DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
This office action is in response to the Applicant’s communication filed on 09/24/2025. In virtue of this communication, claims 1 – 20 are pending in this application.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
This application is a member of a large family of patents and patent applications consisting of 242 members, as of this writing, many of them claiming the subject matter similar to this application. Therefore, this application is a subject to a nonstatutory double patenting rejection against large number of other patents and patent applications within the family. Since it is likely that the claims in this application will be amended to overcome the prior art rejections presented in this office action, it is the examiner’s opinion that it would be unreasonable and would take considerable time to write every single nonstatutory double patenting rejection against every eligible member of the patent family at this point in prosecution. Therefore, the examiner will hold in abeyance the nonstatutory double patenting rejection until the claims in this application are finalized and otherwise ready for allowance over the prior art. At that time, the examiner will analyze the final versions of the claims against claims presented in other patents and patent applications within the family to determine if nonstatutory double patenting rejection is still warranted so that a terminal disclaimer may be filed to overcome any nonstatutory double patenting rejection.
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 16 and 18 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20110090939 (Diener) in view of US 20190064130 (Kanazawa), US 20170366361 (Afkhami) and further in view of RF and Digital Signal Processing for Software-Defined Radio, Chapter 4 - High-Level Requirements and Link Budget Analysis (Link Budget) (all of record).
Regarding claim 1, Diener teaches “A method for automatic signal detection in a radio-frequency (RF) environment, comprising:
learning the RF environment (paragraph 0101: The reference data for the variety of signals that may use the frequency band may be obtained from actual measurement and analysis of those devices. Paragraph 0146: storing in the database the RF signatures of each authorized device. The RF signature may be created by capturing detailed signal pulse characteristics of each authorized device obtained using a device having a SAGE functionality, and storing information describing those characteristics in a database. All this represents “learning”.), including power level measurements of one or more frequency bins within the RF environment (par. 0076 – 0080: collecting and processing “power level measurements of one or more frequency bins”. Detecting average power vs. frequency during a period of time. Paragraph 0092: The measurement engine 50 collects and aggregates output from the SAGE 20 and normalizes the data into meaningful data units for further processing, such as average power, maximum power. Paragraph 0246: a graph created from power measurements taken at a given time interval. The lower line represents a direct graph of the data in a single snapshot of the spectrum at a given instant in time (“power level measurements of one or more frequency bins within the RF environment”.) Although Diener does not explicitly teach the same feature to be performed specifically during the learning, paragraph 0101 teaches that the reference data for the variety of signals that may use the frequency band (which forms “knowledge map” - see below) may be obtained from actual measurement and analysis of those devices, and paragraph 0146 teaches storing in the database the RF signatures of each authorized device by capturing detailed signal pulse characteristics of each authorized device, and storing information describing those characteristics in a database. Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize exactly same method of data processing as disclosed in paragraph 0246 not only while doing the actual test of the unknown RF environment, but also during the learning the RF environment. Doing so would have provided complete power profile for the frequency range);
forming a knowledge map of the RF environment (paragraph 0094: comparing signal data from the measurement engine against a database of information of known signals or signal types (“a knowledge map”). It is implicit that this database was “formed”. For example (paragraph 0094), the signal classification database may be updated with the reference data for new devices that use the frequency band.) based on the power level measurements (paragraph 0101: The reference data (“a knowledge map”) for the variety of signals that may use the frequency band may be obtained from actual measurement and analysis of those devices. Paragraph 0146: storing in the database the RF signatures of each authorized device. The RF signature may be created by capturing detailed signal pulse characteristics of each authorized device obtained using a device having a SAGE functionality, and storing information describing those characteristics in a database. The detailed signal pulse characteristics comprise their power measurements, as was explained above with respect to the previous limitation of this claim);
scrubbing a real-time spectral sweep (paragraph 0070: a real-time spectrum analyzer (SAGE) 20 and a radio receiver or radio transceiver 12 in the device to receive and sample radio frequency energy in the frequency band. Paragraphs 0074 - 0075: The SAGE 20 obtains real-time information about the activity in a frequency band, and comprises a spectrum analyzer (SA) 22. The SA 22 generates data representing a real-time spectrogram of a bandwidth of RF spectrum.) against the knowledge map (paragraph 0094: The classification engine 52 compares outputs of the SAGE 20 (accumulated by the measurement engine 50) against (“scrub” “against”) data templates and related information of known signals (“the knowledge map”) in order to classify signals in the frequency based on energy pulse information detected by the SAGE. Paragraph 0100: The accumulated signal pulse data for the signals to be classified are compared against (“scrubbing” “against”) reference or profile signal pulse data for known signals (“the knowledge map”). Each histogram of the accumulated signal pulse data is compared against a like-kind histogram of the reference signal pulse data.) to create an alert for a spike in power and/or bandwidth for the one or more frequency bins (FIG 17: alerts for operation of microwave oven and cordless phone, both representing examples of “a spike in power and/or bandwidth for the one or more frequency bins”. FIG 18: multiple alerts for Bluetooth headset, cordless phone and microwave oven);
smoothing the real-time spectral sweep with a correction vector (paragraph 0256 and FIG. 23: The bottom line shows the average power over the sampling period (i.e., over the 40,000 FFTs, or 1/10 second). Paragraph 0384 and FIG 22: Typically, 40,000 successive FFTs of the RF spectrum, taken over a total time of 1/10 of a second, are used to construct the statistics for a single message. A first line shows the average power over the sampling period (i.e., over the 40,000 FFTs, or 1/10 second). A second line, which can change rapidly from 1/10 of a second to the next, represents the maximum power per single sampling period. It may clearly be seen in both FIG that the averaged line is much smoother than the other line. Therefore, this averaging represents that “smoothing the real-time spectral sweep”, and the samples collected over the sampling period of 40,000 FFT in aggregate represent “a correction vector”, since it is simply using data from multiple “spectral sweep[s]” (such as 40,000 successive FFTs of the RF spectrum) taken during 1/10 seconds to perform smoothing by averaging)…”
