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 paper is responsive to the response to non-final rejection of December 22, 2025 for the patent application filed September 29, 2023.
Claims 1-30 are currently pending.
Claims 1, 9, 17 and 24 have been amended.
No claims have been cancelled.
Response to Amendment
The amendments comply with the requirements of 37 CFR 1.121(c), find support in the originally-filed specification and are accepted.
Response to Arguments
Applicant’s arguments with respect to claims 1, 9, 17 and 24 filed March 10, 2026 have been fully considered but they are not persuasive.
In particular, the amendments regarding “pruning the second set of candidate resources based at least in part on a shaping pattern of the one or more quality metrics satisfying a quality tolerance threshold, wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs”.
Examiner notes that the broadest reasonable interpretation of the claims supports Jalali rejecting “the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs” because Jalali relates to a plurality of candidates wherein a Gaussian probability reflects an SNR (quality metric). Examiner further notes that the Applicant’s specification defines a shaping pattern in terms of “a shaping pattern of the one or more quality metrics satisfying a quality tolerance threshold” and references Fig. 8. As shown, the Quality metric per CCE is on the Y axis and the different CCEs are in order on the X axis to form a path/shape of the quality. Likewise, Jalali teaches a “shaping pattern” in the form of a Gaussian type curve that is defined by the qualities across more than one CCE.
Applicant’s Figure 8, path 830 illustrates a shaping pattern satisfying a quality tolerance threshold:
PNG
media_image1.png
213
902
media_image1.png
Greyscale
Examiner suggests that the claims be amended to more accurately reflect Fig. 8 as discussed in the Examiner Interview because the term “pattern” is defined in the Oxford English dictionary as “something shaped or designed to serve as a model from which a thing is made; an outline or original. This sense is figurative, meaning an example to be imitated.” Applicant’s claims appear to be using the term “pattern” not as representative of something that can be imitated, but rather as the resulting shape of CCE qualities in a path and “pattern” in terms of a “shape” to be analyzed. Therefore, Examiner notes that the quality metric of ONLY ONE CCE which is within the claim scope would be a POINT and not a path, shape or pattern. Therefore, under the broadest reasonable interpretation of the claims, Jalali teaches a “shaping pattern” of quality metric one or more CCEs in light of the specification.
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.
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 1-30 are rejected under 35 U.S.C. 103 as being unpatentable over US Pat. Pub. 20130294547 to Richard A. Lane et al. (hereinafter Lane) in view of A. Jalali and Z. Ding, "Joint Detection and Decoding of Polar Coded 5G Control Channels," in IEEE Transactions on Wireless Communications, vol. 19, no. 3, pp. 2066-2078, March 2020, doi: 10.1109/TWC.2019.2962113 (hereinafter Jalali).
Regarding claim 1, Lane in view of Jalali teaches A user equipment (UE) for wireless communication, (Lane, Fig. 1, UEs 120) comprising:
one or more memories; (Lane Fig. 7, memory 750)
and
one or more processors coupled to the one or more memories, (Lane Fig. 7 and para. [0065] processor within controller 740) the one or more memories including instructions executable by the one or more processors (Lane Fig. 7 para. [0065]) to cause the UE to:
identify one or more quality metrics associated with a plurality of respective control channel elements (CCEs) of a first set of candidate resources that partially overlaps with a second set of candidate resources, wherein the first set of candidate resources is associated with a first aggregation level and the second set of candidate resources is associated with a second aggregation level; (Lane para. [0068]-[0070] teaches computing quality metrics associated with CCEs. Lane para. [0057] – [0058] and Fig. 3 teaches a first and second set of candidate resources “aliasing” interpreted as overlapping and occupying aggregation level 1 and aggregation level 2 at 345 and 355 at arrow:
PNG
media_image2.png
449
882
media_image2.png
Greyscale
and prune the second set of candidate resources [[based at least in part on a shaping pattern]] of the one or more quality metrics satisfying a quality tolerance threshold, [[wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs]]. (Lane para. [0048] teaches “The next two stages employ a “best” candidate screening process. At 550, the number of candidates is reduced by applying the constraint that each candidate CCE set in the CCE space can only produce one output, i.e., one PDCCH. In other words, a uniqueness constraint is applied which limits each set of CCEs forming a PDCCH to result in only one DCI being output. This constraint is used to prune down the number of candidates after CRC match using a Viterbi QM, or a combination of Viterbi QMs, calculated for each candidate to select only the best candidate from up to 16 candidates, i.e., the eight possible tail-biting solutions from trace-back and the two DCI sizes.”)
