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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 17/952,203 (“203”), fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
Looking to claims 1, 14, 27 and 29, 203 fails to disclose each iteration of the one or more iterations is performed in accordance with a same set of machine learning parameters, and generating the set of machine learning parameters a second set of values of the latent variable or modifying the second plurality of channel estimations associated with the plurality of layers based at least in part on the set of machine learning parameters, as presently claimed, as it does not discuss machine learning parameters at all, let alone how they are update and re-used. Therefore, 203 does not provide support under 112(a) for the present claims and is therefore not entitled to a claim for benefit.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: likelihood module, encoder module and decoder module in claims 13 and 26. (note that the wherein clause of claim 13 does not fall under the processor/memory of claim 1 and therefore lacks sufficient structure).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer.
Claims 1-30 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 13, 25 and 28 of copending Application No. 17/952,203 in view of Xiong, et al. (Xiong, L., Zhang, Z. & Yao, D. A novel real-time channel prediction algorithm in high-speed scenario using convolutional neural network. Wireless Netw 28, 621–634 (2022)).
This is a provisional nonstatutory double patenting rejection.
Regarding claims 1, 14, 27, and 29, claims 1, 13, 25 and 28 of 203 disclose (note that for claims 14, 27 and 29, corresponding claims 13, 25 and 28 are mapped in identical manner to claim 1 and therefore have been omitted)
Claims 1, 14, 27 and 29 of present application
Claims 1, 13, 25 and 28 of 203
receive an assignment of a set of resources associated with a channel comprising a first subset of resources allocated for a data signal and a second subset of resources allocated for a reference signal;
receive an assignment of a set of resources associated with a channel comprising a first subset of resources allocated for a data signal and a second subset of resources allocated for a reference signal;
generate, a first plurality of channel estimations associated with respective layers of a plurality of layers of the channel for the set of resources;
generate a plurality of channel estimations associated with respective layers of a plurality of layers of the channel for the set of resource
The claims of 203 fail to disclose generating, from the reference signal received over the second subset of resources in accordance with a minimum mean square estimation operation, a first plurality of channel estimations associated with respective layers of a plurality of layers of the channel for the set of resources or generate, in accordance with a nonlinear two-dimensional interpolation of the channel for the set of resources, a second plurality of channel estimations and a plurality of values of a latent variable, the second plurality of channel estimations and the plurality of values associated with respective layers of the plurality of layers of the channel for the set of resources, wherein the nonlinear two-dimensional interpolation of the channel is based at least in part on the first plurality of channel estimations. In the same field of endeavor, 203 discloses:
generating, from the reference signal received over the second subset of resources in accordance with a minimum mean square estimation operation, a first plurality of channel estimations associated with respective layers of a plurality of layers of the channel for the set of resources or generate, in accordance with a nonlinear two-dimensional interpolation of the channel for the set of resources, a second plurality of channel estimations and a plurality of values of a latent variable, the second plurality of channel estimations and the plurality of values associated with respective layers of the plurality of layers of the channel for the set of resources, wherein the nonlinear two-dimensional interpolation of the channel is based at least in part on the first plurality of channel estimations (203 discloses generating a first set of channel estimates via a MMSE type algorithm, as MMSE may be used to generate values used for later interpolation [paragraph 0062, 0063 – MMSE used for initial channel estimates for MIMO layers; paragraph 0072 – UE initially determines h(i), the channel estimate, via traditional methods [such as MMSE] and may then perform interpolation/inpainting using a ML model to determine the channel characteristics of the whole channel; fig. 2 – it can be seen that the inpainting/interpolation is in both time and frequency and is therefore two dimensional; paragraph 0064 – non linear techniques may be used]. Finally, the initial estimate/second plurality of channel estimations may be used to calculate latent variables [paragraph 0079, 0083 – a set of latent variables is generated by calculating at least one latent variable per SISO pair/channel].)
perform a refinement operation on the second plurality of channel estimations comprising one or more iterations, wherein, to perform each iteration of the one or more iterations, the one or more processors are configured to cause the wireless communication device to generate respective gradients associated with the second plurality of channel estimations based at least in part on the second plurality of channel estimations for the second subset of resources and measured observations of the second subset of resources, generate, based at least in part on a first set of values of the plurality of values of the latent variable, the second plurality of channel estimations, and the respective gradients, a second set of values of the latent variable and modify the second plurality of channel estimations associated with the plurality of layers based at least in part on the second set of values of the latent variable, the second plurality of channel estimations and the respective gradients. (203 discloses iteratively generating gradients based on the second plurality of channel estimations and measured DMRS for the subset of symbols and using the gradients to generate a second set of latent variables and then modifying the second set of channel estimations based on the second set of latent variables (paragraph 0083 – nearly verbatim this claim language, noting that the performance is repeated for multiple sets of channel estimates from different reference signals based on the gradient, first set of latent variables and second set of latent variables].)
