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 .
This office action is in response to the Applicant’s communication filed on 02/24/2026. Claims 1 – 20 are currently pending in this application.
The applicant' s arguments have been considered but are moot in view of new ground(s) of rejections necessitated by the applicant' s amendment.
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 247 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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 – 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claim 1, as amended, recites “wherein the apparatus is operable to identify a number of users of a network of the at least one signal via analysis of the at least one signal”. In the Remarks, the Applicant states that support for this amendment may be found in paragraphs 0250 and 0273.
Upon review of these paragraphs, nothing has been found in paragraphs 0250 related to this feature. Paragraph 0273 states the following:
“TFE function does not identify each device in an RF environment, but the ASD with TFE function in the present invention is operable to get network fingerprint of radio transmitters. Protocol identification function is operable to identify number of users on a network. The ASD with TFE function in the present invention is operable to detect how many users on a detected signal based on the TFE function even when these users are not always on the detected signal.”
As may be seen from this paragraph, it simply states that “Protocol identification function is operable to identify number of users on a network” without actually enabling this feature. It is not clear how “protocol identification function” or “The ASD with TFE function in the present invention” may allow identification of the number of users on a network. In other words, there is nothing here or anywhere else in the description that would enable identification of number of users based on protocol. A person of ordinary skill in the art would likely not able to use this feature without undue experimentation.
The same argument applies to similarly worded claims 13 and 16.
Claims 2 – 12, 14, 15 and 17 – 20 are rejected as being dependent from the rejected base claims.
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.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20110090939 (Diener) in view of US 20090280748 (Shan), US 20170025996 (Cheung) and further in view of US 20190064130 (Kanazawa) (of record) evidenced by US 20090013210 (McIntosh) and Second Derivative (Siyavula).
Regarding claim 13, Diener teaches “An apparatus for spectrum data management for a radio frequency (RF) environment (shown in Fig. 1, 6, 7, 11 and 12 with corresponding description), comprising:
at least one receiver (12 in Fig. 6 and 11 (including ADC 18); 4000 and 4010 in Fig. 12 (including ADC 18))…”
“…wherein the 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 apparatus is operable to identify a number of users of a network of the at least one signal via analysis of the at least one signal (paragraph 0150: Any device that receives radio frequency energy in the frequency band of interest may be equipped with a SAGE 20 to generate spectrum activity information. FIG. 11 shows an example of such a cognitive radio device. Paragraph 0153: The communication device shown in FIG. 11 may be part of, or correspond to, any of a variety of devices that operate in the frequency band, such as an IEEE 802.11 WLAN AP. In other words, the IEEE 802.11 WLAN AP performs spectrum sensing and analysis functions and is part of the recited by the claim “apparatus for spectrum data management” which also includes various servers shown in FIG 1. Paragraph 0146 teaches STA associating with an AP. Thus, it is a fully functioning wireless network according to IEEE 802.11 standard. Paragraph 0146 goes on to state that each time a STA associates with an AP, its signal pulse characteristics may be compared against the database of information to determine whether it is an authorized device. This procedure ensures only authorized users may connect. This corresponds to recited by the claim “analysis of the at least one signal”.
Further, it is inherent for the IEEE 802.11 networks to know how many users are connected (corresponding to “a number of users of a network of the at least one signal”) based on the such analysis of “the at least one signal” as demodulating and decoding the signal to obtain information contained within the signal. This may be evidenced by McIntosh, FIG 1 and paragraph 0003: The network setup for a wireless local area network generally consists of a modem and a wireless router, as illustrated in FIG. 1. Paragraphs 0102 – 0105: Most routers maintain a DHCP active-lease table of assigned clients. When a client requests DHCP address renewal, it is assigned a "fresh" address. However, this operation may happen only when the purpose of the request is known, which is done by demodulating and decoding the information contained in the signal, or using the wording of the current limitation, “via analysis of the at least one signal.” Based on this requests in general, the DHCP active-lease table of assigned clients is created and maintained which contains information on “a number of users of a network of the at least one signal.”);
wherein the apparatus is operable to identify the at least one signal based on hardware parameters (paragraph 0094: 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. (“identify the at least one signal”). Paragraph 0100: The accumulated signal pulse data for the signals to be classified are compared against reference or profile signal pulse data for known signals. Paragraph 0101 – 0113: 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, and/or from a database of information provided by a regulatory authority, such as a Federal Communication Commission in the U.S. The FCC may maintain and make publicly available a database of transmission parameters for each device permitted to operate in the frequency band. Examples of such parameters are: Transmit spectrum masks and transmit power levels, each representing “hardware parameters”))…”
“…wherein the apparatus is operable to automatically detect the at least one signal in the RF environment by fine-tuning 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 (“automatically detect the at least one signal in the RF environment”). The bwThreshDbm parameter determines the peak floor based on whether `bwThreshDbm` is positive or negative. If bwThreshDbm is positive, then the peak floor is determined dynamically (“fine-tuning 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 “fine-tuned” based on changes in the noise floor of the radio spectrum environment)…”
“…wherein the apparatus is operable to de-bias raw signal data (although Diener in paragraph 0075 teaches spectrum correction algorithm correcting side tone suppression and DC offset, he does not provide any details. However, in paragraph 0011, Diener contains incorporated by reference application 10/246,365. This application was published as US 20030198304 (included in the record) and provides details of the correcting procedure. US 20030198304 in FIG 1 and paragraph 0044 discloses an RF interface positioned between the RF transceiver and spectrum analysis engine. It contains a direct current (DC) correction block 830 and an amplitude/phase correction block 840. FIG 2 and paragraphs 0045 – 0050 describe the interface in more detail. Paragraph 0046: DC, amplitude and phase offset compensation circuits are provided before the Fast Fourier Transform (FFT) to maximize LO and sideband suppression. The Rx baseband signals are sampled at the CLK frequency using two ADCs, one for the in-phase signal (I), and another for the quadrature signal (Q). Both I and Q signals represent “raw signal data”. DC correction is performed adaptively by estimating the DC offset at the ADC output and updating a correction DAC to remove large DC offsets (“de-bias”). Any residual DC offset after coarse correction is removed after the ADC via digital subtraction (also “de-bias”). The MCU estimates the amplitude and phase imbalance and programs the correction values into the appropriate control registers. This is the same as claimed “operable to de-bias raw signal data”)…”
“…wherein the apparatus is operable to subtract the baseline from a real-time spectral sweep to reveal the at least one signal (paragraph 0334, Energy spikes below a peak floor do not qualify, whereas those above the peak floor do qualify. Peak floor corresponds to claimed “baseline” or the threshold above which peaks are detected. Paragraph 0084: The peak detector detects a peak as a set of FFT points in contiguous FFT frequency bins, each above a configured minimum power level (which is also disclosed as threshold or peak floor and corresponds to claimed “baseline”). Once per FFT interval, the peak detector outputs data describing those frequency bins that had a FFT value above a peak threshold (peak floor or “baseline”) and which frequency bin of a contiguous set of frequency bins has a maximum value for that set (“to reveal at least one signal”). In addition, the peak detector passes a power vs. frequency bin data field for each FFT interval. This can be represented by the pseudo code (where k is the frequency bin index) which includes the following formula PDBdiff(k)= PDB(k) - SD_PEAKTH , where PDBdiff(k) appears to represent difference between the absolute value of the signal within kth frequency bin index (PDB(k)) and the value of the peak detection threshold (“baseline”) (SD_PEAKTH). As may be seen, this operation is the same as claimed “subtract the baseline from the real-time spectral sweep”, where “spectral sweep” is given by the multiplicity of PDB(k) and “the baseline” is given by SD_PEAKTH). Everything is done in real-time).”
