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 .
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.
Claim(s) 1-6, 10-13 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klein et al. (US Pub No. 2017/0303830) in view of Lachenbruch et al. (US Pub No. 2017/0224271).
With regards to claims 1 and 11, Klein et al. disclose a system and method of non-invasive determination of a tissue injury of a patient’s tissue, the method comprising:
receiving, by at least two optical sensors (16), intensity and signal distance information from light reflected from the tissue over at least one point of the patient’s skin, including signal distance information measured between at least one light source (14) and the at least two optical sensors (paragraphs [0072], [0081]-[0082], [0106], referring to the system comprising an infrared radiation source (14) and an infrared detector (16) which may be configured to detect short wave infrared (SWIR) radiation, wherein the infrared detector (16) may include one or more [i.e. at least two] component detectors (i.e. at least two optical sensors) that are configured to detect the SWIR radiation in one or more spectral bands and referring to the system being configured to determine the state of subcutaneous (i.e. under skin); paragraphs [0159]-[0161], referring to the detectors being configured to detect reflected radiation that emerges from the tissue surface that is irradiated, wherein signals received from each detector may correspond to the distance (i.e. “signal distance information”) of the detector from the light source and the shape and/or intensity of the signal received by a detector may correspond to a medical condition in the tissue; paragraph [0178], referring to the determination of a distance between the light source and the light sensor may allow determination of a medical condition in the tissue, such as deep tissue injury, due to changes in signal from a detector having a determined distance to the light source; Figures 1-2, 5);
receiving, by at least one physiological sensor (i.e. a pressure sensor or temperature sensor or dedicated sensors which measure vital signs or Raman measurement system which allows determination of concentration of chemical compounds within the tissue, such as myoglobin) over at least one point of the patient’s skin, physiological characteristic information (i.e. temperature or applied pressure or myoglobin concentration) of the tissue (paragraph [0193], referring to Raman spectroscopy analysis allowing determination of concentration of chemical compounds within the tissue, such as myoglobin concentration; paragraph [0196], referring to measuring with a pressure sensor or measuring with a temperature sensor; paragraph [0227], referring to the measurement unit may receive parameters of the subject body (e.g., vital signs) measured with dedicated sensors);
training, by a processor (28), a machine learning (ML) algorithm to determine the tissue injury, wherein the training is carried out on a dataset of intensity and signal distance information and physiological characteristic information of the tissue obtained from the at least two optical sensors and from the at least one physiological sensor (paragraph [0089], referring to the system including a controller (28) which includes a processor; paragraph [0223], referring to, in order to quantify concentration of Propofol and/or identify and/or classify deep tissue injury (DTI), the algorithm may use multivariate classification methods, for instance artificial neural networks (ANN) [i.e. a machine learning (ML) algorithm], etc., wherein such a process may include training and validation steps and wherein the Propofol concentration may be considered as the dependent variables and the other variables (e.g., data matrices A, B, C,D, E) may be the independent variables, wherein the data matrices A,B,C,D,E may refer to matrices of measured values, such as reflectance mode data and deterministic and/or vital signs parameters, etc. and wherein the multivariate classification may allow finding a solution that best predicts the ‘Y” variable as a linear (or non-linear) function of the ‘X’ variables; note that paragraph [0223] discloses that the multivariate classification methods, which can include ANN, can be used to quantify Propfol OR identify deep tissue injury, and therefore it is clear that the ‘Y’ variable can instead correspond to deep tissue injury identification and the ‘X’ variable/data matrices correspond to the data that is used to identify deep tissue injury identification, which, as set forth in paragraphs [0161], [0193] and [0196], the data/dataset of intensity and signal distance information and physiological characteristic information of the tissue obtained from the sensors is used to identify deep tissue injury);