Lane does NOT teach pruning based on a shaping pattern of the quality metrics... wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs
In the analogous art of 3GPP 5G wireless communications, Jalali teaches pruning based on a shaping pattern of the quality metrics wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs. (Jalali teaches pruning candidates by eliminating “false candidates” on page 2071, second column, third paragraph, and using the feature metric f described on page 2072 second paragraph, which is a quality metric reliant on SNR. Jalali further teaches on page 2072, second column, fourth paragraph that a Gaussian probability density function models f very well:
PNG
media_image3.png
431
447
media_image3.png
Greyscale
Jalali teaches on page 2074, first column, second paragraph “Let us denote a = [f1, ··· , fC ] as the array containing the fractional metrics for all the C candidates and let fmin be the minimum value in the array. Based on probability density functions obtained in Fig. 5, the candidate with lowest fractional metric is likely to be the true candidate. However, there is a possibility that p(f|H1) generates a larger f than p(f|H0). The probability of this happening, increases as SNR goes lower and the two density functions get closer to each other. Therefore, we claim the fmin as the true candidate only if it deviates large enough from the rest of candidates.” Examiner interprets the shaping pattern as taught by the probability density function shapes, such as a well-known Gaussian curve of f as a shaping pattern of the quality metric because f is a quality metric based on signal to noise ratios.
Examiner notes that the broadest reasonable interpretation of the claims supports Jalali rejecting “the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs” because Jalali relates to a plurality of candidates wherein a Gaussian probability reflects an SNR (quality metric). Examiner further notes that the Applicant’s specification defines a shaping pattern in terms of “a shaping pattern of the one or more quality metrics satisfying a quality tolerance threshold” and references Fig. 8.
Figure 8, illustrates a shaping pattern satisfying a quality tolerance threshold:
PNG
media_image1.png
213
902
media_image1.png
Greyscale
Examiner suggests that the claims be amended to more accurately reflect Fig. 8 because the term “pattern” is defined in the Oxford English dictionary as “something shaped or designed to serve as a model from which a thing is made; an outline or original. This sense is figurative, meaning an example to be imitated.” Applicant’s claims appear to be using the term “pattern” not as representative of something that can be imitated, but rather as the resulting shape of applying the quality tolerance range, which is not repeatable per se and therefore is only “pattern” in terms of a “shape” to be analyzed. Under the broadest reasonable interpretation of the claims, therefore, Jalali teaches a “shaping pattern” in light of the specification.
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a quality metric with a shaping pattern. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 2, Lane does NOT teach The UE of claim 1, wherein the quality tolerance threshold is associated with a quality tolerance range, and wherein the shaping pattern of the one or more quality metrics satisfying the quality tolerance threshold includes the one or more quality metrics being within the quality tolerance range.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches wherein the quality tolerance threshold is associated with a quality tolerance range, and wherein the shaping pattern of the one or more quality metrics satisfying the quality tolerance threshold includes the one or more quality metrics being within the quality tolerance range. (Jalali page 2072 teaches in the second column, second paragraph “The gray bins of histogram in Fig. 5, corresponds to the event H1 when a valid polar codeword of length 128 was received by the UE, and white bins corresponds to the case when a random bit block of same size was received by the UE, denoted by hypothesis H0. As expected, f tends to be smaller under hypothesis H1. The two histograms become more distinguishable as the SNR and channel estimation improves. Conversely, the separation becomes less pronounced under inaccurate channel estimation.” Jalali page 2073 teaches that a “clustering problem” and fmin is the true candidate “only if it deviates large enough from the rest of the candidates” as explained in Jalali, page 2074 first column, second paragraph. Examiner interprets the quality tolerance range as within the bounds of the “detector” based on the quality metric f as taught on page Jalali page 2074, first column.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 3, Lane does NOT teach The UE of claim 1, wherein the quality tolerance threshold is based at least in part on the first aggregation level.
In the analogous art of 3GPP 5G wireless communication, Jalali teaches wherein the quality tolerance threshold is based at least in part on the first aggregation level. (Jalali teaches on page 2074, second column, a candidate trimming algorithm that takes into account the first aggregation level:
PNG
media_image4.png
294
503
media_image4.png
Greyscale
As shown, the Jalali algorithm takes into account all DCI candidates which would necessarily include the first aggregation level. Examiner notes that Jalali equates an aggregation level with a DCI candidates on page 2069, first column, third paragraph.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a tolerance range based on a first aggregation level. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 4, Lane does NOT teaches The UE of claim 1, wherein a quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches wherein a quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level. (Jalali teaches that the quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level by teaching that f which takes into account the signal to noise level of the candidates associated with the first aggregation level. Jalali Fig. 3 illustrates the first aggregation level and the UE specific search space:
PNG
media_image5.png
456
620
media_image5.png
Greyscale
Jalali page 2069 second column, second paragraph teaches a joint DCI detector in the UE specific search space that includes aggregation level 1. The quality metric f as taught on page Jalali page 2074, first column and a quantity fmin is used in the algorithm for PDCCH candidate trimming shown above.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a tolerance range based on a first aggregation level. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 5, Lane teaches The UE of claim 1, wherein the first set of candidate resources fully overlaps with one or more third sets of candidate resources associated with a third aggregation level. (Lane para. [0029] and Fig. 3 teaches six candidate resources and aggregation levels of “one, two, four, and eight respectively” for UE search spaces 340, 350, 360 and 370. As shown the search spaces overlap:
PNG
media_image2.png
449
882
media_image2.png
Greyscale
Regarding claim 6, Lane teaches The UE of claim 5, wherein the first aggregation level is greater than the third aggregation level, and wherein the one or more memories further include instructions executable by the one or more processors to cause the UE to: (Lane illustrates in Fig. 3 above that the aggregation levels are different sizes, with aggregation level 8 being the largest).
perform, based at least in part on the first aggregation level being greater than the third aggregation level, a first decode operation on the first set of candidate resources [[before selectively performing a second decode operation]] on the one or more third sets of candidate resources. (Lane para. [0058] teaches “the UE needs to decide whether to replace the existing stored message in the output buffer with the new candidate or to discard the new candidate. This decision can be made by comparing quality metrics associated with the stored message and the new candidate message. In one embodiment of the invention, the quality metric used in the comparison is the aggregation level of the DCIs and the DCU with the highest aggregation level is selected.” Lane teaches that the selection is performed using a Viterbi decoding.)
