DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1-120 are objected to because of the following informalities: The acronym CFO is not clearly defined into the claimed invention. Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 17-20 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Mirfakhraei et al. (US Patent 10,797,805, hereinafter Mirfakhraei).
Regarding claim 17, Mirfakhraei discloses a wireless device(Figs. 1,2,7-8), comprising:
a receive (RX) chain (210) to receive incoming signals over a network(Col. 6, lines 1-14, which recites a network interface to receive incoming signals over the network ), the RX chain comprising an analog-to-digital converter (ADC)(Col. 5, lines 35-59, which recites the radio component(s) may include a low noise amplifier (LNA), additional signal amplifiers, an analog-to-digital (A/D) converter, one or more buffers, digital baseband(s), switches, etc);
memory(240); and one or more processors (220) operatively coupled to the memory, the one or more processors to process digital data generated by the ADC, wherein the one or more processors (Col. 6, lines 1-31, which recites The memory 240 comprises a plurality of storage locations that are addressable by the processor 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein. The processor 220 may comprise hardware elements or hardware logic adapted to execute the software programs and manipulate the data structures 245), are to:
track carrier frequency offset (CFO) values of a first plurality of packets received over the network(Col. 14, lines 12-23, which recites a simplified procedure for optimized frequency searching for simultaneously received packet detection in accordance with one or more embodiments described herein. The techniques herein track measured carrier frequency offsets (CFOs) of identified transmitters over a period of activity of the identified transmitters. Through this, in step 715, the techniques herein may then determine predicted CFOs for the identified transmitters);
train, using characteristics of the first plurality of packets received from a second wireless device, a machine learning model (MLM), wherein the MLM is to predict CFO spikes between consecutively received packets(Col. 14, lines 27-47, which recites the techniques herein determine predicted transmitter behavior as a probability of specific transmitters of the identified transmitters being active at given times based on the activity of the identified transmitters. That is, as mentioned above, machine learning algorithms such as an ANN (e.g., a back-propagation trained FFANN), or other types, such as convolution neural networks, or non-linear, non-parametric regression-based statistical prediction models);
receive, after the first plurality of packets, a first packet over a first channel of the network(Col. 14, lines 29 – Col. 15, lines 9, which recites Based on the predicted CFOs and predicted transmitter behavior, therefore, in step 725 the techniques herein may determine CFO ranges that a receiver should expect for upcoming packets, and may then instruct the receiver in step 730 to use the CFO ranges as a prioritized list of dynamically selected CFOs to use to extract single or colliding packets from among potential interferences using frequency demodulation); and
based on one or more characteristics of the first packet, generate an output, via the MLM, indicative of a CFO inconsistency between the first packet and a second packet following the first packet(Abstract, Col. 7, lines 45- Col. 8, lines 14, Col. 14, lines 27 – Col. 15, lines 20 which recites in step 725 the techniques herein may determine CFO ranges that a receiver should expect for upcoming packets, and may then instruct the receiver in step 730 to use the CFO ranges as a prioritized list of dynamically selected CFOs to use to extract single or colliding packets from among potential interferences using frequency demodulation).
Regarding claim 18, Mirfakhraei discloses the wireless device of claim 17, wherein the characteristics of the first plurality of packets comprises a plurality of a current packet CFO estimate, one or more historical CFO estimates, a peak correlation, an acquisition time delay estimate, a preamble validation metric, or a decoded access address validation metric(Abstract, Col. 7, lines 45- Col. 8, lines 14 Col. 14, lines 29 – Col. 15, lines 9).
Regarding claim 19, Mirfakhraei discloses the wireless device of claim 17, wherein the one or more characteristics of the first packet comprises a one or more of a current packet CFO estimate, one or more historical CFO estimates, a peak correlation, an acquisition time delay estimate, a preamble validation metric, or a decoded access address validation metric(Abstract, Col. 7, lines 45- Col. 8, lines 14 Col. 14, lines 29 – Col. 15, lines 9).
Regarding claim 20, Mirfakhraei discloses the e wireless device of claim 17, wherein the output causes the wireless device to modulate a preamble of the second packet without CFO compensation based on historical CFO estimates(Abstract, Col. 7, lines 45- Col. 8, lines 14 Col. 14, lines 29 – Col. 15, lines 9).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1,5,7-8,9,13,and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gaikwad et al. (US Application 2006/0285478, hereinafter Gaikwad) in view Mirfakhraei.