“…detecting at least one signal in the RF environment (paragraph 0094: The classification engine 52 compares outputs of the SAGE 20 against data templates and related information of known signals in order to classify signals in the frequency based on energy pulse information detected by the SAGE. The classification engine 52 can detect, for example, signals that interfere with the operation of one or more devices. The output of the classification engine 52 includes types of signals detected in the frequency band. A classification output may be, for example, "cordless phone", "frequency hopper device", "frequency hopper cordless phone", "microwave oven", "802.11x WLAN device", etc.)…”
“…averaging the real-time spectral sweep (at least paragraph 0256 and FIG 23 showing statistics data including the average power over the sampling period at the bottom. The fact that the data is real-time is given at least in paragraph 0070 referring to real-time spectrum analyzer)…”
“…calculating and storing signal degradation data for the at least one signal (paragraph 0092: higher level application may monitor data and statistics of the measurement engine to determine whether there is a performance degradation of a device or network of devices. An example is given in Fig. 26 with corresponding description in paragraph 0394: the system monitors BER and spectrum activity (block 5010) and makes determination (block 5020 with line going back to block 5010) that BER is good and spectrum activity is low. At some point in time the system determines that this is no longer the case (shown as BER degradation and high spectrum activity). Therefore, the system detects “signal degradation” from good BER and low spectrum activity to otherwise which is all done in real time. If the spectrum activity is high or the BER or PER is high (calculated “signal degradation data for the at least one signal”), the device may compute a signal to interference and noise ratio (SINR) (another example of calculating the “signal degradation data”). Based on the information computed up to this point, the device can in step 5040 determine the cause of degradation as either interference or low signal level. With respect to storing “signal degradation data”, paragraph 0066: the spectrum activity information can be accumulated and stored on a short-term basis or a long-term basis for subsequent analysis. Paragraph 0148: The data mining services 88 involves capturing spectrum activity information for long-term storage in a database. By analyzing the spectrum activity information in non-real-time using queries, network administrators can determine various situations such as at what time of day interference is a problem, in what areas of a region of operation is there the heaviest loading of the spectrum, etc. Since “signal degradation data for the at least one signal” is part of the total data related to the RF signals captured in the environment, including the data mentioned in paragraph 0161 (generated IEEE 802.11 statistics which are indicators of a performance degradation and which are listed in paragraphs 0162-0223) and in paragraph 0394 (data regarding BER and spectrum activity and determination of BER degradation and high spectrum activity), and since the entirety of the spectral activity is stored in the database, it means that “signal degradation data for the at least one signal” is also stored in the database for long-term storage) based at least in part on…” “…environmental parameters (paragraph 0394: in step 5010 the device monitors bit error rate (BER) or PER and other spectrum activity information. If the spectrum activity is high or the BER or PER is high it is noted in step 5020, and in step 5030, the device may compute a signal to interference and noise ratio (SINR) and perform further spectrum analysis using output from the SAGE. Based on the information computed up to this point, the device can in step 5040 determine the cause of degradation as either interference or low signal level. Thus, determination of presence of interference is partially based on monitoring spectrum activity and determination that the spectrum activity is high. This corresponds to the recited “environmental parameters” when “environmental” represents RF/radio spectrum environment - see paragraph 0025, 0065, 0337, 0354 and elsewhere. In other words, a particular case of “environmental parameters” = spectrum activity is high.)…”
Diener does not disclose “applying a gradient detection algorithm to the smoothed real-time spectral sweep to create matched positive and negative gradients”, that detection of the signal is “based on the matched positive and negative gradients”, “removing areas identified by the matched positive and negative gradients, and connecting points between removed areas to determine a baseline”, “creating a reconstructed signal using compressed data for deltas and the baseline” and “wherein determining the baseline is based on averaging past power level measurements and subtracting at least one signal of interest based on the matched positive and negative gradients.”
Kanazawa in paragraphs 0001 – 0005 and FIG 8 describes an existing method of detecting peaks in a spectrum obtained by a spectral device.
Specifically, Kanazawa teaches “applying a gradient detection algorithm to the smoothed … [signal] to create matched positive and negative gradients”, and detection of the signal is “based on the matched positive and negative gradients (paragraph 0004: First, an operation to eliminate a noise from a spectrum is performed. This means that the signal is “smoothed.” Next, a peak top 91 in the signal is detected (FIG. 8(a)). The peak top 91 is, for example, a position where a height of the signal is equal to or more than a predetermined value and the value of the first derivative is 0. Then, a peak start point 92 (“positive gradient”) is detected at a shorter retention time than the peak top 91, and a peak end point 93 (“negative gradient”) is detected at a longer retention time than the peak top 91 (FIG. 8(a)). The start point 92 is a position where the second derivative is positive and the first derivative is equal to or more than a positive predetermined value. Likewise, the end point 93 is a position where the second derivative is positive and the first derivative is equal to or less than a negative predetermined value. Further, such description in paragraph 0004 as “The start point 92 is a position where the second derivative is positive and the first derivative is equal to or more than a positive predetermined value. Likewise, the end point 93 is a position where the second derivative is positive and the first derivative is equal to or less than a negative predetermined value” corresponds to “applying a gradient detection algorithm” since calculation and determination that the second derivative is positive and the first derivative is equal to or more than a positive predetermined value result in determination of a positive gradient, and calculation and determination that the second derivative is positive and the first derivative is equal to or less than a negative predetermined value result in determination of a negative gradient. These two gradients represent “matched positive and negative gradients”. Finally, the signal, comprising start point 92, peak 91 and end point 93, is detected “based on the matched positive and negative gradients” which determines beginning and ending of the peak)).”
Additionally, Kanazawa teaches “removing areas identified by the matched positive and negative gradients and connecting points between removed areas to determine a baseline (paragraph 0005: Based on the peak start and end points 92 and 93 (“areas identified by the matched positive and negative gradients”, as was explained above), the baseline is determined as follows. First, the portion of the spectrum corresponding to the period of retention time when no peak exists, such as between peaks and both ends of the spectrum, is determined as a partial baseline 941 (FIG. 8(b)). In a peak portion, a part where the start point 92 and the end point 93 are connected with a straight line is determined as a partial baseline 942 (FIG. 8(b)). In other words, this determination involves removing the peak (“removing areas identified by the matched positive and negative gradients”) and connecting the points representing start and end points of the peak. The total of the partial baselines 941 and 942 obtained in this way is the baseline 94 of the entire spectrum.).”