Lane does NOT specifically identify decode operations are before selectively performing one or more second decode operations.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches identify a that decode operations are before selectively performing one or more second decode operations after an earlier decoding. (Jalali teaches on page 2069, second column, second paragraph a DCI detector that reduces the set of candidates that the UE must decode and verify “capable of generating a metric that helps the UE to considerably reduce the number of such DCI candidates”.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach selectively performing second decode operations after decode operations. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency.”
Regarding claim 7, Lane teaches The UE of claim 1, wherein the one or more respective CCEs include one or more interleaved CCEs that are mapped to one or more physical resource blocks. (Lane illustrates in Fig. 3 that the CCEs are mapped to PDCCHs 380. Further, Lane para. [0025] teaches that the base station interleaves the received CCEs as shown in Fig. 2A and B).
Regarding claim 8, Lane teaches The UE of claim 7, wherein the one or more physical resource blocks are discontinuous. (Lane Fig. 3, above, illustrates discontinuous PDCCHs 320, 330, 340, 350, 360 and 370).
Regarding claim 9, Lane in view of Jalali teaches A UE for wireless communication, (Lane, Fig. 1, UEs 120) comprising:
one or more memories; (Lane Fig. 7, memory 750)
and
one or more processors coupled to the one or more memories, (Lane Fig. 7 and para. [0065] processor within controller 740) the one or more memories including instructions executable by the one or more processors (Lane Fig. 7 para. [0065]) to cause the UE to:
identify a first set of candidate resources that fully overlaps with one or more second sets of candidate resources, wherein the first set of candidate resources is associated with a first aggregation level and the one or more second sets of candidate resources are associated with a second aggregation level, wherein the first aggregation level is greater than the second aggregation level; (Lane para. [0068]-[0070] teaches computing a quality metrics associated with CCEs. Lane para. [0057] – [0058] and Fig. 3 teaches a first and second set of candidate resources “aliasing” interpreted as overlapping and occupying aggregation level 1 and aggregation level 2 at 345 and 355 at arrow:
PNG
media_image2.png
449
882
media_image2.png
Greyscale
and perform, based at least in part on the first aggregation level being greater than the second aggregation level, a first decode operation on the first set of candidate resources [[before selectively performing one or more second decode operations]] on the one or more second sets of candidate resources [[based at least in part on the first decode operation]]. (Lane para. [0058] teaches “the UE needs to decide whether to replace the existing stored message in the output buffer with the new candidate or to discard the new candidate. This decision can be made by comparing quality metrics associated with the stored message and the new candidate message. In one embodiment of the invention, the quality metric used in the comparison is the aggregation level of the DCIs and the DCU with the highest aggregation level is selected.” Lane teaches that the selection is performed using a Viterbi decoding.)
Lane does NOT specifically identify decode operations are before selectively performing one or more second decode operations on the one or more second sets of candidate resources based at least in part on the first decode operation.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches identify that decode operations are before selectively performing one or more second decode operations on the one or more second sets of candidate resources based at least in part on the first decode operation. (Jalali teaches on page 2069, second column, second paragraph a DCI detector that reduces the set of candidates that the UE must decode and verify “capable of generating a metric that helps the UE to considerably reduce the number of such DCI candidates”. Therefore the reduction is based at least on part on the first decode operation.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach selectively performing second decode operations after decode operations. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency.”
Regarding claim 10, Lane in view of Jalali teaches The UE of claim 9, wherein the first set of candidate resources partially overlaps with a third set of candidate resources, wherein the third set of candidate resources is associated with a third aggregation level, (Lane teaches multiple aggregation levels that overlap in Fig. 3) and wherein the one or more memories further include instructions executable by the one or more processors to cause the UE to:
identify one or more quality metrics associated with one or more respective control channel elements (CCEs) of the first set of candidate resources; (Lane teaches identifying quality metrics for CCEs referred to in Lane as codewords in para. [0007]-[0009].
and
and prune the third set of candidate resources [[based at least in part on a shaping pattern]] of the one or more quality metrics satisfying a quality tolerance threshold, [[wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs]]. (Lane para. [0048] teaches “The next two stages employ a “best” candidate screening process. At 550, the number of candidates is reduced by applying the constraint that each candidate CCE set in the CCE space can only produce one output, i.e., one PDCCH. In other words, a uniqueness constraint is applied which limits each set of CCEs forming a PDCCH to result in only one DCI being output. This constraint is used to prune down the number of candidates after CRC match using a Viterbi QM, or a combination of Viterbi QMs, calculated for each candidate to select only the best candidate from up to 16 candidates, i.e., the eight possible tail-biting solutions from trace-back and the two DCI sizes.”)
