DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
1. Applicant’s arguments filed on 06/18/2026 regarding claims 1, 2, 5-12, 14-19 and 23 in the remarks are fully considered but moot in view of new ground(s) of rejection.
Response to Amendments
Claim Rejections - 35 USC § 103
2. 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.
3. Claim(s) 1, 2, 11, 12, 19 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable Madadi (US PG Pub. No. 2022/0338189) and further in view of Elshafie (US PG Pub. No. 2023/0059139).
As per claim 1:
Madadi teaches a method for channel state information (CSI) feedback (see abstract: teaches a machine learning assisted channel state information reporting or ML assisted CSI prediction), comprising:
transmitting, by a terminal device, first information to a network device (see paragraph [0105], the BS receives a UE capability information, e.g., the support for the ML approach for CSI feedback), the first information at least comprising:
a number of bits required for CSI feedback and error information of different methods for CSI feedback (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art of record),
or a number of bits required for CSI feedback corresponding to a method for CSI feedback that satisfies a requirement (see paragraph [0107], the number of bits used in AI/ML models for high-resolution channel in AI based CSI feedback);
wherein the number of bits required for CSI feedback and the error information of different methods for CSI feedback or the number of bits required for CSI feedback corresponding to the method for CSI feedback that satisfies the requirement are used for the network device to select the method for CSI feedback from a group of methods for CSI feedback (see paragraph [0108], for conventional neural network, the generated AI-CFI refers to the number of bits used in the transmission of the AI-CFI. The size of the AI-CFI relates to the compression ratio, i.e., the ratio of the feedback bits of the original CSI to the AI-CFI. Part or all of the CSI report configuration is set in the RRC IE CSI-ReportConfig) comprising at least one of: a neural-network-based-method for full channel CSI compression feedback, or a neural-network-based-method for full-channel CSI eigenvector compression feedback (see paragraphs [0108], CSI compression involves using dominant eigenvectors per subband in a CNN).
Madadi does not teach wherein in response to the first information comprising the number of bits required for CSI feedback corresponding to the method for CSI feedback that satisfies the requirement, the method for CSI feedback that satisfies the requirement comprises a method for CSI feedback, error information of which is less than a first preset
threshold, and the error information comprises an error between CSI restored by the
network device and sample CSI of the terminal device
Elshafie teaches wherein in response to the first information comprising the number of bits required for CSI feedback corresponding to the method for CSI feedback that satisfies the requirement, the method for CSI feedback that satisfies the requirement comprises a method for CSI feedback, error information of which is less than a first preset
threshold, and the error information comprises an error between CSI restored by the
network device and sample CSI of the terminal device (see paragraph [0100], the UE receives indications of number of bits in which each CSI indication is to be reported. The UE selects a first quantization level which may be selected as the first CSI based on the target BLER and one or more block error rate thresholds, please see paragraph [0102]. The quantization levels may define ranges of thresholds and depending on where the absolute or differential value fall within such ranges a quantization level associated with one or more ranges may be selected for each CSI indication).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the reporting of CSI based on the BLER thresholds (as disclosed in Elshafie) into Madadi as a way of reporting CSI for different operating scenarios such as eMBB, URLLC or other operating scenarios (please see paragraph [0086] of Elshafie). Thus, implementing such improved reporting method results is adequate for communications (please see paragraph [0039] of Elshafie).
As per claim 2:
Madadi in view of Elshafie teaches the method of claim 1, wherein the group of methods for CSI feedback further comprises a code-book based method for CSI feedback (Madadi, see paragraph [0121], codebook parameters for higher layer CSI reporting).
Asper claim 11:
Madadi teaches a method for channel state information (CSI) feedback (see abstract) comprising:
receiving, by a network device, first information transmitted by a terminal device (see paragraph [0105], the BS receives a UE capability information, e.g., the support for the ML approach for CSI feedback), the first information at least comprising:
a number of bits required for CSI feedback and error information of different methods for CSI feedback (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art(s) of record), or
a number of bits required for CSI feedback corresponding to a method for CSI feedback requirement (see paragraph [0107], the number of bits used in AI/ML models for high-resolution channel in AI based CSI feedback);
selecting, by the network device based on the number of bits required for CSI feedback and the error information of different methods for CSI feedback or the number of bits required for CSI feedback corresponding to the method for CSI feedback that satisfies the requirement, the method for CSI feedback from a group of methods for CSI feedback (see paragraph [0108], for conventional neural network, the generated AI-CFI refers to the number of bits used in the transmission of the AI-CFI. The size of the AI-CFI relates to the compression ratio, i.e., the ratio of the feedback bits of the original CSI to the AI-CFI. Part or all of the CSI report configuration is set in the RRC IE CSI-ReportConfig) comprising at least one of: a neural-network-based-method for full channel CSI compression feedback, or a neural-network-based-method for full-channel CSI eigenvector compression feedback (see paragraphs [0108], CSI compression involves using dominant eigenvectors per subband in a CNN).
