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 .
Claims 1-9 are pending in the instant application.
Priority
Receipt is acknowledged of certified copies of foreign priority document (CN) 202410370309.3, however a translation of said application has not been made of record in accordance with 37 CFR 1.55.
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d), a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e). When an English language translation of a non-English language foreign application is required, the translation must be that of the certified copy (of the foreign application as filed) submitted together with a statement that the translation of the certified copy is accurate. See MPEP §§ 215 and 216.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 05/02/2025, 08/15/2025 and 03/06/2026 were filed before the mailing of a first Office action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f), is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f), except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f), except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitations use generic placeholders that are coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitations are:
“a data collection module configured for obtaining channel state information […];
a data processing module configured for denoising the channel state information and extracting features to obtain target feature information […]; and
a posture determination module configured for inputting the target feature information…and outputting a target posture tag […].” in claim 8.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f), applicant may:
(1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or
(2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5 and 7-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Regani et al. [Regani] (US 2020/0064444 A1).
Regarding claim 1,
Regani discloses a wireless network-based posture detection (see at least abstract, apparatus and method for monitoring object expression (e.g., posture or gesture, ¶ 0059) utilizing wireless channel variations, i.e., CI, TSCI, CSI) method, comprising:
obtaining channel state information collected by a signal collection device from a wireless network (¶ 0058, receiver is configured for: receiving a wireless signal from a transmitter through a wireless multipath channel that is impacted by an expression of an object in the venue, wherein the object has at least one movable part and is expressed in the expression with respect to a setup in the venue; and obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the wireless signal received by the receiver);
denoising the channel state information and extracting features from the denoised channel state information to obtain target feature information corresponding to the channel state information (¶0189, A CSI can be used to equalize/undo/minimize /reduce the multipath channel effect (of the transmission channel) to demodulate a signal similar to the one transmitted by the transmitter through the multipath channel. ¶0239, applied to data (e.g. TSCI, autocorrelation, features of TSCI)…denoising);
and inputting the target feature information into a pre-trained posture detection model and outputting a target posture tag corresponding to the channel state information (¶ 0168, cost between the first section of the first-time duration of the first TSCI and the second section of the second time duration of the second TSCI may be modeled with a statistical distribution. At least one of: a scale parameter, location parameter and/or another parameter, of the statistical distribution may be estimated. ¶ 0319, ML (machine learning) model learns…human specific features. ¶ 0325, orientation, presentation, manifestation, dynamic expression, motion, static expression, positioning, scale, placement, state, gesture, pose, posture, body language, body expression, head expression, face expression, vocal expression, arm expression, hand expression, leg expression, and a sequence of expressions of the object. ¶ 0239, applied to data (e.g. TSCI, autocorrelation, features of TSCI)…denoising… least mean square error, recursive least square, constrained least square, batch least square, least absolute error, least mean square deviation, least absolute deviation, local maximization, local minimization, optimization of a cost function, neural network, recognition, labeling, training, clustering, machine learning, supervised learning, unsupervised learning, semi-supervised learning, comparison with another TSCI, similarity score computation, quantization, vector quantization; [Examiner interprets this ML model learns human-specific features by analyzing the autocorrelation of the Time Series of Channel Information (TSCI). After processing steps like denoising and training, the model interprets these autocorrelated features to output a corresponding target posture tag and associated data tags for the Channel State Information (CSI)]).
Regarding claim 2, Regani discloses the method as claimed in claim 1.
