29 assumes the communication with a main processor 30 of the system. Here, the image data is transmitted to the main processor 30 and the main processor provides the sensor system 33 with the parameterization data. “)(Examiner notes that the electrical signals are communicated to the main processor for further processing).
collecting, by a remote application ([0186] “the main processor”), the electrical signals ([0183] “The photoelectric signals are already amplified and digitized in the sensor. These signals are then transmitted in parallel or in series to a microprocessor 26 via a connection line 25.”) from the microcontroller ([0186] “The microprocessor 26 moreover via a communication line 29 assumes the communication with a main processor 30 of the system. Here, the image data is transmitted to the main processor 30”) and processing the electrical signals through a signal processing technique ([0186] “and the main processor provides the sensor system 33 with the parameterization data... The main processor is typically a dual-core computer, with image processing taking place in the first core and the evaluation of the data for determining the tissue and blood values taking place in the second core.”); and
deploying by a remote server, a data-model ([0138] “computational evaluation can be performed in an evaluation apparatus which is separate from the device”; [0135] – [0137] “different multivariate statistical analysis methods…correlation, regression, variant analysis, discriminant analysis and…PCA…”)
Kulcke does not specifically disclose deploying by a trained remote server, a data model comprising an auto-generated series of if-then rules with probability factors tuned for minimal bias, variance, and noise, wherein the data model is trained on datasets correlating the electrical signals to clinically measured values of hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose, and predicting, by the trained remote server, a value for the fluid defined by one or more of hemoglobin bilirubin, oxygen saturation creatinine and random blood glucose using the deployed data model, wherein the predicted value is the value for the fluid defined by the one or more of hemoglobin, bilirubin, oxygen saturation, creatinine, and random blood glucose.
Hong teaches deploying by a trained remote server, a data model comprising an auto-generated series of if-then rules ([0068] – [0070] “Iterative (regression) imputation…matrix factorization problem…”), wherein the data model is trained on datasets correlating the electrical signals to clinically measured values of hemoglobin ([0085]; [0097]“…hemoglobin…”), bilirubin ([0085]; [0088] “…bilirubin…”), oxygen saturation ([0085]; [0102] “o2sat”), creatinine ([0085]; [0091] “creatinine”), and random blood glucose ([0085]; [0094] “glucose”), and to predict a value for the fluid defined by one or more of hemoglobin ([0084] “…predict hemoglobin…”) bilirubin, oxygen saturation creatinine and random blood glucose using the deployed data model ([0084] “…neural net predictive experiment…used a series of low and high frequency clinical data to predict hemoglobin…”), wherein the predicted value is the value for the fluid defined by the one or more of hemoglobin ([0084] “…predict hemoglobin…”), bilirubin, oxygen saturation, creatinine, and random blood glucose.
The motivation for Claim 8 to combine Kulcke and Hong is the same as that disclosed in more detail above in Claim 1. In summary, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the optical glucose sensing system a remote application to collect the signals and perform calculations disclosed by Kulcke with the machine learning network model to predict hemoglobin levels from vital sign measurement input taught by Hong, creating a single hemoglobin monitoring and prediction device that can give users reliable information in advance for more advantageous health outcomes.
Hong does not specifically teach with probability factors tuned for minimal bias, variance, and noise. However, Hong is open to combine with a particular model that specifically teaches probability factors tuned for minimal bias, variance, and noise with [0026] – [0035] “…other forms of regression based machine learning models may be used, including: Linear Regression, Logistic Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Lasso Regression, ElasticNet Regression, Support Vector Regression...other predictive algorithms or methodologies may be utilized without departing from the scope of the disclosure.”