Madadi does not teach wherein in response to the first information comprising the number of bits required for CSI feedback corresponding to the method for CSI feedback that satisfies the requirement, the method for CSI feedback that satisfies the requirement comprises a method for CSI feedback, error information of which is less than a first preset threshold, and the error information comprises an error between CSI restored by the network device and sample CSI of the terminal device.
Elshafie teaches wherein in response to the first information comprising the number of bits required for CSI feedback corresponding to the method for CSI feedback that satisfies the requirement, the method for CSI feedback that satisfies the requirement comprises a method for CSI feedback, error information of which is less than a first preset threshold, and the error information comprises an error between CSI restored by the network device and sample CSI of the terminal device (see paragraph [0100], the UE receives indications of number of bits in which each CSI indication is to be reported. The UE selects a first quantization level which may be selected as the first CSI based on the target BLER and one or more block error rate thresholds, please see paragraph [0102]. The quantization levels may define ranges of thresholds and depending on where the absolute or differential value fall within such ranges a quantization level associated with one or more ranges may be selected for each CSI indication).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the reporting of CSI based on the BLER thresholds (as disclosed in Elshafie) into Madadi as a way of reporting CSI for different operating scenarios such as eMBB, URLLC or other operating scenarios (please see paragraph [0086] of Elshafie). Thus, implementing such improved reporting method results is adequate for communications (please see paragraph [0039] of Elshafie).
Claim 12 is rejected in the same scope as claim 2.
As per claim 19:
Madadi teaches a device for channel state information (CSI) feedback (see Figure 3, paragraph [0081], UE 116), comprising:
a processor (see Figure 3, controller/processor 307);
a memory for storing a computer program (see Figure 3, memory 311 comprising operating system 312 and applications 313);
and a network interface (see Figure 3, RF transceiver 302), wherein the processor is configured to execute the computer program stored in the memory to control the network interface to:
transmit first information to a network device (see paragraph [0105], the BS receives a UE capability information, e.g., the support for the ML approach for CSI feedback), the first information at least comprising:
a number of bits required for CSI feedback and error information of different methods for CSI feedback (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art of record),
or a number of bits required for CSI feedback corresponding to a method for CSI feedback that satisfies a requirement (see paragraph [0107], the number of bits used in AI/ML models for high-resolution channel in AI based CSI feedback);
wherein the number of bits required for CSI feedback and the error information of different methods for CSI feedback or the number of bits required for CSI feedback corresponding to the method for CSI feedback that satisfies the requirement are used for the network device to select the method for CSI feedback from a group of methods for CSI feedback (see paragraph [0108], for conventional neural network, the generated AI-CFI refers to the number of bits used in the transmission of the AI-CFI. The size of the AI-CFI relates to the compression ratio, i.e., the ratio of the feedback bits of the original CSI to the AI-CFI. Part or all of the CSI report configuration is set in the RRC IE CSI-ReportConfig) comprising at least one of: a neural-network-based-method for full channel CSI compression feedback, or a neural-network-based-method for full-channel CSI eigenvector compression feedback (see paragraphs [0108], CSI compression involves using dominant eigenvectors per subband in a CNN).
Madadi does not teach wherein in response to the first information comprising the number of bits required for CSI feedback corresponding to the method for CSI feedback that satisfies the requirement, the method for CSI feedback that satisfies the requirement comprises a method for CSI feedback, error information of which is less than a first preset
threshold, and the error information comprises an error between CSI restored by the
network device and sample CSI of the terminal device
Elshafie teaches wherein in response to the first information comprising the number of bits required for CSI feedback corresponding to the method for CSI feedback that satisfies the requirement, the method for CSI feedback that satisfies the requirement comprises a method for CSI feedback, error information of which is less than a first preset
threshold, and the error information comprises an error between CSI restored by the
network device and sample CSI of the terminal device (see paragraph [0100], the UE receives indications of number of bits in which each CSI indication is to be reported. The UE selects a first quantization level which may be selected as the first CSI based on the target BLER and one or more block error rate thresholds, please see paragraph [0102]. The quantization levels may define ranges of thresholds and depending on where the absolute or differential value fall within such ranges a quantization level associated with one or more ranges may be selected for each CSI indication).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the reporting of CSI based on the BLER thresholds (as disclosed in Elshafie) into Madadi as a way of reporting CSI for different operating scenarios such as eMBB, URLLC or other operating scenarios (please see paragraph [0086] of Elshafie). Thus, implementing such improved reporting method results is adequate for communications (please see paragraph [0039] of Elshafie).
