Detailed Office 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 communication dated 11/12/24
Original claims 1-20 are pending.
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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,567,889. Although the claims at issue are not identical, they are not patentably distinct from each other because a comparison made between claim 1 of the instant application and claims 1, 14 and 18 of Patent reveal the claims at issue define essentially the same invention in different language. Thus, one of ordinary skill in the art would conclude other than minor obvious variations, that the invention defined in claim 1, 14 and 18 at issue is an obvious variation of the invention defined in the claims of the Patent. Thus, examiner asserts the difference describe a subset of all possible conditions being monitored in the Patented claim 1, 14 and 18 These differences are not sufficient to render the claims patentably distinct and therefore a terminal disclaimer is required.
As per dependent claims 2-13, 15-17 and 19-20 , which may correspond dependent claims 2-13, 15-17 and 190- of the patent are analyzed and rejected same ration as clams 1, 14 and 18.
Instant Application 18/944-405
1. A method
of recovering transmitted symbols,
the method comprising: receiving a signal
comprising a codeword, the signal having been affected by channel effects including distortion and noise; identifying a solution space for recovering the transmitted symbols, wherein identifying a solution space comprises finding a sampling center using a full matrix W for minimum mean square error; generating a vector v′, to generate noise, having a predefined variance; applying the vector v′ to apply noise to the solution space; gathering original samples from the solution space, which includes noise from the perturbation vector v′ and symbol information from the received signal to find probabilities for symbols; and using the probabilities, recovering the symbols.
2. The method of claim 1, further comprising: obtaining a change vector; apply the change vector to the vector to create a dependent vector; apply the dependent vector to the solution space to generate additional samples different from the original samples; gathering the additional samples from the solution space to find additional probabilities for symbols; and wherein recovering the symbols comprises using the probabilities and the additional probabilities.
3. The method of claim 1, wherein the vector v′ comprises a zero mean.
4. The method of claim 1, wherein the vector v′ comprises independent and identically distributed random variables.
5. The method of claim 1, wherein the vector v′ comprises Gaussian white noise.
6. The method of claim 1, wherein applying the vector v′ to the solution space comprises translating the vector v′ to the sample space using the full matrix W.
7. The method of claim 1, wherein the probabilities are expressed as log likelihood ratios.
8. The method of claim 1, wherein gathering samples from the solution space comprises using stochastic sampling.
9. The method of claim 1, wherein gathering samples from the solution space comprises using iterative stochastic sampling.
10. The method of claim 1, wherein gathering samples from the solution space comprises using λke≈12maxx∈𝕏k+{-1σ2.Math.y-Hs.Math.2+x∖kT.Math.λ∖ka}-12maxx∈𝕏k-{-1σ2.Math.y-Hs.Math.2+x∖kT.Math.λ∖ka}.
11. The method of claim 1, wherein gathering samples from the solution space comprises using λke≈12maxx∈𝕃k+{-1σ2.Math.y-Hs.Math.2+x∖kT.Math.λ∖ka}-12maxx∈𝕃k-{-1σ2.Math.y-Hs.Math.2+x∖kT.Math.λ∖ka}.
12. The method of claim 1, wherein W=(H.sup.HH+σ.sub.n.sup.2I).sup.−1H.sup.H.
13. The method of claim 1, wherein the predefined variance is defined by spacings between communication constellation points.
14. A computing system comprising: one or more processors; and one or more computer-readable media having stored thereon instructions that are executable by the one or more processors to configure the computer system to recover transmitted symbols, including instructions that are executable to configure the computer system to perform at least the following: receive a signal comprising a codeword, the signal having been affected by channel effects including distortion and noise; identify a solution space for recovering the transmitted symbols, wherein identifying a solution space comprises finding a sampling center using a full matrix W for minimum mean square error; generate a vector v′, to generate noise, having a predefined variance; apply the vector v′ to apply noise to the solution space; gather original samples from the solution space, which includes noise from the vector v′ and symbol information from the received signal to find probabilities for symbols; and using the probabilities, recover the symbol.
15. The computing system of claim 14, wherein the one or more computer-readable media further have stored thereon instructions that are executable by the one or more processors to configure the computer system to perform at least the following: obtain a change vector; apply the change vector to the vector to create a dependent vector; apply the dependent vector to the solution space to generate additional samples different from the original samples; gather the additional samples from the solution space to find additional probabilities for symbols; and wherein recovering the symbols comprises using the probabilities and the additional probabilities.
16. The computing system of claim 14, wherein gathering samples from the solution space comprises using stochastic sampling.
17. The computing system of claim 14, wherein gathering samples from the solution space comprises using iterative stochastic sampling.
18. A receiver comprising: a MIMO detector configured to receive a signal comprising a codeword, the signal having been affected by channel effects including distortion and noise; a channel decoder coupled to the MIMO detector, the channel decoder configured to perform the following: identify a solution space for recovering transmitted symbols, wherein identifying a solution space comprises finding a sampling center using a full matrix W for minimum mean square error; generate a vector v′, to generate noise, having a predefined variance; apply the vector v′ to apply noise to the solution space; gather original samples from the solution space, which includes noise from the vector v′ and symbol information from the received signal to find probabilities for symbols; and using the probabilities, recover the symbols.
19. The receiver of claim 18, further comprising a feedback loop, and wherein the channel decoder is configured to perform iterative stochastic sampling.
