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 .
DETAILED ACTION
The application of Arun Pulasseri Kalam for Activity Detection Using W-Fi Signals filed 7/30/24 has been examined. Claims 1-20 are pending.
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.
Claim(s) 1-3,8,11-12,17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. US Patent Application Publication 20230045129 in view of Bolger US Patent Application Publication 20250000375.
Regarding claim 1, Khan teaches a method for detecting an object in an environment using a wireless signal, the method comprising:
receiving, by a wireless circuitry of a receiver, a wireless signal from the
environment (paragraph 019);
determining, by the wireless circuitry of the receiver, at least one of phase
information or amplitude information associated with the wireless signal (phase and intensity information is extracted, paragraph 019);
generating, by the receiver at least
one of phase data or amplitude data based on the phase information or the amplitude information,
respectively (paragraph 019);
providing, by the wireless circuitry of the receiver, at least one of the phase data
or the amplitude data to an image generation module executed by the receiver (paragraph 020);
processing, by the image generation module executed by the receiver, at least one
of the phase data or the amplitude data to generate an image of the environment and
detecting, by the receiver and based on the image, the object within the
environment (paragraph 019-020). Khan is silent on teaching the use of continuous wavelet transformation. Bolger in an analogous art teaches the use of continuous wavelet transformation to extract signal features from a signal (paragraph 029).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Khan as disclosed by Bolger because such modification represents an improvement over the system of Khan in order to perform a more efficient signal transformation process and extract signal features.
Regarding claim 2, Khan teaches detecting the object further comprises:
receiving, by a machine learning model implemented on the receiver, an input
comprising the image of the environment (paragraph 058);
identifying, by the machine learning model implemented on the receiver, the
object and outputting, by the machine learning model implemented on the receiver, data
indicating the object (paragraph 058-059).
Regarding claim 3, Khan teaches providing a data set comprising at least one of the phase information, the amplitude information, or the data indicating the object to the machine learning model (paragraph 019-020) and retraining the machine learning model using the data set (paragraph 058-059).
. Regarding claim 8, Khan teaches performing, by the receiver, principal component analysis on at least one of the phase information or the amplitude information (phase and intensity information is analyzed, paragraph 19-20).
Regarding claim 11, Khan teaches a system for detecting an object in an environment using a modified wireless signal, comprising:
a transmitter configured to transmit an emitted wireless signal (paragraph 019);
a receiver (11B), comprising:
a wireless circuitry (paragraph 019);
an image generation module (paragraph 020);
one or more processors (20); and
a computer memory comprising instructions that, when executed by the
one or more processors, cause the system to perform operations to:
receive, by the wireless circuitry of a receiver, the modified
wireless signal from the environment, the modified wireless signal based on the
emitted wireless signal modified by the object in the environment (paragraph 043);
determine, by the wireless circuitry of the receiver, at least one of
phase information or amplitude information associated with the modified wireless
signal (phase and intensity information is extracted, paragraph 019);
generate, by the receiver, at least one of phase data or amplitude data based on the phase information or the amplitude information, respectively (paragraph 019);
provide, by the receiver, at least one of the phase data or the
amplitude data to the image generation module executed by the receiver (paragraph 020-021);
process, by the image generation module executed by the receiver,
at least one of the phase data or the amplitude data to generate an image of the
environment and detect, by the receiver and based on the image, the object within
the environment based on the modified wireless signal (paragraph 019-021). Khan is silent on teaching the use of continuous wavelet transformation. Bolger in an analogous art teaches the use of continuous wavelet transformation to extract signal features from a signal (paragraph 029).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Khan as disclosed by Bolger because such modification represents an improvement over the system of Khan in order to perform a more efficient signal transformation process and extract signal features.
Regarding claim 12, Khan teaches the transmitter and the receiver are
configured to provide a wireless network (fig. 1, paragraph 043).
Regarding claim 17, Khan teaches a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform
Operations (paragraph 091) comprising:
receive, by a wireless circuitry of a receiver, a wireless signal from an
environment (paragraph 019);
determine, by the wireless circuitry of the receiver, at least one of phase
information or amplitude information associated with the wireless signal (phase and intensity information is extracted, paragraph 019);
generate, by the receiver at least one of
phase data or amplitude data based on the phase information or the amplitude information,
respectively (paragraph 019) ;
provide, by the wireless circuitry of the receiver, at least one of the phase data or
the amplitude data to an image generation module executed by the receiver(paragraph 020) ;
process, by the image generation module executed by the receiver, at least one of
the phase data or the amplitude data to generate an image of the environment and
detect, by the receiver and based on the image, an object within the environment (paragraph 019-020). Khan is silent on teaching the use of continuous wavelet transformation. Bolger in an analogous art teaches the use of continuous wavelet transformation to extract signal features from a signal (paragraph 029).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Khan as disclosed by Bolger because such modification represents an improvement over the system of Khan in order to perform a more efficient signal transformation process and extract signal features.
