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 § 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 1-5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a method for predicting an inflammation state of a person without significantly more. The claim(s) recite(s) the steps of transmitting and receiving frequency modulated continuous wave signals; providing the signals to a trained machine learning model; and predicting whether the person under observation is in an inflamed state or a non-inflamed state. This judicial exception is not integrated into a practical application because the steps generally links the use of the judicial exception to a particular technological environment, performing well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality and recite the concepts of gathering (i.e. transmitting and receiving) and evaluating (i.e. data analysis, modeling, and predicting) data which can be performed as a mental step or on pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the addition of the antennas to perform transmitting and receiving are recited at a high level of generality and perform the basic functions that would be needed to apply the abstract idea and acquire the signals. The addition of general computer components alone to perform such steps is not sufficient to transform a judicial exception into a patentable one. The computer components are recited at a high level of generality and perform the basic functions of a computer that would be needed to apply the abstract idea via a computer. Merely using generic computer components to perform the basic computer functions to practice or apply the judicial exception does not constitute a meaningful limitation that would amount to significantly more than the judicial exception.
Claims 2-5 are dependent on claim 1 and includes all the limitations of claim 1. Therefore, claims 2-5 recites the same abstract idea of gathering/evaluating data and performing mathematical calculations. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use neural networks are well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, amounting to no more than the abstract idea.
Claims 2-5 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claims 6-14 and 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a method for predicting an inflammation state of a person without significantly more. The claim(s) recite(s) the steps of transmitting and receiving frequency modulated continuous wave signals; converting the data into maps; determining a health indicator; determining a heath metric; providing a machine learning model; and predicting whether the person under observation is in an inflamed state or a non-inflamed state. This judicial exception is not integrated into a practical application because the steps generally links the use of the judicial exception to a particular technological environment, performing well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality and recite the concepts of gathering (i.e. transmitting and receiving) and evaluating (i.e. data analysis, modeling, and predicting) data; and performing mathematical calculations which can be performed as a mental step or on pen and paper. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the addition of the antennas to perform transmitting and receiving are recited at a high level of generality and perform the basic functions that would be needed to apply the abstract idea and acquire the signals.
Claims 7-14, and 22 are dependent on claim 6 and includes all the limitations of claim 6. Therefore, claims 7-14 and 22 recites the same abstract idea of gathering/evaluating data, performing mathematical calculations, and outputting the results. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the use trained classifiers and analyzing health metrics are well-understood, routine and conventional activities previously known to the industry, specified at a high level of generality, amounting to no more than the abstract idea.
Claims 7-14 and 22 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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.
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.
Claim(s) 1-3, 6-12, 14-15, 17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. (US 2023/0346265) in view of Zhao et al. (US 2017/0311901).
With respect to claim 1, Shin et al. discloses a wireless method comprising transmitting frequency-modulated continuous-wave wireless signals from one or more transmitting antennas ([0045]); receiving reflected FMCW wireless signals with one or more receiving antennas ([0045]), at least some of the reflected FMCW wireless signals being reflected from the person ([0046]); repeating steps continuously while the person is under observation ([0045]); producing reflected FMCW wireless data based on the reflected FMCW wireless signals ([0046]); providing the reflected FMCW wireless data as an input to a trained machine learning model, the trained ML model having been trained with ground-truth data ([0082]); and predicting the state with the trained ML model ([0081]). Shin et al. does not teach an inflamed or non-inflamed state. However, Zhao et al. teaches in the same field of endeavor wireless sensing technique comprising transmitting and receiving FMCW wireless signals ([0047-0048]); providing the data to a trained machine learning model ([0081]); and predicting the state of the patient ([0079-0081]). The Examiner notes both Shin et al. and Zhao et al. use FMCW wireless signal data and ML models to predict different states of the patient. Therefore, it would have been obvious to one of ordinary skill in the art to have provided with analyzing and training using ML model to predict various states of the patient as taught Zhao et al. as it is well known for FMCW wireless signal data to be analyzed and trained using ML as a classification model (Shin et al. ([0081]); Zhao et al. [0081]).
With respect to claim 2, Shin et al. discloses a trained neural network ([0082]).
With respect to claim 3, Shin et al. discloses a convolution neural network ([0083]).
With respect to claims 6-7, Shin et al. discloses a wireless method comprising transmitting frequency-modulated continuous-wave wireless signals from one or more transmitting antennas ([0045]); receiving reflected FMCW wireless signals with one or more receiving antennas ([0045]), at least some of the reflected FMCW wireless signals being reflected from the person ([0046]); repeating steps continuously while the person is under observation ([0045]); producing reflected FMCW wireless data based on the reflected FMCW wireless signals ([0046]); converting the raw data into three-dimensional data ([[0070]]); determining a health indicator and one or more quantifiable health metrics; (vital signs measurement; [0043]; [0055]; [0086]); providing the reflected FMCW wireless data as an input to a trained machine learning model, the trained ML model having been trained with ground-truth data ([0082]); and predicting the state with the trained ML model ([0081]). Shin et al. does not teach an inflamed or non-inflamed state. However, Zhao et al. teaches in the same field of endeavor wireless sensing technique comprising transmitting and receiving FMCW wireless signals ([0047-0048]); providing the data to a trained machine learning model ([0081]); and predicting the state of the patient ([0079-0081]). The Examiner notes both Shin et al. and Zhao et al. use FMCW wireless signal data and ML models to predict different states of the patient. Therefore, it would have been obvious to one of ordinary skill in the art to have provided with analyzing and training using ML model to predict various states of the patient as taught Zhao et al. as it is well known for FMCW wireless signal data to be analyzed and trained using ML as a classification model (Shin et al. ([0081]); Zhao et al. [0081]).