applying, by the processor (28), the ML algorithm (i.e. ANN, etc.) on the received intensity and signal distance information and the received physiological characteristic information wherein the received intensity and signal distance information and the received physiological characteristic information are received in real time, to determine a subcutaneous tissue injury in real-time in which liquids accumulate subcutaneously, in accordance with a calculated change in the received signal intensity and signal distance information, and the physiological characteristic information (paragraph [0089], referring to the system including a controller (28) which includes a processor; paragraph [0053], referring to the reflection measurement being indicative of a state of the tissue, wherein the state of the tissue may be indicative of a medical condition including types of deep tissue injury under intact skin in which liquids accumulate subcutaneously; paragraphs [0161], [0193], referring to, for a plurality of detectors with different distances from the radiation source, the shape of the signal may refer to a slope, such as the intensity of absorption line at given wavelength for all detectors and the processor may determine the change in shape of a signal by calculating similarity of measured data sets with the predefined data with known (or calibrated) spectral response (e.g., for healthy tissue) and/or calculate a combined absorption graph (e.g., for a plurality of detectors) and wherein a change in the state of the tissue may be determined when a change in the slope of the combined absorption graph is detected by a processor; paragraph [0196], referring to measuring with a pressure sensor applied pressure in varying magnitude and/or measuring with a temperature sensor applied heat in varying magnitude which causes different absorption and/or scattering of radiation in tissues with deep tissue injuries compared to absorption and/or scattering in healthy tissue; paragraph [0223], referring to, in order to quantify concentration of Propofol and/or identify and/or classify deep tissue injury (DTI), the algorithm may use multivariate classification methods, for instance artificial neural networks (ANN) [i.e. a machine learning (ML) algorithm], etc., wherein such a process may include training and validation steps and wherein the Propofol concentration may be considered as the dependent variables and the other variables (e.g., data matrices A, B, C,D, E) may be the independent variables, wherein the data matrices A,B,C,D,E may refer to matrices of measured values, such as reflectance mode data and deterministic and/or vital signs parameters, etc. and wherein the multivariate classification may allow finding a solution that best predicts the ‘Y” variable as a linear (or non-linear) function of the ‘X’ variables; note that paragraph [0223] discloses that the multivariate classification methods, which can include ANN, can be used to quantify Propfol OR identify deep tissue injury, and therefore it is clear that the ‘Y’ variable can instead correspond to deep tissue injury identification and the ‘X’ variable/data matrices correspond to the data that is used to identify deep tissue injury identification, which, as set forth in paragraphs [0161], [0193] and [0196], the data/dataset of intensity and signal distance information and physiological characteristic information of the tissue obtained from the sensors is used to identify deep tissue injury; paragraphs [0223]-[0224], [0226], [0230], referring to the “real-time” quantification of changes in concentration/substance concentration data, wherein, as set forth in paragraph [0223], Propofol is an example of a particular substance whose concentration is determined, but a deep tissue injury (DTI) may also be identified, wherein, as set forth in paragraphs [0053], [0167], the existence and degree or severity of Deep tissue injury (DTI) is based on the determined concentration of different substances, wherein a medical condition including types of deep tissue injury under intact skin is a condition in which liquids accumulate subcutaneously; note that it follows that DTI is determined via the “real-time” quantification of changes in concentration/substance concentration data particular for DTI and therefore a subcutaneous tissue injury (i.e. DTI) can be determined in “real-time” in which liquids accumulate subcutaneously); and
selecting, by the processor, at least one point of the patient’s skin determined as a potential subdermal injury (paragraph [0169], referring to analysis of the reflectance measurements at the different distances may indicate a depth [i.e. at least one point of the patient’s skin] within the tissue of a detected feature).
However, Klein et al. do not specifically disclose that their method further comprises issuing, by the processor, an alert when the subcutaneous tissue injury is determined.
Lachenbruch et al. disclose a pressure ulcer detection system, wherein a processor is adapted to assess, based on optical properties of outgoing radiation and of incoming radiation, whether or not the tissue site is healthy (Abstract; paragraph [0087]). If the processor determines that tissue is unhealthy, for example that it contains a pressure ulcer or other wound, the processor activates the alarm (paragraph [0087], wherein such an alarm serves as an alert that there is an injury/wound). The alarm provides a communication of the assessment to a destination when an abnormality is present, thus providing an early detection of a skin abnormality (Abstract; paragraphs [0046]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the method of Klein et al. further comprise issuing, by the processor, an alert when the subcutaneous tissue injury is determined, as taught by Lachenbruch et al., in order to provide a communication of the assessment to a destination when an abnormality is present, thus providing an early detection of a skin abnormality which aids in providing early treatment (Abstract; paragraphs [0046]).
