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 action is in response to the application filed on 7/15/2024.
The IDSs filed on 7/15/2024 and 12/27/2025 are considered.
Claims 1-20 are examined and rejected.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1, 10 and 18 recite in part: “… the first dimension is a number of transmitting antenna ports and the second dimension is a number of sub-bands…”. Claim 1 introduces two sets of first dimension and second dimension. The first set is for the target channel information for the CSI-matrix, and the second set is for the target channel information for the CSI-vector. It is unclear if the above underlined limitation is further limiting which set, i.e., the CSI-vector dimension, or is trying to limit a combination of both alternative limitation, making this limitation unclear and hence indefinite.
The dependent claims 2-9, 11-17 and 19-20 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, by virtue of their dependency and for the same reason stated above.
Additionally claims 9, 17 and 20, recite in part: “… in a case where sub-matrices or sub-vectors corresponding to some or all of the K encoding units have different sizes in the first dimension and/or the second dimension, the encoding unit of the K encoding units is first aggregated in descending order according to an antenna port index corresponding to the at least one antenna port basic model, and then aggregated in descending order according to a sub-band index corresponding to the at least one sub-band basic model.”
The underlined claim limitation indicates that: 1) K encoding units with different sizes could be in the first or second dimension; 2) aggregating the K encoding units based on the first dimension (antenna port), and followed by aggregating the K encoding units in the second dimension (sub-band). Here, there is a contradiction as to whether the K encoding units choose the first or second dimension, and if a first dimension is chosen then the aggregation has to be according to this first dimension (antenna port). However, the claim indicates that both the first dimension and second dimension are used to aggregated the K encoding units, making this limitation unclear and hence indefinite.
Allowable Subject Matter
Claims 5-9, 14-17 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4, 10-13, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu (US 20240313838 A1) in view of Liu (WO 2022236785 A1).
Regarding Claim 1, Wu teaches a method for channel information feedback, comprising:
encoding by a transmitting device, target channel information through an encoding network, to obtain a target bitstream (see FIG. 3, para 110, the joint training using neural networks receives, a channel before compression (e.g., a channel, H) as input, which refers to various forms of channel information associated with a signal propagation path between a transmitting device/UE and a receiving device/base station … an autoencoder of an UE for encoding a CSI report or for evaluating a CSI compression scheme … (autoencoder at the UE has many neural networks)); and
transmitting by the transmitting device, the target bitstream to a receiving device (see paras 110--111, autoencoder with neural network nodes provides an output feedback vector which is sent to several decoders or auto decoder (e.g., one or more neural networks) of a base station);
wherein the encoding network is formed by aggregating K encoding units, the target bit stream comprises K sub-bitstreams outputted by the K encoding units respectively, K is a positive integer, and K≥2 (see FIG. 3. para 110, auto encoder provide an output 330, which may be referred to as a feedback vector/i.e., representing aggregated output of the neural networks “NN” of the UE auto encoder/i.e., representing the K encoding units);
wherein
or,
the target channel information is a CSI vector, and an input of an encoding unit of the K encoding units is a sub-vector of the target channel information (see paras 110-11, the auto encoder has one or more neural networks, such as a chain or sequence of i neural networks 325 (e.g., neural networks 325-a-1 through 325-a-i) … a configuration of the auto encoder 320 (e.g., one or more neural networks 325) is implemented in an encoder 240 of a UE 115 (e.g., for encoding a CSI report, or for evaluating a CSI compression scheme … the autoencoder outputs a feedback vector, which then is fed as input to multiple autodecoders/i.e., representing sub-vectors)) in a first dimension a second dimension; the first dimension is a number of transmitting antenna ports and the second dimension is a number of sub-bands
Wu teaches a UE with autoencoders comprising N neural networks for encoding CSI report, and output the csi report as a feedback vector to multiple autodecoders.
Wu does not disclose detials regarding the sub-vectors of the target channel information as a first dimension of antenna ports or a second dimension is a number of sub-bands.