20. The receiver of claim 18, wherein the channel decoder is further configured to perform the following: obtain a change vector; apply the change vector to the vector v′ to create a dependent vector; apply the dependent vector to the solution space to generate additional samples different from the original samples; gather the additional samples from the solution space to find additional probabilities for symbols; and wherein recovering the symbols comprises using the probabilities and the additional probabilities.
U.S. Patent No. 12567889
1. A method of recovering transmitted symbols,
the method comprising: receiving a signal comprising a codeword, the signal having been affected by channel effects including distortion and noise; identifying a solution space for recovering the transmitted symbols, wherein identifying a solution space comprises finding a sampling center using a full matrix W for minimum mean square error; generating a vector v′, to generate noise, having a variance ρσ.sub.n.sup.2 where σ.sub.n.sup.2 is a factor that defines power of added noise and where ρ is a scaling parameter that controls a size of a noise sampleable space; applying the vector v′ to the solution space; gathering original samples from the solution space, which includes noise from a perturbation vector v′ and symbol information from the received signal to find probabilities for symbols; and using the probabilities, recovering the symbols.
2. The method of claim 1, further comprising: obtaining a change vector; apply the change vector to the vector to create a dependent vector; apply the dependent vector to the solution space to generate additional samples different from the original samples; gathering the additional samples from the solution space to find additional probabilities for symbols; and wherein recovering the symbols comprises using the probabilities and the additional probabilities.
3. The method of claim 1, wherein the vector v′ comprises a zero mean.
4. The method of claim 1, wherein the vector v′ comprises independent and identically distributed random variables.
5. The method of claim 1, wherein the vector v′ comprises Gaussian white noise.
6. The method of claim 1, wherein applying the vector v′ to the solution space comprises translating the vector v′ to the sample space using the full matrix W.
7. The method of claim 1, wherein the probabilities are expressed as log likelihood ratios.
8. The method of claim 1, wherein gathering samples from the solution space comprises using stochastic sampling.
9. The method of claim 1, wherein gathering samples from the solution space comprises using iterative stochastic sampling.
10. The method of claim 1, wherein gathering samples from the solution space comprises using λke≈12maxx∈𝕏k+{-1σ2.Math.y-Hs.Math.2+x\kT.Math.λ\ka}-
12maxx∈𝕏k-{-1σ2.Math.y-Hs.Math.2+x\kT.Math.λ\ka}.
11. The method of claim 1, wherein gathering samples from the solution space comprises using λke≈12maxx∈𝕃k+{-1σ2.Math.y-Hs.Math.2+x\kT}-12maxx∈𝕃k-{-1σ2.Math.y-Hs.Math.2+x\kT}.
12. The method of claim 1, wherein W=(HHH+σn2I)-1HH.
13. The method of claim 1, wherein ρ=4.
14. A computing system comprising: one or more processors; and one or more non-transitory computer-readable media having stored thereon instructions that are executable by the one or more processors to configure the computer system to recover transmitted symbols, including instructions that are executable to configure the computer system to perform at least the following: receive a signal comprising a codeword, the signal having been affected by channel effects including distortion and noise; identify a solution space for recovering the transmitted symbols, wherein identifying a solution space comprises finding a sampling center using a full matrix W for minimum mean square error; generate a vector v′, to generate noise, having a variance ρσn2whereσn2 is a factor that defines power of added noise and where p is a scaling parameter that controls a size of a noise sampleable space; apply the vector v′ to the solution space; gather original samples from the solution space, which includes noise from the vector v′ and symbol information from the received signal to find probabilities for symbols; and using the probabilities, recover the symbol.
15. The computing system of claim 14, wherein the one or more non-transitory computer-readable media further have stored thereon instructions that are executable by the one or more processors to configure the computer system to perform at least the following: obtain a change vector; apply the change vector to the vector to create a dependent vector; apply the dependent vector to the solution space to generate additional samples different from the original samples; gather the additional samples from the solution space to find additional probabilities for symbols; and wherein recovering the symbols comprises using the probabilities and the additional probabilities.
16. The computing system of claim 14, wherein gathering samples from the solution space comprises using stochastic sampling.
17. The computing system of claim 14, wherein gathering samples from the solution space comprises using iterative stochastic sampling.
18. A receiver comprising: a MIMO detector configured to receive a signal comprising a codeword, the signal having been affected by channel effects including distortion and noise; a channel decoder coupled to the MIMO detector, the channel decoder configured to perform the following: identify a solution space for recovering transmitted symbols, wherein identifying a solution space comprises finding a sampling center using a full matrix W for minimum mean square error; generate a vector v′, to generate noise, having a variance ρσn2whereσn2 is a factor that defines power of added noise and where p is a scaling parameter that controls a size of a noise sampleable space; apply the vector v′ to the solution space; gather original samples from the solution space, which includes noise from the vector v′ and symbol information from the received signal to find probabilities for symbols; and using the probabilities, recover the symbols.
19. The receiver of claim 18, further comprising a feedback loop, and wherein the channel decoder is configured to perform iterative stochastic sampling.
20. The receiver of claim 18, wherein the channel decoder is further configured to perform the following: obtain a change vector; apply the change vector to the vector v′ to create a dependent vector; apply the dependent vector to the solution space to generate additional samples different from the original samples; gather the additional samples from the solution space to find additional probabilities for symbols; and wherein recovering the symbols comprises using the probabilities and the additional probabilities.
Conclusion
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/ABDULLAHI E SALAD/Primary Examiner, Art Unit 2466