Claim 23 is rejected in the same scope as claim 2.
4. Claims 5, 14 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Madadi in view of Elshafie and further in view of Dimou (US PG Pub. No. 2023/0199535).
As per claim 5:
Liu teaches the method of claim 2 with the exception of:
wherein the method further comprises: receiving, by the terminal device, second information transmitted by the network device, the second information comprising indication information of the selected method for CSI feedback.
Dimou teaches wherein the method further comprises: receiving, by the terminal device, second information transmitted by the network device, the second information comprising indication information of the selected method for CSI feedback (as explained earlier in paragraphs [0147], [0201], the UE may transmit the indication of the error reason 210 as the CSI report in another message).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the transmission of the error reason as CSI feedback (as disclosed in Dimou) into Madadi and Elshafie as a way of enabling the base station to perform suggested changes such as lowering MCS and changing the RBG (please see paragraph [0147] of Dimou).
Claim 14 is rejected in the same scope as claim 5.
As per claim 18:
Madadi in view of Elshafie teaches the method of claim 11 with the exception of:
further comprising: receiving, by the network device, fourth information transmitted by the terminal device, the fourth information comprising updated parameters of a neural network and an updated number of the bits required for CSI feedback of the method for CSI feedback related to the neural network.
Dimou teaches further comprising: receiving, by the network device, fourth information transmitted by the terminal device, the fourth information comprising updated parameters of a neural network and an updated number of the bits required for CSI feedback of the method for CSI feedback related to the neural network (see paragraph [0147], [0201], the UE may indicate {1010} in the bitmap in order to suggest that the base station lowers the MCS and changes the RBG. UE may transmit the indication of the error reason 210, the indication of the suggested radio link adaptation action 215 or both as part of the CSI report 230 (or in another message)).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the transmission of the error reason as CSI feedback (as disclosed in Dimou) into Madadi and Elshafie as a way of enabling the base station to perform suggested changes such as lowering MCS and changing the RBG (please see paragraph [0147] of Dimou).
5. Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Madadi in view of Elshafie further in view of Dimou and Ma (US PG Pub. No. 2023/0284139).
As per claim 6:
Madadi in view of Elshafie and further in view of Dimou teaches the method of claim 5 with the exception of:
wherein the method further comprises:
transmitting, by the terminal device, third information to the network device, the
third information comprising parameters of a neural network corresponding to the
method for CSI feedback.
Ma teaches wherein the method further comprises:
transmitting, by the terminal device, third information to the network device, the
third information comprising parameters of a neural network corresponding to the
method for CSI feedback (see paragraph [0128], disclose the AI might be enabled or disabled depending on UE capability. Also, the AI may be signaled either dynamically or semi-statically indicating the enabling or disabling).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the reporting of the compressed CSI implementing auto-encoding neural network trained between UE and TRP (as disclosed in Ma) into Madadi, Elshafie and Dimou) as a way of reducing overhead and also enabling the network side to restore the original CSI using AI (please see paragraph [0127] of Ma).
Claim 15 is rejected in the same scope as claim 6.
6. Claims 7, 8, 10, 16, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Madadi in view of Elshafie and further in view of Wu (US PG Pub. No. 2024/0313838).
As per claim 7:
Madadi in view of Elshafie teaches the method of claim 1 with the exception of:
further comprising:
training and updating, by the terminal device, parameters of a neural network
when a first preset condition is satisfied.
Wu teaches further comprising:
training and updating, by the terminal device, parameters of a neural network
when a first preset condition is satisfied (see paragraph [0101], CSI compression schemes indicated by the configuration signaling 250 may be updated over time such that the base station may transmit and the UE may receive subsequent indications that may revise, replace, add, cancel or otherwise update CSI compression scheme configurations over time based on machine learning or training of an encoder or training of a decoder. The UE may select a neural network-based CSI compression scheme if certain conditions are met or not met, please see paragraph [0102]).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the various compression schemes updated over time based on ongoing machine learning or training of an encoder or decoder (as disclosed in Wu) into Madadi and Elshafie as a way of taking into consideration several factors that affect the UE such as power availability (i.e., battery status), power consumption and processing load (please see paragraph [0103] of Wu).