With regards to claim 2, Klein et al. disclose that at least one point other than the point determined as a potential subdermal injury is determined as healthy tissue by the processor (paragraphs [0124]-[0125], [0128], referring to differentiating between conditions, such as healthy and diseased tissue; paragraph [0161], referring to the shape and/or intensity of a signal received by a detector may correspond to a medical condition in the tissue, for example shape of a signal changed in measurements for healthy tissue and for deep tissue injury).
With regards to claim 3, Klein et al. disclose that the method further comprises applying modulated lighting so as to accelerate the measurement time by the at least two optical sensors (paragraph [0077], referring to infrared radiation from a single wideband infrared source may be separately channeled via separate spectral band selection devices to form single-band sources, such as by performing separate channeling sequentially to radiate infrared radiation in different spectral bands in quick succession (i.e. modulated lighting which is provided in “quick” succession and thus would accelerate the measurement time by the at least two optical sensors) via a single component aperture of unit aperture).
With regards to claim 4, Klein et al. disclose that the modulated lighting comprises using a different frequency for different light sources simultaneously based on a Fast Fourier Transform algorithm (paragraph [0077], referring to infrared radiation from a single wideband infrared source being separately channeled via separate spectral band selection devices (i.e. prisms, gratings) to form single-band sources and/or dividing radiation from a wideband source and concurrently/simultaneously channeled via different band-selection devices to concurrently/simultaneously radiate in different spectral bands via separate component apertures of the unit aperture, wherein such dividing of the radiation into individual spectral components/bands is based, by definition, on a Fast Fourier Transform algorithm (i.e. FFT which by definition comprises of converting a signal into individual spectral components to thereby provide frequency information about the signal).
With regards to claims 5 and 12, Klein et al. disclose that the signal is received from the at least two optical sensors in a plurality of wavelengths (paragraphs [0058], [0059], referring to two or more radiation detectors being configured to detect different radiation in different spectral bands of the SWIR spectral region and/or the visible spectral region).
With regards to claims 6 and 13, Klein et al. disclose that the physiological characteristic information of the tissue is selected from the group consisting of: blood flow pattern, blood flow rate, blood viscosity, tissue temperature, skin tissue capacitance, pulse wave velocity, skin elasticity, hemoglobin level, and spatial oxygenation (paragraph [0196], referring to measuring with a pressure sensor or measuring with a temperature sensor; paragraph [0227], referring to the measurement unit may receive parameters of the subject body (e.g., vital signs) measured with dedicated sensors).
With regards to claims 10 and 17, Klein et al. disclose that their method further comprises measuring a reference point on a healthy tissue of the patient, in order to get a normalized personalized result for the patient (paragraph [0161], referring to “In some embodiments, the shape and/or intensity of a signal received by a detector may correspond to a medical condition in the tissue, for example shape of a signal changed in measurements for healthy tissue and for deep tissue injury, so that the shape of the signal received for a healthy tissue changes when measuring an injured tissue of the same subject (e.g., person)” and therefore a reference point is measured on a healthy tissue of the patient, thereby providing a normalized personalized result for the patient; paragraph [0162], referring to “In some cases, a reference measurement may be made on the radiation that emerges from the tissue under known conditions (e.g., on a skin surface that is known to overlie healthy tissue, or prior to introduction of a substance into the blood”).
Claim(s) 8-9 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klein et al. in view of Lachenbruch et al. as applied to claims 1 and 11 above, and further in view of Dunn et al. (US Pub No. 2019/0104982).
With regards to claims 8, 15 and 16, as discussed above, the above combined references meet the limitations of claims 1 and 11. However, though Klein et al. do disclose that their device includes a pressure sensor (paragraph [0196]), they do not specifically disclose that their method further comprises initiating a measurement when a pressure signal, from the pressure sensor, is within a predefined pressure threshold range.
Dunn et al. disclose systems, devices, and methods related to the diagnostic measurement of conditions for pressure ulcers, wherein the device comprises a conformable sensor patch device comprising a pressure sensor, a bioimpedance sensor, a temperature sensor, a light sensor, etc. (Abstract; paragraph [0006]). The pressure sensor can be triggered to actuate device operation when a threshold pressure level is sensed for a preset time period or when the pressure reaches a level that could cause an impact injury (paragraph [0006]). Triggering based upon pressure measurements can limit consumption of battery power by actuating device operation only during those times when a particular body location is subject to a pressure that can contribute to pressure ulcer formation (paragraph [0006]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have the method of the above combined references further comprise initiating a measurement when a pressure signal, from the pressure sensor, is within a predefined pressure threshold range, as taught by Dunn et al., in order to limit consumption of power by actuating device operation only during those times when a particular body location is subject to a pressure that can contribute to deep tissue injury, such as a pressure ulcer formation (paragraph [0006]).