Regarding claims 1, 9, Gaikwad discloses a method, a receiver(Figs. 1, 2,4-5) comprising:
a receive (RX) chain (105) to receive incoming signals over a network([0029], which recites PHY layer 100 includes an RF tuner 110, RF filter 115, an analog-to-digital converter 120, an RF demodulator 125, a decoder 130, and a serializer 135. The PHY layer 100 output is coupled to the media access control (MAC) layer. PHY layer 100 also includes a PHY state machine 112, which provides control functions (CONTROL) for certain programmable elements in PHY layer 100.), the RX chain comprising an analog-to-digital converter (ADC)(120); memory(inherent feature of the wireless device); and one or more processors (125,130) operatively coupled to the memory, the one or more processors to process digital data generated by the ADC, wherein the receiver is to
determining a predicted (CFO) estimate(Abstract, [0051]-[0065]), which recites a preamble CFO estimate, ppreamble--stream(m), is made by correlating repeated preamble portions that precede each data packet.); after determining the predicted CFO estimate, receiving a first packet(Abstract, [0051]-[0065]), which recites received data packet that includes one or more M data streams. The data packet has a payload portion preceded by a preamble portion, the payload portion having a plurality of data symbols (L) each of which include a plurality of data tones (K));
correcting frequency offset of a first preamble of the first packet based on the predicted CFO estimate(Abstract, [0051]-[0065]), which recites an initial CFO correction is then made based on the single preamble CFO estimate ppreamble)
determining a first CFO estimate using the corrected first preamble(Abstract, [0052]-[0065], which recites a per-tone CFO estimate is successively averaged over frequency, time and space to derive a data CFO estimate pdata. Step 406, as explained more fully below, is repeated for each l.sup.th data symbol. Finally, according to step 408, any CFO in each received data stream is then corrected according to the data CFO estimate, pdata, and/or the preamble CFO estimate ppreamble));
correcting frequency offset of at least a portion of the first packet based on a combination of the predicted CFO estimate and the first CFO estimate(Abstract, [0051]-[0065]), which recites a per-tone CFO estimate is successively averaged over frequency, time and space to derive a data CFO estimate pdata. Step 406, as explained more fully below, is repeated for each l.sup.th data symbol. Finally, according to step 408, any CFO in each received data stream is then corrected according to the data CFO estimate, pdata, and/or the preamble CFO estimate ppreamble)); and
updating the predicted CFO estimate based on a first output the first output being based on one or more characteristics of the first packet([0051],[0064],[0066], which recites a per-tone CFO estimate is made for each of the K tones in the data symbol, and successively averaged over frequency, time and space to generate a data CFO estimate. The data CFO estimate may be combined with a preamble CFO estimate (see below) to update a packet CFO estimate. The packet CFO estimate is then applied to the data stream passing through the signal processing pipeline. In this fashion, after only a few data symbols have passed through the signal processing pipeline).
As shown on figs. 1 , 2 and described on [0051],[0064],[0066], Gaikwad discloses a PHY state machine( that can be considered as of a machine learning model (MLM)) to output the updated the predicted CFO based on the output of the machine. But, Gaikwad does not explicitly discloses machine learning model (MLM)).
However, Mirfakhraei explicitly teaches a machine learning model (MLM)) to output the updated the predicted CFO based on the output of the machine(Figs. 2, 6, Col. 7, lines 45- 55 and Col. 8, lines 14,30).
Therefore, it would have been obvious for one with ordinary skill in the art before the effective filling date of the claimed invention to combine the teaching of Mirfakhraei with the teaching of Gaikwad by using the above features such as a machine learning model (MLM)) to output the updated the predicted CFO based on the output of the machine as taught by Mirfakhraei for the purpose of instructing the receiver to use the CFO ranges as a prioritized list of dynamically selected CFOs to use to extract single or colliding packets from among potential interferences using frequency demodulation (Abstract).
Regarding claims 5, 13,the combination of Gaikwad Mirfakhraei discloses the method of claim 1, wherein correcting frequency offset of at least the portion of the first packet based on a combination of the predicted CFO estimate and the first CFO estimate comprises: selecting a phase increment based on a combination of the predicted CFO estimate and the first CFO estimate([0021],[0033],[0035]); and processing the portion of the first packet using a phase rotation correction process using the selected phase increment([0021],[0033],[0035]).
Regarding claims 7, 15,the combination of Gaikwad Mirfakhraei discloses the method of claim 1, wherein the one or more characteristics of the first packet comprises a plurality of a current packet CFO estimate, one or more historical CFO estimates, a peak correlation, an acquisition time delay estimate, a preamble validation metric, or a decoded access address validation metric(Abstract, [0052]-[0065]).
Regarding claims 8,16, the combination of Gaikwad Mirfakhraei discloses the method of claim 1, further comprising: before receiving the first packet, receiving a predetermined number of packets; and training, based on characteristics of the predetermined number of packets, the MLM(Figs. 2, 6, Col. 7, lines 45- 55 and Col. 8, lines 14,30).
Allowable Subject Matter
Claims 2-4,6,10-12,14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/DADY CHERY/ Primary Examiner, Art Unit 2418
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