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 office action is in response to the Preliminary amendment filed 10/10/2024.
Claims 1-11, 12-14, 15-20 are pending.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CHINA 202111658528.4, filed on 12/31/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/25/2024, 04/08/2025, 06/04/2025 was filed. The submission 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 § 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.
Claim(s) 1, 2, 3, 12, 13, 15, 16, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over MU et al. ( US 20240265307, hereinafter, MU’s 307 ) in view of Lo et al. ( US 20230006913, Provisional Application No. 63215796 ).
Regarding to the claim 1, US 20240265307 teaches a method associated with a model, comprising:
determining a data processing method for the model (model training, model inference ) [see figure 1 and Paragraph 0097 ] ; and
implementing, according to the data processing method for the model, at least one of the following:
performing model training or performing model inference ( performing mode training or performing model inference ) [see figure 1 and Paragraph 0097 ] [also see figure 24 and Paragraphs 0251-0252].
However, US 20240265307 does not explicitly teach wherein the data processing method for the model comprises at least one of the following: an input data padding method for the model or an output data truncating method for the model; or
the data processing method for the model comprises at least one of the following:
an input data truncating method for the model, or an output data padding method for the model.
( US 20230006913, Provisional Application No. 63215796 ), from the same or similar fields of endeavor, teaches wherein the data processing method for the model comprises at least one of the following:
an input data padding method for the model ) [see Figure 9A & Figure 9B and Paragraphs 0128 -0129 ] [see Provisional Application 63215796, Pages 17-18 and Figure 5 and Figure 6 ] or an output data truncating method for the model; or
the data processing method for the model comprises at least one of the following:
an input data truncating method for the model , or an output data padding method for the model.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the system of US 20240265307 in view of US 20230006913 because US 20230006913suggests that spects of the present disclosure may also be applied to deployment of 5G communication systems, 6G communications systems, or communications using THz bands.
Regarding to the claim 2, US 20240265307 further teaches wherein the determining the data processing method for the model comprises: receiving indication information from a first node, wherein the indication information indicates the data processing method for the model ( receiving indication information from a first node, wherein the indication information indicates the data processing method for the model ) [see Figure 24 and Paragraphs 0250-0252] ; or determining the data processing method for the model as defined in a protocol
Regarding to the claim 3, US 20240265307 and ( US 20230006913, Provisional Application No. 63215796 ) teach the limitations of the claim 1 above.
However, US 20240265307 does not explicitly teach wherein the input data padding method or the output data padding method comprises at least one of the following: a length of padded data, a padding data type, or a data padding rule.
( US 20230006913, Provisional Application No. 63215796 ), from the same or similar fields of endeavor, teaches wherein the input data padding method or the output data padding method comprises at least one of the following: a length of padded data, a padding data type, or a data padding rule (wherein the input data padding method or the output data padding method comprises at least one of the following: a length of padded data, a padding data type, or a data padding rule) [see Paragraphs 0128-0129].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the system of US 20240265307 in view of US 20230006913 because US 20230006913suggests that spects of the present disclosure may also be applied to deployment of 5G communication systems, 6G communications systems, or communications using THz bands.
Regarding to the claim 12, US 20240265307 teaches a method associated with a model, comprising:
Sending indication information to a second node, wherein the indication information indicates a data processing method for the model (sending indication information to a second node wherein the indication information indicates a model training or model inference ) [see Paragraphs 0250-252 and Figure 24 ].
However, US 20240265307 does not explicitly teach wherein the data processing method for the model comprises at least one of the following: an input data padding method for the model or an output data truncating method for the model; or
the data processing method for the model comprises at least one of the following:
an input data truncating method for the model, or an output data padding method for the model.
( US 20230006913, Provisional Application No. 63215796 ), from the same or similar fields of endeavor, teaches wherein the data processing method for the model comprises at least one of the following:
an input data padding method for the model ) [see Figure 9A & Figure 9B and Paragraphs 0128 -0129 ] [see Provisional Application 63215796, Pages 17-18 and Figure 5 and Figure 6 ] or an output data truncating method for the model; or
the data processing method for the model comprises at least one of the following:
an input data truncating method for the model , or an output data padding method for the model.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the system of US 20240265307 in view of US 20230006913 because US 20230006913suggests that spects of the present disclosure may also be applied to deployment of 5G communication systems, 6G communications systems, or communications using THz bands.
Regarding to the claim 13, claim 13 is rejected the same limitations of the claim 3 above.
Regarding to the claim 15, US 20240265307 teaches a communication apparatus, comprising at least one processor coupled to at least one memory, the memory storing programming instructions for execution by the at least one processor to:
determining a data processing method for the model (model training, model inference ) [see figure 1 and Paragraph 0097 ] ; and
implementing, according to the data processing method for the model, at least one of the following:
performing model training or performing model inference ( performing mode training or performing model inference ) [see figure 1 and Paragraph 0097 ] [also see figure 24 and Paragraphs 0251-0252].
