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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/19/2026 has been entered.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claim(s)14-19 is/are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Echigo et al (US20204/0187186) hereinafter Echigo.
Per claim 14 and 16, Echigo discloses a first network node (see figure 15 and corresponding para.), comprising: a memory; and at least one processor coupled to the memory (see figure 15 and item 210), wherein the at least one processor is configured to: obtain, using a function (see figure 2, “ML model”), predicted information (see figure 3A, 3B, CSI based on all samples, ”high density”) based on a first set of signals received from a second network node via a first subset of a set of beams (see col. 0072-0074, low density), beams, wherein to obtain, from the function, the predicted information based on the first set of signals, the at least one processor is configured to obtain a plurality of outputs from a plurality of output ports of the function based on the first set of signals received from the second network node via the first subset of the set of beams, wherein the predicted information is based on at least one output of the plurality of outputs (see figures 2-4 and corresponding paragraphs); determine, based on the predicted information, whether at least one condition associated with the predicted information is satisfied(see col. 0070, the ML model is trained in a way the measured input sampled CSI-RS is able to output the training CSI-RS of figure 3A is output; generate, when the at least one condition is satisfied, beam information associated with at least one first beam of the set of beams based on a second set of signals (test CSI-RS or low density CSI-RS) received from the second network node via a second subset of the set of beams (see figure 4A and 4B and para. 0075-0085); and transmit, to the second network node, a report including the beam information (see para. 0087, the UE may transmit CSI output from the ML model to the BS); wherein each of the plurality of output ports corresponds to at least one of a respective transmission configuration indicator (TCI) state (see para.0302) or a respective reference signal identifier (ID) (see para. 0054), and wherein each of the plurality of output ports (see para. 0066 ports of the CSI-RS) associated with a respective ID for a respective condition of the at least one condition
Per claim 15, Echigo further teaches each of the plurality of output ports is further associated with a respective reporting configuration that indicates at least one of a first set of resources to carry the report, a second set of resources to carry the second set of signals, or a report quantity to be indicated by the report (see figure 4A and 4B and corresponding paragraphs).
Per claim 17, Echigo further teaches an association between each output of the plurality of output ports and the at least one of the respective CSI trigger state ID or the respective ID of the respective reporting configuration is defined based on one of at least one radio resource control (RRC) message or at least one medium access control (MAC) control element (CE) (see para. 0090, show CSI-RS configuration is based on MAC CE).
Per claim 18, Echigo further teaches an association between each output of the plurality of output ports and the at least one of the respective CSI trigger state ID or the respective ID of the respective reporting configuration is defined based on at least one rule (see para. 93, teaches the configuration of the CSI is based on RRC signaling “one rule”).
Per claim 19, Echigo further teaches that wherein each output of the plurality of output ports is associated with a respective ID of one of a transmission configuration indicator (TCI) state or a reference signal, and wherein the second set of signals is received on a set of CSI-RS resources based on the respective ID of the one of the TCI state or the reference signa (See para. 0302).
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.
Claim(s) 1-8, 20, and 22 is/are rejected under 35 U.S.C. 103 as being taught by Echigo et al (US20204/0187186) hereinafter Echigo in further view of Echiogo et al. (WO 2023/013001 A1) hereinafter Echiogo1.
Per claim 1, Echigo discloses a first network node (see figure 15 and corresponding para.), comprising: a memory; and at least one processor coupled to the memory (see figure 15 and item 210), wherein the at least one processor is configured to: obtain, using a function (see figure 2, “ML model”), predicted information (see figure 3A, 3B, CSI based on all samples, ”high density”) based on a first set of signals received from a second network node via a first subset of a set of beams (see col. 0072-0074, low density), beams, wherein to obtain, from the function, the predicted information based on the first set of signals, the at least one processor is configured to obtain a plurality of outputs from a plurality of output ports of the function based on the first set of signals received from the second network node via the first subset of the set of beams, wherein the predicted information is based on at least one output of the plurality of outputs, wherein the predicted information comprises at least one confidence score that is based on the at least one output of the plurality of outputs (see figures 2-4 and corresponding paragraphs), wherein the at least one confidence score is associated with at least one second beam of the set of beams, wherein the at least one confidence score indicates a reliability of at least one predicted value of the predicted information associated with the at least one second beam of the set of beams, and wherein the at least one confidence score comprises at least one of a standard deviation, a variability, a probability, or a likelihood ; determine, based on the predicted information and the at least one confidence score, whether at least one condition associated with the predicted information is satisfied(see col. 0070, the ML model is trained in a way the measured input sampled CSI-RS is able to output the training CSI-RS of figure 3A is output; generate, when the at least one condition is satisfied, beam information associated with at least one first beam of the set of beams based on a second set of signals (test CSI-RS or low density CSI-RS) received from the second network node via a second subset of the set of beams (see figure 4A and 4B and para. 0075-0085); and transmit, to the second network node, a report including the beam information (see para. 0087, the UE may transmit CSI output from the ML model to the BS).
