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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 8 and 15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lin et al. (Lin), U.S. Patent Pub. No. 2024/0103119.
Regarding claims 1, 8 and 15, Lin discloses a method, system and non-transitory CRM (embodiments provide processing systems configured to perform the aforementioned methods as well as those described herein; non-transitory, computer-readable media comprising instructions that, when executed by one or more processors of a processing system, cause the processing system to perform the aforementioned methods as well as those described herein) (0006; see figures 5 and 6) comprising: capturing one or more wireless signals in a geographic area (measuring a plurality of signals within a spatial environment) (0068), wherein each one of the one or more wireless signals comprises channel state information (CSI) data (The measurements may be, for example, CSI measurements) (0068); producing a channel state information (CSI) representation based on the CSI data, wherein the CSI representation indicates a plurality of channel responses (signals reflected back to the device) corresponding to the one or more wireless signals (The measurements may be, for example, CSI measurements generated based on various signals transmitted by the device and reflected back to the device by stationary and non-stationary objects in the spatial environment. These signals may include, for example, CSI reference signals used to measure various signal quality metrics in a wireless communications system or other reference signals that may be transmitted by a device and reflected back to the device by reflection points in the spatial environment) (0068) ; filtering (canceling), by a processing device, the CSI representation to remove at least one of the plurality of channel responses that correspond to a stationary object within the geographic area (The one or more actions may include cancelling one or more components within the plurality of signals based on the determined locations of the stationary reflection points in the spatial environment) (0074), wherein the filtering produces a filtered CSI representation; and predicting a presence of a moving object (non-stationary object) within the geographic area based on the filtered CSI representation (By cancelling components based on the determined locations of the stationary reflection points in the spatial environment, the device may generate signals including components that are predominantly associated with non-stationary reflection points in the spatial environment.) (0074).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2, 9 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Wang et al. (Wang), U.S. Patent Pub. No. 2023/0081472.
Regarding claims 2, 9, and 16, Lin discloses the method, system and CRM of claims 1, 8 and 15 as described above and further discloses wherein the filtering is performed using a motion target indicator (human target) (MTI) filter (the locations of non-stationary reflection points in the spatial environment may include locations of humans in motion in the spatial environment) (0051); (The machine learning models described above may be used for various range-based or location-based tasks. For example, the machine learning models may be used to detect humans by treating humans as a physical filter that produces detectable patterns) (0054).
Lin, however, fails to specifically disclose the method, system and CRM further comprising: computing a weighted historical CSI average based on historical wireless signals comprising historical CSI data received over time; and subtracting the weighted historical CSI average from the CSI representation to produce the filtered CSI representation.
In a similar field of endeavor, Wang discloses a method, apparatus and system for wireless vital monitoring (of humans) using high frequency signals. Wang further teaches a method of computing filtered channel information (CI) by subtracting a weighted average of a number of past (historical) CI’s from current CI (The method/device/system/software of the wireless vital sign monitoring system of Clause A2, further comprising: wherein the filter computes a filtered CI by subtracting a weighted average of a number of past CI from a current CI.) (0447).
Therefore, before the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify Lin with the teachings of Wang. The motivation for this modification would have been to combine prior art elements according to known methods to yield predictable results.
Claim(s) 3, 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Vignon et al. (Vignon), U.S. Patent Pub. No. 2019/0353764.
Regarding claims 3, 10 and 17, Lin discloses the method, system and CRM of claims 1, 9 and 16 wherein the moving object is a human (The machine learning models described above may be used for various range-based or location-based tasks. For example, the machine learning models may be used to detect humans by treating humans as a physical filter that produces detectable patterns (e.g., of signal measurements and timing information derived therefrom) (0054) and further discloses producing a prediction that identifies at least one of the one or more human activities within the geographic area (For example, limited to no Doppler shift between different times may indicate that a human is stationary, while other Doppler shift characteristics may indicate varying types of activity, such as walking, running, jumping, etc.) (0031).
