DETAILED ACTION
This office action is issued in response to communication filed on 12/17/2025. Claims 1-3,5,7,12-13,16,23 and 26 are pending in this Office 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 .
Response to Arguments
Applicant's arguments filed on 12/17/25 with respect to rejection of claims under 35 USC 102 and 103 have been considered but are moot in view of the new ground of rejection.
Applicant’s amendments overcome the 101 rejection. Accordingly, the rejection has been withdrawn.
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.
Claims 1-3,5,7,12,16,23 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Tran.(US Patent Application Publication 2021/0212606 A1, hereinafter “Tran”) and further in view of Frank et al.(US Patent Application Publication 2021/0401330 A1, hereinafter “Frank”)
As to claim 1, Tran teaches a method for generating and using training data from a wearable electronic device worn by a subject to train an inference server communicably coupled to the wearable electronic device, the method, comprising:
receiving, from a first wearable spectrophotometer disposed within a housing of the wearable electronic device worn by the subject, a first measurement of the subject the first measurement comprising a tissue spectra; (Tran par [0101] teaches a non-invasive sensor from a wearable device, a mobile phone or watch can act as a spectrometer which generates a spectrum of light in visible and near infrared regions) ;
receiving a second measurement of the subject, obtained after the first measurement the second measurement obtained by chemical analyte analysis of a sample from the subject; (Tran par [0131] teaches calibrated glucose data from blood-based monitoring device is added to the data )
[receiving a medical state indicated by the second measurement];
updating a training dataset with the first measurement, the second measurement, and the medical state (Tran par [0131] teaches calibrated glucose data from blood-based monitoring device is added to the data ) ; and
instantiating a training operation against the training dataset of a machine learning inference process instantiated by the inference server (Tran par [0131] teaches calibrated glucose data from blood-based monitoring device is added to the data to train the non-invasive glucose determination logic and such data is captured over time and over related markers) ; and
in response to receiving a third measurement obtained from a second wearable spectrophotometer, invoking the machine learning inference process against the third measurement and [receiving in response as output of the machine learning inference process a label corresponding to the medical state].
. (Tran par [0132] ] teaches the neural network is then able to accurately estimate glucose level based on the non-invasive sensor output and the markers that are captured by sensors )
Tran fails to expressly teach receiving a medical state indicated by the second measurement; receiving in response as output of the machine learning inference process a label corresponding to the medical state.
However, Frank teaches receiving a medical state indicated by the second measurement (Frank par [0082] teaches additional data describing other aspects of users may be obtained by the analysis platform); receiving in response as output of the machine learning inference process a label corresponding to the medical state.(Frank par [0089] teaches the diabetes classification 116 output by the prediction system 114’s one or more machine learning models is a label that indicates the person 102 is predicted to have diabetes)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Tran and Frank to achieve the claimed invention. One would have been motivated to make such combination to allow for early detection of diabetes and identify treatment options which may be taken to mitigate potentially adverse health conditions before the user’s diabetes worsens.(Frank par [0032])
As to claim 2, Tran and Frank teach the method of claim 1 wherein the second maturement is a concentration of a chemical constituent of a tissue of the subject.( Tran par [0108] teaches combination of sensors along with machine learning is used to accurately and non-invasively detect glucose)
As to claim 3, Tran and Frank teach the method of claim 2, wherein the chemical constituent is a substance selected from the group consisting of glucose, cortisol, cholesterol, lactate, ethanol, and water. ( Tran par [0108] teaches combination of sensors along with machine learning is used to accurately and non-invasively detect glucose)
As to claim 5,Tran and Frank teach the method of claim1, wherein: the first measurement comprises a first tissue spectrum and a second tissue spectrum of the subject. ( Tran par [0101] teaches measuring glucose using non-invasive sensor that act as a spectrometer which generates a spectrum of light in visible and near infrared regions)
As to claim 7, Tran and Frank teach the method of claim 5, wherein : the first tissue spectrum and the second tissue spectrum are obtained from different locations on the body of the subject. (Tran par [0101] teaches Glucose source is blood within the tissue of an ear lobe, finger or a wrist region suitable for watch-based monitoring. Tran par [0116] teaches measurements are performed continuously or over a certain periods of time)
As to claim 12, Tran and Frank teach the method of claim 1, wherein the training operation comprises clustering. (Tran par [0223] teaches clustering models)
As to claim 16, Tran teaches a method, comprising: receiving a first measurement of a subject, the first measurement being a first tissue spectrum of the subject (Tran par [0101] teaches a non-invasive sensor from a wearable device, a mobile phone or watch can act as a spectrometer which generates a spectrum of light in visible and near infrared regions);
