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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 14 October 2024 was filed after the mailing date of the patent application on 15 July 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings, received on 15 July 2024, are acceptable for examination.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7 and 10-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Niu et al. (WO 2024233166 A1; hereinafter referred to as “Niu”).
Regarding Claim 1, Niu discloses a user equipment (UE) for wireless communication, comprising:
at least one memory (¶159, Niu discloses a user equipment (UE) comprising a memory medium); and
at least one processor coupled with the at least one memory and configured to cause the UE (¶159, Niu discloses the UE further comprising a processor coupled to the memory medium where the processor causes the UE to perform a method) to:
transmit a set of test data (¶108-109 & Fig. 5B & Fig. 5C, Niu discloses transmitting, by the UE to a network or vendor, at least one training data set) comprising at least one of statistics of input data for an encoder model of the UE, a first set of samples of the input data, or a second set of samples representing an expected output of a two-sided artificial intelligence model associated with the first set of samples of the input data (¶108-109 & Fig. 5B & Fig. 5C & ¶132, Niu discloses that the at least one training data set is training date corresponding to an encoder at the UE-side in order to train at least one decoder at the network (NW)-side);
receive a first set of information associated with a first reference artificial intelligence model (¶138-139 & Fig. 7B (724), Niu discloses receiving, by the UE, a new model identifier (ID) announcement associated with a first AI/ML model), where the first reference artificial intelligence model is associated with one or more of a decoder model of a two-sided artificial intelligence model or an encoder model of the two-sided artificial intelligence model (¶108-109, Niu discloses the first AI/ML model is associated an encoder and a decoder of a two-sided model); and
perform a process to obtain the encoder model of the UE (¶139-140 & Fig. 7B (730), Niu discloses performing, by the UE, a process to obtain a new model file corresponding to the new model ID associated with a first AI/ML model) based at least in part on one or more of whether the first reference artificial intelligence model is associated with the encoder model of the two-sided artificial intelligence model or whether the first reference artificial intelligence model is associated with the decoder model of the two-sided artificial intelligence model (¶139-140 & Fig. 7B (730), Niu discloses obtaining the new model file by receiving, by the UE from the NW, the new model file. Here, the new model file is an encoder model of an encoder/decoder pair).
Regarding Claim 2, Niu discloses the UE of claim 1.
Niu further discloses wherein if the first reference artificial intelligence model is associated with the encoder model of the two-sided artificial intelligence model, the at least one processor is configured to cause the UE to perform the process to obtain the encoder model of the UE based at least in part on the first set of information associated with the first reference artificial intelligence model (¶139-140 & Fig. 7B (730), Niu discloses obtaining the new model file by receiving, by the UE from the NW, the new model file based in part on the new model ID and further based on the UE-side model being an encoder model).
Regarding Claim 3, Niu discloses the UE of claim 1.
Niu further discloses wherein if the first reference artificial intelligence model is associated with the decoder model of the two-sided artificial intelligence model, the at least one processor is configured to cause the UE to perform the process to obtain the encoder model of the UE based at least in part on the first set of information associated with the first reference artificial intelligence model and the input data for the encoder model of the UE (¶140 & Fig. 7B & ¶108-109 & Fig. 5B & Fig. 5C, Niu discloses that the UE is configured to receive the new model file based at least in part on the new model ID and the model file is further based upon training data exchanged between the UE and the network).
Regarding Claim 4, Niu discloses the UE of claim 1.
Niu further discloses wherein the at least one processor is configured to cause the UE to transmit an indication of whether the UE successfully obtains the encoder model (¶140 & Fig. 7B, Niu discloses that the UE is caused to transmit an uplink (UL) message to the network in response to successfully obtaining the new model file by receiving, by the UE from the NW, the new model file based in part on the new model ID).
Regarding Claim 5, Niu discloses the UE of claim 4.