Diener does not disclose that “the hardware parameters include antenna position, antenna type, orientation, azimuth, gain, and/or equivalent isotropically radiated power (EIRP) for a transmitter associated with the at least one signal.”
Diener, however, teaches in paragraph 0101 – 0113 that the reference data for the variety of signals that may use the frequency band may be obtained from a database of information provided by a regulatory authority, such as a Federal Communication Commission in the U.S. The FCC may maintain and make publicly available a database of transmission parameters for each device permitted to operate in the frequency band. Examples of such parameters are: Transmit spectrum masks and transmit power levels, each representing “hardware parameters”.
So the difference between the disclosure of Diener and instant claim is that in Diener, it is transmit power level of the transmitter “associated with the at least one signal” which is contained in the database, while one of the parameters recited by instant claim on alternative basis is “equivalent isotropically radiated power (EIRP) for a transmitter”.
In this respect, Shan in paragraphs 0043 – 0044 teaches minimizing interference with the licensed system by using licensed system's channel occupation information provided from the DB server 130 and Equivalent Isotropically Radiated Power (EIRP) information on each of the transmitters operating in the area. The DB server 130 stores information among band usage scheduling information on the licensed system, a usage frequency band range, a transmission start/end time, a geo-location of the transmitter, transmission EIRP of the transmitter (“the hardware parameters include … equivalent isotropically radiated power (EIRP) for a transmitter associated with the at least one signal”), antenna-related information on the transmitter (e.g., an antenna height, an antenna directivity and a directivity pattern, representing “the hardware parameters include antenna position, … orientation, … for a transmitter associated with the at least one signal”, since disclosed antenna height corresponds to the recited “antenna position” and disclosed directivity or directivity pattern correspond to the recited antenna “orientation”).
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 Shan such hardware parameters as at least transmission EIRP of the transmitter, antenna position and orientation, to be stored in the database of Diener. Doing so would have provided additional information in the database that may come useful for the operation of the system.
Diener does not teach that the tuning of the threshold of power level is “on a segmented basis while extracting at least one temporal feature from a knowledge map.”
Cheung in paragraphs 0059 – 0061 teaches that different thresholds can be used for different frequency bands or frequency bins. This maps to the limitation “fine-tuning a threshold of power level on a segmented basis” since each frequency bin represents an individual segment. The thresholds can also be programmed based on the particular application. The thresholds can be determined empirically, and/or adapted over time (e.g., through adaptation algorithms or learning algorithms), thus mapping to the limitation “extracting at least one temporal feature from a knowledge map” since over time adaptation involves looking back at the signal or noise parameters in the times past. 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 (“fine-tune”) detection thresholds for different frequency ranges depending on the noise present at those frequencies.
Lastly, Diener does not disclose “wherein the apparatus is operable to detect positive and negative gradients based on evaluation of an accumulated gradient value”; that detection of the signal is “by matching the positive and negative gradients”, “wherein the apparatus is operable to determine a baseline based on the matched positive and negative gradients”; “wherein the apparatus is operable to process signal data using compressed data for deltas; wherein the deltas are differentials from the baseline.”
Kanazawa in paragraphs 0001 – 0005 and FIG 8 describes an existing method of detecting peaks in a spectrum obtained by a spectral device.
Kanazawa teaches “detect positive and negative gradients based on evaluation of an accumulated gradient value (paragraph 0004: a peak start point 92 is detected at a shorter retention time than the peak top 91, and a peak end point 93 is detected at a longer retention time than the peak top 91 (FIG. 8(a)). The start point 92 (“detect positive … gradient”) 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 (“detect … negative gradient”) is a position where the second derivative is positive and the first derivative is equal to or less than a negative predetermined value. Both cases involve determination of the second derivative. However, it is well-known (see Siyavula) that a second derivative is the derivative of the first derivative and it indicates the change in gradient of the original function. The sign of the second derivative tells if the gradient of the original function is increasing, decreasing or remaining constant. Since no meaningful description is given in the Applicant’s specification of what “accumulated gradient value” is, it appears that establishing that the gradient is increasing or decreasing by means of the second derivative is the same as broadly recited “accumulation of gradient value” of the original function. Assuming for the function plotted in X-Y coordinates, the numerical value of the second derivative serves as an indication of the changes in gradient value of the original function. The second derivative simply measures how much the gradient/tangent slope f′(x) changes as we make small changes in x. i.e. how small changes in x changes the gradient f′(x). However, tracking the change in the gradient value of the original function is the same as “accumulation of gradient value” over X axis, regardless of whether the accumulation is positive or negative.).”