Lane does NOT teach pruning based “on a shaping pattern of the quality metrics”
In the analogous art of 3GPP 5G wireless communications, Jalali teaches pruning based “on a shaping pattern of the quality metrics satisfying a quality tolerance threshold, wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs”. (Jalali teaches pruning candidates by eliminating “false candidates” on page 2071, second column, third paragraph, and using the feature metric f described on page 2072 second paragraph, which is a quality metric reliant on SNR which is a quality metric. Jalali further teaches on page 2072, second column, fourth paragraph that a Gaussian probability density function models f very well:
PNG
media_image3.png
431
447
media_image3.png
Greyscale
Jalali teaches on page 2074, first column, second paragraph “Let us denote a = [f1, ··· , fC ] as the array containing the fractional metrics for all the C candidates and let fmin be the minimum value in the array. Based on probability density functions obtained in Fig. 5, the candidate with lowest fractional metric is likely to be the true candidate. However, there is a possibility that p(f|H1) generates a larger f than p(f|H0). The probability of this happening, increases as SNR goes lower and the two density functions get closer to each other. Therefore, we claim the fmin as the true candidate only if it deviates large enough from the rest of candidates.” Examiner interprets the shaping pattern as taught by the probability density function shapes, such as a well-known Gaussian curve of f as a shaping pattern of the quality metric.
and prune the second set of candidate resources [[based at least in part on a shaping pattern]] of the one or more quality metrics satisfying a quality tolerance threshold, [[wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs]]. (Lane para. [0048] teaches “The next two stages employ a “best” candidate screening process. At 550, the number of candidates is reduced by applying the constraint that each candidate CCE set in the CCE space can only produce one output, i.e., one PDCCH. In other words, a uniqueness constraint is applied which limits each set of CCEs forming a PDCCH to result in only one DCI being output. This constraint is used to prune down the number of candidates after CRC match using a Viterbi QM, or a combination of Viterbi QMs, calculated for each candidate to select only the best candidate from up to 16 candidates, i.e., the eight possible tail-biting solutions from trace-back and the two DCI sizes.”)
Lane does NOT teach pruning based on a shaping pattern of the quality metrics... wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs
In the analogous art of 3GPP 5G wireless communications, Jalali teaches pruning based on a shaping pattern of the quality metrics wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs. (Jalali teaches pruning candidates by eliminating “false candidates” on page 2071, second column, third paragraph, and using the feature metric f described on page 2072 second paragraph, which is a quality metric reliant on SNR. Jalali further teaches on page 2072, second column, fourth paragraph that a Gaussian probability density function models f very well:
PNG
media_image3.png
431
447
media_image3.png
Greyscale
Jalali teaches on page 2074, first column, second paragraph “Let us denote a = [f1, ··· , fC ] as the array containing the fractional metrics for all the C candidates and let fmin be the minimum value in the array. Based on probability density functions obtained in Fig. 5, the candidate with lowest fractional metric is likely to be the true candidate. However, there is a possibility that p(f|H1) generates a larger f than p(f|H0). The probability of this happening, increases as SNR goes lower and the two density functions get closer to each other. Therefore, we claim the fmin as the true candidate only if it deviates large enough from the rest of candidates.” Examiner interprets the shaping pattern as taught by the probability density function shapes, such as a well-known Gaussian curve of f as a shaping pattern of the quality metric because f is a quality metric based on signal to noise ratios.
Examiner notes that the broadest reasonable interpretation of the claims supports Jalali rejecting “the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs” because Jalali relates to a plurality of candidates wherein a Gaussian probability reflects an SNR (quality metric). Examiner further notes that the Applicant’s specification defines a shaping pattern in terms of “a shaping pattern of the one or more quality metrics satisfying a quality tolerance threshold” and references Fig. 8.
Figure 8, illustrates a shaping pattern satisfying a quality tolerance threshold:
PNG
media_image1.png
213
902
media_image1.png
Greyscale
Examiner suggests that the claims be amended to more accurately reflect Fig. 8 because the term “pattern” is defined in the Oxford English dictionary as “something shaped or designed to serve as a model from which a thing is made; an outline or original. This sense is figurative, meaning an example to be imitated.” Applicant’s claims appear to be using the term “pattern” not as representative of something that can be imitated, but rather as the resulting shape of applying the quality tolerance range, which is not repeatable per se and therefore is only “pattern” in terms of a “shape” to be analyzed. Under the broadest reasonable interpretation of the claims, therefore, Jalali teaches a “shaping pattern” in light of the specification.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a quality metric with a shaping pattern. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency.”
