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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/22/2026 has been entered.
Status of Claims
Claims 1-3 and 5-16 are pending in this application. Claim 4 is cancelled and Claims 1-3 and 5-16 have been examined on the merits.
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 1-3 and 5-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites determining a feature contrast of the at least two pattern features and recording the reflection images, and executing a parametrized classification model to output a condition measure of the living organism. The limitation of determining a feature contrast of the at least two pattern features, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind i.e., determining a contrast or a difference between images and patterns. If a Claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Similarly, the limitation of “recording the reflection images” is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind i.e., using pen and paper to record or note the reflection images. If a Claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Further, “executing a parametrized classification model to output a condition measure of the living organism” is directed to an abstract because the claim limitation can be performed via mathematical concepts and mental process, with assistance of basic physical aids or with pen and paper. See MPEP § 2106.04(a)(2)(III)(B). Intellectual Ventures LLCv. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016) established that mental processes encompass acts which, absent anything beyond generic computer components, may be "performed by a human, mentally or with pen and paper." This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element — using one or more generic processors for execution. The processors are recited at a high-level of generality (i.e., the system, processor, an output) such that it amounts no more than mere instructions to apply the exception using processors. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using processors to perform the identifying and determining steps amounts to no more than mere instructions to apply the exception using generic processors. Mere instructions to apply an exception using generic processors cannot provide an inventive concept. Examiner notes that the claim limitations “receiving an image set comprising at least two reflection images” do not sufficient to amount to significantly more than the abstract idea because these claim limitations are directed to extra solution activity of data gathering, and the claims do not positively recite the generation of the image.
The claim is not patent eligible. The other independent claim 12 also recites similar limitations as claim 1, which is also found to be not patent eligible at least for the reasons noted above.
The dependent claims 2-3, 5-11, and 14-16 are also directed to an abstract idea as the depending claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The elements in those claims do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Therefore, the depending claims, are, also not patent eligible.
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.
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, 2, 5, 6,8-15 are rejected under 35 U.S.C. 103 as being unpatentable over Califa (US20220039679A1) in view of Valbusa (US20220409057A1).
Regarding Claim 1,
Califa teaches a method for monitoring a condition of a living organism (corresponding disclosure in at least [0001], where the method is related to monitoring the health-related parameters (condition) of the patient (living organism), “technique specifically relates to monitoring of health-related parameters”) comprising:
(a) receiving an image set comprising at least two reflection images generated at different points in time and an indication of at least one interval between at least two different points in time (corresponding disclosure in at least [0016], where images are received by the image unit at different points in time (sequences of interleaving image data pieces) “configured for providing intervals of coherent illumination pulses separated between them by pulses of field illumination. The first imaging unit may thus collect sequence of interleaving image data pieces and split the collected frames based on illumination conditions”),
wherein a reflection image is generated while the living organism is illuminated by patterned coherent electromagnetic radiation (corresponding disclosure in at least [0005], where the method, which uses speckle pattern, uses electromagnetic radiation “When coherent electromagnetic radiation, e.g. laser beam, is backscattered from an optically rough medium, such as biological tissues, a speckle pattern may appear in the backscattered light”)
and, wherein the reflection image shows at least one pattern feature formed by illuminating at least a part of the living organism by the patterned coherent electromagnetic radiation (corresponding disclosure in at least [0021], where different speckle patterns are formed from illuminating the region “comprises determining contrast variations in image data comprising image data indicative of one or more speckle patterns formed in light returning from illumination spots in said inspection region”),
(b) determining a feature contrast of the at least two pattern features (corresponding disclosure in at least [0021], where the contrast in the speckle patterns are determined “wherein said processing comprises determining contrast variations in image data comprising image data indicative of one or more speckle patterns”)
(c) executing a parametrized classification model to output a condition measure of the living organism based on input of the feature contrast and the indication of the at least one interval (corresponding disclosure in at least [0021], where the conditions of the organism (user) are measured “the determined parameters may comprise at least one of heart rate, heart rate variability, respiratory rate, blood pressure (BP) and pulse wave velocity (PWV)”),
and
(d) outputting the condition measure (corresponding disclosure in at least [0057], where there is a processor for the input data and a parameter determining module, which outputs the condition measures “for processing of the input data where the processing utility 600 includes contrast speckle module 610 and parameter determining module 660”).