Kanazawa further teaches detection of a signal “by matching the positive and negative gradients”, and “operable to determine a baseline based on the matched positive and negative gradients (paragraph 0004: a peak top 91 in the signal is detected (FIG. 8(a)). 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)). Finally, the signal, comprising start point 92, peak 91 and end point 93, is identified “by matching the positive and negative gradients” which determines beginning and ending of the peak. Paragraph 0005: Based on the peak start and end points 92 and 93, 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 (which is “based on 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.).”
Additionally, Kanazawa teaches “process signal data using compressed data for deltas; wherein the deltas are differentials from 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 “process[ing] signal data”. 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 deltas are differentials from the baseline”). The reconstructed signal in FIG 8(c) comprises only the “deltas” Δ with the baseline B subtracted. Therefore, “using compressed data for deltas” 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.).”
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 disclosed by Diener, which would increase reliability of achieved results in peak or signal identification.
Regarding claim 14, Diener teaches “wherein the apparatus includes machine learning to detect the at least one signal (paragraph 0067: The signal classification step 2010 involves processing the output of the spectrum sampling step to measure and classify signals based on characteristics such as power, duration, bandwidth, frequency hopping nature. The output of the signal classification step 2010 is data classifying the signals/devices detected. A classification output may be, for example, "cordless phone", "frequency hopper device", "frequency hopper cordless phone", "microwave oven", "802.11x WLAN device", etc. In other words, “the system is operable to … detect the at least one signal” from "cordless phone", "frequency hopper device", "frequency hopper cordless phone", "microwave oven", "802.11x WLAN device". 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. Further, paragraph 0095 contains incorporated by reference U.S. application Ser. No. 10/246,364. This application, published as US 20040203826, in FIG 14 and paragraph 0073 teaches that the SAGE 400 generates signal pulse data, spectrum analysis statistics, etc., from the received signals to be learned and used for future reference (as data templates to be compared against in the classifying step). This SAGE output is processed by processor 600 executing the classification engine 500. The classification engine 500 accumulates signal pulse data (e.g., builds histograms) and uses those accumulated data to design specific classification algorithms, pulse timing signature templates, etc. In other words, the device learns the environment and creates fingerprints to be used as the reference to detect plurality of signals, therefore, “operable to include machine learning to detect the at least one signal”).”
Regarding claim 15, Diener teaches “wherein the machine learning includes automatic signal variance determination (Diener in paragraph 0095 contains incorporated by reference U.S. application Ser. No. 10/246,364, published as US 20040203826. This publication in FIG 14 and paragraph 0073 teaches “machine learning” that is useful to learn the distinctive profile of a device and create a fingerprint definition. The classification engine 500 processes the SAGE outputs generated based on the transmission in the frequency band by the device 4000, 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. On the other side, Diener in paragraph 0116 describes analysis of a Bluetooth signal in which activity consists of two pulses very close in time. Energy associated with a first pulse may occur at one frequency in the band, and energy associated with a second pulse may occur at another frequency in the band, separated from the first pulse by a time interval that recurs on a consistent basis. Therefore, detection of this pattern involves “automatic signal variance determination” where the “variance” is represented by shifting the signal frequency from pulse to pulse. Taking these two together, learning RF environment using “machine learning” and creating fingerprints definitions specifically for Bluetooth signal in which the pulse frequency varies from pulse to pulse maps to the imitation of this claim.).”
Regarding claim 16, Diener in combination with Shan, Cheung and Kanazawa teaches “A method of spectrum data management for a radio-frequency (RF) environment, comprising:
a node device (various spectrum sensors 1200(i) and WLAN AP 1050(i) in FIG 1 with description in paragraphs 0057 – 0058; also FIG 11 and 12) comprising at least one automatic signal detection (ASD) module (SAGE 20 in Fig. 6, 11 and 12; and 22 - 25 in Fig. 7 together with measurement engine 50 and Classification engine 52, both seen in FIG 6) creating learning data of the RF environment (paragraph 0067: The signal classification step 2010 involves processing the output of the spectrum sampling step to measure and classify signals based on characteristics such as power, duration, bandwidth, frequency hopping nature. The output of the signal classification step 2010 is data classifying the signals/devices detected. A classification output may be "cordless phone", "frequency hopper device", "frequency hopper cordless phone", "microwave oven", "802.11x WLAN device", etc. 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. Further, paragraph 0095 contains incorporated by reference U.S. application Ser. No. 10/246,364. This application, published as US 20040203826, in FIG 14 and paragraph 0073 teaches that the SAGE 400 generates signal pulse data, spectrum analysis statistics, etc., from the received signals to be learned and used for future reference (as data templates to be compared against in the classifying/detecting step) corresponding to the recited “creating learning data”. This SAGE output is processed by processor 600 executing the classification engine 500. The classification engine 500 accumulates signal pulse data (e.g., builds histograms) and uses those accumulated data to design specific classification algorithms, pulse timing signature templates, etc. In other words, the device learns the environment and creates fingerprints to be used as the reference therefore, “creating learning data”);
the node device detecting positive and negative gradients based on evaluation of an accumulated gradient value;
the node device detecting at least one signal in the RF environment by matching the positive and negative gradients (this limitation is rejected as unpatentable over a combination of Diener and Kanazawa, as explained in the rejection of similar limitation of claim 13 above, the explanation being incorporated herein by reference);
the node device identifying a number of users of a network of the at least one signal based on the at least one ASD module analyzing the at least one signal (this limitation is rejected as unpatentable over Diener, as explained in the rejection of similar limitation of claim 13 above, the explanation being incorporated herein by reference);
the node device averaging the real-time spectral sweep (Diener, at least paragraph 0256 and FIG 23 showing statistics data including the average power over the sampling period at the bottom), removing areas identified by the matched positive and negative gradients, and connecting points between removed areas to determine a baseline (Kanazawa, paragraph 0005: Based on the peak start and end points 92 and 93 (“areas identified by 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 (“removing areas identified by the matched positive and negative gradients”) and connecting the points representing start and end points of the peak (“connecting points between removed areas”). The total of the partial baselines 941 and 942 obtained in this way is the baseline 94 of the entire spectrum (“to determine a baseline”).);
the node device identifying the at least one signal based on hardware parameters; and
the node device subtracting the baseline from the real-time spectral sweep to reveal the at least one signal; and
the node device processing signal data using compressed data for deltas;
wherein the hardware parameters include antenna position, antenna type, orientation, azimuth, gain, and/or equivalent isotropically radiated power (EIRP) for a transmitter associated with the at least one signal;
wherein the node device detecting the at least one signal in the RF environment further comprises fine-tuning a threshold of power level measurements on a segmented basis while extracting at least one temporal feature from a knowledge map; and
wherein the deltas are differentials from the baseline (the limitations above are rejected as unpatentable over a combination of Diener, Shan, Cheung and Kanazawa as explained in the rejection of similar limitations of claim 13 above, the explanation being incorporated herein by reference).”