In the same field of endeavor, Liu teaches these limitations: see pages 10-11, FIG. 9., the receiving end device obtains target channel vectors/i.e., representing sub-vectors, of the N CSI feedback periods through a neural network… see page 10, the receiving end device receives K bit streams transmitted by the transmitting end device/i.e., output from encoders, the K bit streams are respectively obtained by encoding channel vectors of K CSI feedback periods in the N CSI feedback periods … page 11, the bit stream in the K bit streams includes information of S sub-bands/i.e., second dimension; … the receiving end device decodes the bit stream from the bit stream on the jth sub-band of the i-th CSI feedback period to the bit stream on the jth sub-band of the i-th CSI feedback period through the second receiving end neural network.
It would be obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the autoencoder/decoder neural network of Wu, to include detials regarding input to the encoder (sub-vectors), output of the encoder (bitstream), and their sub-band granularity as taught by Liu, the motivation being to overcome the fixed codebook design that cannot be dynamically adjusted according to the change of the channel, which makes the precision of the feedback channel information reduced, so as to reduce the pre-coding performance (see Liu, Invention Description).
Regarding Claim 2, Wu teaches the method according to claim 1, wherein in a case where the target channel information is the CSI matrix, the sub-matrix corresponding to the encoding unit of the K encoding units is determined based on a continuous aggregation method, or the sub-matrix corresponding to the encoding unit of the K encoding units is determined based on a non-continuous aggregation method (Examiners Note: The BRI of a process claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The limitation “CSI matrix” is a contingent limitation, since claim 1 recites “CSI vector” as the alternate limitation, which has been chosen as mapped above in the rejection of claim 1. See MPEP section 2111.04).
Regarding Claim 3, Wu teaches the method according to claim 2, wherein in a case where the sub-matrix corresponding to the encoding unit of the K encoding units is determined based on the continuous aggregation method, the sub-matrix corresponding to the encoding unit of the K encoding units occupies a continuous part of the target channel information in the first dimension and/or the second dimension (Examiners Note: The BRI of a process claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The limitation “CSI matrix” is a contingent limitation, since claim 1 recites “CSI vector” as the alternate limitation, which has been chosen as mapped above in the rejection of claim 1. See MPEP section 2111.04).
Regarding Claim 4, Wu teaches the method according to claim 2, wherein in a case where the sub-matrix corresponding to the encoding unit of the K encoding units is determined based on the non-continuous aggregation method, the sub-matrix corresponding to the encoding unit of the K encoding units occupies a non-continuous part of the target channel information in the first dimension and/or the second dimension (Examiners Note: The BRI of a process claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The limitation “CSI matrix” is a contingent limitation, since claim 1 recites “CSI vector” as the alternate limitation, which has been chosen as mapped above in the rejection of claim 1. See MPEP section 2111.04).
Regarding Claim 10, Wu teaches a transmitting device (see FIG. 2., UE/encoder), comprising: a processor and a memory, wherein the memory is configured to store a computer program, the processor is configured to invoke and execute the computer program stored in the memory, to cause the transmitting device to perform:
encoding by a transmitting device, target channel information through an encoding network, to obtain a target bitstream (see FIG. 3, para 110, the joint training using neural networks receives, a channel before compression (e.g., a channel, H) as input, which refers to various forms of channel information associated with a signal propagation path between a transmitting device/UE and a receiving device/base station … an autoencoder of an UE for encoding a CSI report or for evaluating a CSI compression scheme … (autoencoder at the UE has many neural networks)); and
transmitting by the transmitting device, the target bitstream to a receiving device (see paras 110--111, autoencoder with neural network nodes provides an output feedback vector which is sent to several decoders or auto decoder (e.g., one or more neural networks) of a base station);
wherein the encoding network is formed by aggregating K encoding units, the target bit stream comprises K sub-bitstreams outputted by the K encoding units respectively, K is a positive integer, and K≥2 (see FIG. 3. para 110, auto encoder provide an output 330, which may be referred to as a feedback vector/i.e., representing aggregated output of the neural networks “NN” of the UE auto encoder/i.e., representing the K encoding units);
wherein
or,
the target channel information is a CSI vector, and an input of an encoding unit of the K encoding units is a sub-vector of the target channel information (see paras 110-11, the auto encoder has one or more neural networks, such as a chain or sequence of i neural networks 325 (e.g., neural networks 325-a-1 through 325-a-i) … a configuration of the auto encoder 320 (e.g., one or more neural networks 325) is implemented in an encoder 240 of a UE 115 (e.g., for encoding a CSI report, or for evaluating a CSI compression scheme … the autoencoder outputs a feedback vector, which then is fed as input to multiple auto decoders/i.e., representing sub-vectors)) in a first dimension a second dimension; the first dimension is a number of transmitting antenna ports and the second dimension is a number of sub-bands
Wu teaches a UE with autoencoders comprising N neural networks for encoding CSI report, and output the csi report as a feedback vector to multiple auto decoders.
