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 .
Claim Objections
Claim 2 is objected to because of the following informalities: “determining the target input data arrangement type of the current operator comprises” should be “wherein the determining the target input data arrangement type of the current operator comprises.” Appropriate correction is required.
Claim 4 is objected to because of the following informalities: “adjusting the first input data according to the target input data arrangement type of the current operator to obtain the second input data comprises” should be “wherein the adjusting the first input data according to the target input data arrangement type of the current operator to obtain the second input data comprises.” Appropriate correction is required.
Claim 8 is objected to because of the following informalities: “determining the target input data arrangement type of the current operator according to the plurality of data arrangement types corresponding to the plurality of pieces of input data and the plurality of memories occupied by the plurality of pieces of input data comprises” should be “wherein the determining the target input data arrangement type of the current operator according to the plurality of data arrangement types corresponding to the plurality of pieces of input data and the plurality of memories occupied by the plurality of pieces of input data comprises.” Appropriate correction is required.
Claim 9 is objected to because of the following informalities: “the data-arrangement-sensitive operator comprises at least one of following operators” should be “wherein the data-arrangement-sensitive operator comprises at least one of following operators.” Appropriate correction is required.
Claim 10 is objected to because of the following informalities: “the data-arrangement-insensitive operator comprises at least one of following operators” should be “wherein the data-arrangement-insensitive operator comprises at least one of following operators.” Appropriate correction is required.
Claim 20 is objected to because of the following informalities: “wherein he data-arrangement-insensitive operator” should be “wherein the data-arrangement-insensitive operator.” Appropriate correction is required.
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 do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a) an obtaining unit configured to obtain a piece of first input data of a current operator and a data arrangement type of the first input data, b) a determining unit configured to determine a target input data arrangement type of the current operator, c) an adjusting unit configured to adjust the first input data according to the target input data arrangement type of the current operator to obtain a piece of second input data, and d) a computing unit configured to compute based on the second input data and the current operator to obtain an output result in claim 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
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 § 102
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) 1-2, 4, 6-12, 14, 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bao (CN 113449841).
As to claim 1, Bao discloses a neural network computation method, comprising:
obtaining a piece of first input data of a current operator and a data arrangement type of the first input data (see [0092] and [0096]: initial data format);
determining a target input data arrangement type of the current operator (see [0098]: current data format);
adjusting the first input data according to the target input data arrangement type of the current operator to obtain a piece of second input data, wherein a data arrangement type of the second input data is the same as the target input data arrangement type of the current operator (see [0102]-[0104]: when the initial data format of the first operator is different from the current data format, at least one first conversion operator and at least one second conversion operator are inserted … when the initial data format of the first operator is the same as the current data format, it is determined not to insert a transformation operator around the first operator … For operators with input data, a format conversion operator can be inserted before the operator to convert the input data read from memory from the initial data format to the current data format; see [0082]); and
computing based on the second input data and the current operator to obtain an output result, which is a piece of input data of a downstream operator of the current operator or a piece of output data of the neural network (see [0105]).
As to claim 2, Bao further discloses determining the target input data arrangement type of the current operator comprises:
determining the target input data arrangement type of the current operator according to data arrangement requirement of the current operator when the current operator is a data-arrangement-sensitive operator (see [0081]: format-sensitive operators); or
determining the target input data arrangement type of the current operator according to the data arrangement type of the first input data when the current operator is a data-arrangement-insensitive operator (see [0082]: non-format-sensitive operator).
As to claim 4, Bao further discloses adjusting the first input data according to the target input data arrangement type of the current operator to obtain the second input data comprises:
performing a data arrangement conversion on the first input data to obtain the second input data (see [0081] and [0109]); or
determining the first input data as the second input data (see [0082], [0086]).
As to claim 6, Bao further discloses before performing the data arrangement conversion on the first input data to obtain the second input data, further comprising:
determining whether the data arrangement type of the first input data is the same as the target input data arrangement type of the current operator (see [0102]-[0103]);
performing the data arrangement conversion on the first input data to obtain the second input data when the data arrangement type of the first input data is different from the target input data arrangement type of the current operator (see [0102]: when the initial data format of the first operator is different from the current data format, at least one first conversion operator and at least one second conversion operator are inserted); and
taking the first input data as the second input data when the data arrangement type of the first input data is the same as the target input data arrangement type of the current operator (see [0103]: when the initial data format of the first operator is the same as the current data format, it is determined not to insert a transformation operator around the first operator).
As to claim 7, Bao further discloses wherein the first input data comprises a plurality of pieces of input data, and the data arrangement type of the first input data comprises a plurality of data arrangement types corresponding to the plurality of pieces of input data (see [0075]: different data formats including NCHW format and NHWC format), wherein determining the target input data arrangement type of the current operator comprises:
arbitrarily selecting one of the data arrangement types corresponding to the plurality of pieces of input data as the target input data arrangement type of the current operator (see [0075], [0080], [0098]: selecting NCHW format or NHWC format as the initial data format); or
determining the target input data arrangement type of the current operator according to the plurality of data arrangement types corresponding to the plurality of pieces of input data and a plurality of memories occupied by the plurality of pieces of input data.
As to claim 8, Bao further discloses determining the target input data arrangement type of the current operator according to the plurality of data arrangement types corresponding to the plurality of pieces of input data and the plurality of memories occupied by the plurality of pieces of input data comprises: accumulating memories occupied by input data with the same data arrangement type among the plurality of pieces of input data respectively; and determining a data arrangement type corresponding to a maximum memory accumulation result as the target input data arrangement type of the current operator (these features are not required since they are not part of the BRI of claims based on claim 7).