Fortmann-Roe teaches deploying a data model comprising an auto-generated series of if-then rules ([Page 9, Section 4.2. “Bagging and Resampling”] “…Random Forests works by training numerous decision tree each based on a different resampling of the original training data…”); with probability factors tuned for minimal bias ([Page 9, Section 4.2. “Bagging and Resampling”] “…infinite number of trees could be trained without ever increasing bias…continual…decrease in the variance…”), variance ([Page 9, Section 4.2. “Bagging and Resampling”] “By creating many of these trees, in effect a "forest", and then averaging them the variance of the final model can be greatly reduced over that of a single tree…”), and noise ([Page 2 – 3, “Section 1.3. Mathematical Definition”] “That third term, irreducible error, is the noise term in the true relationship that cannot fundamentally be reduced by any model…tradeoff between minimizing the bias and minimizing the variance…”)(Examiner notes that using a Random Forest model is effectively minimizing bias, variance, and noise to the amount that it can be practically minimized. It is further noted that this term is being interpreted that the variance, bias, and noise are being minimized to the extent that is practical, such that all three are not simultaneously at their absolute minimum.)
The motivation for Claim 8 to combine Kulcke in view of Hong with Fortmann-Roe is the same as that described in more detail above in Claim 1. In summary, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the optical biological parameter sensing system and machine learning-based prediction device (for hemoglobin) disclosed by Kulcke in view of Hong with the random forest machine learning model taught by Fortmann-Roe, creating a single hemoglobin monitoring and prediction device using a random forest machine learning model that minimizes bias, variance, and noise factors in order to obtain more accurate results for users.
Regarding Claims 3 and 10, Kulcke in view of Mhaskar discloses as described above, The apparatus of claim 1 and The method of claim 8, respectively. For the remainder of Claims 3 and 10, Kulcke discloses wherein the grating (Fig 2, “diffraction grating” 9) is configured to be used with a spectral analyzer of 340 to 850 nm ([0063] “The device furthermore has a sensor array for recording the spread light.”; [0086] “…a blazed grating in order to enable a high light yield in the diffraction order captured by the camera or the image sensor and in the suitable wavelength range between e.g. 500 nm and 850 nm for measurements of SpO2 concentrations or between 800 nm and 1200 nm for blood sugar measurements.”, 288 pixels based ([All of 0063] including “The CMOS image sensors are highly resolving and typically contain a million pixels or more (the sensor used here has 1.6 MP or even 5 MP)”) with 15 nm resolution ([0093] “the sensors have a pixel resolution that enables a whole spectrum to be recorded at the necessary spectral resolution of less than approximately 5 nm.”), and configured to distribute the reflected light from the object to whole spectra from 310 to 850 nm ([All of 0086], including “The wavelength-dispersive apparatus thus preferably comprises a dispersive optical element, generally an optical grating…enable a high light yield in the diffraction order captured by the camera or the image sensor and in the suitable wavelength range between e.g. 500 nm and 850 nm for measurements of SpO2 concentrations or between 800 nm and 1200 nm for blood sugar measurements.”)
Regarding Claims 7 and 14, Kulcke in view of Mhaskar discloses as described above, The apparatus of claim 1 and The method of claim 8, respectively. For the remainder of Claims 7 and 14, Kulcke discloses wherein the LED array ([0100] “This light is preferably generated by an LED or a combination of LEDs.”) is configured to pass a visible white light ([0198] “A broadband LED 20 typically emits light in the spectral range between 800 and 1200 nm in the direction of the measurement region 3.”; [0100] “By way of example, conventional white-light LEDs are suitable for this.”) to an inner side of a ring finger of a subject ([All of 0198] including “The measurement is performed on a finger. The finger is guided into the measurement region 3.”)(Examiner notes that this could be any finger, including the ring finger.) and penetrate through a finger-tip by passing epidermis, and contact a concentrated peripheral blood ([All of 0198] including “The light re-emerging through the finger is routed into the housing 16 through a second opening in the housing 16”)(Examiner notes that the light is “re-emerging” from going all the way through the finger, therefore it has passed through the epidermis that encloses the finger and the peripheral blood inside the finger.).
Claims 2 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Kulcke (US Patent Application Publication US 2013/0066172 A1), in view of Hong (US 2021/0282649 A1), further in view of Fortmann-Roe (“Understanding the Bias-Variance Tradeoff”, Ref U on PTO-892), as evidenced by InStyle LED (“What’s the difference between Warm, Pure, Daylight and Cool White?”).
