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 Status
Claims 1-20 are pending for examination.
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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yavari (Pub. No.: US 2017/0123058 A1) in view of Monro (Pub. No.: US 2007/0258654 A1).
Regarding claim 1, Yavari teaches a computer-implemented method (System and method for detection of occupancy) comprising:
receiving one or more data signals from at least one WiFi-node-to-Access-Point (AP) link or at least one WiFi node-to-node link (para [0081], “
In some embodiments, the area of coverage can include multiple sensors 10. Multiple sensors can also enable increase of the area of coverage. The multiple sensors can communicate with each other wirelessly. Moreover, multiple sensors 10 can be integrated into a mesh network to increase the size of the area of coverage. The mesh network can be generated using 802.15.4 ZigBee or WiFi, or other protocols, depending on the specifications of the network. Having multiple sensors 10 in an area of coverage can enable redundant and diverse sensor signals. Multiple sensors 10 can also be used the ODS system discussed below to prevent possible failure of a sensor to detect a person at null locations of radio transmission.”. The occupancy detection system (ODS) receives signals from one or more WIFI sensor nodes 10 disposed within a mesh network);
performing a signal pre-processing process on the one or more data signals to generate one or more pre-processed data signals (Fig. 4A, step 402, and para [0088], “ In an embodiment, the process 400 begins at block 402 with receiving signals from the receiver 14. The received signals can correspond to the signals that were transmitted by the transmitter 12 and have returned back through reflection or other physical processes. The received signals may also include noise and other signals of no interest. As discussed above, the sensor 10 can include circuitry 16 that can use signal conditioning to process the received signals. In some embodiments, signal conditioning can be performed entirely by the circuitry 16. In other embodiments, the ODS 100 can perform signal conditioning entirely or in combination with the circuitry 16. Additional signal conditioning can include filtering, amplification, DC removal and the like.”. The ODS performs signal conditioning on the received sensor signals), performing the signal pre-processing process comprising performing a uniform sampling process (Fig. 16a, shows the raw sensor data is sampled and plotted on a graph and para [0131], “In an example simulation of chest movement, a total of 4000 samples (40 s) were generated at a sampling rate of 100 Hz.”), a power normalization process (para [0140], “The data collected from a subject is shown in FIG. 16A for CW operation mode and FIG. 16B for packet operation mode, as an example to show the test result in time and frequency domain. Since the camera measures the displacement directly, different from the radar measurement, for comparison purpose, both raw data were normalized to their maximum measured value.”. The raw sensor data is normalized against the camera data with respect to their maximum measure value), and a filtering process (para [0083], “The ODS 100 processes radar signals 102 received by the receiver 14. In some embodiments, the radar signals are pre-processed through hardware elements as discussed above prior to receiving as input. The ODS 100 implements some of the processes described below. For example, the ODS 100 can also filter signals to remove noise or select particular frequency components. The ODS 100 can also perform signal processing including implementation of correlation algorithms, peak detection, envelope detection, and the like.”);
extracting one or more features using the one or more pre-processed data signals; and
using the one or more features to determine whether a user is present in a defined area (para [0009], “In some embodiments, the method can include extracting a frequency domain feature from the received radio wave. Furthermore, the method can include detecting a presence of a person in an area of coverage based on the extracted time domain feature and the frequency domain feature.”).
Yavari fails to teach an anomaly sample removal process.
However, in the same field of signal processing, Monro teaches a process that removes out-of-range samples and/or inconsistent samples. See para [0012], “These techniques may include, but are not limited to, removal of samples that may be outside of a range, and/or have values that may be inconsistent with the other values being used.”.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Yavari’s ODS with a process that removes out-of-range samples and/or inconsistent samples to increase detection accuracy.
Regarding claim 2, Yavari in the combination teaches the method of claim 1, wherein performing the power normalization process comprises removing an automatic gain control effect from the one or more data signals (Fig. 16A, the normalized graph on the upper right shows the normalized amplitude of the sensor data changed from a scale of 0 – 1V to 1 – -1.5. In other words, the gain or amplitude of the sensor data has been shifted or removed to normalize with the camera data.).
Regarding claim 3, Monro in the combination teaches the method of claim 1, wherein performing the anomaly sample removal process comprises using a rule-based removal method to remove anomalies (para [0012], “These techniques may include, but are not limited to, removal of samples that may be outside of a range, and/or have values that may be inconsistent with the other values being used.”. The rule of removal is based on the sample being out-of-range or inconsistent with other samples).