Therefore since 203 discloses interpolation and iterative refinement, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to perform two dimensional interpolation using ML on the first plurality of channel estimations to form a second plurality of channel estimations that span the entire time-frequency space of the channel and to further use the second plurality of channel estimations to iteratively calculate latent variables and update channel estimates and to repeat the process across multiple sets of reference signals. The motive to combine is to use interpolation to allow the entire channel to be characterized, even resources that do not have reference signals so transmissions on all channels can correctly be decoded based on interpolated best guess channel characteristics to improve performance and to further perform iterative improvements on the channel estimation to allow for refinement of the latent values to be considered in later iterations relative to the correction of the second plurality of channel estimations for better channel estimation accuracy.
The claims of 203 as modified by 203 fail to disclose each iteration of the one or more iterations is performed in accordance with a same set of machine learning parameters, and generating the set of machine learning parameters a second set of values of the latent variable or modifying the second plurality of channel estimations associated with the plurality of layers based at least in part on the set of machine learning parameters. In the same field of endeavor, Xiong discloses each iteration of the one or more iterations is performed in accordance with a same set of machine learning parameters, and generating the set of machine learning parameters a second set of values of the latent variable or modifying the second plurality of channel estimations associated with the plurality of layers based at least in part on the set of machine learning parameters. (Xiong discloses that channel estimation machine learning parameters/weights may remain constant across multiple iterations of the channel estimation until the update threshold related to the error in channel estimation is exceeded, at which time the CNN may be updated based on training data accumulated in the training buffer over multiple iterations of the channel estimation [pages 624-627, in particular page 626, section 3.3].)
Therefore, since Xiong suggests need based training, it would have been obvious to a person of ordinary skill in the art to combine the need based training of Xiong with the system of the claims of 203 as modified by 203 by holding the set of machine learning parameters constant over one or more iterations of the refinement based calculated update thresholds. The motive to combine is to reduce overhead by only performing update of machine learning parameters as needed.
Regarding claims 2, 15, 28 and 30, The claims of 203 as modified by 203 disclose the refinement operation is a first refinement operation and the set of machine learning parameters is a first set of machine learning parameters, and the one or more processors are configured to cause the wireless communication device to: perform a second refinement operation on the second plurality of channel estimations, the second refinement operation comprising one or more second iterations performed in accordance with a same second set of machine learning parameters, wherein the first refinement operation and the second refinement operation are associated with a respective attention calculation of a plurality of attention calculations (as discussed with respect to claim 1, supra, the refinements are repeated for each reference signal, forming a second refinement operation on a second plurality of channel estimates).
Regarding claims 3 and 16, The claims of 203 as previously modified by 203 fail to disclose the plurality of attention calculations comprises a cross-multiple-input and multiple-output calculation. In the same field of endeavor, another portion of 203 discloses the plurality of attention calculations comprises a cross-multiple-input and multiple-output calculation (paragraph 0072 – cross MIMO interference calculated)
Therefore, since 203 further discloses cross MIMO calculations, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to implement cross MIMO calculations in the system of 203 as previously modified by 203. The motive to combine is to allow more accurate calculations by considering cross-mimo interference.
Regarding claims 4 and 17, The claims of 203 as previously modified by 203 fail to disclose perform the minimum mean square estimation operation based on a resource configuration pattern of the second subset of resources allocated for the reference signal, the reference signal comprising a demodulation reference signal. In the same field of endeavor, another portion of 203 discloses perform the minimum mean square estimation operation based on a resource configuration pattern of the second subset of resources allocated for the reference signal, the reference signal comprising a demodulation reference signal (paragraph 0078 – estimation based on DMRS patters, paragraphs 0062-0063 – MMSE may be used for estimation)
Therefore, since 203 further discloses MMSE estimation based on DMRS patters, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine MMST DMRS pattern based estimation with the system of 203 as previously modified by 203 by estimating based on MMSE DMRS patters. The motive to combine is to allow more accurate calculations by considering different DMRS patterns may have different estimation characteristics.