Califa does not teach wherein the parametrized classification model is trained with at least one training dataset comprising a plurality of reflection images formed by illuminating at least a part of a plurality of humans and recording the reflection images and wherein the plurality of humans include humans with known condition measures detectable from corresponding reflection images.
Valbusa, in a similar field of endeavor, teaches a similar concept (feature extraction from an image) wherein the parametrized classification model is trained with at least one training dataset comprising a plurality of reflection images (corresponding disclosure in at least [0062], where there is an algorithm using a plurality of reflection images as the training set “or each feature combination, the configurator causes the segmenter to calculate the reflectance segmentation masks corresponding to the reflectance images of the training set by applying the classification algorithm with this feature combination”)
formed by illuminating at least a part of a plurality of humans and recording the reflection images (corresponding disclosure in at least [0031], where the reflected images are from a human, where the area is a surgical field, and the images are recorded “a pair of corresponding reflectance image 205R and fluorescence image 205F are shown. The reflectance image 205R and the fluorescence image 205F provide a concurrent representation (in terms of reflectance light and fluorescence light, respectively) of a same field of view 103. Particularly, the field of view 103 contains the surgical cavity 109 and some foreign objects, consisting of a surgical instrument 115, two surgeon hands 118 and surrounding skin 124 in the example at issue;”), and wherein the plurality of humans include humans with known condition measures detectable from corresponding reflection images (corresponding disclosure in at least [0050], where the reflection images contact a measurable condition, or measurable property in the image” the segmenter performs a feature extraction step for extracting one or more features from the reflectance image and possibly from the corresponding fluorescence image as well (pre-determined to be the best suited for this purpose, as described in the following); each feature is a (measurable) property that represents a distinctive characteristic of the reflectance/fluorescence image”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated a classification model with a training data set as taught by Valbusa. One of the ordinary skill in the art would have been motivated to incorporate this because the training data further teaches the model for validation and effectiveness and efficiency increases with the use of a model.
Regarding Claim 2, Califa and Valbusa teach the limitations of Claim 1, and Califa further teaches wherein at least three reflection images comprises the at least two reflection images and an additional reflection image (corresponding disclosure in at least [0016], where the imaging unit collects sequences (sets) of image data “configured for providing intervals of coherent illumination pulses separated between them by pulses of field illumination. The first imaging unit may thus collect sequence of interleaving image data pieces and split the collected frames based on illumination conditions,”)
and wherein the at least three reflection images have been generated in a time series with a constant time interval associated with an imaging frequency or changing time intervals associated with a mean imaging frequency (corresponding disclosure in at least [0051], where there is a set sampling rate for the image collection, sampling at a proper rate (frequency) for the imaging of the condition measures (monitoring blood and heart rate) “The first imaging unit 130 is preferably configured for collecting a sequence of images at selected sampling rate, where each image is collected within a selected exposure time, such that the sampling rate is sufficient for monitoring blood perfusion and heart rate of the subject”).
Regarding Claim 5, Califa and Valbusa teach the limitations of Claim 1, and Califa further teaches wherein the reflection images are motion corrected based on motion tracking data associated with the movement of the living organism (corresponding disclosure in at least [0015], where there is a tracking module for correcting the potential shifts of the region “Tracking of the relative location may be used for determining drift of the illumination spot with respect to the inspection region. Such drift may be associated with shift of sub-regions that can be corrected by following selected sub-regions on basis of the inspection region thereby compensating for shifts of the illumination spot”).
