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 .
Priority
Application is a continuation of PCT Application No. PCT/EP2021/084136, filed on
December 03, 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/30/2023 is in compliance
with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being
considered by the examiner.
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) 17-36 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bai et al. Pub. No.: US 20210410219 A1.
Regarding Claim 17 Bai teaches A first apparatus comprising at least one processor, and at least one memory storing instructions, the at least one memory and the instructions configured to, with the at least one processor, cause the first apparatus to:
transmit configuration information to a second apparatus for performance supervision and/or failure detection of at least one machine learning model,
wherein the at least one machine learning model is used by at least one of the first apparatus and the second apparatus. (Bai, paragraphs 0068-0071, teaches a machine learning algorithm run on a User Equipment (UE) that will receive configuration information from a base station. The UE utilizes a machine learning model and is able to, based off of the configuration information, detect failures in its predictions as well as evaluate its performance. The UE is the second apparatus, and the base station is the first apparatus)
Claim 25 is rejected on the same grounds as Claim 17.
Regarding claim 18 Bai teaches The first apparatus according to claim 17, wherein the configuration information comprises at least one of: information related to at least one resource for performance supervision of the at least one machine learning model; information related to at least one signal for performance supervision of the at least one machine learning model; information related to at least one performance metric for failure detection of the at least one machine learning model; information related to a temporal behavior for performance supervision and/or failure detection of the at least one machine learning model; information related to parameters for failure detection of the at least one machine learning model; or information related to rules for failure detection of the at least one machine learning model. (Bai, paragraphs 0068-0071, teaches a machine learning algorithm run on a User Equipment (UE) that will send failure information to a base station this information includes all the failure information collected by the UE (i.e. parameters for failure detection).)
Claim 26 is rejected on the same grounds as Claim 18.
Regarding claim 19 Bai teaches The first apparatus according to claim 18, wherein the information related to at least one resource for performance supervision of the at least one machine learning model and/or the information related to at least one signal for performance supervision of the at least one machine learning model characterizes at least one of: a downlink reference signal; a dedicated signal; or a resource element associated with a data signal. (Bai, paragraphs 44 – 49, teaches the base station and the UE working and sending various signals and information of those signals, Further Bai, paragraph 0127-0130, teaches the use of the reception and transmission components and there information in the sending of information to the base station from the UE for evaluation of failures and performance analysis.)
Claim 27 is rejected on the same grounds as Claim 19.
Regarding claim 20 Bai teaches The first apparatus according to claim 18, wherein the information related to at least one performance metric for failure detection of the at least one machine learning model characterizes at least one of: a mean square error of a variable output by the least one machine learning model; a mean absolute error of a variable output by the least one machine learning model; a recall; a precision; an accuracy; or an F1-score. (Bai, paragraph 0075, teaches the use and the transmission of a differential between a predicted and observed value (i.e. percision).)
Claim 28 is rejected on the same grounds as Claim 20.
Regarding claim 21 Bai teaches The first apparatus according to claim 17, wherein the at least one machine learning model is used by the first apparatus, and wherein the instructions, when executed by the at least one processor, cause the first apparatus to:
receive a failure indication from the second apparatus indicative of a failure of the at least one machine learning model detected in accordance with the configuration information;
and responsive to the failure indication, transmit a model recovery indication to the second apparatus. (Bai, paragraphs 0072-0080. Teaches the base station receiving a failure indication from the UE that was detected in accordance with configuration information. The base station then sends a recovery indication to the UE with an indication of new configuration data, a need for retraining, or instructions to use a non-machine learning fall back.)
Claim 29 is rejected on the same grounds as Claim 21.
Regarding claim 22 Bai teaches The first apparatus according to claim 21, wherein the failure indication comprises a fallback solution for recovering the failure of the at least one machine learning model. (Bai, paragraphs 0072-0080. Teaches the base station sending a fallback solution for recovery to the UE based on the failure.)
Claim 30 is rejected on the same grounds as Claim 22.