Regarding claim 20, Khan teaches performing, by the receiver, principal component analysis on at least one of the phase information or the amplitude information (paragraph 058);
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. US Patent Application Publication 20230045129 in view of Bolger US Patent Application Publication 20250000375 and further in view of LIU US Patent Application Publication 20200193224.
Regarding claim 4, Khan et al. is silent on teaching the machine learning model is a
convolution neural network. Liu in an analogous art teaches the machine learning model is a
convolution neural network (paragraph 028).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Khan in view of Bolger as disclosed by LIU because such modification represents an improvement over the system of Khan in view of Bolger in order to effectively and efficiently train the machine learning model.
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. US Patent Application Publication 20230045129 in view of Bolger US Patent Application Publication 20250000375 and further in view of WU US Patent Application Publication 20220137941.
Regarding claims 4-5, Regarding claim 4, Khan et al. is silent on teaching the machine learning model is a convolution neural network. WU in an analogous art teaches the machine learning model is a
convolution neural network and teaches the convolution neural network is a LeNet-5 model (paragraph 075).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Khan in view of Bolger as disclosed by WU because such modification represents an improvement over the system of Khan in view of Bolger in order to effectively and efficiently train the machine learning model.
Claim(s) 6-7 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. US Patent Application Publication 20230045129 in view of Bolger US Patent Application Publication 20250000375 and further in view of Geyer et al. US Patent Application Publication 20230171728.
Regarding claims 7-8, Khan is silent on teaching the amplitude information comprises CSI
amplitude information and the phase information comprise CSI phase information. Geyer et al. in an analogous art teaches the amplitude information comprises CSI amplitude information and the phase information comprises CSI phase information (paragraph 027). Geyer teaches determining by the receiver, channel state information associated with the wireless signal (paragraph 027).
It would have been ordinary skill in the art to modify the system of Khan in view of Bolger at the time of the invention as disclosed by Geyer because such modification represents an improvement over the system Khan in view of Bolger in order to consider the channel state in order to improve the reliability of detecting the phase and amplitude of the received signal.
Regarding claims 18-19, Khan is silent on teaching the amplitude information comprises CSI
amplitude information and the phase information comprise CSI phase information. Geyer et al. in an analogous art teaches the amplitude information comprises CSI amplitude information and the phase information comprises CSI phase information (paragraph 027). Geyer teaches determining by the receiver, channel state information associated with the wireless signal (paragraph 027).
It would have been ordinary skill in the art to modify the system of Khan in view of Bolger at the time of the invention as disclosed by Geyer because such modification represents an improvement over the system Khan in view of Bolger in order to consider the channel state in order to improve the reliability of detecting the phase and amplitude of the received signal.
Claim(s) 10 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. US Patent Application Publication 20230045129 in view of Bolger US Patent Application Publication 20250000375 and further in view of Kang et al. US Patent Application Publication 20170099449.
Regarding claim 10, Khan et al. is silent on teaching the receiver is a set top box. Kang in an analogous art teaches a set top box as an image generating receiving device (paragraph 09,046-047).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Khan in view of Bolger as disclosed by Kang because such modification represents the substitution of one image generating receiving device for another and providing the predictable result of generating images from a received wireless signal.
Regarding claim 15, Khan et al. is silent on teaching the receiver is a set top box. Kang in an analogous art teaches a set top box as an image generating receiving device (paragraph 09,046-047).
It would have been obvious to one of ordinary skills in the art at the time of the invention to modify the system of Khan in view of Bolger as disclosed by Kang because such modification represents the substitution of one image generating receiving device for another and providing the predictable result of generating images from a received wireless signal.
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. US Patent Application Publication 20230045129 in view of Bolger US Patent Application Publication 20250000375 and further in view of Takeda et al. US Patent Application Publication 2022/0198615.
Regarding claim 13-14, Khan is silent on teaching detection of the object within the
environment further comprises detecting motion of the object. Takeda in an analogous art teaches the detection of the object within the environment further comprises detecting motion of the object (paragraph 035-036). Takeda teaches a classification model to classify the motion of the object into one or more classifications (paragraph 0219).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Khan et al. in view of Bolger as disclosed by Takeda because such modification improves the image generation process and ensures the effects of the motion on the image generation are considered.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. US Patent Application Publication 20230045129 in view of Bolger US Patent Application Publication 20250000375 and further in view of Bergstrom US Patent Application Publication 2025/0232433.
Regarding claim 16, Khan is silent on teaching an edge AI machine learning model. Bergstrom in an analogous art teaches the use of an edge AI machine learning model (paragraph 049).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Khan in view of Bolger as disclosed by Bergstrom because such modification represents the substitution of one type of AI learning model for another and producing the predictable result of generating an image from the wireless signal.
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Conclusion
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/VERNAL U BROWN/Primary Examiner, Art Unit 2686
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