Regarding Claim 6, Califa and Valbusa teach the limitations of Claim 1, and Califa further teaches wherein the coherent electromagnetic radiation has a wavelength between 900 nm and 1000 nm (corresponding disclosure in at least [0048], where a wavelength range between 800-1550 nm is used, which encompasses the wavelength “the use of near Infrared illumination, e.g. wavelength range of 800-1550 nm may be preferred providing certain penetration through the skin and being non-visible illumination”).
Regarding Claim 8, Califa and Valbusa teach the limitations of Claim 1, and Califa further teaches wherein the condition measure comprises at least one of the heart rate, the blood pressure and/or the aspiration level (corresponding disclosure in at least [0007], where different biological parameters are measured “thereby enabling to determine biological parameters such as heart rate, heart rate variability (HRV), blood pressure (BP)”).
Regarding Claim 9, Califa and Valbusa teach the limitations of Claim 1, and Califa further teaches wherein outputting the condition measure is substituted by: generating a signal indicating a condition-based action based on a comparison of the condition measure with a threshold associated with a critical condition measure (corresponding disclosure in at least [0069], where there is a threshold for an abnormal condition (critical condition), determined after receiving the signal determined of the condition “system 100 may be located in front of a driver/pilot/operator or any other person/passenger in a moving vehicle and directed for inspection a region of the relevant subject for determining biological parameters such as heart rate… This allows monitoring of health condition of the subject and generating alert if a variation is detected beyond selected threshold”)
outputting the signal indicating the condition-based action (corresponding disclosure in at least [0069], where the signal result based on the critical condition (beyond threshold) is provided “determining biological parameters such as heart rate, heart rate variability etc., or existence of a person in a selected seat by detecting existence of heart rate. This allows monitoring of health condition of the subject and generating alert if a variation is detected beyond selected threshold”).
Regarding Claim 10, Califa and Valbusa teach the limitations of Claim 9, and Califa further teaches wherein the threshold is specific for the living organism (corresponding disclosure in at least [0070], where the measurements and detection of parameters are for an individual “a method for detecting biomedical parameters of an individual according to some embodiments of the present technique”).
Regarding Claim 11, Califa and Valbusa teach the limitations of Claim 1, and Califa further teaches A non-transitory computer-readable data medium storing a computer program including instructions for executing steps of the method (corresponding disclosure in at least [0009], where there is a storage utility (medium) for storing the input data for execution of the processing steps “ A control unit, generally comprising at least one processor and storage utility, is configured for receiving input data in the form of one or more sequences of image data pieces collected by the first imaging unit, and for processing the input data to determine one or more selected parameters of the subject”).
Regarding Claim 12,
Califa teaches a system for monitoring a condition of a living organism (corresponding disclosure in at least [0001], where the method is related to monitoring the health-related parameters (condition) of the patient (living organism), “technique specifically relates to monitoring of health-related parameters”) comprising:
(a) an input for receiving an image set comprising at least two reflection images generated at different points in time and an indication of at least one interval between at least two different points in time (corresponding disclosure in at least [0016], where images are received by the image unitat different points in time (sequences of interleaving image data pieces) “configured for providing intervals of coherent illumination pulses separated between them by pulses of field illumination. The first imaging unit may thus collect sequence of interleaving image data pieces and split the collected frames based on illumination conditions”),
wherein a reflection is generated while the living organism is illuminated by patterned coherent electromagnetic radiation (corresponding disclosure in at least [0005], where the method, which uses speckle pattern, uses electromagnetic radiation “When coherent electromagnetic radiation, e.g. laser beam, is backscattered from an optically rough medium, such as biological tissues, a speckle pattern may appear in the backscattered light”),