Still additionally, Kanazawa teaches “creating a reconstructed signal using compressed data for deltas and the baseline (the original signal is shown in FIG 8(a), while creating the signal shown in FIG 8(c) with the baseline removed corresponds to creation of “a reconstructed signal”. Paragraph 0005: By subtracting this baseline 94 from the spectrum, peaks of the spectrum are determined (FIG. 8(c)). For the sake of explanation, let the original data signal be “S” and the baseline be “B”. Mathematically baseline subtraction is expressed as S – B. In other words, the differential between the original signal data S and the baseline B is Δ. The reconstructed signal in FIG 8(c) comprises only the “deltas” Δ with the baseline B subtracted. Therefore, “using compressed data for deltas and the baseline” since the baseline is subtracted from the original signal; the data is “compressed” since the portion common to the entire spectrum in the form of baseline is removed from all of the data.)” and “wherein determining the baseline is based on averaging past … [signal] measurements (paragraph 0004: an operation to eliminate noise is performed. Although not explicitly disclosed, the Examiner takes an official notice that using averaging of already recorded measurements to eliminate noise was well known and widely used in the art at the effective filing date of the application) and subtracting at least one signal of interest based on the matched positive and negative gradients (paragraph 0005: Based on the peak start and end points 92 and 93 (representing “at least one signal of interest based on the matched positive and negative gradients”), the baseline is determined as follows. First, the portion of the spectrum corresponding to the period of retention time when no peak exists, such as between peaks and both ends of the spectrum, is determined as a partial baseline 941 (FIG. 8(b)). In a peak portion, a part where the start point 92 and the end point 93 are connected with a straight line is determined as a partial baseline 942 (FIG. 8(b)). In other words, this determination involves removing the peak (“subtracting at least one signal of interest”) and connecting the points representing start and end points of the peak. The total of the partial baselines 941 and 942 obtained in this way is the baseline 94 of the entire spectrum.).”
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize an existing method of detecting peaks in a spectrum obtained by a spectral device, disclosed by Kanazawa, and based on matching positive and negative gradients and operation with the baseline, in the system of Diener. Doing so would have provided another method of peak determination in addition to those which may have already been disclosed by Diener, which would increase reliability of achieved results in peak or signal identification.
Since in the system of Diener it is “power level measurements” of “the real-time spectral sweep” which are determined for each frequency bin, and most other processing is performed on these measurements, the determination of gradients and subsequent baseline operations would be performed on the “power level measurements” of the “the real-time spectral sweep”. Further, as was explained above, Diener in paragraph 0256 and FIG 23 teaches smoothing “the real-time spectral sweep” using averaging, and Kanazawa in paragraphs 0004 also teaches removing noise from the signal prior to performing the rest of the method. Therefore, baseline determination disclosed by Kanazawa would be applied specifically to disclosed by Diener “past power level measurements” smoothed by the averaging.
Next, Diener does not disclose “wherein the detecting the at least one signal in the RF environment comprises automatically fine-tuning a threshold of power level on a segmented basis while extracting at least one temporal feature from the knowledge map; wherein a pre-recognition delay parameter sets a minimum number of consecutive scans of the RF environment to determine if the at least one signal is a signal of interest.”
Afkhami teaches “detecting the at least one signal in the RF environment comprises automatically fine-tuning a threshold of power level on a segmented basis while extracting at least one temporal feature from the knowledge map (paragraph 0102: the device processor may adjust one or more thresholds (therefore, in segments, or “on a segmented basis”) based on one or more of the determined signal characteristics to increase sensitivity for detection of one or more signal characteristics of an RF signal that may enable the received RF signal to be determined as a valid signal (“a signal of interest” – see below). Conversely, the device processor may adjust one or more thresholds to decrease sensitivity for detection of one or more signal characteristics representative of an RF signal that is determined to be a known noise signal, or an RF signal that is not valid. This corresponds to the recited “automatically fine-tuning a threshold of power level”. Paragraphs 0048 – 0053: In response to determining that the signal strength of the received RF signal exceeds the threshold signal strength, the device processor may determine one or more signal characteristics of the received RF signal in block 308. The device processor may determine whether the received RF signal is a valid signal in determination block 316 by comparing determined signal characteristics to a database of known valid signals stored in memory (i.e. recognizing the signal). This is the same as the claimed “while extracting at least one temporal feature from the knowledge map” where the recited “at least one temporal feature” is the characteristics of a signal in the database (“knowledge map”).); wherein a pre-recognition delay parameter sets a minimum number of consecutive scans of the RF environment to determine if the at least one signal is a signal of interest (paragraph 0102: the device processor may observe and build up over time information about regularly detected RF signals such as based on information detected during a sliding window duration of time (e.g., over the previous 10 seconds which corresponds to the claimed “a pre-recognition delay parameter”). Based on one or more of the determined signal characteristics (or based on one or more of the determined signal characteristics over time), the device processor may adjust one or more thresholds to increase sensitivity for detection of one or more signal characteristics of an RF signal. This sliding window duration of time implicitly “sets a minimum number of consecutive scans”. In other words, whatever number of “consecutive scans” happens within the previous 10 seconds is set as “a minimum number of consecutive scans”.).”
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize disclosed by Afkhami automatic adjustment of thresholds based on desired sensitivity for detection of valid signals based on information accumulation delay, in the system of Diener. Doing so would have allowed to improve the functionality of the automatic signal detection module based on observed and/or accumulated parameters of the radiofrequency environment.
Lastly, Diener does not disclose that calculation of the signal degradation data is also based on “noise figure parameters, hardware parameters” “wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP).”
As was explained above, Diener in paragraph 0394 teaches determination of the cause of degradation of a signal as either interference or low signal level. The Examiner takes an official notice that it is well-known in the art that a low signal level may be caused by excessively large range (distance) between the transmitter of the signal and the receiver.
Link Budget on p. 13 teaches predicting the range and performance of the radio by performing link budget analysis using such channel and transceiver parameters as transmit power, system noise figure, carrier frequency and channel bandwidth, transmit and receive antenna gains, temperature, the environment (indoor, outdoor, LOS, etc.), and the required SNR. Further, p. 20 teaches that the link budget equation can be expressed as:
PNG
media_image1.png
108
1014
media_image1.png
Greyscale
(4.26)
where
Ptx is the transmit power in dBm
Gtx is the transmitter antenna gain in dB
Grx is the receiver antenna gain in dB
Pl is the average path loss
The relation in (4.26) excludes the losses due to cables and VSWR mismatches.
However, Ptx + Gtx + (losses due to cables and VSWR mismatches) is well known to be “effective isotropic radiated power (EIRP)”.
In other words, the link budget takes into account “noise figure parameters (shown as NFsystemdB in the formula above) and hardware parameters; and wherein the hardware parameters comprise … effective isotropic radiated power (EIRP) (shown in the formula above as Ptx + Gtx with or without losses due to cables and VSWR mismatches).”
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize disclosed by Link Budget link budget analysis. Doing so would have not only allowed prediction of the range of the radio signal propagation with acceptable performance, but also utilized this calculation in further determination of whether the disclosed by Diener degradation of a signal is due to low signal level, if the link budget shows so, or if not, due to interference.