Regarding claim 11, Lane does NOT teach The UE of claim 10, wherein the quality tolerance threshold is associated with a quality tolerance range, and wherein the shaping pattern of the one or more quality metrics satisfying the quality tolerance threshold includes the one or more quality metrics being within the quality tolerance range.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches wherein the quality tolerance threshold is associated with a quality tolerance range, and wherein the shaping pattern of the one or more quality metrics satisfying the quality tolerance threshold includes the one or more quality metrics being within the quality tolerance range. (Jalali page 2072 teaches in the second column, second paragraph “The gray bins of histogram in Fig. 5, corresponds to the event H1 when a valid polar codeword of length 128 was received by the UE, and white bins corresponds to the case when a random bit block of same size was received by the UE, denoted by hypothesis H0. As expected, f tends to be smaller under hypothesis H1. The two histograms become more distinguishable as the SNR and channel estimation improves. Conversely, the separation becomes less pronounced under inaccurate channel estimation.” Jalali page 2073 teaches that a “clustering problem” and fmin is the true candidate “only if it deviates large enough from the rest of the candidates” as explained in Jalali, page 2074 first column, second paragraph. Examiner interprets the quality tolerance range as within the bounds of the “detector” based on the quality metric f as taught on page Jalali page 2074, first column.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a quality metric range. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 12, Lane does NOT teach The UE of claim 10, wherein the quality tolerance threshold is based at least in part on the first aggregation level.
In the analogous art of 3GPP 5G wireless communication, Jalali teaches wherein the quality tolerance threshold is based at least in part on the first aggregation level. (Jalali teaches on page 2074, second column a candidate trimming algorithm that takes into account the first aggregation level:
PNG
media_image4.png
294
503
media_image4.png
Greyscale
As shown, the Jalali algorithm takes into account all candidates which would necessarily include the first aggregation level.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a tolerance range based on a first aggregation level. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 13, Lane does NOT teach The UE of claim 10, wherein a quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches wherein a quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level. (Jalali teaches that the quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level by teaching that f which takes into account the signal to noise level of the candidates associated with the first aggregation level. Jalali Fig. 3 illustrates the first aggregation level and the UE specific search space:
PNG
media_image5.png
456
620
media_image5.png
Greyscale
Jalali page 2069 second column, second paragraph teaches a joint DCI detector in the UE specific search space that includes aggregation level 1. The quality metric f as taught on page Jalali page 2074, first column and a quantity fmin is used in the algorithm for PDCCH candidate trimming shown above.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a quantity of the one or more quality metrics satisfying the quality tolerance threshold is based on a first aggregation level. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 14, Lane teaches The UE of claim 10, wherein the one or more respective CCEs include one or more interleaved CCEs that are mapped to one or more physical resource blocks. (Lane illustrates in Fig. 3 that the CCEs are mapped to PDCCHs 380. Further, Lane para. [0025] teaches that the base station interleaves the received CCEs as shown in Fig. 2A and B).
Regarding claim 15, Lane teaches The UE of claim 14, wherein the one or more physical resource blocks are discontinuous. (Lane Fig. 3, above, illustrates discontinuous PDCCHs 320, 330, 340, 350, 360 and 370).
Regarding claim 16, Lane teaches The UE of claim 9, wherein the first set of candidate resources includes one or more physical downlink control channel (PDCCH) candidate resources. (Lane Fig. 3, above, illustrates discontinuous PDCCHs 320, 330, 340, 350, 360 and 370).
Regarding claim 17, Lane in view of Jalali teaches A method of wireless communication performed by a user equipment (UE), (Lane, Fig. 1, UEs 120) comprising:
identifying one or more quality metrics associated with a plurality of respective control channel elements (CCEs) of a first set of candidate resources that partially overlaps with a second set of candidate resources, wherein the first set of candidate resources is associated with a first aggregation level and the second set of candidate resources is associated with a second aggregation level; (Lane para. [0068]-[0070] teaches computing a quality metrics associated with CCEs. Lane para. [0057] – [0058] and Fig. 3 teaches a first and second set of candidate resources “aliasing” interpreted as overlapping and occupying aggregation level 1 and aggregation level 2 at 345 and 355 at arrow:
PNG
media_image2.png
449
882
media_image2.png
Greyscale
and pruning the second set of candidate resources [[based at least in part on a shaping pattern]] of the one or more quality metrics satisfying a quality tolerance threshold, [[wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs]]. (Lane para. [0048] teaches “The next two stages employ a “best” candidate screening process. At 550, the number of candidates is reduced by applying the constraint that each candidate CCE set in the CCE space can only produce one output, i.e., one PDCCH. In other words, a uniqueness constraint is applied which limits each set of CCEs forming a PDCCH to result in only one DCI being output. This constraint is used to prune down the number of candidates after CRC match using a Viterbi QM, or a combination of Viterbi QMs, calculated for each candidate to select only the best candidate from up to 16 candidates, i.e., the eight possible tail-biting solutions from trace-back and the two DCI sizes.”)
