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 .
Drawings
The drawings are objected to because Fig. 3 item 306 and Fig. 4 item 410 misspell “Unifled” believe should be Unified. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 4 and 12 are objected to because of the following informalities: The claims as written do not appear to be facially ambiguous; however, one may argue that it may be ambiguous as to what constitutes infringement with respect to the claimed subject matter. As written, the claims appear to describe an inherent feature – a radar receives whatever it receives.
Appropriate correction is required. The Examiner suggests wherein the following for claim 12: “The mmWave radar system of claim 9, wherein the AI decoder determines position and/or velocity of a non-living object from the environmental data. Support can be found in the specification at Para. 15, which states “The mmWave radar sensor 102 is used to detect environmental data, and the unified AI model 104 is used to process the environmental data and generate a plurality of task results. The unified AI model 104 may comprise an AI encoder and an AI decoder. The AI encoder is trained to adapt the radar input data into a unified representation for multi-tasking. The AI decoder learns to utilize the unified representation and decode it into the multi-task results. The control unit 106 operates the interactive device according to the plurality of task results. In an embodiment, the environmental data includes heart rate, respiratory rate, gesture, posture, position and/or velocity of a living being. In another embodiment, the environmental data includes position and/or velocity of a non-living object or a plant.”
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)(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, 7 – 13, 15 – 17 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chao (US 20220300080 A1) same inventive entity published more than a year prior.
As to claims 1 and 9, Wang discloses a mmWave radar system, comprising:
an mmWave radar sensor, configured to detect environmental data (Paras. 27 and 31);
a digital signal processor, coupled to the mmWave radar sensor, and configured to perform digital signal preprocessing on the environmental data to generate preprocessed data (Fig. 1 shows multiple FFT’s and phase extractor 120) and
a unified artificial intelligence (AI) model comprising:
an AI encoder, coupled to the mmWave radar sensor, and configured to compress the environmental data to generate a unified representation (Para. 28 “compression algorithm”); and
an AI decoder, coupled to the AI encoder, and configured to analyze the unified representation for generating a plurality of task results (Para. 21 “The array processor 1510 may be linked to the weight modulating engine 158 and used to receive the decompressed weight Wd and the gesture data Dg, and recognize the gesture 199 using a recognition algorithm according to the decompressed weight Wd and the gesture data Dg. Because the compressed weight Wc and the decompressed weight Wd have been stored in the weight modulating engine 158, the required storage space may be decreased.” Here, recognition meets the scope of task. Decompression meets the scope of decoding.).
As to claims 2 and 10, Wang discloses the mmWave radar method/system of claims 1/9, wherein the plurality of task results comprise a presence detection result, an object and user tracking result, a posture analysis result, and/or a gesture recognition result (Para. 21).
As to claims 3 and 11, Wang discloses the mmWave radar method/system of claims 1/9, wherein the environmental data comprises heart rate, respiratory rate, gesture, posture, position and/or velocity of a living being (Gestures as cited for in claims 2 and 10).
As to claims 4 and 12, the features as currently written appear to be inherent. The Examiner will also address recommended language of claims 4 and 12 in the 103 rejection below.
As to claims 5 and 13, Wang does not teach the mmWave radar system of claim 9, further comprising: an interactive device, coupled to the AI decoder, configured to respond according to the plurality of task results (Para. 25 interactive game).
As to claim 7 and 15, Wang discloses the mmWave radar system of claim 9, wherein the mmWave radar sensor transmits a plurality of frequency modulated continuous wave (FMCW) signals and receives a plurality of reflected FMCW signals (Para. 27).
As to claim 8 and 16, Wang discloses the mmWave radar system of claim 15, wherein the digital signal processor preprocesses the plurality of reflected FMCW signals to generate the preprocessed data (FMCW would be the signals processed; e.g., compressed, because that would be the signals transmitted and received.).
As to claim 20, Wang discloses the mmWave radar system of claim 9, wherein the digital signal preprocessing comprises fast Fourier transform (FFT), digital Fourier transform (DFT) decimation, wavelet transform, and/or point-cloud analysis (Para. 15).