However, US 20240265307 does not explicitly teach wherein the data processing method for the model comprises at least one of the following: an input data padding method for the model or an output data truncating method for the model; or
the data processing method for the model comprises at least one of the following:
an input data truncating method for the model, or an output data padding method for the model.
( US 20230006913, Provisional Application No. 63215796 ), from the same or similar fields of endeavor, teaches wherein the data processing method for the model comprises at least one of the following:
an input data padding method for the model ) [see Figure 9A & Figure 9B and Paragraphs 0128 -0129 ] [see Provisional Application 63215796, Pages 17-18 and Figure 5 and Figure 6 ] or an output data truncating method for the model; or
the data processing method for the model comprises at least one of the following:
an input data truncating method for the model , or an output data padding method for the model.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the system of US 20240265307 in view of US 20230006913 because US 20230006913suggests that spects of the present disclosure may also be applied to deployment of 5G communication systems, 6G communications systems, or communications using THz bands.
Regarding to the claim 16, claim 16 is rejected the same limitations of the claim 2 above.
Regarding to the claim 17, claim 17 is rejected the same limitations of the claim 3 above.
Claim(s) 4, 5, 6 , 7, 8, 9, 10, 14, 18, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over MU et al. ( US 20240265307, hereinafter, MU’s 307 ) in view of Lo et al. ( US 20230006913, Provisional Application No. 63215796 ), and further view of Mohassel et al. ( US 20220092216, hereinafter, Mohassel’ 216 ).
Regarding to the claim 4, US 20240265307 and US 20230006913 teach the limitations of the claim 1 above.
However, US 20240265307 does not explicitly teach wherein the output data truncating method or the input data truncating method comprises at least one of the following: a length of data after truncating, or a data truncating rule.
US 20220092216, from the same or similar fields of endeavor, teaches wherein the output data truncating method or the input data truncating method comprises at least one of the following: a length of data after truncating, or a data truncating rule (wherein the output data truncating method or the input data truncating method comprises at least one of the following: a length of data after truncating, or a data truncating rule) [see Paragraphs 0238 & 0248 ] [also see the paragraphs 0126 & 0009 & 0147 & 0156].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the combined system (US 20240265307 and US 20230006913 ), and further in view of US 20220092216 because US 20220092216 suggests that privacy-preserving machine learning offers guarantees that provide a strong first line of defense which can be strengthened when combined with orthogonal mechanisms such as differential privacy.
Regarding to the claim 5, US 20240265307 and US 20230006913 teach the limitations of the claim 1 above.
However, US 20240265307 does not explicitly teach performing data padding on first input data according to the input data padding method, to obtain second input data; determining first output data based on the second input data and the model; and performing data truncating on the first output data according to the output data truncating method.
US 20220092216, from the same or similar fields of endeavor, teaches performing data padding on first input data according to the input data padding method, to obtain second input data; determining first output data based on the second input data and the model; and performing data truncating on the first output data according to the output data truncating method ( performing data padding on first input data according to the input data padding method, to obtain second input data; determining first output data based on the second input data and the model; and performing data truncating on the first output data according to the output data truncating method) [see Paragraphs 0238 & 0248 ] [also see the paragraphs 0126 & 0009 & 0147 & 0156].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the combined system (US 20240265307 and US 20230006913 ), and further in view of US 20220092216 because US 20220092216 suggests that privacy-preserving machine learning offers guarantees that provide a strong first line of defense which can be strengthened when combined with orthogonal mechanisms such as differential privacy.
Regarding to the claim 6, US 20240265307 and US 20230006913 teach the limitations of the claim 1 above.
However, US 20240265307 does not explicitly teach performing data padding on first input training data according to the input data padding method, to obtain second input training data; determining first output training data based on the second input training data and the model; and performing data truncating on the first output training data according to the output data truncating method, to obtain second output training data; and performing parameter adjustment on the model based on the second output training data.
US 20220092216, from the same or similar fields of endeavor, teaches performing data padding on first input training data according to the input data padding method, to obtain second input training data; determining first output training data based on the second input training data and the model; and performing data truncating on the first output training data according to the output data truncating method, to obtain second output training data; and performing parameter adjustment on the model based on the second output training data (performing data padding on first input training data according to the input data padding method, to obtain second input training data; determining first output training data based on the second input training data and the model; and performing data truncating on the first output training data according to the output data truncating method, to obtain second output training data; and performing parameter adjustment on the model based on the second output training data ) [see Paragraphs 0238 & 0248 ] [also see the paragraphs 0126 & 0009 & 0147 & 0156].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the combined system (US 20240265307 and US 20230006913 ), and further in view of US 20220092216 because US 20220092216 suggests that privacy-preserving machine learning offers guarantees that provide a strong first line of defense which can be strengthened when combined with orthogonal mechanisms such as differential privacy.
Regarding to the claim 7, US 20240265307 and ( US 20230006913, Provisional Application No. 63215796 ) teach the limitations of the claim 1 above.
However, US 20240265307 does not explicitly teach performing data truncating on first input data according to the input data truncating method, to obtain second input data; determining first output data based on the second input data and the model; and performing data padding on the first output data according to the output data padding method.