Lane does NOT teach pruning based on a shaping pattern of the quality metrics... wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs
In the analogous art of 3GPP 5G wireless communications, Jalali teaches pruning based on a shaping pattern of the quality metrics wherein the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs. (Jalali teaches pruning candidates by eliminating “false candidates” on page 2071, second column, third paragraph, and using the feature metric f described on page 2072 second paragraph, which is a quality metric reliant on SNR. Jalali further teaches on page 2072, second column, fourth paragraph that a Gaussian probability density function models f very well:
PNG
media_image3.png
431
447
media_image3.png
Greyscale
Jalali teaches on page 2074, first column, second paragraph “Let us denote a = [f1, ··· , fC ] as the array containing the fractional metrics for all the C candidates and let fmin be the minimum value in the array. Based on probability density functions obtained in Fig. 5, the candidate with lowest fractional metric is likely to be the true candidate. However, there is a possibility that p(f|H1) generates a larger f than p(f|H0). The probability of this happening, increases as SNR goes lower and the two density functions get closer to each other. Therefore, we claim the fmin as the true candidate only if it deviates large enough from the rest of candidates.” Examiner interprets the shaping pattern as taught by the probability density function shapes, such as a well-known Gaussian curve of f as a shaping pattern of the quality metric because f is a quality metric based on signal to noise ratios.
Examiner notes that the broadest reasonable interpretation of the claims supports Jalali rejecting “the shaping pattern is defined by the one or more quality metrics across the plurality of respective CCEs” because Jalali relates to a plurality of candidates wherein a Gaussian probability reflects an SNR (quality metric). Examiner further notes that the Applicant’s specification defines a shaping pattern in terms of “a shaping pattern of the one or more quality metrics satisfying a quality tolerance threshold” and references Fig. 8.
Figure 8, illustrates a shaping pattern satisfying a quality tolerance threshold:
PNG
media_image1.png
213
902
media_image1.png
Greyscale
Examiner suggests that the claims be amended to more accurately reflect Fig. 8 because the term “pattern” is defined in the Oxford English dictionary as “something shaped or designed to serve as a model from which a thing is made; an outline or original. This sense is figurative, meaning an example to be imitated.” Applicant’s claims appear to be using the term “pattern” not as representative of something that can be imitated, but rather as the resulting shape of applying the quality tolerance range, which is not repeatable per se and therefore is only “pattern” in terms of a “shape” to be analyzed. Under the broadest reasonable interpretation of the claims, therefore, Jalali teaches a “shaping pattern” in light of the specification.
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a quality metric with a shaping pattern. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 18, Lane does NOT teach The method of claim 17, wherein the quality tolerance threshold is associated with a quality tolerance range, and wherein the shaping pattern of the one or more quality metrics satisfying the quality tolerance threshold includes the one or more quality metrics being within the quality tolerance range.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches wherein the quality tolerance threshold is associated with a quality tolerance range, and wherein the shaping pattern of the one or more quality metrics satisfying the quality tolerance threshold includes the one or more quality metrics being within the quality tolerance range. (Jalali page 2072 teaches in the second column, second paragraph “The gray bins of histogram in Fig. 5, corresponds to the event H1 when a valid polar codeword of length 128 was received by the UE, and white bins corresponds to the case when a random bit block of same size was received by the UE, denoted by hypothesis H0. As expected, f tends to be smaller under hypothesis H1. The two histograms become more distinguishable as the SNR and channel estimation improves. Conversely, the separation becomes less pronounced under inaccurate channel estimation.” Jalali page 2073 teaches that a “clustering problem” and fmin is the true candidate “only if it deviates large enough from the rest of the candidates” as explained in Jalali, page 2074 first column, second paragraph. Examiner interprets the quality tolerance range as within the bounds of the “detector” based on the quality metric f as taught on page Jalali page 2074, first column.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a quality metric range. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 19, Lane does NOT teach The method of claim 17, wherein the quality tolerance threshold is based at least in part on the first aggregation level.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches wherein the quality tolerance threshold is based at least in part on the first aggregation level. In the analogous art of 3GPP 5G wireless communication, Jalali teaches wherein the quality tolerance threshold is based at least in part on the first aggregation level. (Jalali teaches on page 2074, second column a candidate trimming algorithm that takes into account the first aggregation level:
PNG
media_image4.png
294
503
media_image4.png
Greyscale
As shown, the Jalali algorithm takes into account all candidates which would necessarily include the first aggregation level.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a quality tolerance threshold is based at least in part on the first aggregation level. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 20, Lane does NOT teach The method of claim 17, wherein a quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches wherein a quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level. (Jalali teaches that the quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level by teaching that f which takes into account the signal to noise level of the candidates associated with the first aggregation level which Jalali equates to a DCI candidate Jalali Fig. 3 illustrates the first aggregation level and the UE specific search space:
PNG
media_image5.png
456
620
media_image5.png
Greyscale
Jalali page 2069 second column, second paragraph teaches a joint DCI detector in the UE specific search space that includes aggregation level 1. The quality metric f as taught on page Jalali page 2074, first column.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 21, Lane teaches The method of claim 17, wherein the first set of candidate resources fully overlaps with one or more third sets of candidate resources associated with a third aggregation level. (Lane para. [0029] and Fig. 3 teaches six candidate resources and aggregation levels of “one, two, four, and eight respectively” for UE search spaces 340, 350, 360 and 370. As shown the search spaces overlap:
PNG
media_image2.png
449
882
media_image2.png
Greyscale
Regarding claim 22, Lane teaches The method of claim 21, wherein the first aggregation level is greater than the third aggregation level, (Lane illustrates different sized aggregation levels in Fig. 3, above) the method further comprising:
performing, based at least in part on the first aggregation level being greater than the third aggregation level, a first decode operation on the first set of candidate resources [[before selectively performing a second decode operation]] on the one or more third sets of candidate resources. (Lane para. [0058] teaches “the UE needs to decide whether to replace the existing stored message in the output buffer with the new candidate or to discard the new candidate. This decision can be made by comparing quality metrics associated with the stored message and the new candidate message. In one embodiment of the invention, the quality metric used in the comparison is the aggregation level of the DCIs and the DCU with the highest aggregation level is selected.” Lane teaches that the selection is performed using a Viterbi decoding.)