Regarding claim 9, Diener teaches “A system for automatic signal detection in a radio-frequency (RF) environment (shown in FIG 1 with corresponding description), comprising:
at least one apparatus for detecting signals in the RF environment (shown in Fig. 1 (as 1200(N), or 1030(N), or 1050(N)), 6, 7, 11 and 12 with corresponding description); and
wherein the at least one apparatus is operable to sweep and learn the RF environment (paragraph 0101: The reference data for the variety of signals that may use the frequency band may be obtained from actual measurement and analysis of those devices. Paragraph 0146: storing in the database the RF signatures of each authorized device. The RF signature may be created by capturing detailed signal pulse characteristics of each authorized device obtained using a device having a SAGE functionality, and storing information describing those characteristics in a database. All this represents “sweep and learn”.) including power level measurements of one or more frequency bins within the RF environment (par. 0076 – 0080: collecting and processing “power level measurements of one or more frequency bins”. Detecting average power vs. frequency during a period of time. Paragraph 0092: The measurement engine 50 collects and aggregates output from the SAGE 20 and normalizes the data into meaningful data units for further processing, such as average power, maximum power. Paragraph 0246: a graph created from power measurements taken at a given time interval. The lower line represents a direct graph of the data in a single snapshot of the spectrum at a given instant in time (“power level measurements of one or more frequency bins within the RF environment”.) Although Diener does not explicitly teach the same feature to be performed specifically during the learning, paragraph 0101 teaches that the reference data for the variety of signals that may use the frequency band (which forms “knowledge map” - see below) may be obtained from actual measurement and analysis of those devices, and paragraph 0146 teaches storing in the database the RF signatures of each authorized device by capturing detailed signal pulse characteristics of each authorized device, and storing information describing those characteristics in a database. Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize exactly same method of data processing as disclosed in paragraph 0246 not only while doing the actual test of the unknown RF environment, but also during the learning the RF environment. Doing so would have provided complete power profile for the frequency range);
wherein the at least one apparatus is operable to form a knowledge map (paragraph 0094: comparing signal data from the measurement engine against a database of information of known signals or signal types (“a knowledge map”). It is implicit that this database was “formed”. For example (paragraph 0094), the signal classification database may be updated with the reference data for new devices that use the frequency band.) based on the power level measurements (paragraph 0101: The reference data (“a knowledge map”) for the variety of signals that may use the frequency band may be obtained from actual measurement and analysis of those devices. Paragraph 0146: storing in the database the RF signatures of each authorized device. The RF signature may be created by capturing detailed signal pulse characteristics of each authorized device obtained using a device having a SAGE functionality, and storing information describing those characteristics in a database. The detailed signal pulse characteristics comprise their power measurements, as was explained above with respect to the previous limitation of this claim);
wherein the at least one apparatus is operable to scrub a real-time spectral sweep (paragraph 0070: a real-time spectrum analyzer (SAGE) 20 and a radio receiver or radio transceiver 12 in the device to receive and sample radio frequency energy in the frequency band. Paragraphs 0074 - 0075: The SAGE 20 obtains real-time information about the activity in a frequency band, and comprises a spectrum analyzer (SA) 22. The SA 22 generates data representing a real-time spectrogram of a bandwidth of RF spectrum.) against the knowledge map (paragraph 0094: The classification engine 52 compares outputs of the SAGE 20 (accumulated by the measurement engine 50) against (“scrub” “against”) data templates and related information of known signals (“the knowledge map”) in order to classify signals in the frequency based on energy pulse information detected by the SAGE. Paragraph 0100: The accumulated signal pulse data for the signals to be classified are compared against (“scrubbing” “against”) reference or profile signal pulse data for known signals (“the knowledge map”). Each histogram of the accumulated signal pulse data is compared against a like-kind histogram of the reference signal pulse data.) to create an alert for a spike in power and/or bandwidth for the one or more frequency bins (FIG 17: alerts for operation of microwave oven and cordless phone, both representing examples of “a spike in power and/or bandwidth for the one or more frequency bins”. FIG 18: multiple alerts for Bluetooth headset, cordless phone and microwave oven);
wherein the at least one apparatus is operable to smooth the real-time spectral sweep with a correction vector (paragraph 0256 and FIG. 23: The bottom line shows the average power over the sampling period (i.e., over the 40,000 FFTs, or 1/10 second). Paragraph 0384 and FIG 22: Typically, 40,000 successive FFTs of the RF spectrum, taken over a total time of 1/10 of a second, are used to construct the statistics for a single message. A first line shows the average power over the sampling period (i.e., over the 40,000 FFTs, or 1/10 second). A second line, which can change rapidly from 1/10 of a second to the next, represents the maximum power per single sampling period. It may clearly be seen in both FIG that the averaged line is much smoother than the other line. Therefore, this averaging represents that “the at least one apparatus is operable to smooth the real-time spectral sweep”, and the samples collected over the sampling period of 40,000 FFT in aggregate represent “a correction vector”, since it is simply using data from multiple “spectral sweep[s]” (such as 40,000 successive FFTs of the RF spectrum) taken during 1/10 seconds to perform smoothing by averaging)…”
“…wherein the at least one apparatus is operable to detect at least one signal in the RF environment (paragraph 0094: The classification engine 52 compares outputs of the SAGE 20 against data templates and related information of known signals in order to classify signals in the frequency based on energy pulse information detected by the SAGE. The classification engine 52 can detect, for example, signals that interfere with the operation of one or more devices. The output of the classification engine 52 includes types of signals detected in the frequency band. A classification output may be, for example, "cordless phone", "frequency hopper device", "frequency hopper cordless phone", "microwave oven", "802.11x WLAN device", etc.)…”
“…wherein the at least one apparatus is operable to average the real-time spectral sweep (at least paragraph 0256 and FIG 23 showing statistics data including the average power over the sampling period at the bottom. The fact that the data is real-time is given at least in paragraph 0070 referring to real-time spectrum analyzer)…”
“…wherein the at least one apparatus is operable to automatically fine-tune a threshold of power level (Par. 0334 – 0337: A pulse is defined by a series of time-contiguous, and bandwidth continuous peaks. A peak floor (“a threshold of power level”) is established to determine which spikes of radio energy qualify as a valid peak. Energy spikes below this peak floor do not qualify, whereas those above the peak floor do qualify. If bwThreshDbm parameter is positive, then the peak floor is determined dynamically (“fine-tune a threshold of power level”) based on the current noise floor: peak floor dBm=noise floor dBm+bwThreshDbm. The noise floor based mechanism is used almost exclusively because it responds well to changes in the radio spectrum environment. All this means that the “threshold of power level” is “automatically fine-tuned” based on changes in the noise floor of the radio spectrum environment) on a segmented basis (since the above method with respect to controlling peak floor is implemented in the digital domain, the changing of the threshold is also performed in digital domain and thus represents discrete small steps each of which may also be called a “segment”, which is consistent with the dictionary definition of the term “segment” as “one of the parts into which something naturally separates or is divided; a division, portion, or section”. Therefore, digitally changing the threshold in small steps would be the same as changing it on “a segmented basis”, each “segment” representing the smallest step or resolution in setting the threshold)…”