Wu does not disclose detials regarding the sub-vectors of the target channel information as a first dimension of antenna ports or a second dimension is a number of sub-bands.
In the same field of endeavor, Liu teaches these limitations: see pages 10-11, FIG. 9., the receiving end device obtains target channel vectors/i.e., representing sub-vectors, of the N CSI feedback periods through a neural network… see page 10, the receiving end device receives K bit streams transmitted by the transmitting end device/i.e., output from encoders, the K bit streams are respectively obtained by encoding channel vectors of K CSI feedback periods in the N CSI feedback periods … page 11, the bit stream in the K bit streams includes information of S sub-bands/i.e., second dimension; … the receiving end device decodes the bit stream from the bit stream on the jth sub-band of the i-th CSI feedback period to the bit stream on the jth sub-band of the i-th CSI feedback period through the second receiving end neural network.
It would be obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the autoencoder/decoder neural network of Wu, to include detials regarding input to the encoder (sub-vectors), output of the encoder (bitstream), and their sub-band granularity as taught by Liu, the motivation being to overcome the fixed codebook design that cannot be dynamically adjusted according to the change of the channel, which makes the precision of the feedback channel information reduced, so as to reduce the pre-coding performance (see Liu, Invention Description).
Regarding Claim 11, Wu teaches the transmitting device according to claim 10, wherein in a case where the target channel information is the CSI matrix, the sub-matrix corresponding to the encoding unit of the K encoding units is determined based on a continuous aggregation method, or the sub-matrix corresponding to the encoding unit of the K encoding units is determined based on a non-continuous aggregation method (Examiners Note: The BRI of a process claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The limitation “CSI matrix” is a contingent limitation, since claim 1 recites “CSI vector” as the alternate limitation, which has been chosen as mapped above in the rejection of claim 1. See MPEP section 2111.04).
Regarding Claim 12, Wu teaches the transmitting device according to claim 11, wherein in a case where the sub-matrix corresponding to the encoding unit of the K encoding units is determined based on the continuous aggregation method, the sub-matrix corresponding to the encoding unit of the K encoding units occupies a continuous part of the target channel information in the first dimension and/or the second dimension (Examiners Note: The BRI of a process claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The limitation “CSI matrix” is a contingent limitation, since claim 1 recites “CSI vector” as the alternate limitation, which has been chosen as mapped above in the rejection of claim 1. See MPEP section 2111.04).
Regarding Claim 13, Wu teaches the transmitting device according to claim 11, wherein in a case where the sub-matrix corresponding to the encoding unit of the K encoding units is determined based on the non-continuous aggregation method, the sub-matrix corresponding to the encoding unit of the K encoding units occupies a non-continuous part of the target channel information in the first dimension and/or the second dimension (Examiners Note: The BRI of a process claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met. The limitation “CSI matrix” is a contingent limitation, since claim 1 recites “CSI vector” as the alternate limitation, which has been chosen as mapped above in the rejection of claim 1. See MPEP section 2111.04).