Regani further discloses wherein denoising the channel state information and extracting the features from the denoised channel state information to obtain the target feature information corresponding to the channel state information comprises: clipping the channel state information to obtain the clipped channel state information (¶ 0239, processing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI)… clipping, soft clipping … Operations may be applied jointly on multiple time series or functions);
determining whether the clipped channel state information comprises noise, and performing a denoising processing to obtain the denoised channel state information when the clipped channel state information is determined to comprise noise (¶ 0239, processing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI)…. Fourier transform (FT), discrete time FT (DTFT), discrete FT (DFT), fast FT (FFT). ¶ 0240, The function (e.g. function of operands) may comprise…estimation, feature extraction, learning network, feature extraction, denoising);
and extracting the features from the denoised channel state information to obtain the target feature information corresponding to the channel state information (¶ 0254, A set of selected significant local peaks may be selected from the set of identified significant local peaks based on a selection criterion (e.g. a quality criterion, a signal quality condition). The characteristics/spatial-temporal information [¶ 0250, characteristics (e.g. characteristics of motion of an object in the venue) may comprise at least one of…e.g., history, historical characteristics, average characteristics, current characteristics, past characteristics, future characteristics, predicted characteristics] of the object may be computed based on the set of selected significant local peaks and frequency values associated with the set of selected significant local peaks. ¶ 0256, selection criterion may always correspond to select the strongest peaks in a range).
Regarding claim 3, Regani discloses the method as claimed in claim 1.
Regani further discloses wherein a training process of the pre-trained posture detection model comprises: obtaining training data, wherein the training data comprises historical channel state information and posture tags of the wireless network, and each historical channel state information is corresponding to one of the posture tags (¶ 0183, classifier of the at least one event may be re-trained based on at least one of: the re-trained projection, the training TSCI associated with the at least one events, and/or at least one current TSCI. ¶ 0205, characteristics and/or spatial-temporal information (e.g. motion information) may comprise: … e.g., gait, head motion, repeated motion, periodic motion, impulsive motion, sudden motion, fall-down motion, frequency of motion, time trend, temporal profile, temporal characteristics, change, change in frequency, change in timing, change of gait cycle, timing, starting time, ending time, duration, history of motion, motion type….hand motion rate, hand motion direction, leg motion, body motion, walking rate, hand motion rate, positional characteristics, characteristics associated with movement (e.g. change in position/location) of the object; [Examiner interprets the above, e.g., history of motion characteristics with the disclosure of TSCI training projections, to be equivalent to historical channel state information]); preprocessing and extracting features from the historical channel state information to obtain the feature information corresponding to the historical channel state information (¶ 0249, At least one characteristics of a function (e.g. auto-correlation function, auto-covariance function, cross-correlation function, cross-covariance function, power spectral density, time function, frequency domain function, frequency transform) may be determined … characteristics of the function may include … at least one argument of the function associated with the at least one characteristics of the function may be identified. Some quantity (e.g. spatial-temporal information of the object) may be determined based on the at least one argument of the function. ¶ 0250, characteristics (e.g. characteristics of motion of an object in the venue) may comprise at least one of…e.g., history, historical characteristics, average characteristics, current characteristics, past characteristics, future characteristics, predicted characteristics);
and training the posture detection model to be trained based on the posture tags and the historical feature information, and obtaining the trained posture detection model upon completion of training (¶ 0134, At least one respective time series of training CI (training TSCI) may be obtained. ¶ 0137, classifier may be applied to classify at least one current TSCI obtained…The classifier may also be applied to associate the current event with a known event, a class/category/group/grouping/list/set of known events, an unknown event, a class/category/group/grouping/list/set of unknown events, and/or another event/class/category/group/grouping/list/set. ¶ 0180, The classifier of the at least one event may be trained based on the projection and the training TSCI associated with the at least one event. The at least one current TSCI may be classified/categorized based on the projection and the current TSCI).
Regarding claim 4, Regani discloses the method as claimed in claim 3.