Niu further discloses wherein if the UE is not able to successfully obtain the encoder model of the UE, the at least one processor is configured to cause the UE to:
receive a second set of information associated with a second reference artificial intelligence model associated with a different decoder model (¶138-139 & Fig. 7B (724), Niu discloses receiving, by the UE, a second new model identifier (ID) announcement associated with a second AI/ML model); and
perform the process to obtain the encoder model of the UE based at least in part on the second set of information and the input data (¶139-140 & Fig. 7B (730), Niu discloses performing, by the UE, a process to obtain a second new model file corresponding to the second new model ID associated with the second AI/ML model).
Regarding Claim 6, Niu discloses the UE of claim 4.
Niu further discloses wherein if the UE is not able to successfully obtain the encoder model of the UE, the at least one processor is configured to cause the UE to receive a message indicating an inability to train an encoder-decoder pair (¶139-140 & ¶5, Niu discloses that, if the UE does not support the model, then the UE is configured to activate, deactivate, fallback, switch, or update a particular AI/ML- based functionality (or of particular AI/ML-based models, as identified by their respective model IDs and/or versions) via existing 3GPP signaling (e.g., RRC, MAC-CE, DCI)).
Regarding Claim 7, Niu discloses the UE of claim 4.
Niu further discloses wherein if the UE is not able to successfully obtain the encoder model of the UE, the at least one processor is configured to cause the UE to receive a message comprising an instruction to implement a fallback process (¶139-140 & ¶5, Niu discloses that, if the UE does not support the model, then the UE is configured to fallback to a particular AI/ML- based functionality (or of particular AI/ML-based models, as identified by their respective model IDs and/or versions) via existing 3GPP signaling (e.g., RRC, MAC-CE, DCI)).
Regarding Claim 10, Niu discloses the UE of claim 1.
Niu further discloses wherein the first set of information comprises one or more of
a set of parameters representing one or more weights of a neural network model,
a set of parameters representing a structure of the neural network model,
a set of samples representing an input/output of the neural network model, or
one or more identifiers associated with at least one of model parameters, model structure (¶138-139 & Fig. 7B (724), Niu discloses receiving, by the UE, a new model identifier (ID) announcement associated with a first AI/ML model), or an associated set of samples.
Regarding Claim 11, Niu discloses the UE of claim 1.
Niu further discloses wherein the at least one processor is configured to cause the UE to perform the process to obtain the encoder model of the UE based at least in part on one or more of an instruction received from a different node or a different process executed at the UE (¶139-140 & Fig. 7B (730), Niu discloses obtaining the new model file by receiving, by the UE from the NW, the new model file based the need of the UE 702 to compile the newly downloaded model ( or version) and/or request a UE server to compile the model).
Regarding Claim 12, Claim 12 is rejected on the same basis as Claim 1.
Regarding Claim 13, Niu discloses a network equipment for wireless communication, comprising:
at least one memory (¶159, Niu discloses a base station (BS) or network node comprising a memory medium); and
at least one processor coupled with the at least one memory and configured to cause the network equipment (¶159, Niu discloses the UE further comprising a processor coupled to the memory medium where the processor causes the UE to perform a method) to:
receive a set of test data (¶108-109 & Fig. 5B & Fig. 5C, Niu discloses receiving, from the UE to a network or vendor, at least one training data set) comprising at least one of statistics of input data for an encoder model of a user equipment (UE), a first set of samples of the input data, or a second set of samples representing an expected output of a two-sided artificial intelligence model associated with the first set of samples of the input data (¶108-109 & Fig. 5B & Fig. 5C & ¶132, Niu discloses that the at least one training data set is training date corresponding to an encoder at the UE-side in order to train at least one decoder at the network (NW)-side); and
transmit, based at least in part on the set of test data, a first set of information associated with a first reference artificial intelligence model (¶138-139 & Fig. 7B (724), Niu discloses transmitting, to the UE, a new model identifier (ID) announcement associated with a first AI/ML model), where the first reference artificial intelligence model is associated with one or more of a decoder model of a two-sided artificial intelligence model or an encoder model of the two-sided artificial intelligence model (¶108-109, Niu discloses the first AI/ML model is associated an encoder and a decoder of a two-sided model).