Regarding Claim 18, Wu teaches a receiving device (see FIG. 2. Base station/decoder), comprising: a processor and a memory, wherein the memory is configured to store a computer program, the processor is configured to invoke and execute the computer program stored in the memory, to cause the receiving device to perform:
receiving a target bitstream transmitted by a transmitting device, wherein the target bitstream comprises K sub-bitstreams output by K encoding units respectively (see para 111, the feedback vector/i.e., representing the target bitstreams and its associated sub-bitstreams, is received as input by the autodecoders); and
decoding the target bitstream through a decoding network, to obtain target channel information (see FIG. 3. depicting the multiple autodecoders each having N neural network associated/i.e., decoding network);
wherein the decoding network is formed by aggregating K decoding units, the K decoding units correspond to the K encoding units respectively, and an input of a decoding unit of the K decoding units is a sub-bitstream output by a corresponding encoding unit, K is a positive integer, and K≥2 (see FIG. 3., output of the autoencoders has 2 branches that go into two auto decoders);
wherein
or,
the target channel information is a CSI vector, and an output of the decoding unit of the K decoding units is a sub-vector of the target channel information in a first dimension and/or a second dimension; the first dimension is a number of transmitting antenna ports, and the second dimension is a number of sub-bands (see para 111, for joint training, the output (e.g., the feedback vector) is provided to two or more auto decoders 340 (e.g., auto decoder 340-a, associated with a first CSI compression scheme, auto decoder 340-b, associated with a second CSI compression scheme), each of which may be described as being associated with a different branch of a joint training. Each of the auto decoders is configured according to one or more respective decoding techniques for decompressing an information payload… the different auto decoders or associated branches may each output a recovered channel, which may be recovered at different resolution, granularity, precision, or accuracy. In some examples, an auto decoder involves one or more neural networks, such as a chain or sequence of j neural networks 345 in the auto decoder 340-a (e.g., neural networks 345-a-1 through 345-a-j), or a chain or sequence of k neural networks 345 in the auto decoder 340-b (e.g., neural networks 345-b-1 through 345-b-k) implemented in a CSI compression evaluation component at a UE 115 (e.g., for evaluating CSI compression schemes associated with a CSI report 225)).
Wu does not disclose detials regarding the sub-vectors of the target channel information as a first dimension of antenna ports or a second dimension is a number of sub-bands.
In the same field of endeavor, Liu teaches these limitations: see pages 11-12, decoding the bit stream from the first sub-band to the Sth sub-band in the ith CSI feedback period through the second receiving end neural network, obtaining the target channel vector on the jth sub-band of the ith CSI feedback period; and the receiving end device obtaining the K target channel vectors according to the target channel vector on the jth sub-band of the ith CSI feedback period… The CSI on the jth sub-band of the ith CSI feedback period is jointly recovered (i.e., a target channel vector on the jth sub-band of the ith CSI feedback period is determined).
.It would be obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the autoencoder/decoder neural network of Wu, to include detials regarding input to the encoder (sub-vectors), output of the encoder (bitstream), and their sub-band granularity as taught by Liu, the motivation being to overcome the fixed codebook design that cannot be dynamically adjusted according to the change of the channel, which makes the precision of the feedback channel information reduced, so as to reduce the pre-coding performance (see Liu, Invention Description).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Namgoong (WO 2022040655 A1) teaches that a client (UE) uses neural network that may be trained to learn dependence of measured qualities on individual parameters, isolate the measured qualities through various layers of the one or more neural networks (also referred to as "operations"), and compress measurements in a way that limits compression loss. The client may transmit the compressed measurements to a base station. The base station decodes the compressed measurements using decompression operations and reconstruction operations associated with one or more neural networks. The one or more decompression and reconstruction operations are based on a set of features of the compressed data set to produce reconstructed measurements…. the client and server may use autoencoder pairs for compressing and reconstructing information. In some cases, autoencoder pairs may be trained using federated learning. Federated learning is a machine learning technique that enables multiple clients to collaboratively learn neural network models, while the server does not collect the data from the clients. In a typical case, federated learning techniques involve a single global neural network model trained from the data stored on multiple clients. For example, in a Federated Averaging (FedAvg) algorithm, the server sends the neural network model to the clients. Each client trains the received neural network model using its own data and sends back an updated neural network model to the server. The server averages the updated neural network models from the clients to obtain a new neural network model; also see FIG.6 18-20.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEEPA BELUR whose telephone number is (571)270-3722. The examiner can normally be reached M-F 8 am - 4:30 pm.
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, Kevin Bates can be reached at 571-272-3980. 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.
/DEEPA BELUR/Primary Examiner, Art Unit 2472
Prosecution Insights