Regani discloses wherein preprocessing and extracting the features from the historical channel state information to obtain the feature information corresponding to the historical channel state information comprises: clipping the historical channel state information to obtain the clipped historical channel state information (¶ 0239, processing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI)… clipping, soft clipping … Operations may be applied jointly on multiple time series or functions);
denoising the clipped historical channel state information to obtain the denoised historical channel state information (¶ 0239, processing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI)…. Fourier transform (FT), discrete time FT (DTFT), discrete FT (DFT), fast FT (FFT). ¶ 0240, The function (e.g. function of operands) may comprise…estimation, feature extraction, learning network, feature extraction, denoising. ¶ 0256, selection criterion may always correspond to select the strongest peaks in a range);
and extracting the features from the denoised historical channel state information to obtain the feature information corresponding to the historical channel state information (¶ 0259, a similarity score and/or component similarity score may be computed … based on a pair of temporally adjacent CI of a TSCI….similarity score and/or component similar score may be/comprise: time reversal resonating strength (TRRS), correlation, cross-correlation, auto-correlation, correlation indicator, covariance, cross-covariance, auto-covariance, inner product of two vectors, distance score, norm, metric, quality metric, signal quality condition, statistical characteristics, discrimination score, neural network, deep learning network, machine learning, training, discrimination, weighted averaging, preprocessing, denoising, signal conditioning, filtering, time correction, timing compensation, phase offset compensation, transformation, component-wise operation, feature extraction, finite state machine, and/or another score. The characteristics and/or spatial-temporal information may be determined/computed based on the similarity score).
Regarding claim 5, Regani discloses the method as claimed in claim 4.
Regani further discloses wherein clipping the historical channel state information to obtain the clipped historical channel state information comprises: performing a first noise analysis on the historical channel state information to obtain a first noise included in the historical channel state information (¶ 0239, processing may be applied to data (e.g. TSCI, autocorrelation, features of TSCI)…. Fourier transform (FT), discrete time FT (DTFT), discrete FT (DFT), fast FT (FFT). ¶ 0251, at least one local signal-to-noise-ratio-like (SNR-like) parameter may be computed for each pair of adjacent local maximum and local minimum. The SNR-like parameter may be a function (e.g. linear, log, exponential function, monotonic function) of a fraction of a quantity (e.g. power, magnitude, etc.) of the local maximum over the same quantity of the local minimum. It may also be the function of a difference between the quantity of the local maximum and the same quantity of the local minimum); and removing the first noise in the historical channel state information to obtain the historical channel state information without the first noise (¶ 0254, A set of selected significant local peaks may be selected from the set of identified significant local peaks based on a selection criterion (e.g. a quality criterion, a signal quality condition). The characteristics/spatial-temporal information of the object may be computed based on the set of selected significant local peaks and frequency values associated with the set of selected significant local peaks. ¶ 0256, selection criterion may always correspond to select the strongest peaks in a range).
Regarding claim 7, Regani discloses the method as claimed in claim 4.
Regani further discloses wherein extracting the features from the denoised historical channel state information to obtain the feature information corresponding to the historical channel state information comprises: classifying the denoised historical channel state information based on the posture tags, obtaining the tag data corresponding to each of the posture tags, and determine a maximum amount of data of the tag data (¶ 0252, Significant local peaks may be identified or selected. Each significant local peak may be a local maximum with SNR-like parameter greater than a threshold. ¶ 0270, the quantity may be compared with a reference data); performing a sample-synthesis processing based on the tag data to obtain synthesized data corresponding to each of the tag data (¶ 0265, Regression may be performed using regression function to fit sampled data (e.g. CI, feature of CI, component of CI) or another function (e.g. autocorrelation function) in a regression window… The regression function may be linear function, quadratic function, cubic function, polynomial function, and/or another function; [Examiner interprets regression analysis as a form of data synthesis, i.e., regression synthesizes data observations into a simplified predictive model]);
determining supplementary data for each of the tag data in the synthesized data based on the maximum amount of data, and obtaining category data corresponding to each of the posture tags based on the supplementary data and the tag data, wherein each of the category data is corresponding to one of the posture tags (¶ 0270, the quantity may be compared with a reference data. ¶ 0271, regression window may be determined based on at least one of: the movement (e.g. change in position/ location) of the object, quantity associated with the object, the at least one characteristics and/or spatial-temporal information of the object associated with the movement of the object, estimated location of the local extremum, noise characteristics, estimated noise characteristics. ¶ 0137, classifier may be applied to classify at least one current TSCI obtained…The classifier may also be applied to associate the current event with a known event, a class/category/group/grouping/list/set of known events);
and extracting the features from the category data to obtain the feature information corresponding to the historical channel state information (¶ 0137, the classifier may also be applied to associate the current event with a known event, a class/category/group/grouping /list/set of known events. ¶ 0259, a similarity score and/or component similarity score may be computed … based on a pair of temporally adjacent CI of a TSCI….similarity score and/or component similar score may be/comprise: correlation, cross-correlation, auto-correlation, correlation indicator, covariance, cross-covariance, auto-covariance, inner product of two vectors, distance score, norm, metric, quality metric, signal quality condition, statistical characteristics…weighted averaging…denoising, filtering).