Regarding claim 17, Diener teaches “further comprising the node device classifying the at least one signal based on machine learning software (paragraph 0067: The signal classification step 2010 (“classifying the at least one signal”) involves processing the output of the spectrum sampling step to measure and classify signals based on characteristics such as power, duration, bandwidth, frequency hopping nature. The output of the signal classification step 2010 is data classifying the signals/devices detected. A classification output may be, for example, "cordless phone", "frequency hopper device", "frequency hopper cordless phone", "microwave oven", "802.11x WLAN device", etc. 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. Further, paragraph 0095 contains incorporated by reference U.S. application Ser. No. 10/246,364. This application, published as US 20040203826, in FIG 14 and paragraph 0073 teaches that the SAGE 400 generates signal pulse data, spectrum analysis statistics, etc., from the received signals to be learned and used for future reference (as data templates to be compared against in the classifying step). This SAGE output is processed by processor 600 executing the classification engine 500. The classification engine 500 accumulates signal pulse data (e.g., builds histograms) and uses those accumulated data to design specific classification algorithms, pulse timing signature templates, etc. In other words, the device learns the environment and creates fingerprints to be used as the reference therefore, “based on machine learning software”).”
Regarding claim 18, Diener teaches “further comprising the node device comparing the real-time spectral sweep of the RF environment based on the power level measurements to the knowledge map (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. Paragraph 0094: The classification engine 52 compares outputs of the SAGE 20 (accumulated by the measurement engine 50) against data templates and related information of known signals (“comparing a real-time spectral sweep” “to 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 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. Par. 0076 – 0080: collecting and processing “power level measurements” for each frequency bin. 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.).”
Regarding claim 19, Diener teaches “further comprising the node device generating at least one report for the RF environment (for example, paragraph 0119: reporting on Bluetooth protocol device. Paragraphs 0302: reporting on spectrum event message which includes Bluetooth. Paragraphs 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; paragraphs 0067, 0068, 0119, 0357, Fig. 16-25 with corresponding description all deal with generating reports and displaying resulting data.).”
Regarding claim 20, Diener teaches “further comprising the node device generating In-Phase and Quadrature (I/Q) data for at least one target bandwidth (in Fig. 7 shown as Data I and Data Q) determined by a learning and conflict detection engine (paragraph 0093: In response to requests from other software programs or systems (such as the network spectrum interface, the classification engine 52 or the location engine 54), the measurement engine 50 (together comprising “the learning and conflict detection engine”) responds to configure the SAGE 20 and or radio 12, according to the type of data requested); the node device distilling the I/Q data (demodulation function is disclosed in paragraphs 0171, 0198, 0211, 0213 and 0217. Although Diener does not disclose what type of data is supplied to the demodulator, the Examiner takes an official notice that usage of demodulation to perform processing (“distill”) of I/Q data is well known in the art. Therefore, it would have been obvious to a person of ordinary skill in the art to install demodulator after the ADC simply by design choice with predictable results such as demodulation being performed on I/Q data) and storing actionable I/Q data (implemented in described in paragraph 0076 dual port RAM (DPR) 28 which stores duty cycle vs. frequency during a period of time; average power vs. frequency during a period of time; maximum (max) power vs. frequency during a period of time; and number of peaks during a period of time. Since this information comes from stats buffer which receives its input from the FFT block, as seen in Fig. 7, and which performs processing on I and Q data received from ADC, this information represents “actionable I/Q data”. Similarly, as disclosed in paragraph 0083, the SD 23 identifies signal pulses in the received signal data, filters these signals based on their spectral and temporal properties, and passes characteristic information about each pulse to the dual port RAM (DPR) 28. Also, from Fig. 7, it may be seen that information from snapshot buffer and decimator is also transferred to the dual port RAM (DPR) 28. Since all of these parts receive their inputs from the FFT block, as seen in Fig. 7, and which performs processing on I and Q data received from ADC, all this information represents “actionable I/Q data”.); and the node device performing a fast Fourier transform (FFT) based on a wideband sweeping of the RF environment (paragraph 0075: The windowing block performs pre-FFT windowing on the I and Q data. The FFT block provides (I and Q) FFT data for each of 256 frequency bins that span the bandwidth of frequency band of interest. As disclosed in paragraph 0070, the bandwidth of frequency band of interest may be wideband. Paragraph 0231: in monitoring the spectrum, session control messages tell the NSI how wide the bandwidth should be (narrowband or wideband), and the center frequency of the bandwidth being monitored. Paragraph 0245: The SAGE 20 will analyze a frequency band centered at a frequency which may be controlled. Moreover, the bandwidth of the frequency band analyzed may be controlled. For example, substantially an entire frequency band may be analyzed, such as 100 MHz (wideband mode). The selected frequency band, is divided into a plurality of frequency bins) and extracting metadata based on FFT data (The analysis process is disclosed in paragraphs 0073 – 0090 and includes determination of various parameters of detected signals in the RF environment after the FFT (“extracting meta data”).).”
