CTNF 18/894,770 CTNF 87247 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Status This Office Action is in response to communications filed on 12/17/2025. Claims 1-20 are pending for examination. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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 nonobviousness. 07-20-02-aia AIA 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. 07-21-aia AIA Claim s 1-4, 7-13 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zou et al. (U.S. Patent Application Pub. U.S. 20220124154) in view of Nakano et al. (JP 2024052104) . Regarding claim 1, Zou teaches a method for determining a status of a person using wireless signals, the method comprising: collecting, at a wireless receiver, channel state information from received packets transmitted by a wireless transmitter (¶016-¶017; teaches CSI platform receiving channel state information from Wi-Fi and able IoT devices); annotating, using a computer system, the selected channel state information segments with a class indicative of a status of the person based on a gait (¶ 016; teaches using unique fine-grained gait patterns of each person revealed from the Wi-Fi channel state information measurements referred to as shape collect signatures for human identification) and at least some of a plurality of biometric features of the person extracted from the channel state information (¶036; teaches producing an identity estimation that is a person’s unique gait and body movement can be characterized by small continuous fraction in this zoning human identification can be readily achieved by learning the shape let’s and be used as a signature or fingerprint of a person; ¶ 079; teaches the gait information of a person is related to their weight height and age); identifying, using a machine learning model, the status of the person based on the gait and the at least some of the plurality of biometric features of the person, wherein the machine learning model is trained using classifier training and training data comprising information from the selected channel state information segments and gait and biometric information of persons having different statuses (¶ 075 – ¶ 077; teaches shapelet’s learned body classifier across different time in environmental settings are used as a unique gait signature to identify each person and further walking traces of people are collected in used to train a classifier on the identity of the person). Zou does not explicitly disclose the method wherein determining, using the computer system and the machine learning model and based on the status of the person, one or more settings of a machine located in a space that includes the wireless receiver and the wireless transmitter, wherein the one or more settings correspond to the status of the person; and controlling, by the computer system and using the one or more settings, operation of the machine in response to detecting the status of the person. Nakano from an analogous art teaches a method wherein determining, using the computer system and the machine learning model and based on the status of the person, one or more settings of a machine located in a space that includes the wireless receiver and the wireless transmitter (paragraph 089; times the home appliance usage space type of the home appliance with the home appliance usage time data acquired) identifying, using the computer system and the one or more parameters of the selected channel state information segments a presence of the particular person based on additional packets received by the wireless receiver subsequent to performing classifier training (¶ 096; teaches user identification device calculating joint probability of each individual being occupant of the space for each home appliance) and controlling, by the computer system and using the one or more settings, operation of the machine in response to detecting the status of the person (¶ 114; teaches the operation of the home appliance was detected and base usage history can be calculated). Therefore, it would’ve been obvious to one of ordinary skill in the art before the invention was filed to modify Zou by including the sentence of a machine to determine a user identification and use the appliance as taught by Nakano in order to better identify users and control the use of the machines/appliances. Regarding claim 2, Zou and Nakano teach the method of claim 1, and Zou further teaches the method further comprising pre-processing the channel state information using amplitude information from the received packets to determine the gait and ones of the plurality of biometric features of the person (¶ 037, ¶ 053, ¶ 054; teaches leveraging CSI amplitudes using frame and gait information of each individual can be extracted from the CSI series data and characterized by subsea sequences at critical times and using a biometric signature to identify the person). Regarding claim 3, Zou and Nakano teach method of claim 2, and Zou further teaches wherein the pre-processing of the channel state information comprises using phase information from the received packets to determine the gait and ones of the plurality of biometric features of the person (¶ 041; teaches CSI phase differences readings across one internal pair in one subcarrier for six different gestures and these observations verify that the CSI time series data can be leveraged to identify various gestures). Regarding claim 4, Zou and Nakano teach method of claim 1, and Zou further teaches wherein performing classifier training of the machine learning model comprises using a two-dimensional convolution neural network to determine the status of the person (¶ 049; teaches analyzing signal propagation variations caused by