Regarding claims 5 and 18, The claims of 203 as previously modified by 203 fail to disclose to generate the respective gradients, the one or more processors are configured to cause the wireless communication device to: generate respective sets of values of a residual variable based at least in part on a difference between the measured observations of the second subset of resources and the second plurality of channel estimations for the second subset of resources; and combine the respective sets of values of the residual variable, the second subset of resources, and a quantity of mask bits. In the same field of endeavor, another portion of 203 discloses to generate the respective gradients, the one or more processors are configured to cause the wireless communication device to: generate respective sets of values of a residual variable based at least in part on a difference between the measured observations of the second subset of resources and the second plurality of channel estimations for the second subset of resources; and combine the respective sets of values of the residual variable, the second subset of resources, and a quantity of mask bits (paragraph 0086, 0116 – estimation based on mask bits and residual values)
Therefore, since 203 further discloses difference estimation, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine difference estimation with the system of 203 as previously modified by 203 by determining residual values based on differences and one or more mask bits. The motive to combine is to allow more accurate calculations by using gradient descent based on difference while using a mask to limit calculations to relevant subsets of resources.
Regarding claims 6 and 19, The claims of 203 as previously modified by 203 discloses to generate the second set of values of the latent variable, the one or more processors are configured to cause the wireless communication device to combine the second plurality of channel estimations for the second subset of resources, the respective gradients, and respective values of the first set of values of the latent variable based at least in part on generation of the respective gradients (as discussed in the independent claim, supra, the generation of the second set of latent values depends on the gradient, the first set of latent values and the second plurality of channel estimates).
Regarding claims 7 and 20, The claims of 203 as previously modified by 203 fail to disclose to generate the second set of values of the latent variable, the one or more processors are configured to cause the wireless communication device to: model a correlation between resources of each resource block of a group of resource blocks and other resource blocks of the group of resource blocks. In the same field of endeavor, another portion of 203 discloses to generate the second set of values of the latent variable, the one or more processors are configured to cause the wireless communication device to model a correlation between resources of each resource block of a group of resource blocks and other resource blocks of the group of resource blocks (paragraphs 0096-0099)
Therefore, since 203 further discloses correlation based latent generation, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention to combine correlation based latent generation with the system of 203 as previously modified by 203 by using inter resource block correlation to calculate the second set of values of the latent variable. The motive to combine is to allow more accurate calculations inter resource block calculations.
Regarding claims 8 and 21, claim 5 of 203 discloses model a correlation between resources of each group of a plurality of groups of resources of the set of resources and other groups of the plurality of groups of resources, wherein each group of the plurality of groups of resources comprises a plurality of resource blocks.
Regarding claims 9 and 22, claim 6 of 203 discloses model a correlation between each layer of the plurality of layers for the set of resources.
Regarding claims 10 and 23, The claims of 203 as previously modified by 203 disclose modify the second plurality of channel estimations, the one or more processors are configured to cause the wireless communication device to combine the second set of values of the latent variable, the second plurality of channel estimations, and the respective gradients based at least in part on the set of machine learning parameters (as discussed in the independent claim, supra, the ML parameters are used in the calculation/modification of the second plurality of channel estimations)
Regarding claims 11 and 24, he claims of 203 as previously modified by 203 discloses the nonlinear two-dimensional interpolation of the channel is based at least in part on a machine learning model. (as discussed in the independent claim, supra, the ML parameters are used for the intepolation)
Regarding claims 12 and 25, claim 8 of 203 discloses the first plurality of channel estimations and the second plurality of channel estimations are associated with a plurality of single-input and single-output antenna pairs.
Regarding claims 13 and 26, The claims of 203 as previously modified by 203 disclose each refinement network executes according to the same set of machine learning parameters. Claim 11 of 203 further discloses each iteration of the one or more iterations is performed by a refinement network comprising a likelihood module, an encoder module, and a decoder module, the refinement network comprising a machine learning model.
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.
Claims 1-30 is/are rejected under 35 U.S.C. 103 as being obvious over Pratik, et al. (US Pre Grant Publication No. 2024/0113917) in view of Xiong, et al. (Xiong, L., Zhang, Z. & Yao, D. A novel real-time channel prediction algorithm in high-speed scenario using convolutional neural network. Wireless Netw 28, 621–634 (2022))
The applied reference has a common inventor with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2).
This rejection under 35 U.S.C. 103 might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C.102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B); or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement. See generally MPEP § 717.02.