Lin, however, fails to specifically disclose, the method, system and CRM further comprising: transforming the filtered CSI representation into a Doppler trace image; inputting the Doppler trace image into a Bayesian convolutional neural network (CNN) that is trained to detect one or more human activities; and producing, by the processing device using the Bayesian CNN, a prediction that identifies at least one of the one or more human activities within the geographic area.
It is noted that Lin does disclose the use of various machine learning models such as the Bayesian model (a Gaussian mixture model may be a probabilistic model, implemented as a convolutional neural network (see 0032)… the Gaussian mixture model may be a Bayesian model) (0033) and uses doppler shift information to detect human presence and activity (For example, limited to no Doppler shift between different times may indicate that a human is stationary, while other Doppler shift characteristics may indicate varying types of activity, such as walking, running, jumping, etc.) (0031); (A model that uses signal measurements and timing information extracted from these signal measurements to predict the presence of non-stationary objects (e.g., humans in motion) in a spatial environment and the locations of these non-stationary objects may, in some aspects, be a Gaussian mixture model.) (0032); (the Gaussian mixture model may be a Bayesian model) (0033).
Lin fails to specifically disclose the use of doppler trace images for representing the CSI based on doppler shift.
In a similar field of endeavor, Vignon discloses the use of well-known doppler technology in systems and methods for ultrafast imaging and uses doppler image data to represent doppler shift information for display (In some embodiments, the signals from the signal processor 126 may also be coupled to a Doppler processor 160, which may be configured to estimate the Doppler shift and generate Doppler image data. The Doppler image data may include color data which is then overlaid with B-mode (i.e. grayscale) image data for display) (0065).
Therefore, before the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify Lin with the teachings of Vignon for the purpose of providing a visual representation of the doppler information described in Lin.
Allowable Subject Matter
Claims 4-7, 11-14 and 18-20 are 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: Regarding claims 4, 11 and 18, the closest prior art of record, Lin, generally teaches the use of doppler and Bayesian technology for detecting and representing data as described above. Lin, however, taken individually or collectively, fails to suggest or render obvious a method, system and CRM, as claimed as a whole, training the Bayesian CNN in a self-supervised training mode, wherein the training further comprises: creating an augmented Doppler trace image from the Doppler trace image, wherein the augmented Doppler trace image is a deformable transform of the Doppler trace image relevant to one or more physical properties of the one or more wireless signals; inputting the Doppler trace image and the augmented Doppler trace image into the Bayesian CNN, wherein the Bayesian CNN transforms the Doppler trace image and the augmented Doppler trace image into an original latent space representation and an augmented latent space representation, respectively; computing a contrastive loss between the original latent space representation and the augmented latent space representation; and adjusting one or more properties of the Bayesian CNN based on the contrastive loss, as explicitly described.
Regarding claims 5-7, 12-14, 19 and 20, they are indicated allowable solely based on their dependence from claims 4, 11 and 18.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Omer et al., U.S. Patent Pub. No. 2024/0372749, discloses filtering channel responses for motion detection.
Resor, U.S. Patent Pub. No. 2024/0163838, discloses signal source mobility classification.
Edge et al., U.S. Patent Pub. No. 2023/0421993, discloses crowd sensing using RF sensing from multiple wireless nodes.
Wu et al., U.S. Patent Pub. No. 2020/0191943, discloses a method, apparatus and system for wireless object tracking.
Zhu et al., U.S. Patent Pub. No. 2024/0175983, discloses a method, apparatus and system for wireless sensing based on deep learning.
Zhang et al., U.S. Patent Pub. No. 2019/0020425, discloses a method for determining a doppler frequency shift of a wireless signal directly reflected by a moving object.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TEMICA M. BEAMER whose telephone number is (571)272-7797. The examiner can normally be reached Monday thru Friday; 9:00 AM to 3:00 PM.
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/TEMICA M BEAMER/Primary Examiner, Art Unit 2646
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