and, wherein the reflection image shows at least one pattern feature formed by illuminating at least a part of the living organism by the patterned coherent electromagnetic radiation (corresponding disclosure in at least [0021], where different speckle patterns are formed from illuminating the region “comprises determining contrast variations in image data comprising image data indicative of one or more speckle patterns formed in light returning from illumination spots in said inspection region”),
(b) a processor for - determining a feature contrast of the at least two pattern features (corresponding disclosure in at least [0021], where the contrast in the speckle patterns are determined “wherein said processing comprises determining contrast variations in image data comprising image data indicative of one or more speckle patterns”); and- determining a condition measure of the living organism based on the feature contrast and the indication of the at least one interval (corresponding disclosure in at least [0021], where the conditions of the organism (user) are measured “the determined parameters may comprise at least one of heart rate, heart rate variability, respiratory rate, blood pressure (BP) and pulse wave velocity (PWV)”),
wherein the condition measure is determined using an algorithm (corresponding disclosure in at least [0082], where algorithms are implemented for the results “The method is based on common hardware and employs simple algorithms to achieve these results” and further in [0021], where the conditions of the organism (user) are measured “the determined parameters may comprise at least one of heart rate, heart rate variability, respiratory rate, blood pressure (BP) and pulse wave velocity (PWV)”) and reflection images of a plurality of humans with known condition measures (corresponding disclosure in at least [0005], where the method, which uses speckle pattern, uses electromagnetic radiation “When coherent electromagnetic radiation, e.g. laser beam, is backscattered from an optically rough medium, such as biological tissues, a speckle pattern may appear in the backscattered light”),
(c) an output for outputting the condition measure (corresponding disclosure in at least [0057], where there is a processor for the input data and a parameter determining module, which outputs the condition measures “for processing of the input data where the processing utility 600 includes contrast speckle module 610 and parameter determining module 660”).
Califa does not teach wherein the parametrized classification model is trained with at least one training dataset comprising a plurality of reflection images formed by illuminating at least a part of a plurality of humans and recording the reflection images and wherein the plurality of humans include humans with known condition measures detectable from corresponding reflection images.
Valbusa, in a similar field of endeavor, teaches a similar concept (feature extraction from an image) wherein the parametrized classification model is trained with at least one training dataset comprising a plurality of reflection images (corresponding disclosure in at least [0062], where there is an algorithm using a plurality of reflection images as the training set “or each feature combination, the configurator causes the segmenter to calculate the reflectance segmentation masks corresponding to the reflectance images of the training set by applying the classification algorithm with this feature combination”)
formed by illuminating at least a part of a plurality of humans and recording the reflection images (corresponding disclosure in at least [0031], where the reflected images are from a human, where the area is a surgical field, and the images are recorded “a pair of corresponding reflectance image 205R and fluorescence image 205F are shown. The reflectance image 205R and the fluorescence image 205F provide a concurrent representation (in terms of reflectance light and fluorescence light, respectively) of a same field of view 103. Particularly, the field of view 103 contains the surgical cavity 109 and some foreign objects, consisting of a surgical instrument 115, two surgeon hands 118 and surrounding skin 124 in the example at issue;”), and wherein the plurality of humans include humans with known condition measures detectable from corresponding reflection images (corresponding disclosure in at least [0050], where the reflection images contact a measurable condition, or measurable property in the image” the segmenter performs a feature extraction step for extracting one or more features from the reflectance image and possibly from the corresponding fluorescence image as well (pre-determined to be the best suited for this purpose, as described in the following); each feature is a (measurable) property that represents a distinctive characteristic of the reflectance/fluorescence image”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated a classification model with a training data set as taught by Valbusa. One of the ordinary skill in the art would have been motivated to incorporate this because the training data further teaches the model for validation and effectiveness and efficiency increases with the use of a model.