“…wherein the at least one apparatus is operable to calculate signal degradation data for the at least one signal (paragraph 0092: higher level application may monitor data and statistics of the measurement engine to determine whether there is a performance degradation of a device or network of devices. An example is given in Fig. 26 with corresponding description in paragraph 0394: the system monitors BER and spectrum activity (block 5010) and makes determination (block 5020 with line going back to block 5010) that BER is good and spectrum activity is low. At some point in time the system determines that this is no longer the case (shown as BER degradation and high spectrum activity). Therefore, the system detects “signal degradation” from good BER and low spectrum activity to otherwise which is all done in real time. If the spectrum activity is high or the BER or PER is high (calculated “signal degradation data for the at least one signal”), the device may compute a signal to interference and noise ratio (SINR) (another example of calculating the “signal degradation data”). Based on the information computed up to this point, the device can in step 5040 determine the cause of degradation as either interference or low signal level.) based at least in part on…” “…environmental parameters (paragraph 0394: in step 5010 the device monitors bit error rate (BER) or PER and other spectrum activity information. If the spectrum activity is high or the BER or PER is high it is noted in step 5020, and in step 5030, the device may compute a signal to interference and noise ratio (SINR) and perform further spectrum analysis using output from the SAGE. Based on the information computed up to this point, the device can in step 5040 determine the cause of degradation as either interference or low signal level. Thus, determination of presence of interference is partially based on monitoring spectrum activity and determination that the spectrum activity is high. This corresponds to the recited “environmental parameters” when “environmental” represents RF/radio spectrum environment - see paragraph 0025, 0065, 0337, 0354 and elsewhere. In other words, a particular case of “environmental parameters” = spectrum activity is high)…”
Diener does not disclose “wherein the at least one apparatus is operable to apply a gradient detection algorithm to the smoothed real-time spectral sweep to create matched positive and negative gradients”, that detection of the signal is “based on the matched positive and negative gradients”, “remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline; wherein the at least one apparatus is operable to create a reconstructed signal using compressed data for deltas and the baseline” and “wherein determining the baseline is based on averaging past power level measurements and subtracting at least one signal of interest based on the matched positive and negative gradients.” However, these limitations are rejected in view of Kanazawa as explained in the rejection of similar imitations of claim 1 above.
Diener does not disclose “wherein a pre-recognition delay parameter sets a minimum number of consecutive scans of the RF environment to determine if the at least one signal is a signal of interest.” However, these limitations are rejected in view of Afkhami as explained in the rejection of similar imitations of claim 1 above.
Lastly, Diener does not disclose that calculation of the signal degradation data is also based on “noise figure parameters and hardware parameters” “and wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP).” However, these limitations are rejected in view of Link Budget as explained in the rejection of similar imitations of claim 1 above.
Regarding claim 18, Diener teaches “A system for automatic signal detection in a radio-frequency (RF) environment (shown in FIG 1 with corresponding description), comprising:
at least one apparatus for detecting signals in the RF environment (shown in Fig. 1 (as 1200(N), or 1030(N), or 1050(N)), 6, 7, 11 and 12 with corresponding description); and
wherein the at least one apparatus is operable to sweep and learn the RF environment, thereby creating learning data (paragraph 0101: The reference data for the variety of signals that may use the frequency band may be obtained from actual measurement and analysis of those devices. Paragraph 0146: storing in the database the RF signatures of each authorized device. The RF signature may be created by capturing detailed signal pulse characteristics of each authorized device obtained using a device having a SAGE functionality, and storing information describing those characteristics in a database. All this represents “sweep and learn”.) including power level measurements of one or more frequency bins within the RF environment (par. 0076 – 0080: collecting and processing “power level measurements of one or more frequency bins”. Detecting average power vs. frequency during a period of time. Paragraph 0092: The measurement engine 50 collects and aggregates output from the SAGE 20 and normalizes the data into meaningful data units for further processing, such as average power, maximum power. Paragraph 0246: a graph created from power measurements taken at a given time interval. The lower line represents a direct graph of the data in a single snapshot of the spectrum at a given instant in time (“power level measurements of one or more frequency bins within the RF environment”.) Although Diener does not explicitly teach the same feature to be performed specifically during the learning, paragraph 0101 teaches that the reference data for the variety of signals that may use the frequency band (which forms “knowledge map” - see below) may be obtained from actual measurement and analysis of those devices, and paragraph 0146 teaches storing in the database the RF signatures of each authorized device by capturing detailed signal pulse characteristics of each authorized device, and storing information describing those characteristics in a database. Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize exactly same method of data processing as disclosed in paragraph 0246 not only while doing the actual test of the unknown RF environment, but also during the learning the RF environment. Doing so would have provided complete power profile for the frequency range);
wherein the at least one apparatus is operable to form a knowledge map (paragraph 0094: comparing signal data from the measurement engine against a database of information of known signals or signal types (“a knowledge map”). It is implicit that this database was “formed”. For example (paragraph 0094), the signal classification database may be updated with the reference data for new devices that use the frequency band.) based on the power level measurements of the one or more frequency bins within the RF environment (paragraph 0101: The reference data (“a knowledge map”) for the variety of signals that may use the frequency band may be obtained from actual measurement and analysis of those devices. Paragraph 0146: storing in the database the RF signatures of each authorized device. The RF signature may be created by capturing detailed signal pulse characteristics of each authorized device obtained using a device having a SAGE functionality, and storing information describing those characteristics in a database. The detailed signal pulse characteristics comprise their power measurements, as was explained above with respect to the previous limitation of this claim);
wherein the at least one apparatus is operable to scrub a real-time spectral sweep (paragraph 0070: a real-time spectrum analyzer (SAGE) 20 and a radio receiver or radio transceiver 12 in the device to receive and sample radio frequency energy in the frequency band. Paragraphs 0074 - 0075: The SAGE 20 obtains real-time information about the activity in a frequency band, and comprises a spectrum analyzer (SA) 22. The SA 22 generates data representing a real-time spectrogram of a bandwidth of RF spectrum.) against the knowledge map (paragraph 0094: The classification engine 52 compares outputs of the SAGE 20 (accumulated by the measurement engine 50) against (“scrub” “against”) data templates and related information of known signals (“the knowledge map”) in order to classify signals in the frequency based on energy pulse information detected by the SAGE. Paragraph 0100: The accumulated signal pulse data for the signals to be classified are compared against (“scrubbing” “against”) reference or profile signal pulse data for known signals (“the knowledge map”). Each histogram of the accumulated signal pulse data is compared against a like-kind histogram of the reference signal pulse data.) to create an alert for a spike in power and/or bandwidth for the one or more frequency bins (FIG 17: alerts for operation of microwave oven and cordless phone, both representing examples of “a spike in power and/or bandwidth for the one or more frequency bins”. FIG 18: multiple alerts for Bluetooth headset, cordless phone and microwave oven);