Regarding Claims 2 and 9, Kulcke in view of Mhaskar discloses as described above, The apparatus of claim 1 and The method of claim 8, respectively. For the remainder of Claims 2 and 9, Kulcke discloses wherein the LED array ([[0100] “This light is preferably generated by an LED or a combination of LEDs.”) is defined by 440-660 nm ([0190] “This LED has a good intensity distribution, particularly in the spectral range between 500 nm and 650 nm”) and a color temperature 7000 K (All of [0100] including, “conventional white-light LEDs “, “…which white-light LEDs have a broadband light emission as a result of an additional superposed fluorescent dye. Inorganic fluorescent dyes, which for example have ytterbium or other rare earths in YAG or similar host lattices, can be used as dyes.”; [All of 0190], including “Shown here is the spectrum of a suitable broadband LED which, like a white-light LED, has a blue emitter (450 nm) for exciting a dye.”, “use could also be made of a white-light LED with a substantially lower color temperature…”)(Examiner notes a person having ordinary skill in the art would recognize that conventional white-light LEDs broadly include those with a color temperature of 7000k, as shown referring to the scale below by In Style LED. Refer to “What’s the difference between Warm, Pure, Daylight and Cool White?” for more information about white LEDs. The disclosed color temperature is on the “bluer”, “cool” side of the white light scale, which would include the disclosed “white-light LED with a blue emitter” of a higher color temperature such as 7000 K, as opposed to a “white-light LED with a substantially lower color temperature”).
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Figure A: Excerpt color temperature scale from InStyle LED, an exemplar scale of well-known information about described “white light” in lighting arts.
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kulcke in view of in view of Hong (US 2021/0282649 A1), further in view of Fortmann-Roe (“Understanding the Bias-Variance Tradeoff”, Ref U on PTO-892), further in view of Whang, et. al., “Designing Uniform Illumination Systems by Surface-Tailored Lens and Configurations of LED Arrays”, and further in view of Park, (US Patent US 10,016,153 B2).
Regarding Claims 4 and 11, Kulcke in view of Hong and Fortmann-Roe discloses as described above, The apparatus of claim 1 and The method of claim 8, respectively. For the remainder of Claims 4 and 11, Kulcke discloses wherein the LED array corresponds to a white LED ([0100] “By way of example, conventional white-light LEDs are suitable for this…”), a combination of 6 LEDs ([0100] ” This light is preferably generated by an LED or a combination of LEDs.”) to produce a concentrated light beam ([0171] “the beam path”)(Examiner notes that the disclosed LED light forms a light beam the has a “beam path” after it has been produced that is ultimately concentrated onto the “image sensor 12”).
Kulcke does not specifically disclose LED ring, a combination of 6 LEDs of luminous intensity of 18 mcd placed angularly. However, Kulcke is open to including LEDs arranged in a ring configuration for, as “a combination of LEDs” is broadly disclosed, along with a need for the LED to provide [0190] “good intensity distribution”. Furthermore, Kulcke is open to the LEDs having a luminous intensity of 18 mcd, as the specific luminosity is not disclosed, and the amount of light put out by the LEDs is safe for the body such that [0022] “The light source must not be too strong so that there are no burns on the finger.” A low intensity of 18 mcd would be align with providing safe lighting for a finger. Furthermore, Kulcke’s disclosed system has capabilities to [0127] “make[s] it possible to detect very weak variations in the intensity” of measured light as a result of the LED source.
Whang teaches methods by which to optimize the configuration of sets of LEDS in order to achieve uniform illumination distribution. Specifically for Claims 4 and 11, Whang teaches wherein the LED array corresponds to a LED ring ([Page 94, Section II. Uniform Illumination System] Fig 1(b), Circular configuration of LED array) a combination of 6 LEDs ([All of Section D.1. Circular Ring LED Arrays] “The irradiance E is given by the sum of the irradiance for N ≥ 4 (even number) LEDs”) placed angularly ([Section II. Uniform Illumination System, Paragraphs 1 - 2] “It is, therefore, important that when utilizing fewer LEDs to achieve better uniformity, the desired beam angle is also obtained.”, “a configuration of LED arrays with a limited number of LEDs and the desired angular distribution”; [Page 97, All of Section D. Circular Ring LED Arrays] to produce a concentrated light beam ([Page 94, Section II. Uniform Illumination System, Paragraph 1] “It is, therefore, important that when utilizing fewer LEDs to achieve better uniformity, the desired beam angle is also obtained. The system presented in this paper can do this.; [Page 101, Section VII. Results of Simulation, A. Uniform Illumination System of Circular Ring LED Array”], All of Section and Fig 19.)
