Response to Amendment
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 .
The remarks and/or response dated 4/2/26 has been received and made of record.
Applicant argument with respect claims 1, 20, 29, 46 54-57 has been fully considered but are not persuasive for the following reasons.
Applicant's arguments filed 4/26/26 have been fully considered but they are not persuasive for the following reasons.
First, it appears applicant attacking references individually , thus by applicant's the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). In this case Ren discloses a method for wireless communications at a user equipment (UE),(computing platform 100) comprising:
receiving, from a base station(computing platform , OOD detection system 200 ) for detecting a data sample that falls outside of a dataset used to train a first machine learning model(see the abstract, fig.2-3 and par. 0085-0086, where the OOD detection module may receive data sample outside the set of training data where OOD detection module may detect out-of-distribution data sample that falls outside of a dataset), determining that an out-of-distribution detection event has occurred based at least in part on determining that at least one data sample generated by the first machine learning model falls outside of the dataset based at least in part on the out-of-distribution (see fig.3, steps 302-308 and par. 0088-0093 , the output 150 can include a determination of whether an event or the testing data is an out-of-distribution data or not, i.e., whether the testing data is generated based on the same distribution used to generate the collection of data including the training data), and transmitting, to the base station, an indication that the out-of-distribution detection event has been determined for the at least one data sample(see fig. 2 and par. 0087, 0098, step 214, transmitted for additional processing).
However, Ren is silent regarding using an out-of-distribution detection rule configuration. In this regard Narang discloses detecting using an out-of-distribution detection rule configuration (see pars. 0064, 0157, 0187, showing using rule event detection that associated with any or all of the outputs is deemed too high (e.g., above a set of one or more predetermined thresholds, not satisfying of a set of validation constraints and/or rules, etc.) and enabling a determination of whether or not the input data can be trusted to be determined. this enables out-of-distribution test data to be determined with high confidence.
Second, applicant alleges the art of record fails to discloses receiving, from a base station, control signaling indicating an out- of-distribution detection rule configuration for detecting a data sample that falls outside of a dataset used to train a first machine learning model; and transmitting, to the base station, an indication that the out-of- distribution detection event has been determined for the at least one data sample.
In response examiner respectfully disagrees and asserts Ren discloses receiving, from a base station, control signaling indicating an out- of-distribution for detecting a data sample that falls outside of a dataset used to train a first machine learning model(see fig. 3 and pars 0088-0092, where the computing system can obtain a set of in-distribution training data that includes a plurality of in-distribution training examples) and transmitting, to the base station, an indication that the out-of- distribution detection event has been determined for the at least one data sample.(see par. 0087, where the system 200 can provide the data input 204 to one or more additional analysis components 214 such as, for example, a machine-learned classifier model for classification relative to a plurality of in-distribution classes.
Here, Ren does not explicitly disclose using detection rule configuration
Narang discloses detecting event based an out-of-distribution detection rule configuration (see pars. 0064, 0157, 0187). For example, Narang discloses using an out-of-distribution detection rule configuration ( that is using detection rule to determine whether at least one data sample falls outside of the dataset)(see pars. 0064, 0157, 0187, showing using rule event detection that associated with any or all of the outputs is deemed too high (e.g., above a set of one or more predetermined thresholds, not satisfying of a set of validation constraints and/or rules, etc.) and enabling a determination of whether or not the input data can be trusted to be determined. this enables out-of-distribution test data to be determined with high confidence. Thus, enabling implementing a set of predefined rules which are unable to handle every scenario.
Finally, examiner respectfully disagrees and asserts Ren’s’ computing device operates in wireless environment which can include any type of computing device including base station (see fig, 1a and pars 00054-0058, 0088-0091).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) are: “means for receiving----; means for determining----; and means for transmitting---” in claims 54 and 55
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
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.
Claims 1-57 are rejected under 35 U.S.C. 103 as being unpatentable over Ren et al . U.S. Patent Application Publication No. 20220253747 [hereinafter Ren ] in view of Narang et al. U.S. Patent Application Publication No. 2022/0011776 [hereinafter Narang]
As per claim 1, 20, 29, 46, and 54-57 Ren discloses a method for wireless communications at a user equipment (UE),(computing platform 100) comprising:
receiving, from a base station(computing platform , OOD detection system 200 ) for detecting a data sample that falls outside of a dataset used to train a first machine learning model(see the abstract, fig.2-3 and par. 0085-0086, where the OOD detection module may receive data sample outside the set of training data where OOD detection module may detect out-of-distribution data sample that falls outside of a dataset)
determining that an out-of-distribution detection event has occurred based at least in part on determining that at least one data sample generated by the first machine learning model falls outside of the dataset based at least in part on the out-of-distribution (see fig.3, steps 302-308 and par. 0088-0093 , the output 150 can include a determination of whether an event or the testing data is an out-of-distribution data or not, i.e., whether the testing data is generated based on the same distribution used to generate the collection of data including the training data); and
transmitting, to the base station, an indication that the out-of-distribution detection event has been determined for the at least one data sample(see fig. 2 and par. 0087, 0098, step 214, transmitted for additional processing).
Ren does not explicitly disclose using an out-of-distribution detection rule configuration
Narang discloses detecting using an out-of-distribution detection rule configuration (see pars. 0064, 0157, 0187, showing using rule event detection that associated with any or all of the outputs is deemed too high (e.g., above a set of one or more predetermined thresholds, not satisfying of a set of validation constraints and/or rules, etc.) and enabling a determination of whether or not the input data can be trusted to be determined. this enables out-of-distribution test data to be determined with high confidence. Therefore, it would have been obvious to one having ordinary skill in the art prior to effective filing date of the claimed invention to incorporate the teachings of Narang into the system of Ren wherein when an -distribution event is detected and that the uncertainty is deemed too high, the system selects the fallback response (e.g., rule configuration ,or rule-based processes thereby enabling a determination of whether or not the input data can be trusted to be determined. this enables out-of-distribution test data to be determined with high confidence.