wherein the at least one apparatus is operable to smooth the real-time spectral sweep with a correction vector (paragraph 0256 and FIG. 23: The bottom line shows the average power over the sampling period (i.e., over the 40,000 FFTs, or 1/10 second). Paragraph 0384 and FIG 22: Typically, 40,000 successive FFTs of the RF spectrum, taken over a total time of 1/10 of a second, are used to construct the statistics for a single message. A first line shows the average power over the sampling period (i.e., over the 40,000 FFTs, or 1/10 second). A second line, which can change rapidly from 1/10 of a second to the next, represents the maximum power per single sampling period. It may clearly be seen in both FIG that the averaged line is much smoother than the other line. Therefore, this averaging represents that “the at least one apparatus is operable to smooth the real-time spectral sweep”, and the samples collected over the sampling period of 40,000 FFT in aggregate represent “a correction vector”, since it is simply using data from multiple “spectral sweep[s]” (such as 40,000 successive FFTs of the RF spectrum) taken during 1/10 seconds to perform smoothing by averaging)…”
“…wherein the at least one apparatus is operable to detect at least one signal in the RF environment (paragraph 0094: The classification engine 52 compares outputs of the SAGE 20 against data templates and related information of known signals in order to classify signals in the frequency based on energy pulse information detected by the SAGE. The classification engine 52 can detect, for example, signals that interfere with the operation of one or more devices. The output of the classification engine 52 includes types of signals detected in the frequency band. A classification output may be, for example, "cordless phone", "frequency hopper device", "frequency hopper cordless phone", "microwave oven", "802.11x WLAN device", etc.)…”
“…wherein the at least one apparatus is operable to average the real-time spectral sweep (at least paragraph 0256 and FIG 23 showing statistics data including the average power over the sampling period at the bottom. The fact that the data is real-time is given at least in paragraph 0070 referring to real-time spectrum analyzer)…”
“…wherein the at least one apparatus is operable to calculate signal degradation data for the at least one signal (paragraph 0092: higher level application may monitor data and statistics of the measurement engine to determine whether there is a performance degradation of a device or network of devices. An example is given in Fig. 26 with corresponding description in paragraph 0394: the system monitors BER and spectrum activity (block 5010) and makes determination (block 5020 with line going back to block 5010) that BER is good and spectrum activity is low. At some point in time the system determines that this is no longer the case (shown as BER degradation and high spectrum activity). Therefore, the system detects “signal degradation” from good BER and low spectrum activity to otherwise which is all done in real time. If the spectrum activity is high or the BER or PER is high (calculated “signal degradation data for the at least one signal”), the device may compute a signal to interference and noise ratio (SINR) (another example of calculating the “signal degradation data”). Based on the information computed up to this point, the device can in step 5040 determine the cause of degradation as either interference or low signal level.) …”
Diener does not disclose “wherein the at least one apparatus is operable to apply a gradient detection algorithm to the smoothed real-time spectral sweep to create matched positive and negative gradients”, that detection of the signal is “based on the matched positive and negative gradients”, “remove areas identified by the matched positive and negative gradients, and connect points between removed areas to determine a baseline; wherein the at least one apparatus is operable to create a reconstructed signal using compressed data for deltas and the baseline” and “wherein determining the baseline is based on averaging past power level measurements and subtracting at least one signal of interest based on the matched positive and negative gradients.” However, these limitations are rejected in view of Kanazawa as explained in the rejection of similar imitations of claim 1 above.
Diener does not disclose “wherein the at least one apparatus is operable to automatically fine-tune a threshold of power level on a segmented basis while extracting at least one temporal feature from the knowledge map”; “wherein a pre-recognition delay parameter sets a minimum number of consecutive scans of the RF environment to determine if the at least one signal is a signal of interest; wherein the at least one apparatus is operable to tune an automatic signal detection (ASD) sensitivity with a temporal feature extraction (TFE) system; wherein the TFE system uses an aggregation of signal data over time.”
Afkhami teaches “automatically fine-tune a threshold of power level on a segmented basis while extracting at least one temporal feature from the knowledge map (paragraph 0102: the device processor may adjust one or more thresholds (therefore, in segments, or “on a segmented basis”) based on one or more of the determined signal characteristics to increase sensitivity for detection of one or more signal characteristics of an RF signal that may enable the received RF signal to be determined as a valid signal (“a signal of interest” – see below). Conversely, the device processor may adjust one or more thresholds to decrease sensitivity for detection of one or more signal characteristics representative of an RF signal that is determined to be a known noise signal, or an RF signal that is not valid. This corresponds to the recited “automatically fine-tune a threshold of power level”. Paragraphs 0048 – 0053: In response to determining that the signal strength of the received RF signal exceeds the threshold signal strength, the device processor may determine one or more signal characteristics of the received RF signal in block 308. The device processor may determine whether the received RF signal is a valid signal in determination block 316 by comparing determined signal characteristics to a database of known valid signals stored in memory (i.e. recognizing the signal). This is the same as the claimed “while extracting at least one temporal feature from the knowledge map” where the recited “at least one temporal feature” is the characteristics of a signal in the database (“knowledge map”).)”, “wherein a pre-recognition delay parameter sets a minimum number of consecutive scans of the RF environment to determine if the at least one signal is a signal of interest (paragraph 0102: the device processor may observe and build up over time information about regularly detected RF signals such as based on information detected during a sliding window duration of time (e.g., over the previous 10 seconds which corresponds to the claimed “a pre-recognition delay parameter”). Based on one or more of the determined signal characteristics (or based on one or more of the determined signal characteristics over time), the device processor may adjust one or more thresholds to increase sensitivity for detection of one or more signal characteristics of an RF signal that may enable the received RF signal to be determined as a valid signal (“to determine if the at least one signal is a signal of interest”). This sliding window duration of time implicitly “sets a minimum number of consecutive scans”. In other words, whatever number of “consecutive scans” happens within the previous 10 seconds is set as “a minimum number of consecutive scans”.); wherein the at least one apparatus is operable to tune an automatic signal detection (ASD) sensitivity (paragraph 0102: the device processor may adjust one or more thresholds based on one or more of the determined signal characteristics to increase sensitivity for detection (“tune an automatic signal detection (ASD) sensitivity”) of one or more signal characteristics of an RF signal that may enable the received RF signal to be determined as a valid signal. Conversely, the device processor may adjust one or more thresholds to decrease sensitivity for detection of one or more signal characteristics representative of an RF signal that is determined to be a known noise signal, or an RF signal that is not valid.) with a temporal feature extraction (TFE) system; wherein the TFE system uses an aggregation of signal data over time (paragraph 0102: In block 702, the device processor may adjust one or more thresholds based on one or more of the determined signal characteristics. For example, the device processor may observe and build up over time information about regularly detected RF signals (“the TFE system uses an aggregation of signal data over time”).).”