Features of claims 8 and 9 correspond to features of claim 1, and are therefore rejected using the same rationale and prior art applied to claim 1, above.
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 non-obviousness.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Regani in view of Kachroo et al. [Kachroo] (US 11,616,553 B1).
Regarding claim 6, Regani discloses the method as claimed in claim 4.
Regani does not explicitly teach wherein denoising the clipped historical channel state information to obtain the denoised historical channel state information, comprises: performing a second noise analysis on the clipped historical channel state information; removing a second noise obtained from the second noise analysis when a presence of noise is determined during the second noise analysis, and filling the historical channel state information of which the second noise is removed with data to obtain the denoised historical channel state information; and taking the clipped historical channel state information as the denoised historical channel state information when it is determined that there is no noise present during the second noise analysis.
However, in an analogous field of endeavor, Kachroo teaches wherein denoising the clipped historical channel state information to obtain the denoised historical channel state information, comprises: performing a second noise analysis on the clipped historical channel state information (see at least abstract, second CSI samples corresponding to the subset, the method determines a motion condition or a no-motion condition within a geographical region. See fig. 7, ref. 708);
removing a second noise obtained from the second noise analysis when a presence of noise is determined during the second noise analysis, and filling the historical channel state information of which the second noise is removed with data to obtain the denoised historical channel state information (Col 10, lines 18-50, process the second set of CSI samples to remove low-frequency and high-frequency noise components or at least remove the effects of the low-frequency and high-frequency noise components by adjusting amplitude values in the CSI samples. In short, the processing logic can process the second set of CSI samples to be within a band of interest in which motion and no-motion conditions can be detected);
and taking the clipped historical channel state information as the denoised historical channel state information when it is determined that there is no noise present during the second noise analysis (Col 19 lines 45-55, if the processing logic determines that the CSI data is clean, the processing logic can process the collected CSI data. Col 20 lines 44-47, in the case that the CSI data is clean at block 502, the processing logic can use a simplistic approach that does not need to turn the ACI filter on and off. Col. 10 lines 51-60, CSI data being clean… because the CSI data is not disrupted by interference and congestion in the wireless channel).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Regani with the above-mentioned features as disclosed by Kachroo, since doing so would have achieved the desirable result of maintaining continuous channel tracking.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nammi et al. (US 2018/0123848 A1); Nammi et al. teaches filtering channel-state data to reduce signal interference. See ¶¶ 0034-43.
KIM (US 2023/0004755 A1); Kim teaches AI training data generation for object detection. See Fig. 1, ¶ 0004-07.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAJDI ALSOMIRI whose telephone number is (571) 270-0427. The examiner can normally be reached 7AM-5PM. 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, Ayman Abaza can be reached at (571) 270-0422. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.A./Examiner, Art Unit 2465
/AYMAN A ABAZA/Primary Examiner, Art Unit 2465
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