With respect to claim 8, Zhao et al. discloses a support vector machine classifier model ([0081]).
With respect to claim 11, Shin et al. discloses respiratory signals ([0007]).
With respect to claim 12, Shin et al. discloses a second health indicator (e.g. heart rate [0056]).
With respect to claim 14, Shin et al. discloses sending an output signal to a device ([0003]).
With respect to claims 15 and 19, Shin et al. discloses wireless-tracking system comprising one or more transmitting antennas ([0045]); one or more receiving antennas ([0045]); one or more processing circuit ([0005]; [0044]; Fig. 4A); a power supply ([0087]); and one or more non-transitory computer-readable memory ([0054]). The Examiner notes that the functions of the processor are recited as intended use.
Claim(s) 4-5, 9-10, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. (US 2023/0346265) in view of Zhao et al. (US 2017/0311901) as applied to claims 1, 6, 15, and 19, further in view of Peyman (US 2022/0240779).
With respect to claims 4-5, 9-10, 18, and 20, Shin et al. discloses three-dimensional data ([[0070]]). Shin et al. does not teach providing the three-dimensional data as input to the trained ML model. However, Peyman teaches in the same field of endeavor providing 3D images as input to be analyzed or trained by a machine learning algorithm ([0471]). Therefore, it would have been obvious to one of ordinary skill in the art to have provided the three-dimensional data as input to the trained ML model as taught by Peyman as it is well known for 3D data to be analyzed and trained by machine learning.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. (US 2023/0346265) in view of Zhao et al. (US 2017/0311901) as applied to claim 12, further in view of Coffey et al. (US 2023/0000396).
Shin et al. discloses the subject matter substantially as claimed except for a second health metric including gate speed. However, Coffey et al. teaches in the same field of endeavor wherein determining movement includes determining gate speed ([0094]). Therefore, it would have been obvious to one of ordinary skill in the art to have provided Shin et al. with gate speed as taught by Coffey et al. in order to monitor the changes in the patient physiological capability for potential physical decline ([0094]).
Claim(s) 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shin et al. (US 2023/0346265) in view of Zhao et al. (US 2017/0311901) as applied to claim 15, further in view of Hristov et al. (US 2021/0321938).
Shin et al. discloses the subject matter substantially as claimed except for wherein the antennas are evenly spaced along two orthogonal axes. However, Hristov et al. teaches in the same field of endeavor wherein the transmitting and receiving antennas are evenly spaced along two orthogonal axes ([0010]). Therefore, it would have been obvious to one of ordinary skill in the art to have provided the arrangement as taught by Hristov et al. as a rearrangement of parts is well within the skill level of one of ordinary skill in the art. (MPEP 2144.04(VI)(C)).
Allowable Subject Matter
Claim 21 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the prior art of record fails to disclose or render obvious the claimed combination of subject matter including further cause the second processor circuit to overlay the three-dimensional reflected wireless signal maps to form a heatmap that represents accumulated position data for the person under observation; and determine the health indicator and/or the one or more quantifiable health metrics using the heatmap.
Response to Arguments
Applicant's arguments filed 1/9/2026 have been fully considered but they are not persuasive.
Applicant’s arguments with respect to claims rejected under 35 USC 101 have been considered but they are not persuasive. The addition of general computer components alone to perform such steps is not sufficient to transform a judicial exception into a patentable one. The computer components are recited at a high level of generality and perform the basic functions of a computer that would be needed to apply the abstract idea via a computer. Merely using generic computer components to perform the basic computer functions to practice or apply the judicial exception does not constitute a meaningful limitation that would amount to significantly more than the judicial exception.
Applicant argues that the steps cannot be performed in the human mind. However, the Examiner’s position is that the claims merely recite steps at a high level of generality and fail to set forth specific algorithms or calculations that are sufficient to amount to significantly more than the judicial exception. The Examiner notes that performing repetitive calculations are considered as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(d)(II)).
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant argues the reference does not teach input into a trained ML model. However, the Examiner respectfully disagrees with the applicant. Shin et al. discloses the data input into a trained ML model (416; [0081-0082]) and predicting using the trained ML model ([0081]).
Applicant’s arguments with respect to claim 4 have been considered, but they are not persuasive. Shin et al. discloses acquiring mapping to the environment and a respective physical location in a room in which the person is located ([0087]). Therefore, the data acquired corresponds to a respective physical location in a room in which the person is located.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER LUONG whose telephone number is (571)270-1609. The examiner can normally be reached M-F 9-6.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anhtuan T Nguyen can be reached at (571)272-4963. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PETER LUONG/Primary Examiner, Art Unit 3797