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize disclosed by Afkhami automatic adjustment of thresholds based on desired sensitivity for detection of valid signals based on information accumulation delay, in the system of Diener. Doing so would have allowed to improve the functionality of the automatic signal detection module based on observed and/or accumulated parameters of the radiofrequency environment.
Lastly, Diener does not disclose that calculation of the signal degradation data is based on “noise figure parameters and hardware parameters; and wherein the hardware parameters comprise antenna position, antenna type, orientation, and/or effective isotropic radiated power (EIRP).” However, these limitations are rejected in view of Link Budget as explained in the rejection of similar imitations of claims 1 and 9 above.
Regarding claims 2 and 10, Diener teaches “further comprising creating a profile of the RF environment based on the knowledge map (Fig. 16 – 24 in combination with corresponding description disclose “a profile of the RF environment based on the knowledge map”. For example, Fig. 17 and 18 show detection of microwave ovens, Bluetooth headsets, cordless headset, etc., determination of which was made using database of known signals (“based on the knowledge map”)), wherein the profile comprises a highest power level at each frequency during a learning period (paragraph 0246: a graph created from power measurements taken at a given time interval. The upper jagged line represents the peak values seen in the RF spectrum over the entire testing period to the present instant (“a highest power level at each frequency”). With respect to doing the same during the “learning period”, Diener’s paragraph 0101 teaches that the reference data for the variety of signals that may use the frequency band (which forms “knowledge map”) may be obtained from actual measurement and analysis of those devices, and paragraph 0146 teaches storing in the database the RF signatures of each authorized device by capturing detailed signal pulse characteristics of each authorized device, and storing information describing those characteristics in a database. Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize exactly same method of data processing not only while doing the actual test of the unknown RF environment, but also while performing “learning” the RF environment. Doing so would have provided complete power profile for the frequency range).”
Regarding claims 3 and 11, Diener teaches “further comprising periodically reevaluating the RF environment and updating the knowledge map (paragraph 0094: the signal classification database (“the knowledge map”) may be updated for new devices that use the frequency band).”
Regarding claim 4, Diener teaches “further comprising a temporal feature extraction (TFE) system aggregating signal data over time (Diener, par. 0076 – 0080: collecting and processing “signal data over time”. Detecting average power vs. frequency during a period of time representing “a temporal feature extraction (TFE) system”. Paragraph 0092: The measurement engine 50 (also representing “a temporal feature extraction (TFE) system”) collects and aggregates output from the SAGE 20 and normalizes the data into meaningful data units for further processing, such as average power, maximum power.).”
Regarding claims 5, 15 and 19, Diener teaches “further comprising displaying the knowledge map and detecting results in real time on a remote device (Fig. 1: “a remote device” 1090 comprising display monitor 1096. Paragraph 0066: the spectrum activity information (“detecting results”) is reported locally, or remotely, to other devices to display, analyze and/or generate real-time alerts related to activity in the frequency band. Although Diener does not seem to disclose “displaying the knowledge map” implemented as a database of information of known signals or signal types, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to implement the capability of displaying information contained in the database, such as known signal parameters, for example, in a table or a visual form. Doing so would have allowed the administrator/user of the system to know what is actually contained in the database and what is being compared to the detected signal).”
Regarding claims 6 and 13, Diener teaches “further comprising learning the RF environment to a settled percent (paragraph 0100: accumulated signal pulse data for the signals to be classified are compared against reference or profile signal pulse data for known signals. Each histogram of the accumulated signal pulse data is compared against a like-kind histogram of the reference signal pulse data. The degree of match represents how close to each other the histograms are and for certain reference signal pulses, a very close match on certain pulse data must be found. For example, when comparing accumulated signal pulse data with reference data, there must be very precise matches between the pulse duration, bandwidth and time between pulses histograms in order to declare a match. A scoring system may be used, where a numeric value is assigned to the comparison results between each signal characteristic. For certain signal types, if the total numeric value (e.g., total score) is at least as great as a certain value, then a match may be declared. It would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize percentage of similarity between the histograms as the score disclosed by Diener simply by design choice with predictable results. Therefore, simply as an example, if the percentage of similarity equals 90%, this would have meant that 90 percent of information contained in the accumulated signal pulse histogram is the same as the information contained in the reference histogram. In view of the teaching in par. 0101 that the reference data for the variety of signals that may use the frequency band may be obtained from actual measurement and analysis of signal emitting devices, the above mentioned 90% means that the spectrum management apparatus of Diener has seen 90% of newly accumulated signal pulse histogram before, which is equivalent to recited in the claim “a settled percent” as per applicant’s spec. Therefore, since the system is capable of matching the accumulated signal pulse histogram to the reference histogram with assigning a certain percentage of similarity (“a settled percent"), this means that the system has learned “the RF environment to a settled percent”).”
Regarding claims 7 and 14, Diener teaches “further comprising indexing the power level measurements for each frequency interval in a spectrum section (FIG 41 and paragraph 0513: an example of the information contained in the L2 SUM 380 (“SUM” - spectrum utilization map – see paragraph 0469). Each Fast Fourier Transform (FFT) frequency bin (of a plurality of frequency bins that span the frequency band (“a spectrum section”)) has an associated maximum power statistic, average power statistic. This means that the bin number in the first row at the top of the table serves as an “index” to corresponding power level measurements in the third and fourth rows) during a learning period (Although Diener does not explicitly teach the same feature to be performed specifically during “learning period”, paragraph 0101 teaches that the reference data for the variety of signals that may use the frequency band (which forms “knowledge map”) may be obtained from actual measurement and analysis of those devices, and paragraph 0146 teaches storing in the database the RF signatures of each authorized device by capturing detailed signal pulse characteristics of each authorized device, and storing information describing those characteristics in a database. Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize exactly same method of data processing as disclosed in paragraph 0513 not only while doing the actual test of the unknown RF environment, but also while performing the “learning” of the RF environment. Doing so would have provided complete statistics for each frequency bin in a convenient format).”