Regarding claim 23 Bai teaches The first apparatus according to claim 21, wherein the model recovery indication comprises at least one of: an indication whether a failure recovery was successful; at least one update of the at least one machine learning model; configuration parameters associated with the failure recovery; or a time offset characterizing a start of operation based on a recovered model obtained by the failure recovery. (Bai, paragraphs 0072-0080, teaches the recovery indication being configuration parameters for the UE to use to either retrain or use a fall back.)
Regarding claim 24 Bai teaches The first apparatus according to claim 17, wherein: the first apparatus is a base station and the second apparatus is a terminal device, or the first apparatus is a terminal device and the second apparatus is a base station, or the first apparatus is a terminal device and the second apparatus is a terminal device. (Bai, paragraphs 0072-0080, teaches the first apparatus being a base station and the second apparatus being a User Equipment (i.e. a terminal device)
Regarding claim 31 Bai teaches A second apparatus comprising at least one processor, and at least one memory storing instructions, the at least one memory and the instructions configured to, with the at least one processor, cause the second apparatus to: receive configuration information from a first apparatus for performance supervision and/or failure detection of at least one machine learning model, wherein the at least one machine learning model is used by at least one of the first apparatus and the second apparatus. (Bai, paragraphs 0068-0071, teaches a machine learning algorithm run on a User Equipment (UE) that will receive configuration information from a base station. The UE utilizes a machine learning model and is able to, based off of the configuration information, detect failures in its predictions as well as evaluate its performance. The UE is the second apparatus, and the base station is the first apparatus)
Regarding claim 32 Bai teaches The second apparatus according to claim 31, wherein the configuration information comprises at least one of: information related to at least one resource for performance supervision of the at least one machine learning model; information related to at least one signal for performance supervision of the at least one machine learning model; information related to at least one performance metric for failure detection of the at least one machine learning model; information related to a temporal behavior for performance supervision and/or failure detection of the at least one machine learning model; information related to parameters for failure detection of the at least one machine learning model; or information related to rules for failure detection of the at least one machine learning model. (Bai, paragraphs 0068-0071, teaches a machine learning algorithm run on a User Equipment (UE) that will send failure information to a base station this information includes all the failure information collected by the UE (i.e. parameters for failure detection).)
Regarding claim 33 Bai teaches The second apparatus according to claim 31, wherein the at least one machine learning model is used by the first apparatus, and wherein the instructions, when executed by the at least one processor, cause the second apparatus to: transmit a failure indication indicative of a failure of the at least one machine learning model detected in accordance with the configuration information to the first apparatus; and receive a model recovery indication from the first apparatus responsive to the failure indication. (Bai, paragraphs 0072-0080. Teaches the base station receiving a failure indication from the UE that was detected in accordance with configuration information. The base station then sends a recovery indication to the UE with an indication of new configuration data, a need for retraining, or instructions to use a non-machine learning fall back.)
Regarding claim 34 Bai teaches The second apparatus according to claim 33, wherein the failure indication comprises a fallback solution for recovering the failure of the at least one machine learning model. (Bai, paragraphs 0072-0080. Teaches the base station sending a fallback solution for recovery to the UE based on the failure.)
Regarding claim 35 Bai teaches The second apparatus according to claim 33, wherein the model recovery indication comprises at least one of: an indication whether a failure recovery was successful; at least one update of the at least one machine learning model; configuration parameters associated with the failure recovery; or a time offset characterizing a start of operation based on a recovered model obtained by the failure recovery. (Bai, paragraphs 0072-0080, teaches the recovery indication being configuration parameters for the UE to use to either retrain or use a fall back.)
Regarding claim 36 Bai teaches The second apparatus according to claim 31, wherein: the first apparatus is a base station and the second apparatus is a terminal device, or the first apparatus is a terminal device and the second apparatus is a base station, or the first apparatus is a terminal device and the second apparatus is a terminal device. (Bai, paragraphs 0072-0080, teaches the first apparatus being a base station and the second apparatus being a User Equipment (i.e. a terminal device)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhou et al. Pub No.: US 20230041404 A1 and Norman et al. Patent. No: US 12132619 B2 have similar teachings to the cited prior art.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 7:20am-5:20pm; F: Out of Office.
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/THOMAS BERNARD LANE/Examiner, Art Unit 2142
/HAIMEI JIANG/Primary Examiner, Art Unit 2142
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