US 20220092216, from the same or similar fields of endeavor, teaches performing data truncating on first input data according to the input data truncating method, to obtain second input data; determining first output data based on the second input data and the model; and performing data padding on the first output data according to the output data padding method ( performing data truncating on first input data according to the input data truncating method, to obtain second input data; determining first output data based on the second input data and the model; and performing data padding on the first output data according to the output data padding method) [see Paragraphs 0238 & 0248 ] [also see the paragraphs 0126 & 0009 & 0147 & 0156].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the combined system (US 20240265307 and US 20230006913 ), and further in view of US 20220092216 because US 20220092216 suggests that privacy-preserving machine learning offers guarantees that provide a strong first line of defense which can be strengthened when combined with orthogonal mechanisms such as differential privacy.
Regarding to the claim 8, US 20240265307 and ( US 20230006913, Provisional Application No. 63215796 ) teach the limitations of the claim 1 above.
However, US 20240265307 does not explicitly teach performing data truncating on first input training data according to the input data truncating method, to obtain second input training data; determining first output training data based on the second input training data and the model; performing data padding on the first output training data according to the output data padding method, to obtain second output training data; and performing parameter adjustment on the model based on the second output training data.
US 20220092216, from the same or similar fields of endeavor, teaches performing data truncating on first input training data according to the input data truncating method, to obtain second input training data; determining first output training data based on the second input training data and the model; performing data padding on the first output training data according to the output data padding method, to obtain second output training data; and performing parameter adjustment on the model based on the second output training data ( performing data truncating on first input training data according to the input data truncating method, to obtain second input training data; determining first output training data based on the second input training data and the model; performing data padding on the first output training data according to the output data padding method, to obtain second output training data; and performing parameter adjustment on the model based on the second output training data) [see Paragraphs 0238 & 0248 ] [also see the paragraphs 0126 & 0009 & 0147 & 0156].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the combined system (US 20240265307 and US 20230006913 ), and further in view of US 20220092216 because US 20220092216 suggests that privacy-preserving machine learning offers guarantees that provide a strong first line of defense which can be strengthened when combined with orthogonal mechanisms such as differential privacy.
Regarding to the claim 9, US 20240265307 and ( US 20230006913, Provisional Application No. 63215796 ) teach the limitations of the claim 1 above.
However, US 20240265307 does not explicitly teach determining first output data of the first sub-model; and performing data truncating on the first output data according to the output data truncating method.
US 20220092216, from the same or similar fields of endeavor, teaches determining first output data of the first sub-model; and performing data truncating on the first output data according to the output data truncating method ( determining first output data of the first sub-model; and performing data truncating on the first output data according to the output data truncating method) [see Paragraphs 0238 & 0248 ] [also see the paragraphs 0126 & 0009 & 0147 & 0156].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the combined system (US 20240265307 and US 20230006913 ), and further in view of US 20220092216 because US 20220092216 suggests that privacy-preserving machine learning offers guarantees that provide a strong first line of defense which can be strengthened when combined with orthogonal mechanisms such as differential privacy.
Regarding to the claim 10, US 20240265307 and ( US 20230006913, Provisional Application No. 63215796 ) teach the limitations of the claim 1 above.
However, US 20240265307 does not explicitly teach performing data padding on first input data according to the input data padding method, to obtain second input data; and determining first output data based on the second input data and the second sub-model.
US 20220092216, from the same or similar fields of endeavor, teaches performing data padding on first input data according to the input data padding method, to obtain second input data; and determining first output data based on the second input data and the second sub-model ( performing data padding on first input data according to the input data padding method, to obtain second input data; and determining first output data based on the second input data and the second sub-model)
[see Paragraphs 0238 & 0248 ] [also see the paragraphs 0126 & 0009 & 0147 & 0156].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing data of the claimed invention to modify the combined system (US 20240265307 and US 20230006913 ), and further in view of US 20220092216 because US 20220092216 suggests that privacy-preserving machine learning offers guarantees that provide a strong first line of defense which can be strengthened when combined with orthogonal mechanisms such as differential privacy.
Regarding to the claim 14, claim 14 is rejected the same limitations of the claim 4 above.
Regarding to the claim 18, claim 18 is rejected the same limitations of the claim 9 above.
Regarding to the claim 19, claim 19 is rejected the same limitations of the claim 10 above.
Allowable Subject Matter
Claim 11 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.
The following is an examiner’s statement of reasons for allowance:
The prior art fails to disclose adjusting, based on the input training data of the first sub-model and the output training data of the second sub-model, at least one of the following: a model parameter of the first sub-model, or a model parameter of the second sub-model.
Claim 20 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.
The following is an examiner’s statement of reasons for allowance:
The prior art fails to disclose adjust, based on the input training data of the first sub-model and the output training data of the second sub-model, at least one of the following: a model parameter of the first sub-model, or a model parameter of the second sub-model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHUONG T HO whose telephone number is (571)272-3133. The examiner can normally be reached 7:30-4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles C Jiang can be reached at 571-270-7191. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHUONG T HO/Examiner, Art Unit 2412
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