Regarding claim 8, Diener teaches “wherein learning the RF environment is based on statistical learning techniques (paragraph 0101: The reference data for the variety of signals that may use the frequency band may be obtained from actual measurement and analysis of those devices. Paragraph 0146: storing in the database the RF signatures of each authorized device. The RF signature may be created by capturing detailed signal pulse characteristics of each authorized device obtained using a device having a SAGE functionality, and storing this information in a database. All this represents “learning”. With respect to “based on statistical learning techniques”, paragraph 0095 contains incorporated by reference U.S. application Ser. No. 10/246,364. This application was published as US 20040203826. FIG 14 and paragraph 0073 of US 20040203826 illustrates an environment to learn the distinctive profile of a device and create a fingerprint definition. A device 4000 that transmits a radio signal to learn is turned on. The SAGE 400 generates signal pulse data, spectrum analysis statistics, etc., from the signal that it receives from the device 4000. This SAGE output may be processed by processor 600 executing the classification engine 500. The classification engine 500 processes the SAGE outputs, accumulates signal pulse data (e.g., builds histograms) and uses those histograms as the appropriate set of fingerprint definitions to classify/identify the device 4000. Alternatively, the accumulated data can be used to design specific classification algorithms, pulse timing signature templates, etc., to classify/identify signals of the device 4000. All this represents “statistical learning techniques” (especially including histograms representing statistical distributions to learn patterns, and classification algorithm)).”
Regarding claim 12, Diener teaches “wherein the at least one apparatus is operable to send a notification and/or an alarm to an operator after detecting the at least one signal (paragraph 0066: the spectrum activity information (or the raw data used to generate it) is reported locally, or remotely, to other devices to display, analyze and/or generate real-time alerts related to activity in the frequency band. Paragraph 0067: The signal classification information generated by processing the spectrum activity information may be reported to local or remote locations, and used to generate real-time alerts. The real-time alert may take the form of a graphical display, audio, email message, paging message, etc.).”
Regarding claim 16, Diener teaches “wherein a frequency resolution of the knowledge map is based on a Fast Fourier Transform (FFT) size setting (Paragraph 0146: storing in the database the RF signatures of each authorized device. The RF signature may be created by capturing signal pulse characteristics of each authorized device by a device having a SAGE functionality, and storing information describing those characteristics in a database. Capturing signal pulse characteristics is disclosed in paragraphs 0075 – 0090 and the resolution is based on the number of bins in FFT. Therefore, since it is these signals for the authorized devices that will be stored in the database (“the knowledge map”), and the resolution for these signals is based on the number of bins in FFT, the resolution of the signals stored in the database will also be based on the number of bins in FFT.).”
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over US 20110090939 (Diener) in view of US 20190064130 (Kanazawa), US 20170366361 (Afkhami) and RF and Digital Signal Processing for Software-Defined Radio, Chapter 4 - High-Level Requirements and Link Budget Analysis (Link Budget) as applied to claim 9 above, and further in view of US 20080133190 (Peretz) (of record).
Regarding claim 17, Diener does not teach “wherein the environmental parameters include rain, fog, and haze based on a delta correction factor table and a provided precipitation rate.”
As was explained above, Diener in paragraph 0394 teaches determination of the cause of degradation of a signal as either interference or low signal level. The Examiner takes an official notice that it is well-known in the art that a low signal level may be caused by excessively large range (distance) between the transmitter of the signal and the receiver causing high signal attenuation.
Peretz, paragraph 0170 teaches that the amount of attenuation of a wireless signal due to the effect of rain (“wherein the environmental parameters include rain” and “a provided precipitation rate”) may be estimated by the following equation:
A=a*Rb
wherein "A" stands for attenuation measured in db/km, "R" for the rain rate (mm/hr) (“a provided precipitation rate”), and wherein "a" and "b" are parameters that depend on rain drop size and signal frequency, respectively. Since no meaningful description is given in the specification as filed of what “a delta correction factor” may be, within the concept of broadest reasonable interpretation this is mapped to parameters "a" and "b" in the above formula, since they make the formula correct at least based on a specific frequency. In other words, disclosed by the claim “a delta correction factor” is mapped to "a" and "b" in the above formula. And since paragraph 0172 teaches that the inputs to the model including such “environmental parameters” as current weather conditions, air humidity, smog are stored in the storage unit, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to also store such additional “environmental parameters” as “fog, and haze” together with "a" and "b", in the form of a list or “a table”, both of these formats are well-known in the art, to be used in the formula above so that there would be no need to turn to external sources of these data every time when they need to be utilized in calculations, thus resulting in meeting the limitation of “a delta correction factor table”.
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize disclosed by Peretz usage of “environmental parameters” for determination of the signal attenuation due to the effect of these environmental parameters, in the system of Diener. Doing so would have allowed to more accurately model propagation of the signal between transmitter and receiver.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over US 20110090939 (Diener) in view of US 20190064130 (Kanazawa), US 20170366361 (Afkhami) and RF and Digital Signal Processing for Software-Defined Radio, Chapter 4 - High-Level Requirements and Link Budget Analysis (Link Budget) as applied to claim 18 above, and further in view of US 20170025996 (Cheung) (of record).
Regarding claim 20, Diener teaches “wherein the threshold of power level is variable (paragraphs 0334 – 0337: a pulse is defined by a series of time-contiguous, and bandwidth continuous peaks. A peak floor is established to determine which spikes of radio energy qualify as a valid peak. Energy spikes below this peak floor do not qualify, whereas those above the peak floor do qualify. This peak floor acting as a threshold for qualifying peaks corresponds to “the threshold of power level” since it determines sensitivity of the system. In Diener, the peak floor is variable and is based on the current noise floor, which is variable, because it responds well to changes in the radio spectrum environment.)…” “…and a power level of the at least one signal is determined to be greater than the threshold of power level (paragraph 0334: A peak floor (“the threshold of power level”) is established to determine which spikes of radio energy qualify as a valid peak. Energy spikes below this peak floor do not qualify, whereas those above the peak floor do qualify (“a power level of the at least one signal is determined to be greater than…”).).”
Diener does not disclose that the threshold is variable “between the one or more frequency bins.”
Cheung in paragraphs 0059 – 0061 teaches that different thresholds can be used for different frequency bands or frequency bins. The thresholds can also be programmed based on the particular application. Although Cheung discloses this feature for analyzing the electromagnetic spectrum for presence of arc, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application that it can also be used for detection of any of the signals in the electromagnetic spectrum.
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize disclosed by Cheung usage of different threshold of power levels for different frequency bins, in the system of Diener. Doing so would have allowed to more accurately set detection thresholds for different frequency ranges depending on the noise present at those frequencies.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GENNADIY TSVEY whose telephone number is (571)270-3198. The examiner can normally be reached Mon-Fri 9-5:30.
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, Wesley Kim can be reached at 571-272-7867. 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.
/GENNADIY TSVEY/ Primary Examiner, Art Unit 2648