Whang does not teach LEDs having a luminous intensity of 18 mcd. However, Whang is open to including LEDs of any intensity, as the teaching is for arranging the LEDs in advantageous orientations for relative to each other in order to achieve uniform illumination distribution in a circular orientation. The luminous intensity of the individual LEDs is not limited, as the equations taught by Whang calculate collective luminous intensity based on customizable inputs for the LED’s individual specifications and placement.
Park teaches a photoplethysmographic measurement method for measuring a biosignal with an light source. Specifically regarding Claims 4 and 11, Park teaches of luminous intensity of 18 mcd ([Col 6, Lines 60 – 62] “An intensity of the light emitted from the light source of the light emitter 120 may range from 20 to 200 millicandelas per square meter (mdc/m^2)).
For the combination of Kulcke and Whang, Whang teaches that the arrangement of LEDs can be optimized into different-shaped orientations, such as a circle (particularly with N = even number of LEDs) to achieve uniform illumination distribution. The circular orientation of LEDs of Whang would perform the same function of providing a light source for an object if combined with the “LED or a combination of LEDs” light source of Kulcke. Therefore, it would have been predictable to use the circular orientation of LEDs in Whang in any similar LED-based light source process as it would continue to operate with the function of supplying light to an object with LEDs. Further, Kulcke discloses that a “good intensity distribution” of the illumination is advantageous for its biosignal measurement applications, and so Kulcke is open to ways to use a “combination of LEDs” with a good and uniform illumination distribution. This could include the circular orientation of LEDs for uniform illumination distribution as taught by Whang. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the “LED or a combination of LEDs” light source disclosed in Kulcke and the circular orientation of LEDs taught by Whang, creating a single apparatus to supply uniform illumination distribution to an object for measuring biosignals. All claimed elements are known in prior art and could have been combined with no change to their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time the invention was properly filed.
For a particular light intensity for the LED array, Kulcke may be combined with Whang, as described above, and further combined with Park. Park teaches that biosignals from the blood such as oxygen can be measured spectrally using an instrument with an applied light source. This is the same function as Kulcke’s applied light from light source 20, “an LED or a combination of LEDs” spectrally measuring oxygen saturation. As described above, Kulcke discloses a requirement for its light source to “not be too strong so that there are no burns on the finger.” Park teaches that a low level of light intensity can successfully yield oxygen saturation measurements. Therefore, Kulcke discloses a motivation to substitute a light source that specifically has a low level of light intensity for use in a biomedical application, such as the “light emitter” from Park, thereby handily avoiding burn risks.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the “LED or a combination of LEDs” of Kulcke with the “light emitter” with an intensity of 20 – 200 millicandelas per square meter of Park. Kulcke discloses applied light from “an LED or a combination of LEDs” light source to spectrally measuring oxygen saturation, which is the same function as Park’s “light emitter” with an intensity of 20 – 200 millicandelas per square meter to spectrally measure blood oxygen. Therefore it would yield a predictable result to substitute Park’s low intensity level for the “light emitter” for the “an LED or a combination of LEDs” of Kulcke. The substitution of Park’s low intensity level for the light emitter would not affect the combination with Whang, as Whang’s teachings are for arranging the LEDs in advantageous orientations for achieving uniform illumination distribution, with the luminous intensity as a variable in the equations of sensor placement. The individual LEDs can be any light intensity in regard to Whang. The simple substitution of one known element for another is likely to be obvious when predictable results are achieved.
Response to Arguments
Applicant's arguments filed 05 February 2026 have been fully considered but they are not persuasive.
Regarding 35 U.S.C. 103 Rejections
Applicant’s arguments with respect to claims 1 – 4, 7 – 11, and 14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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/MELISSA JO MONTGOMERY/Examiner, Art Unit 3791
/PATRICK FERNANDES/Primary Examiner, Art Unit 3791
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