Further, with regards to claim 16, the limitation concerning the pressure signal is “from a damping pressure mechanism” is directed to an intended use of the claimed system and/or a manner of operating the claimed system/apparatus. A recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim. Since the processor of the above combined references is capable of receiving a pressure signal from any mechanism, including from a damping pressure mechanism, the above combined references meet the limitation.
With regards to claim 9, Klein et al. disclose that the pressure sensor is accommodated in an elastomeric ring (paragraph [0086], referring to their system comprising a protective cover (20) which is made out of suitable materials, such as thermoplastic elastomers, etc., which is elastomeric material; paragraph [0229], referring to the sensors having an external ring that is adhered to the skin around the blood vessel).
Claim(s) 18-20 and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klein et al. in view of Lachenbruch et al. as applied to claims 1 and 11 above, and further in view of Gurevich et al. (US Pub No. 2018/0028079).
With regards to claims 21-22, as discussed above, the above combined references meet the limitations of claims 1 and 11. Further, with regards to claim 18, the above combined references disclose most of the limitation of claim 18, including a device for non-invasive determination of a tissue injury of a patient’s tissue, the device comprising at least two optical sensors, at least one physiological sensor and a processor configured to perform the steps as set forth in claims 1 and 11 [see above rejection of claims 1 and 11] and Klein et al. further set forth that the device comprises a light source (14) (paragraph [0072], referring to the infrared radiation source (14); Figure 1).
Klein et al. further disclose that the ML algorithm comprises a classifier to determine a relationship between the received information and the determined subcutaneous tissue injury (paragraph [0223], referring to identifying and/or classifying deep tissue injury (DTI) using multivariate classification methods, including PLS, PCR, LDA, SVM, etc.).
However, the above combined references do not specifically disclose that the classifier is specifically a logistic regression classifier.
Gurevich et al. disclose methods and systems for characterizing tissue of a subject, wherein various learning models may be used for predictive analytics of the tissue, including, for example, error based learning (logistic regression, support vector machines, artificial neural networks), or a combination thereof (Abstract; paragraph [0154]).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to substitute the classifier of the above combined references with a classifier comprising of a logistic regression classifier, as taught by Gurevich et al., as the substation of one known classifier for another yields predictable results (i.e. classifying tissue) to one of ordinary skill in the art. One of ordinary skill in the art would have been able to carry out such a substation and the results are reasonably predictable.
With regards to claim 19, Klein et al. disclose that a signal is received from the at least two optical sensors in a plurality of wavelengths (paragraphs [0058], [0059], referring to two or more radiation detectors being configured to detect different radiation in different spectral bands of the SWIR spectral region and/or the visible spectral region).
With regards to claim 20, Klein et al. disclose that their method further comprises measuring a reference point on a healthy tissue of the patient, in order to get a normalized personalized result for the patient (paragraph [0161], referring to “In some embodiments, the shape and/or intensity of a signal received by a detector may correspond to a medical condition in the tissue, for example shape of a signal changed in measurements for healthy tissue and for deep tissue injury, so that the shape of the signal received for a healthy tissue changes when measuring an injured tissue of the same subject (e.g., person)” and therefore a reference point is measured on a healthy tissue of the patient, thereby providing a normalized personalized result for the patient; paragraph [0162], referring to “In some cases, a reference measurement may be made on the radiation that emerges from the tissue under known conditions (e.g., on a skin surface that is known to overlie healthy tissue, or prior to introduction of a substance into the blood”).
Response to Arguments
Applicant's arguments filed August 13, 2025 have been fully considered but they are not persuasive.
With regards to claim 1, Applicant argues that none of Klein and Lachenbruch, alone or in combination, describe determination of a subcutaneous tissue injury in which liquids accumulate simultaneously based on real-time measured information. Applicant asserts that Klein’s use of neural networks to determine Propofol concentration is not the same thing as applying a machine learning algorithm for determination of subcutaneous tissue injury in which liquids subcutaneously based on real-time measured information as required by amended independent claim 1.