Lane does NOT specifically identify decode operations are before selectively performing one or more second decode operations.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches identify a that decode operations are before selectively performing one or more second decode operations after an earlier decoding. (Jalali teaches on page 2069, second column, second paragraph a DCI detector that reduces the set of candidates that the UE must decode and verify “capable of generating a metric that helps the UE to considerably reduce the number of such DCI candidates”.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach selectively performing second decode operations after decode operations. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency.”
Regarding claim 23, Lane teaches The method of claim 17, wherein the one or more respective CCEs include one or more interleaved CCEs that are mapped to one or more physical resource blocks. (Lane illustrates in Fig. 3 that the CCEs are mapped to PDCCHs 380. Further, Lane para. [0025] teaches that the base station interleaves the received CCEs as shown in Fig. 2A and B).
Regarding claim 24, Lane in view of Jalali teaches A method of wireless communication performed by a user equipment (UE), (Lane, Fig. 1, UEs 120) comprising:
identifying a first set of candidate resources that fully overlaps with one or more second sets of candidate resources, wherein the first set of candidate resources is associated with a first aggregation level and the one or more second sets of candidate resources are associated with a second aggregation level, wherein the first aggregation level is greater than the second aggregation level; (Lane para. [0068]-[0070] teaches computing a quality metrics associated with CCEs. Lane para. [0057] – [0058] and Fig. 3 teaches a first and second set of candidate resources “aliasing” interpreted as overlapping and occupying aggregation level 1 and aggregation level 2 at 345 and 355 at arrow:
PNG
media_image2.png
449
882
media_image2.png
Greyscale
and performing, based at least in part on the first aggregation level being greater than the second aggregation level, a first decode operation on the first set of candidate resources [[before selectively performing one or more second decode operations]] on the one or more second sets of candidate resources [[based at least in part on the first decode operation]]. (Lane para. [0058] teaches “the UE needs to decide whether to replace the existing stored message in the output buffer with the new candidate or to discard the new candidate. This decision can be made by comparing quality metrics associated with the stored message and the new candidate message. In one embodiment of the invention, the quality metric used in the comparison is the aggregation level of the DCIs and the DCU with the highest aggregation level is selected.” Lane teaches that the selection is performed using a Viterbi decoding.)
Lane does NOT specifically identify decode operations are before selectively performing one or more second decode operations on the one or more second sets of candidate resources based at least in part on the first decode operation.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches identify that decode operations are before selectively performing one or more second decode operations on the one or more second sets of candidate resources based at least in part on the first decode operation. (Jalali teaches on page 2069, second column, second paragraph a DCI detector that reduces the set of candidates that the UE must decode and verify “capable of generating a metric that helps the UE to considerably reduce the number of such DCI candidates”. Therefore the reduction is based at least on part on the first decode operation.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach selectively performing second decode operations after decode operations. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency.”
Regarding claim 25, Lane teaches The method of claim 24, wherein the first set of candidate resources partially overlaps with a third set of candidate resources, (Lane Fig. 3 above illustrates candidate resources overlapping including resource 340, 350 and 370) and wherein the third set of candidate resources is associated with a third aggregation level, (Lane Fig. 3 illustrates each of resource 340, 350 and 370 is associated with different aggregation levels, see AL=1, AL=2 and AL=8 in Fig. 3) the method further comprising:
identifying one or more quality metrics associated with one or more respective control channel elements (CCEs) of the first set of candidate resources; (Lane para. [0068]-[0070] teaches computing a quality metrics associated with CCEs. Lane para. [0057] – [0058] and Fig. 3 teaches a first and second set of candidate resources “aliasing” interpreted as overlapping and occupying aggregation level 1 and aggregation level 2 at 345 and 355 at arrow:
PNG
media_image2.png
449
882
media_image2.png
Greyscale
and pruning the third set of candidate resources [[based at least in part on a shaping pattern]] of the one or more quality metrics satisfying a quality tolerance threshold. (Lane para. [0048] teaches “The next two stages employ a “best” candidate screening process. At 550, the number of candidates is reduced by applying the constraint that each candidate CCE set in the CCE space can only produce one output, i.e., one PDCCH. In other words, a uniqueness constraint is applied which limits each set of CCEs forming a PDCCH to result in only one DCI being output. This constraint is used to prune down the number of candidates after CRC match using a Viterbi QM, or a combination of Viterbi QMs, calculated for each candidate to select only the best candidate from up to 16 candidates, i.e., the eight possible tail-biting solutions from trace-back and the two DCI sizes.”)