human emotions. ,makes device free gesture recognition feasible… Signal can be modeled as a chain impulse response in the OFD M receiver is able to provide a sample version of the signal spectrum of each subcarrier in the frequency domain which contains both amplitude attenuation and phase shift as complex numbers). Regarding claim 7, Zou and Nakano teach the method of claim 1, and Zou further teaches wherein performing the classifier training of the machine learning model comprises using a sequence model to determining a temporal pattern of motion of the person (¶ 077; teaches trainer classifier using the 200 walking traces of the 20 human subjects). Regarding claim 8, Zou and Nakano teach the method of claim 1, and Nakano further teaches wherein the machine is a home appliance (¶ 011; teaches system includes identifies the use of home appliance by each individual and household). Therefore, it would’ve been obvious to one of ordinary skill in the art before the invention was filed to modify Zou by including the machine is a home appliance, as taught by Nakano in order to control the use of the appliances. Regarding claim 9, Zou and Nakano teach the method of claim 1, and Zou further teaches wherein the status of the person indicates whether the person is a child or an adult (¶036; and Zou further teaches producing an identity estimation that is a person’s unique gait and body movement can be characterized by small continuous fraction in this zoning human identification can be readily achieved by learning the shape let’s and be used as a signature or fingerprint of a person; ¶ 079; teaches the gait information of a person is related to their weight height and age). Regarding claims 10 and 19, Zou teaches a system for determining a status of a person using wireless signals and a non-transitory computer-readable medium storing instructions that, when executed by a computer system, cause the computer system to carry out operations, the system comprising: a wireless receiver configured to receive packets transmitted by a wireless transmitter, and further configured to collect channel state information from the packets (¶016-¶017; teaches CSI platform receiving channel state information from Wi-Fi and able IoT devices); and a computer system associated with the wireless receiver (¶ 016; using unique fine-grained gait patterns of each person revealed from the Wi-Fi channel state information measurements and also ¶036; teaches producing an identity estimation that is a person’s unique gait and body movement can be characterized by small continuous fraction in this zoning human identification can be readily achieved by learning the shape let’s and be used as a signature or fingerprint of a person), wherein the computer system is configured to: annotate the selected channel state information segments with a class indicative of a status of the person based on a gait and at least some of a plurality of biometric features of the person extracted from the channel state information (¶ 016; teaches using unique fine-grained gait patterns of each person revealed from the Wi-Fi channel state information measurements referred to as shape collect signatures for human identification); identify, using a machine learning model, the status of the person based on the gait and the at least some of the plurality of biometric features of the person, wherein the machine learning model is trained using classifier training and training data comprising information from the selected channel state information segments and gait and biometric information of persons having different statuses (¶ 075 – ¶ 077; teaches shapelet’s learned body classifier across different time in environmental settings are used as a unique gait signature to identify each person and further walking traces of people are collected in used to train a classifier on the identity of the person); Zou does not explicitly disclose the system/ non-transitory computer-readable medium wherein determining, using the computer system and the machine learning model and based on the status of the person, one or more settings of a machine located in a space that includes the wireless receiver and the wireless transmitter, wherein the one or more settings correspond to the status of the person; and controlling, by the computer system and using the one or more settings, operation of the machine in response to detecting the status of the person. Nakano from an analogous art teaches a system/ non-transitory computer-readable medium wherein determining, using the computer system and the machine learning model and based on the status of the person, one or more settings of a machine located in a space that includes the wireless receiver and the wireless transmitter (paragraph 089; times the home appliance usage space type of the home appliance with the home appliance usage time data acquired) identifying, using the computer system and the one or more parameters of the selected channel state information segments a presence of the particular person based on additional packets received by the wireless receiver subsequent to performing classifier training (¶ 096; teaches user identification device calculating joint probability of each individual being occupant of the space for each home appliance) and controlling, by the computer system and using the one or more settings, operation of the machine in response to detecting the status of the person (¶ 114; teaches the operation of the home appliance was detected and base usage history can be calculated). Therefore, it would’ve been obvious to one of ordinary skill in the art before the invention was filed to modify Zou with a machine to determine a user identification and use the appliance