Regarding Claim 14, Niu discloses the network equipment of claim 13.
Niu further discloses wherein the at least one processor is configured to cause the network equipment to receive an indication of whether the UE is able to successfully obtain the encoder model (¶140 & Fig. 7B, Niu discloses that the NW is caused to receive an uplink (UL) message in response to the UE successfully obtaining the new model file by receiving, by the UE from the NW, the new model file based in part on the new model ID).
Regarding Claim 15, Claim 15 is rejected on the same basis as Claim 5.
Regarding Claim 16, Claim 16 is rejected on the same basis as Claim 6.
Regarding Claim 17, Claim 17 is rejected on the same basis as Claim 7.
Regarding Claim 18, Claim 18 is rejected on the same basis as Claim 10.
Regarding Claim 19, Niu discloses the network equipment of claim 13.
Niu further discloses wherein the at least one processor is configured to cause the network equipment to transmit, to the UE, an instruction to perform a process to obtain the encoder model of the UE (¶139-140 & Fig. 7B (730), Niu discloses obtaining the new model file by receiving, by the UE from the NW, the new model file based the need of the UE 702 to compile the newly downloaded model ( or version) and/or request a UE server to compile the model).
Regarding Claim 20, Claim 20 is rejected on the same basis as Claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Niu in view of Saber et al. (US 20230412230 A1; hereinafter referred to as “Saber”).
Regarding Claim 8, Niu discloses the UE of claim 1.
However, Niu does not disclose wherein the at least one processor is configured to cause the UE to perform measurements of a radio channel, and wherein the input data is associated with the measurements of the radio channel.
Saber, a prior art reference in the same field of endeavor, teaches wherein the at least one processor is configured to cause the UE to perform measurements of a radio channel (¶227, Saber discloses that the UE performs channel state information (CSI) measurements of a channel), and wherein the input data is associated with the measurements of the radio channel (¶227, Saber discloses that the training set were the training set is associated with channel state information (CSI) measurements).
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to modify Niu by requiring that the at least one processor is configured to cause the UE to perform measurements of a radio channel and that the input data is associated with the measurements of the radio channel as taught by Saber because reporting channel information, such as a precoding matrix and/or CQI, using one or more artificial intelligence and/or machine learning models is improved by enabling compression of channel information (Saber, ¶8).
Regarding Claim 9, Niu in view of Saber discloses the UE of claim 1.
Saber, a prior art reference in the same field of endeavor, further teaches wherein the input data comprises at least one of a representation of a measured channel matrix of a radio channel or a precoder for the radio channel (¶227, Saber discloses that the training set comprises CSI measurements).
It would have been obvious to one having ordinary skill in the art prior to the effective filing date of the claimed invention to modify Niu in view of Saber by requiring that the input data comprises at least one of a representation of a measured channel matrix of a radio channel or a precoder for the radio channel as taught by Saber because reporting channel information, such as a precoding matrix and/or CQI, using one or more artificial intelligence and/or machine learning models is improved by enabling compression of channel information (Saber, ¶8).
Internet Communications
Applicant is encouraged to submit a written authorization for Internet communications (PTO/SB/439, http://www.uspto.gov/sites/default/files/documents/sb0439.pdf) in the instant patent application to authorize the examiner to communicate with the applicant via email. The authorization will allow the examiner to better practice compact prosecution. The written authorization can be submitted via one of the following methods only: (1) Central Fax which can be found in the Conclusion section of this Office action; (2) regular postal mail; (3) EFS WEB; or (4) the service window on the Alexandria campus. EFS web is the recommended way to submit the form since this allows the form to be entered into the file wrapper within the same day (system dependent). Written authorization submitted via other methods, such as direct fax to the examiner or email, will not be accepted. See MPEP § 502.03.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NOWLIN whose telephone number is (313)446-6544. The examiner can normally be reached M-F 12:00PM-10:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Thier can be reached at (571) 272-2832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC NOWLIN/Examiner, Art Unit 2474
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