Regarding claims 1, 14, 27, and 29, Pratik discloses an apparatus for wireless communication at a wireless communication device, comprising one or more memories; and one or more processors coupled with the one or more memories and configured to cause the wireless communication device to a method for wireless communication at a wireless communication device, comprising, a wireless communication device for wireless communication, comprising and aA non-transitory computer-readable medium storing code for wireless communication at a wireless communication device, the code comprising instructions executable by one or more processors to cause the wireless communication device to:
receive an assignment of a set of resources associated with a channel comprising a first subset of resources allocated for a data signal and a second subset of resources allocated for a reference signal (paragraph 0077; note also claim 1, which is a part of the original disclosure)
generate, a first plurality of channel estimations associated with respective layers of a plurality of layers of the channel for the set of resources (see claim 1, which is a part of the original disclosure).
generating, from the reference signal received over the second subset of resources in accordance with a minimum mean square estimation operation, a first plurality of channel estimations associated with respective layers of a plurality of layers of the channel for the set of resources or generate, in accordance with a nonlinear two-dimensional interpolation of the channel for the set of resources, a second plurality of channel estimations and a plurality of values of a latent variable, the second plurality of channel estimations and the plurality of values associated with respective layers of the plurality of layers of the channel for the set of resources, wherein the nonlinear two-dimensional interpolation of the channel is based at least in part on the first plurality of channel estimations (203 discloses generating a first set of channel estimates via a MMSE type algorithm, as MMSE may be used to generate values used for later interpolation [paragraph 0062, 0063 – MMSE used for initial channel estimates for MIMO layers; paragraph 0072 – UE initially determines h(i), the channel estimate, via traditional methods [such as MMSE] and may then perform interpolation/inpainting using a ML model to determine the channel characteristics of the whole channel; fig. 2 – it can be seen that the inpainting/interpolation is in both time and frequency and is therefore two dimensional; paragraph 0064 – non linear techniques may be used]. Finally, the initial estimate/second plurality of channel estimations may be used to calculate latent variables [paragraph 0079, 0083 – a set of latent variables is generated by calculating at least one latent variable per SISO pair/channel].)
perform a refinement operation on the second plurality of channel estimations comprising one or more iterations, wherein, to perform each iteration of the one or more iterations, the one or more processors are configured to cause the wireless communication device to generate respective gradients associated with the second plurality of channel estimations based at least in part on the second plurality of channel estimations for the second subset of resources and measured observations of the second subset of resources, generate, based at least in part on a first set of values of the plurality of values of the latent variable, the second plurality of channel estimations, and the respective gradients, a second set of values of the latent variable and modify the second plurality of channel estimations associated with the plurality of layers based at least in part on the second set of values of the latent variable, the second plurality of channel estimations and the respective gradients. (203 discloses iteratively generating gradients based on the second plurality of channel estimations and measured DMRS for the subset of symbols and using the gradients to generate a second set of latent variables and then modifying the second set of channel estimations based on the second set of latent variables (paragraph 0083 – nearly verbatim this claim language, noting that the performance is repeated for multiple sets of channel estimates from different reference signals based on the gradient, first set of latent variables and second set of latent variables].)
203 fails to disclose each iteration of the one or more iterations is performed in accordance with a same set of machine learning parameters, and generating the set of machine learning parameters a second set of values of the latent variable or modifying the second plurality of channel estimations associated with the plurality of layers based at least in part on the set of machine learning parameters. In the same field of endeavor, Xiong discloses each iteration of the one or more iterations is performed in accordance with a same set of machine learning parameters, and generating the set of machine learning parameters a second set of values of the latent variable or modifying the second plurality of channel estimations associated with the plurality of layers based at least in part on the set of machine learning parameters. (Xiong discloses that channel estimation machine learning parameters/weights may remain constant across multiple iterations of the channel estimation until the update threshold related to the error in channel estimation is exceeded, at which time the CNN may be updated based on training data accumulated in the training buffer over multiple iterations of the channel estimation [pages 624-627, in particular page 626, section 3.3].)
Therefore, since Xiong suggests need based training, it would have been obvious to a person of ordinary skill in the art to combine the need based training of Xiong with the system of 203 by holding the set of machine learning parameters constant over one or more iterations of the refinement based calculated update thresholds. The motive to combine is to reduce overhead by only performing update of machine learning parameters as needed.