Regarding claim 4, Monro in the combination teaches the method of claim 1, wherein performing the anomaly sample removal process comprises using a clustering removal method to remove anomalies (para [0012], “These techniques may include, but are not limited to, removal of samples that may be outside of a range, and/or have values that may be inconsistent with the other values being used.”. The process removes samples that are out-of-range or inconsistent with other samples. For instance, the one or more out-of-range samples are considered as a cluster that the process removes for being out-of-range.).
Regarding claim 5, Yavari in the combination teaches the method of claim 1, wherein using the one or more features to determine whether the user is present in the defined area comprises using the one or more features in a machine learning process, the machine learning process using one or more features extracted from one or more data signals from the at least one WiFi node-to-AP link or the at least one WiFi node-to-node link (para [0096], “In an embodiment, the process 400 can include comparison of signal or portions of the signal using a machine learning or neural network algorithm. The neural network can be constructed with three layer networks: the input layer inputs key vectors, response vectors, and the associative relation between vectors. In an embodiment, the neural network is a Back-Propagation (BP) Neural Network. By providing input examples and known-good output, the network learns what type of behavior is expected and adapts the threshold (as discussed above) for different environment. The neural network can discriminate the human cardiopulmonary motion from other types of motion, such as regular mechanical movement, including examining the interval between the peaks in the signal waveform.”. The ODS determines a human motion by using machine learning process based on respiration pattern extraction.).
Regarding claim 6, Yavari in the combination teaches the method of claim 1, wherein using the one or more features to determine whether the user is present in the defined area comprises performing a rule-based process using the one or more features (para [0091], “At block 404, the ODS 100 can compare the moving average calculation with a threshold. If the moving average calculation exceeds the threshold, the ODS 100 can determine that a target has been detected.”).
Regarding claim 7, Yavari in the combination teaches the method of claim 6, wherein performing the rule-based process comprises calculating margin values and determining a presence of the user based on the calculated margin values and a buffer (para [0091], “In some embodiments, the threshold is dynamically updated by the ODS 100 based on a calculation of an average of the moving average over time and the standard deviation of the moving average calculation shown above.”. The threshold includes calculating the standard deviation of the moving average over time.).
Regarding claim 8, recites a system that performs the method of claim 1. Therefore, the claim is rejected for the same reason.
Yavari further teaches the structure of a presence detection system, comprising:
one or more transceivers; and
a hub comprising a processor (Fig. 2, the ODS comprises of a transceiver to receive signals 102, 104 and a processor to perform the process of 400).
Regarding claim 9, recites a system that performs the method of claim 2. Therefore, the claim is rejected for the same reason.
Regarding claim 10, recites a system that performs the method of claim 3. Therefore, the claim is rejected for the same reason.
Regarding claim 11, recites a system that performs the method of claim 4. Therefore, the claim is rejected for the same reason.
Regarding claim 12, recites a system that performs the method of claim 5. Therefore, the claim is rejected for the same reason.
Regarding claim 13, recites a system that performs the method of claim 6. Therefore, the claim is rejected for the same reason.
Regarding claim 14, recites a system that performs the method of claim 7. Therefore, the claim is rejected for the same reason.
Regarding claim 15, recites a computer-readable medium that performs the method of claim 1. Therefore, the claim is rejected for the same reason.
Regarding claim 16, recites a computer-readable medium that performs the method of claim 2. Therefore, the claim is rejected for the same reason.
Regarding claim 17, recites a computer-readable medium that performs
the method of claim 3. Therefore, the claim is rejected for the same reason.
Regarding claim 18, recites a computer-readable medium that performs the method of claim 4. Therefore, the claim is rejected for the same reason.
Regarding claim 19, recites a computer-readable medium that performs the method of claim 5. Therefore, the claim is rejected for the same reason.
Regarding claim 20, recites a computer-readable medium that performs the method of claims 6-7. Therefore, the claim is rejected for the same reason.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Omer (Pat. No.: US 10,952,181 B1) teaches a system and method for detecting a location of motion using wireless signal in a wireless mesh network that includes leaf nodes.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHEN Y WU whose telephone number is (571)272-5711. The examiner can normally be reached Monday-Friday, 10AM-6PM, EST.
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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.
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/ZHEN Y WU/Primary Examiner, Art Unit 2685
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