as taught by Nakano in order to better identify users and control the use of the machines/appliances. Regarding claim 11, Zou and Nakano teach the system of claim 10, and Zou further teaches wherein the computer system is further configured to pre-process the channel state information using amplitude information from the received packets to determine the gait and ones of the plurality of biometric features of the person (¶ 037, ¶ 053, ¶ 054; teaches leveraging CSI amplitudes using frame and gait information of each individual can be extracted from the CSI series data and characterized by subsea sequences at critical times and using a biometric signature to identify the person). . Regarding claim 12, Zou and Nakano teach the system of claim 11, and Zou further teaches wherein pre-processing of the channel state information comprises using phase information from the received packets to determine the gait and ones of the plurality of biometric features of the person (¶ 041; teaches CSI phase differences readings across one internal pair in one subcarrier for six different gestures and these observations verify that the CSI time series data can be leveraged to identify various gestures). Regarding claim 13, Zou and Nakano teach method of claim 10, and Zou further teaches wherein performing classifier training of the machine learning model comprises using a two-dimensional convolution neural network to determine the status of the person (¶ 049; teaches analyzing signal propagation variations caused by human emotions. ,makes device free gesture recognition feasible… Signal can be modeled as a chain impulse response in the OFD M receiver is able to provide a sample version of the signal spectrum of each subcarrier in the frequency domain which contains both amplitude attenuation and phase shift as complex numbers). Regarding claim 16, Zou and Nakano teach the method of claim 10, and Zou further teaches, wherein performing the classifier training of the machine learning model comprises using a sequence model to determining a temporal pattern of motion of the person (¶ 077; teaches trainer classifier using the 200 walking traces of the 20 human subjects). Regarding claim 17, Zou and Nakano teach the system of claim 10, and Nakano further teaches wherein the machine is a home appliance (¶ 011; teaches system includes identifies the use of home appliance by each individual and household). Therefore, it would’ve been obvious to one of ordinary skill in the art before the invention was filed to modify Zou by including the machine is a home appliance, as taught by Nakano in order to control the use of the appliances. Regarding claim 18, Zou and Nakano teach the system of claim 10, and Zou further teaches wherein the status of the person indicates whether the person is a child or an adult (¶036; and Zou further teaches producing an identity estimation that is a person’s unique gait and body movement can be characterized by small continuous fraction in this zoning human identification can be readily achieved by learning the shape let’s and be used as a signature or fingerprint of a person; ¶ 079; teaches the gait information of a person is related to their weight height and age). Regarding claim 20, Zou and Nakano teach the method of claim 19, and Nakano further teaches wherein the machine is a home appliance (¶ 011; teaches system includes identifies the use of home appliance by each individual and household). Therefore, it would’ve been obvious to one of ordinary skill in the art before the invention was filed to modify Zou by including the machine is a home appliance, as taught by Nakano in order to control the use of the appliances . 07-21-aia AIA Claim s 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zou et al. (U.S. Patent Application Pub. U.S. 20220124154) in view of Nakano et al. (JP 2024052104) further in view of Wang et al. (U.S. Patent 11,408,978) and still further in view of Pantfoerder (CN 116157861) . Regarding claims 5 and 14, Zou and Nakano teach the method/system of claim 1 and claim 10, but both are silent on extracting a plurality of time-domain features to determine a variability of wireless signals of the selected channel state information segments; and extracting a plurality of frequency-domain features to determine spectral bandwidth, spectral flatness, and peak frequency of the wireless signals of the selected channel state information segments including subcarrier correlations. Wang from an analogous receiver art teaches the concept of extracting a plurality of time-domain features to determine a variability of wireless signals of the selected channel state information segments (col 3:63 – col 4:7; receiving, using N2 receive antennas of a second heterogeneous wireless device, the wireless signal through the wireless multipath channel; obtaining a plurality of time series of channel information (TSCI) of the wireless multipath channel from the wireless signal, wherein: N1 and N2 are positive integers, and each of the plurality of TSCI is associated with a transmit antenna of the first heterogeneous wireless device and a receive antenna of the second heterogeneous wireless device; computing a timing information of the repetitive motion based on the plurality of TSCI; and monitoring the repetitive motion of the object based on the timing information Pantfoerder from an analogous receiver art teaches the concept of extracting a plurality of frequency-domain features to determine spectral bandwidth, spectral flatness, and peak frequency of the wireless signals of the selected channel state information segments including subcarrier correlations (claim 4; wherein the feature to be extracted is in a group comprising a frequency domain