Echigo doesn’t expressly teaches the utilization of the confidence score. Echigo 2 teaches such feature (see translation page 10, such as a prediction value with 95% confidence score or the likelihood of error between the predict value of an AI model and actual measured value). It would have been obvious to one of ordinary skill the art before the effective filing date of the claim invention to incorporated the confidence score with the predicted/estimate value in order to provide the user or system the quality level of the predicted/forecasted value.
Per claim 2, Echigo further teaches that the function comprises at least one of a machine learning model, a minimum mean square error (MMSE) filtering model, a Bayesian optimization model, or another neural network model (see figure 2).
Per claim 3, Echigo further teaches that at least one processor is further configured to receive a resource configuration from the second network node before the at least one condition is satisfied, wherein the resource configuration indicates at least one a set of resources on which to transmit the report or a set of resources on which to receive the second set of signals (see figure 3A, 3B, 4A and 4B and corresponding para.)
Per claim 4, Echigo further teaches that the at least one processor is further configured to: receive, from the second network node, a function configuration indicating a set of parameters for the function, wherein the function configuration is associated with a prediction function at the second network node; and apply the set of parameters to the function (see figure 5 and para. 0083, the information of pre-trained ML model is given by the BS to the MS).
Per claim 5, Echigo further teaches that the at least one processor is further configured to transmit, to the second network node, a channel state information (CSI) report for processing by the second network node using a prediction function at the second network node, wherein the CSI report is based on the first set of signals received from the second network node via the first subset of a set of beams, and wherein the second set of signals is received based on the CSI report (see figure 2 and corresponding para.).
Per claim 6, Echigo further teaches that the at least one processor is further configured to transmit a set of sounding reference signals (SRSs) to the second network node for processing by the second network node using the prediction function at the second network node, wherein the second set of signals is received further based on the set of SRSs (see para. 159 and 175).
Per claim 7, Echigo further teaches that the at least one processor is further configured to: transmit at least a portion of the predicted information to the second network node; and receive the second set of signals based on the at least a portion of the predicted information (see figure 4A and 4B and para. 0075-0085).
Per claim 8, Echigo further teaches that reception of the second set of signals is further based on an acknowledgement (ACK) message from the second network node that is associatedwith the at least a portion of the predicted information (see para. 0211).
Per claim 12, combination of Echigo and Echigo1 further teaches the at least one condition comprises a first condition that is satisfied when the at least one predicted value satisfies a first threshold, and further comprises a second condition that is satisfied when the at least one confidence score satisfies a second threshold (see Echigo figure 4B and para. 0079, the confidence score could be the given range of error such as 95% confidence level as taught by Echigo1).
Per claim 20, Echigo further teaches that the at least one processor is configured to transmit the report on one of a set of aperiodic resources, a set of periodic resources, or a set of semi-persistent resources (see figure 6A, 6B and 7A and 7B, periodically update the CSI-RS configuration).
Per claim 22, Echigo further teaches that the at least one condition is defined by a non-signaled configuration (see para. 0083, the UE already has the information of the pre-trained ML model, no signal needed from the BS).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Echigo and Echigo1 in view of Fang (US2022/0121641) hereinafter Fang.
Echigo and Echigo1 discloses a mobile terminal utilize a predict function to train and predict a beamform information for a BS as depicted in claim 1. Combination of Echigo and Echigo 1 doesn’t teach that he predicted information comprises at least one predicted value and an applicable timestamp associated with the at least one predicted value. Fang teaches such limitation (see para. 0042 and 0043). It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant invention to provide a timestamp to the predicted value in order to track all the prediction for a later usage.
Allowable Subject Matter
Claim 13 is 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUWEN PAN whose telephone number is (571)272-7855. The examiner can normally be reached M-F 9:00am-5:00pm.
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.
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.
YUWEN . PAN
Supervisory Patent Examiner
Art Unit 2649
/YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649
Prosecution Insights