Regarding Claim 13, Califa and Valbusa teach the limitations of Claim 1, and Califa further teaches wherein the system further comprises a camera for generating the at least two reflection images and/or an illumination source for illuminating the living organism by the patterned coherent electromagnetic radiation (corresponding disclosure in at least [0072], where the reflection images are captured using a camera and further the patterned light (the polarized laser) “To calculate the speckle contrast and remote PPG in reflection (rPPGr) signals, a non-collimated, polarized, 850 nm laser was used to illuminate the entire finger with an intensity of 300 μW/cm2. Two additional cameras were used to record the backscattered light from the finger”).
Regarding Claim 14, Califa and Valbusa teach the limitations of Claim 1, and Califa further teaches the method comprising using the condition measure in a condition controlling system (corresponding disclosure in at least [0021], where the control unit (controlling system) takes the condition measures for processing “a control unit comprising at least one processor, the control unit is configured for receiving input data comprising said at least one sequence of image data piece and for processing the image data pieces for generating data indicative of one or more parameters of the individual”).
Regarding Claim 15, Califa and Valbusa teach the limitations of Claim 1, and Califa further teaches a condition controlling system comprising:(a) an input for receiving a condition measure obtained by the method (corresponding disclosure in at least [0056], where there is an input module configured with the processing “the control unit may be configured as a computer unit or system including one or more processors, memory utility and input/output modules”)
and a threshold associated with a critical condition measure (corresponding disclosure in at least [0069], where there is a threshold showing a condition beyond it (critical condition) “This allows monitoring of health condition of the subject and generating alert if a variation is detected beyond selected threshold”);
(b) a processor for generating a signal indicating a condition-based action based on a comparison of the condition measure with the threshold associated with a critical condition measure (corresponding disclosure in at least [0014], where there is a processor included for comparison of the condition measures “the processor unit may operate for determining, within received image data pieces, speckle regions associated with the illumination spot and having speckle patterns. The processor unit may divide the speckle region to two or more sub-regions and determine separate contrast measure for the different sub-regions”; examiner notes the claim does not further specify what is meant by “critical” condition or what the “condition” measured is, and a broadest reasonable interpretation is used); and
(c) an output for outputting the signal indicating a condition-based action (corresponding disclosure in at least [0056], where there is a control unit which includes the processor alongside an output module “the control unit may be configured as a computer unit or system including one or more processors, memory utility and input/output modules”).
Claims 3, 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Califa (US20220039679A1) and Valbusa (US20220409057A1) as applied in Claims 1 and 2 and in further view of Drori (WO2021074920A1).
Regarding Claim 3, Califa and Valbusa teaches the limitations of Claim 2, but does not teach wherein the imaging frequency is at least twice the motion frequency being associated with an expected periodic motion of a body fluid.
Drori, in a similar field of endeavor, teaches wherein the imaging frequency is at least twice the motion frequency being associated with an expected periodic motion of a body fluid (corresponding disclosure in at least [pg. 55, lns 28-32], where the imaging frequency will be twice the body fluid (human pulse) “Measuring human pulse, which is typically in a range of 40 beats per minute and above. Such measurement needs analyzing vibrations at a frequency of 1 Hertz and even somewhat less. When such analyzing is performed by analyzing image frames of a video sequence, it is sufficient to analyze image frames at approximately double the rate of the frequency being measured, that is, for example, approximately 2 frames per second or above”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated an imaging frequency that is twice the motion of fluid as taught by Drori. One of the ordinary skill in the art would have been motivated to incorporate this because it ensures that the blood flow is being fully measured with each image frame for analysis. One of ordinary skill in the signal processing arts would also have recognized that sampling at twice the frequency rate prevents aliasing, i.e. the Nyquist theorem.