Regarding claims 2, 15, 28 and 30, 203 disclose the refinement operation is a first refinement operation and the set of machine learning parameters is a first set of machine learning parameters, and the one or more processors are configured to cause the wireless communication device to: perform a second refinement operation on the second plurality of channel estimations, the second refinement operation comprising one or more second iterations performed in accordance with a same second set of machine learning parameters, wherein the first refinement operation and the second refinement operation are associated with a respective attention calculation of a plurality of attention calculations (as discussed with respect to claim 1, supra, the refinements are repeated for each reference signal, forming a second refinement operation on a second plurality of channel estimates).
Regarding claims 3 and 16, 203 discloses the plurality of attention calculations comprises a cross-multiple-input and multiple-output calculation (paragraph 0072 – cross MIMO interference calculated)
Regarding claims 4 and 17, 203 discloses perform the minimum mean square estimation operation based on a resource configuration pattern of the second subset of resources allocated for the reference signal, the reference signal comprising a demodulation reference signal (paragraph 0078 – estimation based on DMRS patters, paragraphs 0062-0063 – MMSE may be used for estimation)
Regarding claims 5 and 18, 203 discloses to generate the respective gradients, the one or more processors are configured to cause the wireless communication device to: generate respective sets of values of a residual variable based at least in part on a difference between the measured observations of the second subset of resources and the second plurality of channel estimations for the second subset of resources; and combine the respective sets of values of the residual variable, the second subset of resources, and a quantity of mask bits (paragraph 0086, 0116 – estimation based on mask bits and residual values)
Regarding claims 6 and 19, 203 discloses to generate the second set of values of the latent variable, the one or more processors are configured to cause the wireless communication device to combine the second plurality of channel estimations for the second subset of resources, the respective gradients, and respective values of the first set of values of the latent variable based at least in part on generation of the respective gradients (as discussed in the independent claim, supra, the generation of the second set of latent values depends on the gradient, the first set of latent values and the second plurality of channel estimates).
Regarding claims 7 and 20, 203 discloses to generate the second set of values of the latent variable, the one or more processors are configured to cause the wireless communication device to model a correlation between resources of each resource block of a group of resource blocks and other resource blocks of the group of resource blocks (paragraphs 0096-0099)
Regarding claims 8 and 21, claim 5 of 203 (a part of the original disclosure) discloses model a correlation between resources of each group of a plurality of groups of resources of the set of resources and other groups of the plurality of groups of resources, wherein each group of the plurality of groups of resources comprises a plurality of resource blocks.
Regarding claims 9 and 22, claim 6 of 203 (a part of the original disclosure) discloses model a correlation between each layer of the plurality of layers for the set of resources.
Regarding claims 10 and 23, 203 discloses modify the second plurality of channel estimations, the one or more processors are configured to cause the wireless communication device to combine the second set of values of the latent variable, the second plurality of channel estimations, and the respective gradients based at least in part on the set of machine learning parameters (as discussed in the independent claim, supra, the ML parameters are used in the calculation/modification of the second plurality of channel estimations)
Regarding claims 11 and 24, 203 discloses the nonlinear two-dimensional interpolation of the channel is based at least in part on a machine learning model. (as discussed in the independent claim, supra, the ML parameters are used for the interpolation)
Regarding claims 12 and 25, claim 8 of 203 (a part of the original disclosure) discloses the first plurality of channel estimations and the second plurality of channel estimations are associated with a plurality of single-input and single-output antenna pairs.
Regarding claims 13 and 26, 203 discloses each iteration of the one or more iterations is performed by a refinement network comprising a likelihood module, an encoder module, and a decoder module, the refinement network comprising a machine learning model; and each refinement network executes according to the same set of machine learning parameters (see discussion supra, note also claim 11 of 203, a part of the original disclosure).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Mohammadi, et al. (US Pre Grant Publication No. 2024/0364562) – disclosing ML based channel estimation
Kwon, et al. (US Pre Grant Publication No. 2022/0337455) – disclosing time/frequency interpolation as a part of performing neural network channel estimation
Kim, et al. (US Pre Grant Publication No. 2025/0070837) – disclosing ML based channel estimation
Xue, et al. (P. Xue and Y. Zhao, "A Self-Learning Channel Modeling Approach Based on Explainable Neural Network," in IEEE Wireless Communications Letters, vol. 12, no. 7, pp. 1289-1293, July 2023) – disclosing XAI methods for channel estimation
Arvinte, et al. (Marius Arvinte, Jonathan I. Tamir, MIMO channel estimation using score-based generative models,” IEEE Trans. Wireless Commun., vol. 22, no. 6, pp. 3698–3713, June 2023) – disclosing ML channel estimation
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/CHRISTOPHER M CRUTCHFIELD/Primary Examiner, Art Unit 2466