As per claim 2. Ren discloses the method of claim 1, wherein receiving the control signaling comprises: receiving, from the base station, the control signaling that indicates a model configuration for configuring the first machine learning model to generate the at least one data sample and that indicates the out-of-distribution detection rule configuration for configuring a second machine learning model for detecting data samples that fall outside of the dataset used to train the first machine learning model(see figs 2-3 and pars 0082-0085 and 0089-0093)
As per claim 3, Ren discloses the method of claim 1, wherein receiving the control signaling comprises: receiving, from the base station, the control signaling that indicates a model configuration for configuring the first machine learning model to generate the at least one data sample and detect data samples that fall outside of the dataset used to train the first machine learning model (see figs 2-3 and pars 0082-0085 and 0089-0093)
As per claim 4 Reno discloses the method of claim 1, wherein receiving the control signaling comprises: receiving the control signaling that jointly configures the UE with the out-of-distribution detection rule configuration and a model configuration for the first machine learning model (see fig 1b and pars 0082-0085 for configuring and training model1 to model N)
As per claim 5, Ren discloses The method of claim 1, wherein receiving the control signaling comprises: receiving the control signaling that indicates the out-of-distribution detection rule configuration that is a common out-of-distribution detection rule configuration for a plurality of machine learning models.
As per claim 6-7, Ren discloses the method of claim 1, wherein receiving the control signaling comprises: receiving the control signaling indicating the out-of-distribution detection rule configuration that indicates a probability distribution range for a probability distribution for data samples generated by the first machine learning model(see par. 0086, showing out of range threshold value ).
As per claim 8, Ren discloses the method of claim 1, wherein receiving the control signaling comprises: receiving the control signaling indicating the out-of-distribution detection rule configuration that indicates a confidence value threshold for the first machine learning model(see par. 0086, likelihood ratio value to a threshold value
As per claim 9, Ren discloses The method of claim 8, wherein transmitting the indication that the out-of-distribution detection event has been determined comprises: transmitting the indication based at least in part on the at least one data sample satisfying the confidence value threshold (see par. 0064, 0086, satisfying a likelihood ratio value )
As per claim 10, Ren discloses The method of claim 1, wherein receiving the control signaling comprises: receiving the control signaling indicating the out-of-distribution detection rule configuration that indicates a reconstruction error threshold for the first machine learning model(see par. 0006, 0067).
As per claim 11, Ren discloses The method of claim 10, wherein transmitting the indication that the out-of-distribution detection event has been determined comprises: transmitting the indication based at least in part on the at least one data sample satisfying the reconstruction error threshold(see par. 00083, process the data input 204 to generate or reconstruct a second likelihood value 208 for the data input 204.)
As per claims 12-13 Narang discloses the method of claim 1, wherein receiving the control signaling comprises: receiving the control signaling indicating the out-of-distribution detection rule configuration that indicates a feature statistics distribution range and a latent feature location for the first machine learning model(see par. 0155).
As per claim 14, Ren discloses the method of claim 1, wherein receiving the control signaling further comprises: receiving the control signaling indicating the out-of-distribution detection rule configuration that indicates an out-of-distribution detection pattern, wherein the at least one data sample is determined to fall outside of the dataset according to the out-of-distribution detection pattern(see par. 0085-0087)
As per claim 15, Ren discloses The method of claim 14, wherein the out-of-distribution detection pattern indicates a fixed period of instances for the UE to determine whether one or more data samples generated by the first machine learning model fall outside of the dataset used to train the first machine learning model.(see par.0085)
As per claim 16, Ren discloses The method of claim 14, wherein the out-of-distribution detection pattern indicates specific instances for the UE to determine whether one or more data samples generated by the first machine learning model fall outside of the dataset used to train the first machine learning model(see par. 0085-0087)
As per claim 17, Ren discloses The method of claim 1, wherein receiving the control signaling further comprising: receiving the control signaling indicating the out-of-distribution detection rule configuration that indicates one or more parameters to implicitly indicate an out-of-distribution detection pattern(see 0058, 0086 determine a likelihood ratio value for a data input based on outputs from the model(s) 120 and detect whether the data input is OOD based on the likelihood ratio value).
As per claim 18, Ren discloses The method of claim 1, wherein transmitting the indication that the out-of-distribution detection event has been determined comprises: transmitting the indication that indicates a measurement value for the at least one data sample(see par. 0086).
As per claim 19, Ren discloses The method of claim 1, wherein transmitting the indication that the out-of-distribution detection event has been determined comprises: transmitting the indication that the out-of-distribution detection event has been determined based at least in part on a reporting trigger condition being satisfied, a pre-defined reporting pattern, a reporting configuration, or a combination thereof(see par. 0058, determine a likelihood ratio value for a data input based on outputs from the model(s) 120 and detect whether the data input is OOD based on the likelihood ratio value.
As per dependent claims 21-28 , 30-43 and 47-53, the claims are similar with claims 2-19 discussed above and are rejected same rational as claims 2-29
THIS ACTION IS MADE FINAL. 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDULLAHI ELMI SALAD whose telephone number is (571)272-4009. The examiner can normally be reached 9:30AM-6:PM.
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/ABDULLAHI E SALAD/Primary Examiner, Art Unit 2466
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