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 .
Response to Arguments
Applicant’s arguments with respect to independent claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Note: Examiner interpretation is that the one complex neural network which provides analysis data(A) and generates the parameters for transmitter(T) can be considered as two separate neural networks ((A, T) or (A+T, T) or (A, A+T)).
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 4, 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over D1 US 20200225321 A1 in view of D0 US 20220082654 A1.
Regarding claims 1, 23 D1 teaches
1, 23. (New) A network entity apparatus comprising:
a processor;(1422 or 1464)
wireless communication hardware; and[0119]
computer-readable storage media storing instructions that, when executed by the processor, cause the processor and the wireless communication hardware to(inherent)
operate as a radar signal receiver of a bistatic radar(fig. 14) by receiving a reflected version of a radar signal[0029] and generating radar data by processing the reflected version of the radar signal using a deep neural network[0031-0032][0051], the radar signal transmitted by a second device associated with a transmitter of the bistatic radar and reflected off an object, the radar data comprising information about the object; (fig. 14)and
operate as a feedback signal transmitter by generating a feedback signal using the deep neural network and transmitting the feedback signal to the recipient/second device, the feedback signal being based on the radar data.[0116]
[0090] explicitly teaches single processor communicating with multiple receivers and generating single encoded message which inherently means at least transmitting different inputs from multiple receivers to the processing device
But does not teach while D0 teaches
the radar signal being generated using a first deep neural network associated with a second device, the radar signal being transmitted by a radar signal transmitter of the bistatic radar and reflected off an object;[0005-0007]
generating radar data by processing the reflected version of the radar signal using a second deep neural network that has been jointly trained with the first deep neural network[0005-0007](generated radar signal (obtained with neural network) after feedback procedure is retransmitted and repeated for multiple pulses. )
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D0 in order decrease the interference and noise. [0007]
D1 also teaches
4. (Currently amended) The method of claim 1, wherein:
the radar data comprises at least one of the following:
raw digital samples of the reflected version of the radar signal;
range-Doppler maps; or
interferometry data; and
information within the radar data comprises at least one of the following:
position information associated with the object;
movement information associated with the object; [D1: 0029]
size information associated with the object; or
material composition information associated with the object.
22. (New) The method of claim 1, wherein: the device comprises: a user equipment; or a base station.(D1: fig. 14)
Claim(s) 2, 3, 7-10, 12-13 and 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over D1 US 20200225321 A1 and D0 in view of D2 US 20200293860 A1.
Regarding claim 7 D1 teaches
7. (Currently amended) A method performed by a first device(1420), the method comprising:
operating as a radar signal transmitter(1424) of a bistatic radar(fig. 14)
transmitting the radar signal, the radar signal reflected off an object; and(fig. 14)[0029]
Also teaches that recipient receives feedback signal from a second device the feedback signal being based on radar data generated by the second device by processing the radar signal.
And radar signal being processed by neural network at second device.
but does not explicitly teach
operating as a feedback signal receiver by
receiving a feedback signal from a second device and determining information about the object by processing the radar data using the deep neural network,
generating a radar signal using a deep neural network
D1 also does not teach but D0 teaches
generating radar data by processing the reflected version of the radar signal using a second deep neural network that has been jointly trained with the first deep neural network[D0: 0005-0007](generated radar signal (obtained with neural network) after feedback procedure is retransmitted and repeated for multiple pulses. )
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D0 in order decrease the interference and noise. [D0: 0007]
D2 teaches generating a radar signal using a deep neural network [D2: 0022]
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D2 in order encode information/behavior which will allows minimization of the noise. (D2: [0022])
Although D1 does not explicitly teach “operating as a feedback signal receiver by
receiving a feedback signal from a second device and determining information about the object by processing the radar data using the deep neural network” D1 explicitly teaches receiving signal by second device and sending some signals associated with the received signal to the recipient and if the recipient is at the first device it would be obvious to send it to the first device. Also, D1 teaches performing classification analysis using neural network on second device instead of the first device but this is just matter of the optimization of the computational powers. If nodes do not include high computational power it would be obvious to send as feedback raw signals so main computers at recipient cite perform neural network analysis.
Regarding claim 12 D1 teaches
12. (Currently amended) The method of claim 7, further comprising:
selecting multiple devices[0100], the multiple devices including the second device; and [0100](second radar sensor)
to the multiple devices to enable the multiple devices to receive reflected versions of the radar signal and transmit respective feedback signals.[0100 with previous rejection using fig. 14 and feedback generation]
But does not explicitly say
transmitting an activation message
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 to transmit activation message to all the sensors of interest in order to turn on the sensors of interest.