Examiner respectfully disagrees and notes that Klein discloses in paragraph [0053] that “The reflection or transmission measurement may be indicative of a state of the tissue… The state of the tissue may be indicative of a medical condition in the patient. For example, a medical condition may include otitis media, early stages of pressure ulcers, or other types of deep tissue injury under intact skin in which liquids accumulate subcutaneously. A state of the tissue may include a concentration of a substance in the blood or other subcutaneous fluids… A processor, may determine state of the tissue…and based on the determined concentration of the different substances, determine the existence and degree or severity of DTI”. Therefore Klein does disclose a determination of a subcutaneous tissue injury (i.e. deep tissue injury) in which liquids accumulate (i.e. “deep tissue injury under intact skin in which liquids accumulate subcutaneously”). Klein further sets forth in paragraphs [0223]-[0224], [0226] and [0230] “real-time” quantification of changes in concentration/substance concentration data, wherein, as set forth in paragraph [0223], Propofol is an example of a particular substance whose concentration is determined, but a deep tissue injury (DTI) may also be identified, wherein, as set forth in paragraphs [0053] and [0167] of Klein, the existence and degree or severity of Deep tissue injury (DTI) is based on the determined concentration of different substances. It follows that DTI can be determined via the “real-time” quantification of changes in concentration/substance concentration data particular for DTI and therefore a subcutaneous tissue injury (i.e. DTI) in which liquids accumulate subcutaneously can be determined in “real-time”.
Examiner emphasizes that Klein is not solely limited to determine Propofol concentration, but rather the measurements of Klein may be carried out for determination of other substance concentration in the blood stream and/or in other subcutaneous tissue, including, as set forth in paragraph [0053] of Klein, concentrations of different substances which can be used to determine the existence and degree or severity of Deep tissue injury (DTI). Further see paragraph [0168] of Klein which sets forth that deep tissue injuries may include increasing concentration (over time) of substances that are products of ischemic processes (causing damage to the tissue) such as free fatty acids and/or glycerol which are the breakdown products of the fatty part of the tissue (triglyceride) and the accumulation of other substances (e.g., protease or myoglobin) may also indicate presence of a deep tissue injury. Paragraph [0170] of Klein further sets forth “…non-invasive measurement may be carried out for determination of drug (e.g., intravenous delivered substance such as Propofol) or other substance concentration in the blood stream and/or in other subcutaneous tissue (e.g., of a mammal) using optical sensors” and further, as was set forth in the previous action, paragraph [0223] of Klein sets forth that “According to some embodiments, in order to quantify concentration of anesthetic drugs (e.g., Propofol, etc.) and/or identify and/or classify deep tissue injury (DTI), the algorithm may use the multivariate classification methods, for instance including at least one of partial-least square (PLS), principal component regression (PCR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), naive bayesian classifier (NBC), support vector machine (SVM), artificial neural networks (ANN), etc.”. It is thus clear that Klein’s invention is not solely limited to determining Propofol concentration, but can be used to identify and/or classify deep tissue injury (DTI) (i.e. a “subcutaneous injury in which liquids accumulate subcutaneously”).
With regards to claim 2, Applicant argues that Klein relies on other medical indications to determine healthy tissue, whereas claim 2 of the present application refers to determining a tissue as healthy tissue by the processor so that there is no need for prior knowledge on the condition of that tissue as required by Klein.
Examiner respectfully disagrees and notes that the claim does not set forth that no prior knowledge on the condition of the tissue is required, but rather only sets forth that “at least one point other than the point determined as a potential subdermal injury is determined as healthy tissue by the processor”. Paragraphs [0124]-[0125] of Klein does disclose this as these paragraphs set forth that the differential absorption may be related to the state of the tissue “e.g., presence, absence, degree, or other state of a medical condition”, wherein the “absence” of a medical condition would correspond to healthy tissue and further discloses differentiating between healthy and diseased tissue and additionally sets forth that reflection measurements may be made on a region (i.e. “at least one point other than the point determined as a potential subdermal injury”) of a tissue surface when the underlying tissue is expected to be healthy. Paragraph [0161] of Klein further refers to receiving signals for healthy tissue changes. As such, Klein does appear to teach the limitations of claim 2.
The claims therefore remain rejected under the previously applied prior art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KATHERINE L FERNANDEZ/ Primary Examiner, Art Unit 3798