Regarding claim 1, Diener teaches “An apparatus for spectrum data management for a radio frequency (RF) environment (shown in Fig. 1, 6, 7, 11 and 12 with corresponding description), comprising:
an automatic signal detection (ASD) module (at least SAGE 20 in Fig. 6, 11 and 12; and 22 - 25 in Fig. 7 together with measurement engine 50 and Classification engine 52, both seen in FIG 6);
wherein the ASD module includes machine learning (paragraph 0067: The signal classification step 2010 involves processing the output of the spectrum sampling step to measure and classify signals based on characteristics such as power, duration, bandwidth, frequency hopping nature. The output of the signal classification step 2010 is data classifying the signals/devices detected. A classification output may be "cordless phone", "frequency hopper device", "frequency hopper cordless phone", "microwave oven", "802.11x WLAN device", etc. In other words, signals from "cordless phone", "frequency hopper device", "frequency hopper cordless phone", "microwave oven", "802.11x WLAN device" are “detected” by the “automatic signal detection (ASD) module”. 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. Further, paragraph 0095 contains incorporated by reference U.S. application Ser. No. 10/246,364. This application, published as US 20040203826, in FIG 14 and paragraph 0073 teaches that the SAGE 400 generates signal pulse data, spectrum analysis statistics, etc., from the received signals to be learned and used for future reference (as data templates to be compared against in the classifying/detecting step). This SAGE output is processed by processor 600 executing the classification engine 500. The classification engine 500 accumulates signal pulse data (e.g., builds histograms) and uses those accumulated data to design specific classification algorithms, pulse timing signature templates, etc. In other words, the device learns the environment and creates fingerprints to be used as the reference, therefore, “the ASD module includes machine learning”);
wherein the apparatus is operable to form a knowledge map based on past power level measurements of the RF environment over time (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. 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 (“based on past power level measurements of the RF environment over time”).);
wherein the apparatus is operable to automatically detect and/or classify at least one signal from the RF environment based on a comparison of the real-time spectral sweep to the knowledge map (paragraph 0067: The signal classification step 2010 (“classify at least one signal from the RF environment”) involves processing the output of the spectrum sampling step to measure and classify signals based on characteristics such as power, duration, bandwidth, frequency hopping nature. The output of the signal classification step 2010 is data classifying the signals/devices detected. A classification output may be, for example, "cordless phone", "frequency hopper device", "frequency hopper cordless phone", "microwave oven", "802.11x WLAN device", etc. Paragraph 0094: The classification engine 52 compares outputs of the SAGE 20 (“the real-time spectral sweep”) 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 (“based on the comparison of the real-time spectral sweep to the knowledge map”).);
wherein the apparatus is operable to identify a number of users of a network of the at least one signal via analysis of the at least one signal (paragraph 0150: Any device that receives radio frequency energy in the frequency band of interest may be equipped with a SAGE 20 to generate spectrum activity information. FIG. 11 shows an example of such a cognitive radio device. Paragraph 0153: The communication device shown in FIG. 11 may be part of, or correspond to, any of a variety of devices that operate in the frequency band, such as an IEEE 802.11 WLAN AP. In other words, the IEEE 802.11 WLAN AP performs spectrum sensing and analysis functions and is part of the recited by the claim “apparatus for spectrum data management” which also includes various servers shown in FIG 1. Paragraph 0146 teaches STA associating with an AP. Thus, it is a fully functioning wireless network according to IEEE 802.11 standard. Paragraph 0146 goes on to state that each time a STA associates with an AP, its signal pulse characteristics may be compared against the database of information to determine whether it is an authorized device. This procedure ensures only authorized users may connect. This also corresponds to recited by the claim “analysis of the at least one signal”.
Further, it is inherent for the IEEE 802.11 networks to know how many users are connected (corresponding to “a number of users of a network of the at least one signal”) based on the such analysis of “the at least one signal” as demodulating and decoding the signal to obtain information contained within the signal. This may be evidenced by McIntosh, FIG 1 and paragraph 0003: The network setup for a wireless local area network generally consists of a modem and a wireless router, as illustrated in FIG. 1. Paragraphs 0102 – 0105: Most routers maintain a DHCP active-lease table of assigned clients. When a client requests DHCP address renewal, it is assigned a "fresh" address. However, this operation may happen only when the purpose of the request is known, which is done by demodulating and decoding the information contained in the signal, or using the wording of the current limitation, “via analysis of the at least one signal.” Based on this requests in general, the DHCP active-lease table of assigned clients is created and maintained which contains information on “a number of users of a network of the at least one signal.”);
wherein the apparatus is operable to identify the at least one signal based on hardware parameters (paragraph 0094: 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. (“identify the at least one signal”). Paragraph 0100: The accumulated signal pulse data for the signals to be classified are compared against reference or profile signal pulse data for known signals. Paragraph 0101 – 0113: 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, and/or from a database of information provided by a regulatory authority, such as a Federal Communication Commission in the U.S. The FCC may maintain and make publicly available a database of transmission parameters for each device permitted to operate in the frequency band. Examples of such parameters are: Transmit spectrum masks and transmit power levels, each representing “hardware parameters”))…”
“…wherein the apparatus is operable to automatically detect the at least one signal in the RF environment by fine-tuning 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 (“automatically detect the at least one signal in the RF environment”). The bwThreshDbm parameter determines the peak floor based on whether `bwThreshDbm` is positive or negative. If bwThreshDbm is positive, then the peak floor is determined dynamically (“fine-tuning 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 “fine-tuned” based on changes in the noise floor of the radio spectrum environment)…”
“…wherein the apparatus is operable to subtract the baseline from the real-time spectral sweep to reveal the at least one signal (paragraph 0334, Energy spikes below a peak floor do not qualify, whereas those above the peak floor do qualify. Peak floor corresponds to claimed “baseline” or the threshold above which peaks are detected. Paragraph 0084: The peak detector detects a peak as a set of FFT points in contiguous FFT frequency bins, each above a configured minimum power level (which is also disclosed as threshold or peak floor and corresponds to claimed “baseline”). Once per FFT interval, the peak detector outputs data describing those frequency bins that had a FFT value above a peak threshold (peak floor or “baseline”) and which frequency bin of a contiguous set of frequency bins has a maximum value for that set (“to reveal the at least one signal”). In addition, the peak detector passes a power vs. frequency bin data field for each FFT interval. This can be represented by the pseudo code (where k is the frequency bin index) which includes the following formula PDBdiff(k)= PDB(k) - SD_PEAKTH , where PDBdiff(k) appears to represent difference between the absolute value of the signal within kth frequency bin index (PDB(k)) and the value of the peak detection threshold (“baseline”) (SD_PEAKTH). As may be seen, this operation is the same as claimed “subtract the baseline from the real-time spectral sweep”, where “spectral sweep” is given by the multiplicity of PDB(k) and “the baseline” is given by SD_PEAKTH). Everything is done in real-time)…”
Diener does not teach that the tuning of the threshold of power level is “on a segmented basis while extracting at least one temporal feature from a knowledge map.”