13. (Currently amended) The method of claim 7, further comprising:
receiving a reflected version of the radar signal or a reflected version of the feedback signal (D1 [0029]); and
determining additional information about the object by processing the reflected version of the radar signal or the reflected version of the feedback signal using the deep neural network.(D1 [0029])
17. (New) The method of claim 7, wherein:
the device comprises: a user equipment; or a base station.(D1 fig. 14)
18. (New) The method of claim 7, further comprising:
modulating a reference signal onto the radar signal; or modulating the radar signal onto the reference signal.[D1: 0029 with 0089 and 0106](0029 teaches FMCW signal and [0089 0106] comparing it with reference signals which means reference signal should be FMCW too )
19. (New) The method of claim 18, wherein:
the reference signal comprises an uplink reference signal or a downlink reference signal.(implicit as reference signal can have only two origins whether uplink or downlink , the fact that D1 teaches comparison to reference signal one of them is used. According to the [0089 and 0106] denoiser which is another processing element receives the reference signal and original signal and hence can be considered downlink especially [0090] teaches receiving multiple radar inputs from different receivers and generating single encoded representation and hence every different receiver send the signal and reference signal to the preprocessing unit 800 in [0090] which is then denoised and encoded , hence downlink is implicit)
20. (New) The method of claim 19, wherein:
the reference signal comprises the uplink reference signal; and the uplink reference signal comprises a sounding reference signal[D1: 0089](regarding uplink or downlink it is just matter of design choice regarding where to process the data )
21. (New) The method of claim 19, wherein:
the reference signal comprises the downlink reference signal; and the downlink [D1: 0090+0089]reference signal comprises a primary synchronization signal, a secondary synchronization signal, a demodulation reference signal, a phase-tracking reference signal[D1: 0088][D1: 0089](phase adjustment template), a channel-state-information reference signal, or a tracking reference signal.
Regarding claims 2, 8 D1 teaches
a first deep neural network (see rejection of claim 1 [0031-0032][0051])
generating of the radar data comprises generating the radar data using the first deep neural network(generating classification data )
D0 teaches
the neural network comprises a first receiver deep neural network data output/input and a second transmitter deep neural network data output/input; the generating of the radar data comprises generating the radar data using the first receiver deep neural network; and the generating of the feedback signal comprises generating the feedback signal using the second transmitter deep neural network.[0005-0007] [0028]
D0/D1 does not teach but D2 teaches
2, 8. (Original) The method of claim 1, wherein:
the second deep neural network comprises a first receiver deep neural network and a second transmitter deep neural network; the generating of the radar data comprises generating the radar data using the first receiver deep neural network; (D2 clarifies that different neurons in neural network correspond to different outputs (fig. 1))
D2 also teaches
3, 9. (Currently amended) The method of claim 1, wherein the generating of the feedback signal comprises: accepting, by the second deep neural network, the radar data; and generating, by the second deep neural network, digital samples[D2: 0050](implicit digital circuitry generates digital samples) based on the radar data, the digital samples associated with the feedback signal.[D2: 0022]
Claim 9, digital samples based on the at least one radar waveform property; and generating the radar signal using the digital samples.[D2: 0064](implicit/obvious with D2 [0022])
10. (Currently amended) The method of claim 9, wherein: the at least one radar waveform property comprises at least one of the following: a center frequency; a bandwidth; a pulse-repetition frequency; a beamforming configuration; or a modulation type.[D2: 0064]
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D2 in order generate transmission signal that classifies information associated with the one or more signals and/or output information associated with the one or more signals and decreases the noise.
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over D1 US 20200225321 A1 and D0 in view of D3 US 20180025272 A1.
Regarding claim 5 D1 does not teach but D3 teaches
5. (Currently amended) The method of any previous claim1, further comprising:
receiving a configuration message from the second device; and modifying a neural network formation configuration of the second deep neural network based on the configuration message.[D3: 0160,0186]
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D3 in order to perform neural network on desired node or server when local node computational power is not sufficient.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over D1 US 20200225321 A1 and D0 in view of D4 US 20190064341 A1.
Regarding claim 6 D1 does not teach but D4 teaches
6. wherein the radar signal is associated with a first frequency band; and
the transmitting of the feedback signal comprises at least one of the following:
transmitting the feedback signal using a second frequency band that is different than the first frequency band; or transmitting the feedback signal using an assigned timeslot.[D4: 0027]
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D4 in order differentiate the signals from different nodes.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over D1 US 20200225321 A1 and D0 in view of D2 US 20200293860 A1 and further in view of D5 US 20190129026 A1
Regarding claim 11 D1/D0/D2 does not teach but D5 teaches
11. (Currently amended) The method of any one of claims 7 , further comprising:
modeling radio frequency (RF) signal propagation paths within an operating environment based on the information about the object; and [D5: 0835]
adjusting beamforming configurations associated with wireless communication based on the modeled RF signal propagation paths.(D5:[0927], [0849][0886][0890])
It would be obvious to one of ordinary skills in the art at the time of the filing to modify invention by D1 with invention by D5 in order to perform energy efficient radar interrogation/communication in complex environments.
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 HELENA SERAYDARYAN whose telephone number is (571)270-0706. The examiner can normally be reached on M-T, 7:30-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha Desai can be reached on (571)270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HELENA H SERAYDARYAN/ Examiner, Art Unit 3648
/RESHA DESAI/ Supervisory Patent Examiner, Art Unit 3648
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