feature; maximum value, mean value, median, standard deviation, variance, skewness, kurtosis, average absolute deviation, the 25 bits, the 75 bits, entropy, zero-crossing rate, peak factor, duration of the first peak and/or the second peak in the pattern, the duration between the first peak and the second peak in the pattern, the duration between the second peak of the first pattern and the first peak of the subsequent pattern, Mel frequency cepstrum coefficient, pitch chroma, frequency spectrum flatness, frequency spectrum peak, frequency spectrum slope, frequency spectrum slope, frequency spectrum entropy, main frequency, bandwidth, spectrum center of mass spectral flux, frequency spectrum roll-off, type information, severity information, position information, weight information, additional information and/or other parameters, or a combination of these parameters). Therefore, it would’ve been obvious to one of ordinary skill in the art before the invention was filed to modify Zou- Nakano by including extracting a plurality of time-domain features to determine a variability of wireless signals of the selected channel state information segments; and extracting a plurality of frequency-domain features to determine spectral bandwidth, spectral flatness, and peak frequency of the wireless signals of the selected channel state information segments including subcarrier correlations, as taught by Wang and Pantfoerder in order to enhance signal analysis . 07-21-aia AIA Claim s 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zou et al. (U.S. Patent Application Pub. U.S. 20220124154) in view of Nakano et al. (JP 2024052104) further in view of Wang et al. (U.S. Patent 11,408,978) and still further in view of Pantfoerder et al. (CN 116157861) and even still further in view of Baek et al. (U.S. Patent Application Pub. U.S 20220197961) . Regarding claim 6, Zou, Nakano, Wang and Pantfoerder teach the method of claim 5, but all are silent on wherein using a sequence model having a bidirectional gated recurrent unit (BiGRU) with an attention mechanism and a transformer. Baek from an analogous art teaches the concept wherein using a sequence model having a bidirectional gated recurrent unit (BiGRU) with an attention mechanism and a transformer (¶ 087; Additionally or alternatively, the RNN 324 and/or the RNN 312 may include other sequence analysis models (e.g., seq2seq models such as a long short-term memory neural network, a gated recurrent unit, etc.) Further, in some embodiments, the RNN 324 and/or the RNN 312 may include one or more optimization layers, such as neural attention mechanisms or transformers (e.g., a BERT model as described in Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, CoRR, abs/1810.04805, 2018, the entire contents of which are expressly incorporated herein by reference). Therefore, it would’ve been obvious to one of ordinary skill in the art before the invention was filed to modify Zou- Nakano by using a sequence model having a bidirectional gated recurrent unit (BiGRU) with an attention mechanism and a transformer, as taught by Baek in order to further enhance signal analysis. Regarding claim 15, Zou, Nakano, Wang and Pantfoerder teach the system of claim 14, but all are silent on wherein using a sequence model having a bidirectional gated recurrent unit (BiGRU) with an attention mechanism and a transformer. Baek from an analogous art teaches the concept wherein using a sequence model having a bidirectional gated recurrent unit (BiGRU) with an attention mechanism and a transformer (¶ 087; Additionally or alternatively, the RNN 324 and/or the RNN 312 may include other sequence analysis models (e.g., seq2seq models such as a long short-term memory neural network, a gated recurrent unit, etc.) Further, in some embodiments, the RNN 324 and/or the RNN 312 may include one or more optimization layers, such as neural attention mechanisms or transformers (e.g., a BERT model as described in Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, CoRR, abs/1810.04805, 2018, the entire contents of which are expressly incorporated herein by reference). Therefore, it would’ve been obvious to one of ordinary skill in the art before the invention was filed to modify Zou- Nakano by using a sequence model having a bidirectional gated recurrent unit (BiGRU) with an attention mechanism and a transformer, as taught by Baek in order to further enhance signal analysis. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANCIL H LITTLEJOHN JR whose telephone number is (571)270-3718. The examiner can normally be reached M-F 8:30-5 (CST). 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, Quan-Zhen Wang can be reached at (571) 272-3114. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MANCIL LITTLEJOHN JR/Examiner, Art Unit 2685 /QUAN ZHEN WANG/Supervisory Patent Examiner, Art Unit 2685 Application/Control Number: 18/894,770 Page 2 Art Unit: 2685 Application/Control Number: 18/894,770 Page 3 Art Unit: 2685 Application/Control Number: 18/894,770 Page 4 Art Unit: 2685 Application/Control Number: 18/894,770 Page 5 Art Unit: 2685 Application/Control Number: 18/894,770 Page 6 Art Unit: 2685 Application/Control Number: 18/894,770 Page 7 Art Unit: 2685 Application/Control Number: 18/894,770 Page 8 Art Unit: 2685 Application/Control Number: 18/894,770 Page 9 Art Unit: 2685 Application/Control Number: 18/894,770 Page 10 Art Unit: 2685 Application/Control Number: 18/894,770 Page 11 Art Unit: 2685 Application/Control Number: 18/894,770 Page 12 Art Unit: 2685 Application/Control Number: 18/894,770 Page 13 Art Unit: 2685 Application/Control Number: 18/894,770 Page 14 Art Unit: 2685 Application/Control Number: 18/894,770 Page 15 Art Unit: 2685
Prosecution Insights