However, this limitation is rejected in view of Cheung as explained in the rejection of similar limitations in claim 13 above, the explanation being incorporated herein by reference.
Lastly, Diener does not disclose that “the hardware parameters include antenna position, antenna type, orientation, azimuth, gain, and/or equivalent isotropically radiated power (EIRP) for a transmitter associated with the at least one signal.”
However, this limitation is rejected in view of Shan as explained in the rejection of similar limitations in claim 13 above, the explanation being incorporated herein by reference.
Lastly, Diener does not disclose “wherein the apparatus is operable to detect positive and negative gradients based on evaluation of an accumulated gradient value; wherein the apparatus is operable to determine a baseline based on an average of past power level measurements with at least one signal of interest subtracted by matching the positive and negative gradients”; “wherein the apparatus is operable to process signal data using compressed data for deltas; and wherein the deltas are differentials from the baseline.”
Kanazawa in paragraphs 0001 – 0005 and FIG 8 describes an existing method of detecting peaks in a spectrum obtained by a spectral device.
Kanazawa teaches “detect positive and negative gradients based on evaluation of an accumulated gradient value (paragraph 0004: a peak start point 92 is detected at a shorter retention time than the peak top 91, and a peak end point 93 is detected at a longer retention time than the peak top 91 (FIG. 8(a)). The start point 92 (“detect positive … gradient”) 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 (“detect … negative gradient”) is a position where the second derivative is positive and the first derivative is equal to or less than a negative predetermined value. Both cases involve determination of the second derivative. However, it is well-known (see Siyavula) that a second derivative is the derivative of the first derivative and it indicates the change in gradient of the original function. The sign of the second derivative tells if the gradient of the original function is increasing, decreasing or remaining constant. Since no meaningful description is given in the Applicant’s specification of what “accumulated gradient value” is, it appears that establishing that the gradient is increasing or decreasing by means of the second derivative is the same as broadly recited “accumulation of gradient value” of the original function. Assuming for the function plotted in X-Y coordinates, the numerical value of the second derivative serves as an indication of the changes in gradient value of the original function. The second derivative simply measures how much the gradient/tangent slope f′(x) changes as we make small changes in x. i.e. how small changes in x changes the gradient f′(x). However, tracking the change in the gradient value of the original function is the same as “accumulation of gradient value” over X axis, regardless of whether the accumulation is positive or negative.).”
Kanazawa further teaches “determine a baseline based on an average of past power level measurements (paragraph 0004: First, an operation to eliminate a noise is performed. Although Kanazawa does not explicitly state that this noise elimination is performed by averaging the past measurements, this method is well known in the art, as may be evidenced by Diener, Paragraph 0384 and FIG 22 – 23: 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, the averaging eliminates noise. It would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize averaging to eliminate noise in the method of Kanazawa) with at least one signal of interest subtracted by matching the positive and negative gradients; wherein the apparatus is operable to subtract the baseline from the” “spectral sweep to reveal the at least one signal (Kanazawa, paragraph 0004: a peak top 91 in the signal is detected (FIG. 8(a)). 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)). Finally, the signal, comprising start point 92, peak 91 and end point 93, is identified “by matching the positive and negative gradients” which determines beginning and ending of the peak. Paragraph 0005: Based on the peak start and end points 92 and 93, 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 (subtracting) the peak (“at least one signal of interest” which is by means of “matching the positive and negative gradients”) and connecting the points representing start and end points of the peak thus “revealing the at least one signal” in FIG 8(c). The total of the partial baselines 941 and 942 obtained in this way is the baseline 94 of the entire spectrum.);.”
Kanazawa further teaches “process signal data using compressed data for deltas; and wherein the deltas are differentials from 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 “process[ing] signal data”. 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 deltas are differentials from the baseline”). The reconstructed signal in FIG 8(c) comprises only the “deltas” Δ with the baseline B subtracted. Therefore, “using compressed data for deltas” 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.).”
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 disclosed by Diener, which would increase reliability of achieved results in peak or signal identification.
Regarding claim 2, Diener teaches “wherein the ASD module includes automatic signal variance determination (paragraph 0116 describes analysis of a Bluetooth signal in which activity consists of two pulses very close in time. Energy associated with a first pulse may occur at one frequency in the band, and energy associated with a second pulse may occur at another frequency in the band, separated from the first pulse by a time interval that recurs on a consistent basis. Therefore, detection of this pattern involves “automatic signal variance determination” where the “variance” is represented by shifting the signal frequency from pulse to pulse.).”
Regarding claim 3, Diener does not teach “wherein the machine learning includes an artificial neural network (ANN)”.
However, in the previous office action the Examiner took an Official Notice that artificial neural networks were well known and widely used in machine learning before the effective filing date of this application containing this specific limitation. Since the applicant failed to properly traverse the Official Notice, this common knowledge or well-known in the art statement is now taken to be admitted prior art.
Therefore, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application containing this specific limitation to take well known and widely used artificial neural networks and use them in machine learning disclosed by Diener. Doing so would have allowed to learn from past experience, and derive conclusions from a complex set of input information.
Regarding claim 4, Diener teaches “further comprising at least one receiver operable to create measured data based on the RF environment (12 in Fig. 6 and 11 (including ADC 18); 4000 and 4010 in Fig. 12 (including ADC 18)).”