As to claim 17, Wang discloses the mmWave radar system of claim 9, wherein the digital signal processor performs digital signal post-processing on the plurality of task results to generate a plurality of post-processed results (Para. 22 “The first micro controller unit 180 may also be used to send data to a cloud server 388 for weight training.” Data has to be formatted (inherent) to be sent over cloud. Rationale applies to items 130 and 170 of Fig. 1. Claims 18 – 19 are more narrowly directed to post-processing.)
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 4 and 12 are rejected under 35 U.S.C. 103 as being obvious over Wang in view of Baheti (US 20190094350 A1).
As to claims 4 and 12, Wang does not teach the limitation of the mmWave radar system of claim 9, wherein the AI decoder determines position and/or velocity of a non-living object from the environmental data.
In the same field of endeavor, Baheti teaches “With respect to fan 114, the coarse target detection step 302 identifies the location of fan 114 and macro-Doppler filtering step 304 characterizes the motion of fan 114. Based on the output of macro-Doppler filtering step 304, classifier stage 308 determines that fan 114 is non-human and a micro-Doppler analysis is not performed. In an alternative embodiment, a micro-Doppler analysis may also be performed on fan 114 (Para. 38).”
In view of the teachings of Baheti, it would have been obvious to one having ordinary skill in the art before filing to filter interfering harmonics from a source, e.g. fan, from the gesture analysis thereby improving accuracy.
Claims 5-6 and 13-14 are rejected under 35 U.S.C. 103 as being obvious over Wang in view of Trotta (US 20180074173 A1).
As to claims 5 and 13, Wang teaches the mmWave radar system of claim 9, further comprising: an interactive device, coupled to the AI decoder, configured to respond according to the plurality of task results (Para. 25). Proceed to claims 6 and 14.
As to claims 6 and 14, Wang does not teach the mmWave radar method/ system of claim 5/13, wherein the interactive device is a smart fan, a smart television, a spatial audio, an air conditioner, smart lighting, security surveillance system,or an electric door.
To the extent necessary, the Examiner takes official notice of any particular device. The type of device is not esoteric. The Examiner has observed other references directed to gesture control of a variety of different appliances. The motivation would be marketability.
In the same field of endeavor, Trotta teaches “During operation, the position and gestures of hand 732 may be detected by radar transceiver device 702 and/or other processing circuitry coupled thereto. For example, radar transceiver device 702 may be coupled to a computer system, appliance, or other device, and the detected gestures may be used as input to the computer system or various device. For example, a gesture of two fingers tapping each other could be interpreted as a “button press,” or a gesture of a rotating thumb and finger may be interpreted as a turning a dial (Para. 52).”
In view of Trotta, it would have been obvious to the ordinary skill in the art before filing to apply gesture control to coupled interactive smart devices given the growing market demand for such and direct coupling is less prone to interference relative to wireless.
Claims 18 – 19 are rejected under 35 U.S.C. 103 as being obvious over Wang in view of Das (US 11537819 B1).
As to claim 18, Wang does not teach the mmWave radar system of claim 17, wherein the digital signal post-processing comprises kalman filtering, particle filtering, and/or clustering.
As to claim 19, Wang does not teach the mmWave radar system of claim 17, wherein the AI encoder compresses fusion of the preprocessed data and a plurality of prior post-processed results to generate the unified representation. NOTE: Prior post-processed data is data processed with previous processed data; e.g., feed-back loop, recursive filters such as Kalman and IIR filters.
In the same field, Das teaches “the computing device may train a machine learned model of the covariance model to accept observation data as input and output observation covariance values that may represent covariances between each input or variable that is input to the Kalman filter or similar system (col. 8 ll. 61 – 65).”
In view of Das, it would have been obvious to the artisan before filing to apply Kalman filtering to the neural networks because it is known that Kalman filters improves tracking and prediction accuracy thus improving overall accuracy.
Wang already teaches neural network compression. See Para. 28. Das also considers dimensionality reduction algorithms such as principal component analysis PCA, thus it would have been obvious to apply both Kalman and compression together to reduce data memory size processing thus improving processing speed. See Das col. 21 ll. 11 – 56.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W JUSTICE whose telephone number is (571)270-7029. The examiner can normally be reached 7:30 - 5:30 M-F.
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/MICHAEL W JUSTICE/Examiner, Art Unit 3648
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