Lane does NOT teach pruning based on a shaping pattern of the quality metrics.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches pruning based on a shaping pattern of the quality metrics. (Jalali teaches pruning candidates by eliminating “false candidates” on page 2071, second column, third paragraph, and using the feature metric f described on page 2072 second paragraph, which is a quality metric reliant on SNR. Jalali further teaches on page 2072, second column, fourth paragraph that a Gaussian probability density function models f very well:
PNG
media_image3.png
431
447
media_image3.png
Greyscale
Jalali teaches on page 2074, first column, second paragraph “Let us denote a = [f1, ··· , fC ] as the array containing the fractional metrics for all the C candidates and let fmin be the minimum value in the array. Based on probability density functions obtained in Fig. 5, the candidate with lowest fractional metric is likely to be the true candidate. However, there is a possibility that p(f|H1) generates a larger f than p(f|H0). The probability of this happening, increases as SNR goes lower and the two density functions get closer to each other. Therefore, we claim the fmin as the true candidate only if it deviates large enough from the rest of candidates.” Examiner interprets the shaping pattern as taught by the probability density function shapes, such as a well-known Gaussian curve of f as a shaping pattern of the quality metric because f is a quality metric based on signal to noise ratios.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a quality metric with a shaping pattern. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 26, Lane does NOT teach The method of claim 25, wherein the quality tolerance threshold is associated with a quality tolerance range, and wherein the shaping pattern of the one or more quality metrics satisfying the quality tolerance threshold includes the one or more quality metrics being within the quality tolerance range.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches wherein the quality tolerance threshold is associated with a quality tolerance range, and wherein the shaping pattern of the one or more quality metrics satisfying the quality tolerance threshold includes the one or more quality metrics being within the quality tolerance range. (Jalali page 2072 teaches in the second column, second paragraph “The gray bins of histogram in Fig. 5, corresponds to the event H1 when a valid polar codeword of length 128 was received by the UE, and white bins corresponds to the case when a random bit block of same size was received by the UE, denoted by hypothesis H0. As expected, f tends to be smaller under hypothesis H1. The two histograms become more distinguishable as the SNR and channel estimation improves. Conversely, the separation becomes less pronounced under inaccurate channel estimation.” Jalali page 2073 teaches that a “clustering problem” and fmin is the true candidate “only if it deviates large enough from the rest of the candidates” as explained in Jalali, page 2074 first column, second paragraph. Examiner interprets the quality tolerance range as within the bounds of the “detector” based on the quality metric f as taught on page Jalali page 2074, first column.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a quality metric range. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 27, Lane does NOT teach The method of claim 25, wherein the quality tolerance threshold is based at least in part on the first aggregation level.
In the analogous art of 3GPP 5G wireless communication, Jalali teaches wherein the quality tolerance threshold is based at least in part on the first aggregation level. (Jalali teaches on page 2074, second column a candidate trimming algorithm that takes into account the first aggregation level, which Jalali equates to a DCI candidate on page 2069, first column, third paragraph.
PNG
media_image4.png
294
503
media_image4.png
Greyscale
As shown, the Jalali algorithm takes into account all DCI candidates including a first aggregation level (DCI candidate) which would necessarily include the first aggregation level.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a tolerance range based on a first aggregation level. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 28, Lane does NOT teach The method of claim 25, wherein a quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level.
In the analogous art of 3GPP 5G wireless communications, Jalali teaches wherein a quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level. (Jalali teaches that the quantity of the one or more quality metrics satisfying the quality tolerance threshold is associated with the first aggregation level by teaching that f which takes into account the signal to noise level of the candidates associated with the first aggregation level. Jalali Fig. 3 illustrates the first aggregation level and the UE specific search space:
PNG
media_image5.png
456
620
media_image5.png
Greyscale
Jalali page 2069 second column, second paragraph teaches a joint DCI detector in the UE specific search space that includes aggregation level 1. The quality metric f as taught on page Jalali page 2074, first column.)
It would have been obvious to one of ordinary skill in the art to have combined Lane and Jalali to teach a tolerance range based on a first aggregation level. Each of Jalali and Lane are in the field in the wireless communications. One of ordinary skill in the art would have been motivated to combine Jalali with Lane in order to reduce costs of a high complexity decoders on all DCI candidates “in terms of computation, latency, and battery” and improve “both detection performance and computation efficiency” as taught in Jalali page 2066, second column, second paragraph.
Regarding claim 29, Lane teaches The method of claim 25, wherein the one or more respective CCEs include one or more interleaved CCEs that are mapped to one or more physical resource blocks. (Lane Fig. 3, above, illustrates discontinuous PDCCHs 320, 330, 340, 350, 360 and 370).
Regarding claim 30, Lane teaches The method of claim 29, wherein the one or more physical resource blocks are discontinuous. (Lane Fig. 3, above, illustrates discontinuous PDCCHs 320, 330, 340, 350, 360 and 370).
Conclusion
THIS ACTION IS MADE FINAL. 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 MARGARET MARIE ANDERSON whose telephone number is (703)756-1068. The examiner can normally be reached M-F.
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, CHARLES JIANG can be reached at 571-270-7191. 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.
/MMA/Examiner, Art Unit 2412
/CHARLES C JIANG/Supervisory Patent Examiner, Art Unit 2412
Prosecution Insights