Regarding claim 5, Diener teaches “wherein the at least one receiver comprises a primary receiver (may be mapped to receiver 12 in Fig. 6 and 11 (including ADC 18); 4000 and 4010 in Fig. 12 (including ADC 18). In Fig. 7 partially shown as ADC with input from the receiver) and a secondary receiver (may be mapped to shown in Fig. 7 and described in paragraph 0075 a windowing block, a NFFT=256-point complex FFT block, and a spectrum correction block.), wherein the primary receiver is configured to generate In-Phase and Quadrature (I/Q) data for at least one target bandwidth (in Fig. 7 shown as Data I and Data Q) based on a learning and conflict detection engine (paragraph 0093: In response to requests from other software programs or systems (such as the network spectrum interface, the classification engine 52 or the location engine 54), the measurement engine 50 (together comprising “the learning and conflict detection engine”) responds to configure the SAGE 20 and or radio 12 (“a primary receiver”), according to the type of data requested), and wherein the secondary receiver is configured to perform a fast Fourier transform (FFT) based on a wideband sweeping of the RF environment (paragraph 0075: The windowing block performs pre-FFT windowing on the I and Q data. The FFT block provides (I and Q) FFT data for each of 256 frequency bins that span the bandwidth of frequency band of interest. As disclosed in paragraph 0070, the bandwidth of frequency band of interest may be wideband. Paragraph 0231: in monitoring the spectrum, session control messages tell the NSI how wide the bandwidth should be (narrowband or wideband), and the center frequency of the bandwidth being monitored. Paragraph 0245: The SAGE 20 will analyze a frequency band centered at a frequency which may be controlled. Moreover, the bandwidth of the frequency band analyzed may be controlled. For example, substantially an entire frequency band may be analyzed, such as 100 MHz (wideband mode). The selected frequency band, is divided into a plurality of frequency bins).”
Regarding claim 6, Diener teaches “further comprising an I/Q buffer (shown in Fig. 7 and described in paragraph 0087 snapshot buffer (SB) 24), wherein the apparatus is operable to determine whether to keep the I/Q data in the I/Q buffer (paragraph 0087: The SB 24 collects a set of raw digital signal samples of the received signal (shown in Fig. 7 as Data I and Data Q) useful for signal classification. The SB 24 can be triggered to begin sample collection from either the SD 23 or from an external trigger source using the snapshot trigger signal SB_TRIG. When a snapshot trigger condition is detected, SB 24 buffers up a set of digital samples and asserts an interrupt to a processor. Therefore, determination “whether to keep the I/Q data in the I/Q buffer” may be expressed by using signal SB_TRIG from the external trigger source. On the other side, paragraph 0120 teaches using snapshot buffer data collected by the measurement engine 50 to perform time difference of arrival (TDOA), and paragraph 0500 teaches that the L0 SAGE engine 120 receives configuration information for several of its components from L1 engines. For example, it receives configuration information for the snapshot buffer from the L1 location engine 210, and upon an appropriate triggering event, supplied snapshot buffer content to the L1 location engine 210. In other words, control of the snapshot buffer, such as its configuration which determines triggering event resulting in collection of the data by the snapshot buffer (“determine whether to keep the I/Q data in the I/Q buffer”) is performed by the location engine. In view of this, it would have been obvious to a person of ordinary skill in the art to perform triggering of the snapshot buffer (and thus “determine whether to keep the I/Q data in the I/Q buffer”) using signal SB_TRIG from the external trigger source, disclosed in paragraph 0087, controlled by the location engine simply to implement functionality disclosed in paragraphs 0120 and 0500).”
Regarding claim 7, Diener teaches “further comprising a demodulator configured to distill the I/Q data (demodulation function is disclosed in paragraphs 0171, 0198, 0211, 0213 and 0217. Therefore, “demodulator” is implicitly present in the system. Although Diener does not disclose what type of data is supplied to the demodulator, the Examiner takes an official notice that usage of demodulator to perform processing (“distill”) of I/Q data is well known in the art. Therefore, it would have been obvious to a person of ordinary skill in the art to install demodulator after the ADC simply by design choice with predictable results such as demodulation being performed on I/Q data) and store actionable I/Q data (implemented in described in paragraph 0076 dual port RAM (DPR) 28 which stores duty cycle vs. frequency during a period of time; average power vs. frequency during a period of time; maximum (max) power vs. frequency during a period of time; and number of peaks during a period of time. Since this information comes from stats buffer which receives its input from the FFT block, as seen in Fig. 7, and which performs processing on I and Q data received from ADC, this information represents “actionable I/Q data”. Similarly, as disclosed in paragraph 0083, the SD 23 identifies signal pulses in the received signal data, filters these signals based on their spectral and temporal properties, and passes characteristic information about each pulse to the dual port RAM (DPR) 28. Also, from Fig. 7, it may be seen that information from snapshot buffer and decimator is also transferred to the dual port RAM (DPR) 28. Since all of these parts receive their inputs from the FFT block, as seen in Fig. 7, and which performs processing on I and Q data received from ADC, all this information represents “actionable I/Q data”.), wherein the actionable I/Q data comprises signal metrics, protocol data, radio identification (ID), network ID and layer 3 data (since I and Q data received from ADC completely represents all information contained in the received signal, anything which is contained in the signal as well as the signal parameters (“signal metrics”) will be represented by the I and Q data received from ADC. Therefore, the I and Q data received from ADC will contain directly or indirectly any type of data or information contained in the received signal, including “protocol data, radio identification (ID), network ID and layer 3 data”, if available in the received signal. Alternatively, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to explicitly extract any type of data directly or indirectly contained in the I and Q data received from ADC if desired by the designer of the system or operator, if this data is available by the nature of the received signal (for example, the signal received from a microwave oven will not contain any of these data, while the signal received from a device on a cellular network will likely contain this information), without any patentable significance).”