As per claim 8:
Madadi in view of Elshafie and further in view of Wu teaches the method of claim 7.
Madadi and Elshafie do not teach wherein the first preset condition is satisfied in one of following conditions: when error information of the method for CSI feedback related to the neural network exceeds a second preset threshold; or, when first indication information of the network device is received by the terminal device, the first indication information informing the terminal device to return back to a codebook-based method for CSI feedback.
Wu teaches wherein the first preset condition is satisfied in one of following conditions:
when error information of the method for CSI feedback related to the neural network exceeds a second preset threshold (see paragraph [0104], when the condition of P1* α<P2 is not met, the UE may select an encoding or decoding in accordance with the neural network-based CSI compression scheme, indicating that the decoder should be configured in accordance with the neural network-based CSI compression scheme);
or, when first indication information of the network device is received by the terminal device, the first indication information informing the terminal device to return back to a codebook-based method for CSI feedback (see paragraph [0124], of the MSE output is worse than a codebook-based compression, the UE may be configured to switch the configured codebook. In some examples, the UE may signal a preferred or new codebook).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the various compression schemes updated over time based on ongoing machine learning or training of an encoder or decoder (as disclosed in Wu) into Madadi and Elshafie as a way of taking into consideration several factors that affect the UE such as power availability (i.e., battery status), power consumption and processing load (please see paragraph [0103] of Wu).
As per claim 10:
Madadi in view of Elshafie and further in view of Wu teaches the method of claim 8.
Madadi and Elshafie do not teach wherein in case that the first preset condition is satisfied when error information of the method for CSI feedback related to the neural network exceeds the second preset threshold, the method further comprises:
transmitting, by the terminal device, second indication information to the network device, the second indication information informing the network device to return back to the codebook-based method for CSI feedback.
Wu teaches wherein in case that the first preset condition is satisfied when error information of the method for CSI feedback related to the neural network exceeds the second preset threshold (see paragraph [0124], if an error (i.e., MSE) of a neural network-based CSI compression is greater than an error of a compression codebook-based CSI compression, the UE may report a configured codebook-based compression), the method further comprises:
transmitting, by the terminal device, second indication information to the network device, the second indication information informing the network device to return back to the codebook-based method for CSI feedback (see paragraph [0124], of the MSE output is worse than a codebook-based compression, the UE may be configured to switch the configured codebook. In some examples, the UE may signal a preferred or new codebook).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the various compression schemes updated over time based on ongoing machine learning or training of an encoder or decoder (as disclosed in Wu) into Madadi and Elshafie as a way of taking into consideration several factors that affect the UE such as power availability (i.e., battery status), power consumption and processing load (please see paragraph [0103] of Wu).
As per claim 16:
Madadi in view of Elshafie teaches the method of claim 11 with the exception of:
further comprising: transmitting first indication information to the terminal device when a second preset condition is not satisfied, the first indication information informing the terminal device to return back to a codebook-based method for CSI feedback.
Wu teaches further comprising: transmitting first indication information to the terminal device when a second preset condition is not satisfied, the first indication information informing the terminal device to return back to a codebook-based method for CSI feedback (see paragraph [0124], in another example, if an error (e.g., MSE) of a neural network-based CSI compression is greater than an error of a codebook-based CSI compression, the UE may report a configured codebook-based compression (e.g., compressing or encoding a CSI report in accordance with a codebook-based encoder, indicating that the UE selected a codebook-based compression, indicating, an indication 255, that a channel report has been encoded). Note: Said indication 255 is sent by the base station as show in figure 2).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the various compression schemes updated over time based on ongoing machine learning or training of an encoder or decoder (as disclosed in Wu) into Madadi and Elshafie as a way of taking into consideration several factors that affect the UE such as power availability (i.e., battery status), power consumption and processing load (please see paragraph [0103] of Wu).
Claim 17 is rejected in the same scope as claim 10.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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PRINCE AKWASI. MENSAH
Examiner
Art Unit 2474
/PRINCE A MENSAH/Examiner, Art Unit 2474
/Michael Thier/Supervisory Patent Examiner, Art Unit 2474
Prosecution Insights