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
1. 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.
2. Claims 1-9 and 14-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al. (US 2024/0188124).
Regarding claim 1, Li teaches a noise detector (abstract Fig. 2, and pages 2, paragraphs 29). Li teaches that a sample counter to count a number of data points received, wherein each data point represents a frequency value (page 1, paragraphs 4 – 6 and Fig. 1, 2, where teaches receiver may further include a counter configured to generate a first count of frequency deviation samples exceeding the predetermined threshold frequency in N frequency deviation samples of the received signal, and the adaptive number may be N in response to the first count being greater than or equal to a predetermined threshold count and the adaptive number may be 2×N in response to the first count being less than the predetermined threshold count), a frequency comparator to compare each data point to an expected range of values, and to detect frequency outliers as those data points having a frequency value outside the expected range of values (Fig. 5, 11 and pages 4, paragraphs 45 – pages 5, paragraphs 50, where teaches comparing each frequency deviation sample in the window to a predetermined frequency deviation threshold and counts how many frequency deviation samples of the window exceed the predetermined frequency deviation threshold, and comparing and updating outputs (e.g., an indication that the current window of frequency deviation samples includes BLE1M, BLE2M, or Zigbee data) and stores the window for use by signal detector based on the current window of samples), a frequency outlier counter to count a number of frequency outliers (Fig. 5, 11 and pages 4, paragraphs 45 – pages 5, paragraphs 50, where teaches noise detector compares each frequency deviation sample in the window to a predetermined frequency deviation threshold and counts how many frequency deviation samples of the window exceed the predetermined frequency deviation threshold (e.g., using a counter or control logic)), a threshold selector to select a threshold based on the number of data points received (Fig. 5, 11 and pages 5, paragraphs 47 – pages 7, paragraphs 56, where teaches determining how many frequency deviation samples of the two windows have values that exceed the predetermined threshold frequency value to generate the combined count based on a count of the number of continuous frequency deviation samples in the window that have a frequency deviation below a predetermined frequency deviation threshold), a noise comparator to perform a comparison of the number of frequency outliers to the threshold, wherein, if the number of frequency outliers is greater than the threshold, noise is detected (page 1, paragraphs 4 – 6, Fig. 5, 11 and pages 4, paragraphs 45 – pages 5, paragraphs 50, where teaches receiver may further include a counter configured to generate a first count of frequency deviation samples exceeding the predetermined threshold frequency in N frequency deviation samples of the received signal, and the adaptive number may be N in response to the first count being greater than or equal to a predetermined threshold count, and comparing each frequency deviation sample in the window to a predetermined frequency deviation threshold and counts how many frequency deviation samples of the window exceed the predetermined frequency deviation threshold, and comparing and updating outputs (e.g., an indication that the current window of frequency deviation samples includes BLE1M, BLE2M, or Zigbee data).
Regarding claim 2, Li teaches that the threshold increases for greater numbers of data points received (page 1, paragraphs 4 – 6, Fig. 5, 11 and pages 4, paragraphs 43 – pages 5, paragraphs 50).
Regarding claim 3, Li teaches that the incoming data points are grouped into windows, wherein the threshold is selected based on a number of windows received (Fig. 5, 11 and pages 5, paragraphs 47 – pages 7, paragraphs 56).
Regarding claim 4, Li teaches that the frequency outlier counter counts a total number of frequency outliers detected in all of the windows (Fig. 5, 11 and pages 5, paragraphs 47 – pages 7, paragraphs 56).
Regarding claim 5, Li teaches that each window comprises 4 microseconds (Fig. 5, 11 and pages 5, paragraphs 45 – pages 6, paragraphs 51).
Regarding claim 6, Li teaches that the threshold comparator performs the comparison after each window is completely filled (Fig. 5, 11 and pages 5, paragraphs 47 – pages 7, paragraphs 56).
Regarding claim 7, Li teaches that a segment counter, wherein the frequency outlier counter counts a number of frequency outliers per segment (Fig. 5, 11 and pages 4, paragraphs 45 – pages 5, paragraphs 50).
Regarding claim 8, Li teaches that the noise comparator performs the comparison of the number of frequency outliers received in the last N segments to the threshold (page 1, paragraphs 4 – 6, Fig. 5, 11 and pages 4, paragraphs 43 – pages 5, paragraphs 50).
Regarding claim 9, Li teaches that N is initially a first value and changes to a second value, larger than the first value (page 1, paragraphs 4 – 6, Fig. 5, 11 and pages 4, paragraphs 43 – pages 5, paragraphs 50).
Regarding claim 14, Li teaches all the limitation as discussed in claims 1 and 8. Furthermore, Li further teaches that a window/segment counter to group the incoming data points into a plurality of segments (page 1, paragraphs 4 – 6, Fig. 5, 11 and pages 4, paragraphs 43 – pages 5, paragraphs 50, where teaches noise detector for counting number of windows), a frequency outlier counter to count a number of frequency outliers per segment (page 1, paragraphs 4 – 6, Fig. 5, 11 and pages 4, paragraphs 43 – pages 5, paragraphs 50, where teaches counting number of frequency deviation samples per window), and a noise comparator to perform a comparison of the number of frequency outliers in a last N segments to a threshold, wherein, if the number of frequency outliers is greater than the threshold, noise is detected (page 1, paragraphs 4 – 6, Fig. 5, 11 and pages 4, paragraphs 45 – pages 5, paragraphs 50, where teaches receiver may further include a counter configured to generate a first count of frequency deviation samples exceeding the predetermined threshold frequency in N frequency deviation samples of the received signal, and the adaptive number may be N in response to the first count being greater than or equal to a predetermined threshold count, and comparing each frequency deviation sample in the window to a predetermined frequency deviation threshold and counts how many frequency deviation samples of the window exceed the predetermined frequency deviation threshold, and comparing and updating outputs (e.g., an indication that the current window of frequency deviation samples includes BLE1M, BLE2M, or Zigbee data).
Regarding claim 15, Li teaches all the limitation as discussed in claim 14. Furthermore, Li further teaches that N is constant (page 1, paragraphs 4 – 6, Fig. 5, 11 and pages 4, paragraphs 45 – pages 5, paragraphs 50).
Regarding claim 16, Li teaches all the limitation as discussed in claim 14. Furthermore, Li further teaches that N is initially a first value and changes to a second value, larger than the first value (page 1, paragraphs 4 – 6, Fig. 5, 11 and pages 4, paragraphs 45 – pages 5, paragraphs 50).
Allowable Subject Matter
3. Claims 10-13 and 17-20 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 prior art of record fails to disclose the limitation “a receive circuit to generate the incoming date points, a demodulator to detect a packet and receive the packet, and a scheduler in communication with the noise detector and the demodulator, wherein the scheduler changes a frequency channel of the demodulator so as to scan multiple channels sequentially” as specified the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wallace et al. (US 2008/0002794) discloses Single-Burst Acquisition for Wireless Communication System.
Zhibo et al. (US 2021/0006448) discloses Packet Detection and Timing Synchronization for High Performance Wireless Communication in Substation Automation.
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J.L
April 16, 2026
John J Lee
/JOHN J LEE/
Primary Examiner, Art Unit 2649