Regarding claim 8, Diener teaches “wherein the apparatus is configured for conflict recognition and anomaly identification (Par. 0067: detection of interference condition (presence of another signal in the frequency band of operation, adjacent frequency channel of operation, etc., of a device or network of devices in the frequency band) (which is “conflict recognition and anomaly identification”). A real-time alert may be generated to advise a network administrator about the condition including recommendations to a user or to a network administrator to make adjustments to a device or network of devices operating in the frequency band. This also means that a conflict or anomaly were recognized or identified. Par. 0068: The policy execution step 2020 involves determining what should be done about the information output by the signal classification step 2010. In processing the spectrum activity information, controls may be generated to adjust one or more operating parameters of devices or networks of devices operating in the frequency band. The spectrum actions step 2030 generates the particular controls to effect the actions such as: assigning a device to a different frequency sub-band or channel in the frequency band, executing interference mitigation or co-existence algorithms, executing spectrum etiquette procedures, executing spectrum priority schemes, or re-assigning STAs to APs in a WLAN This also means that a conflict or anomaly were recognized or identified based on which the actions were taken or recommended. Par. 0094: The classification engine 52 can detect signals that interfere with the operation of one or more devices (e.g., occupy or occur in the same channel of the unlicensed band as a device operating in the band – “conflict recognition and anomaly identification”). 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", etc. (all of which represent certain conflict or anomaly for the network, therefore, “conflict recognition and anomaly identification”). Another 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) (“anomaly identification”).).”
Regarding claim 9, Diener teaches “wherein the apparatus is operable to tune the ASD module automatically (“ASD module” was mapped to at least SAGE 20 together with measurement engine 50 and Classification engine 52. Paragraph 0093: In response to requests from other software programs or systems (such as the network spectrum interface, the classification engine 52 or the location engine 54), the measurement engine 50 responds to configure the SAGE 20 (“ASD module”) and or radio 12, according to the type of data requested. Paragraph 0245: The measurement engine 50 supplies the configuration parameters to the SAGE drivers 15).”
Regarding claim 10, Diener does not teach “wherein the ASD module is operable to detect a narrowband signal with a bandwidth from 1 kHz to 60 kHz inside a wideband signal with a bandwidth up to 100 MHz across a 6 GHz spectrum.”
However, this limitation is merely a statement of intended use or environment in which the device operates and does not restrict the device to any particular structure or the method to any particular steps. “[a]n intended use or purpose usually will not limit the scope of the claim because such statements usually do no more than define a context in which the invention operates.” See Boehringer Ingelheim Vetmedica, Inc. v. Schering-Plough Corp., 320 F.3d 1339, 1345 (Fed. Cir. 2003). Although “[s]uch statements often . . . appear in the claim’s preamble,” a statement of intended use or purpose can appear elsewhere in a claim. In re Stencel, 828 F.2d 751, 754 (Fed. Cir. 1987).
Additionally or alternatively, it would have been obvious to a person of ordinary skill in the art at the effective filing date of the application to utilize device and system of Diener to detect the type of signal recited in the claim, if required to do so, by simply using appropriate histograms and/or templates from the database of known signals, and/or pulse definitions described in paragraph 0086 including ranges for power, center frequency, bandwidth and duration (defined by the pulse detector configuration information). Doing so would have expanded the type of signals that may be detected by the device.
Regarding claim 11, Diener teaches “wherein the apparatus is operable to use a calibration vector to de-bias raw signal data (although Diener in paragraph 0075 teaches spectrum correction algorithm correcting side tone suppression and DC offset, he does not provide any details. However, in paragraph 0011, Diener contains incorporated by reference application 10/246,365. This application was published as US 20030198304 (included in the record) and provides details of the correcting procedure. US 20030198304 in FIG 1 and paragraph 0044 discloses an RF interface positioned between the RF transceiver and spectrum analysis engine. It contains a direct current (DC) correction block 830 and an amplitude/phase correction block 840. FIG 2 and paragraphs 0045 – 0050 describe the interface in more detail. Paragraph 0046: DC, amplitude and phase offset compensation circuits are provided before the Fast Fourier Transform (FFT) to maximize LO and sideband suppression. The Rx baseband signals are sampled at the CLK frequency using two ADCs, one for the in-phase signal (I), and another for the quadrature signal (Q). Both I and Q signals represent “raw signal data”. DC correction is performed adaptively by estimating the DC offset at the ADC output and updating a correction DAC to remove large DC offsets (“de-bias”). Any residual DC offset after coarse correction is removed after the ADC via digital subtraction (also “de-bias”). The MCU estimates the amplitude and phase imbalance and programs the correction values (thus being “calibration vector”) into the appropriate control registers. This is the same as claimed “operable to use a calibration vector to de-bias raw signal data”).”
Regarding claim 12, Diener teaches “wherein the apparatus further comprises a learning and conflict detection engine (items 50, 52, 54 and 56 in Fig. 6. Indeed, par. 0094: The classification engine 52 can detect signals that interfere with the operation of one or more devices (e.g., occupy or occur in the same channel of the unlicensed band as a device operating in the band, thus representing “conflict”). 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. (“conflict detection”). 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”, and, therefore, this function is implemented by an “engine”).”
Claim 10 is alternatively rejected under 35 U.S.C. 103 as being unpatentable over US 20110090939 (Diener) in view of US 20090280748 (Shan), US 20170025996 (Cheung) and US 20190064130 (Kanazawa) and further in view of US 20150319768 (Abdelmonem).
Regarding claim 10, Diener does not teach “wherein the ASD module is operable to detect a narrow band signal with a bandwidth from 1 kHz to 60 kHz inside a wideband signal with a bandwidth up to 100 MHz across a 6 GHz spectrum.”
Abdelmonem in paragraph 0084 teaches probability distribution functions (PDFs) of a typical DSSS signal and a complementary cumulative distribution functions (CCDFs) of a typical DSSS signal, which may be used to establish a criteria used to determine [narrowband] channels disposed within a [wideband] signal.
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 Abdelmonem criteria for determination of narrowband channels disposed within a wideband signal, in the system of Diener. Doing so would have expanded the type of signals that may be detected by the device.
Regarding specific values for the bandwidths, it would have been obvious to a person of ordinary skill in the art to apply disclosed by Abdelmonem criteria for any specific bandwidths of signals, since it has been held that discovering an optimum value of a result effective variable involves only routine skill in the art. In re Boesch, 617 F.2d 272, 205 USPQ 215 (CCPA 1980) and/or it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or workable ranges involves only routine skill in the art. In re Aller, 105 USPQ 233.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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