receiving a second measurement of the subject, the second measurement being a second tissue spectrum of the subject taken at a different location from the first tissue spectrum; (Tran par [0101] teaches Glucose source is blood within the tissue of an ear lobe, finger or a wrist region suitable for watch-based monitoring)
receiving a third measurement of the subject, the third measurement being a chemical analyte sample taken from the subject contemporaneously with the first measurement and the second measurement (Tran par [0131] teaches calibrated glucose data from blood-based monitoring device is added to the data);
[receiving an indication of a health state of the subject after analysis of the first measurement, the second measurement, and the third measurement ]
updating a training dataset with the first measurement, the second measurement, the third measurement, and the indication, the training dataset including respective first, second, and third measurements (Tran par [0131] teaches calibrated glucose data from blood-based monitoring device is added to the data to train the non-invasive glucose determination logic and such data is captured over time and over related markers) and
[respective indications taken received in respect of at least two other subjects different from the subject ]; and
initiating a training operation based on the training dataset in which a machine learning inference process is trained to provide as output the indication in response to receiving as input the first measurement, the second measurement, and the third measurement; (Tran par [0131] teaches calibrated glucose data from blood-based monitoring device is added to the data to train the non-invasive glucose determination logic and such data is captured over time and over related markers)
wherein the subject is a first subject (Tran par [003] teaches a user), and
[ the method comprises: receiving a fourth measurement in respect of a second subject from a wearable electronic device worn by the second subject and generating, using the machine learning inference process based on the fourth measurement, an estimate of an aspect of the health state of the second subject].
Tran fails to expressly teach receiving an indication of a health state of the subject after analysis of the first measurement, the second measurement, and the third measurement receiving an indication of a health state of the subject after analysis of the first measurement, the second measurement, and the third measurement respective indications taken received in respect of at least two other subjects different from the subject and receiving a fourth measurement in respect of a second subject from a wearable electronic device worn by the second subject and generating, using the machine learning inference process based on the fourth measurement, an estimate of an aspect of the health state of the second subject.
.
However, Frank teaches receiving an indication of a health state of the subject after analysis of the first measurement, the second measurement, and the third measurement receiving; respective indications taken received in respect of at least two other subjects different from the subject (Frank par [0082] teaches additional data describing other aspects of users may be obtained by the analysis platform) and receiving a fourth measurement in respect of a second subject from a wearable electronic device worn by the second subject and generating, using the machine learning inference process based on the fourth measurement, an estimate of an aspect of the health state of the second subject.
.( Frank par [0089] teaches the diabetes classification 116 output by the prediction system 114’s one or more machine learning models is a label that indicates the person 102 is predicted to have diabetes)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Tran and Frank to achieve the claimed invention. One would have been motivated to make such combination to allow for early detection of diabetes and identify treatment options which may be taken to mitigate potentially adverse health conditions before the user’s diabetes worsens.(Frank par [0032])
As to claim 23, Tran and Frank teach the method of claim 16, wherein the first tissue spectrum and the second tissue spectrum are obtained at different points in time.( Tran par [0116] teaches measurements are performed continuously or over a certain periods of time)
As to claim 26,Tran and Frank teach the method of claim 21, wherein the training dataset comprises an image of a portion of the first subject.(Tran par [0214] teaches collecting data from plurality of cameras and uses the 3D images technology to determine if the patient needs help)
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Tran and Frank and further in view of Bertsimas et al.(US Patent Application Publication 2022/0011224 A1, hereinafter “Bertsimas”)
As to claim 13, Tran and Frank teach the method of claim 1 but fail to teach wherein the machine learning training process comprises dimensionality reduction.
However, Bertsimas teaches wherein the machine learning training process comprises dimensionality reduction.(Bertsimas par [0050] teaches a machine learning model that takes as input a set of features with reduced dimensionality from that of the spectral data)
Therefore , it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Tran , Frank and Bertsimas to achieve the claimed invention. One would have been motivated to make such combination to improve accuracy of the machine learning model.(Bertsimas par [0050])
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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/HIEN L DUONG/Primary Examiner, Art Unit 2147