Regarding Claim 7, Califa and Valbusa teach the limitations of claim 1 and wherein the patterned coherent electromagnetic radiation are emitted from an illumination source (corresponding disclosure in at least [0016], where the method of using speckles (patterns) is accomplished with an illumination light source “the illumination unit may comprise, in additional to the coherent illumination light source, a wide field illumination source”),
and wherein the image set comprising at least two reflection images is received from a camera (corresponding disclosure in at least [0016], where the reflection images are received by a camera (video channel, first imaging unit) “The first imaging unit may thus collect sequence of interleaving image data pieces and split the collected frames based on illumination conditions, where frames illuminated by coherent illumination are directed for speckle pattern analysis and frames illuminated by field illumination are directed to a video channel”), but does not teach wherein a distance between the living organism and the camera and/or the distance between the living organism and the illumination source is 5 cm and 150 cm.
Drori, in a similar field of endeavor, teaches wherein a distance between the living organism and the camera and/or the distance between the living organism and the illumination source is 5 cm and 150 cm (corresponding disclosure in at least [pg. 46, ln 31-32], where the distance between the camera and the patient is 40 cm “Figure 15A shows an image of a patient’s arm in human- visible wavelengths, taken at a distance of approximately 40 centimeters from the arm”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated the distance between the living organism and the illumination source as taught by Drori. One of the ordinary skill in the art would have been motivated to incorporate this because the distance between the light and patient allows for the light to properly illuminate the targeted region for imaging.
Regarding Claim 16, Califa and Valbusa teach the limitations of claim 1 and Califa further teaches at least two reflection images (corresponding disclosure in at least [0021], where different speckle patterns are formed from illuminating the region “comprises determining contrast variations in image data comprising image data indicative of one or more speckle patterns formed in light returning from illumination spots in said inspection region”), but does not teach wherein the at least two reflection images are separated by a constant time interval associated with an imaging frequency or changing time intervals.
Drori, in a similar field of endeavor, teaches a similar concept of a constant time interval associated with an imaging frequency or changing time intervals (corresponding disclosure in at least [pg. 55, lns 28-32], where the imaging frequency is at a constant time interval (the interval is associated with the imaging frequency of 1 Hz or less, which is consistent) “Measuring human pulse, which is typically in a range of 40 beats per minute and above. Such measurement needs analyzing vibrations at a frequency of 1 Hertz and even somewhat less. When such analyzing is performed by analyzing image frames of a video sequence, it is sufficient to analyze image frames at approximately double the rate of the frequency being measured, that is, for example, approximately 2 frames per second or above”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to have incorporated a constant time interval associated with an imaging frequency as taught by Drori. One of the ordinary skill in the art would have been motivated to incorporate this because it ensures that imaging is completed with consistent timing.
Response to Arguments
Applicant's arguments filed 04/22/2026 regarding the Drawings objections have been fully considered and the objection is withdrawn in light of the amendments.
Applicant's arguments filed 04/22/2026 regarding the 35 U.S.C. 101 rejections have been fully considered but they are not persuasive. As recited in the office action above (see 35 U.S.C. 101 rejection), “determining a feature contrast” and “recording the reflection images, and executing a parametrized classification model to output a condition measure of the living organism” are still processes covering limitation in the mind. The other limitations, such as “wherein a reflection image is generated while the living organism is illuminated by patterned coherent electromagnetic radiation" are not positively recited and amounts only to receiving an image set, which could have also been received in the past or through memory storage. Ultimately, the limitation is rendered as an extra solution activity. Further, step c in Claim 1 (and similarly claim 12) is an abstract idea, where a person may perform the calculation steps with pen and paper. Therefore, the amendments do not further integrate the judicial exception into a practical application.
Applicant's arguments filed 04/22/2026 regarding the 35 U.S.C. 112b rejections have been fully considered and the rejection is withdrawn in light of the amendments.
Applicant’s arguments regarding the 35 U.S.C. 103 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
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Such prior arts include Frank (US20200390337A1) in regards to detection of features and reflection images, and Jones (US20220133241A1) in regards to assessing different conditions of a person using reflected signals.
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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/K.E.K./Examiner, Art Unit 3797
/JOSEPH M SANTOS RODRIGUEZ/Primary Examiner, Art Unit 3797