As to claim 9, Bao further discloses the data-arrangement-sensitive operator comprises at least one of following operators: an operator that has an explicit requirement on a data arrangement type of input data; an operator that has no explicit requirement on the data arrangement type of the input data, but the input data contains a mask, an index, and a tensor related to an arrangement order; and an operator whose input data dimension is different from an output dimension, or an operator that depends on an adjacent relationship of dimensions (see [0081]).
As to claim 10, Bao further discloses the data-arrangement-insensitive operator comprises at least one of following operators: an operator with the same computation logic for all input elements; and an operator that operates on a specified dimension and has no dependency on the adjacent relationship of the dimensions, or an addition operator, a subtraction operator, a multiplication operator, or a division operator (see [0082]).
As to claim 11, Bao discloses a neural network computing device (FIG. 19), comprising:
an obtaining unit (see [0180]) configured to obtain a piece of first input data of a current operator and a data arrangement type of the first input data (see [0092] and [0096]: initial data format);
a determining unit (see [0180]) configured to determine a target input data arrangement type of the current operator (see [0098]: current data format);
an adjusting unit (see [0180]) configured to adjust the first input data according to the target input data arrangement type of the current operator to obtain a piece of second input data, wherein a data arrangement type of the second input data is the same as the target input data arrangement type of the current operator (see [0102]-[0104]: when the initial data format of the first operator is different from the current data format, at least one first conversion operator and at least one second conversion operator are inserted … when the initial data format of the first operator is the same as the current data format, it is determined not to insert a transformation operator around the first operator … For operators with input data, a format conversion operator can be inserted before the operator to convert the input data read from memory from the initial data format to the current data format); and
a computing unit (see [0180]) configured to compute based on the second input data and the current operator to obtain an output result, which is a piece of input data of a downstream operator of the current operator or a piece of output data of the neural network (see [0105]).
As to claims 12, 14, 16-20, claims 12, 14, 16-20 recite the same features as those recited in claims 2, 4, 6-10, respectively, and are therefore rejected for the same reasons as used above.
Claim(s) 1-2, 4, 6-12, 14, 16-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Xie et al (US 20230342147).
As to claim 1, Xie discloses a neural network computation method (FIG. 12), comprising:
obtaining a piece of first input data of a current operator and a data arrangement type of the first input data (FIG. 12, input data in NCHW format);
determining a target input data arrangement type of the current operator (FIG. 12 and [0125], data in NHWC format of the Cuda core);
adjusting the first input data according to the target input data arrangement type of the current operator to obtain a piece of second input data, wherein a data arrangement type of the second input data is the same as the target input data arrangement type of the current operator (FIG. 12 and [0125], T operator converts data from NCHW format to NHWC format); and
computing based on the second input data and the current operator to obtain an output result, which is a piece of input data of a downstream operator of the current operator or a piece of output data of the neural network (FIG. 12 and [0126], output data).
As to claim 2, Xie further discloses determining the target input data arrangement type of the current operator comprises:
determining the target input data arrangement type of the current operator according to data arrangement requirement of the current operator when the current operator is a data-arrangement-sensitive operator (FIG. 12 and [0089]: deconvolution operator (DConV) is data-arrangement-sensitive); or
determining the target input data arrangement type of the current operator according to the data arrangement type of the first input data when the current operator is a data-arrangement-insensitive operator (FIG. 12 and [0089]: RELU is data-arrangement-insensitive).
As to claim 4, Xie further discloses adjusting the first input data according to the target input data arrangement type of the current operator to obtain the second input data comprises:
performing a data arrangement conversion on the first input data to obtain the second input data (FIG. 12 and [0125]-[0126]: T operator converts data from NCHW format to NHWC format for DconV in Cuda core); or
determining the first input data as the second input data (FIG. 12 and [0126]: no conversion performed between DconV and RELU, the data is in NHWC format).
As to claim 6, Xie further discloses before performing the data arrangement conversion on the first input data to obtain the second input data, further comprising:
determining whether the data arrangement type of the first input data is the same as the target input data arrangement type of the current operator (see [0126]: data in NCHW or NHWC formats);
performing the data arrangement conversion on the first input data to obtain the second input data when the data arrangement type of the first input data is different from the target input data arrangement type of the current operator (FIG. 12 and [0125]-[0126]: T operator converts data from NCHW format to NHWC format for DconV in Cuda core); and
taking the first input data as the second input data when the data arrangement type of the first input data is the same as the target input data arrangement type of the current operator (FIG. 12 and [0126]: no conversion performed between DconV and RELU, the data is in NHWC format).
As to claim 7, Xie further discloses wherein the first input data comprises a plurality of pieces of input data, and the data arrangement type of the first input data comprises a plurality of data arrangement types corresponding to the plurality of pieces of input data (see [0120]: and [0127]: dimensions of the NX format should be at least four dimensions, to include dimensions in an NZ format, an ND format, an NCHW format, and an NHWC format), wherein determining the target input data arrangement type of the current operator comprises:
arbitrarily selecting one of the data arrangement types corresponding to the plurality of pieces of input data as the target input data arrangement type of the current operator (see [0124] and [0127]); or
determining the target input data arrangement type of the current operator according to the plurality of data arrangement types corresponding to the plurality of pieces of input data and a plurality of memories occupied by the plurality of pieces of input data.
As to claim 8, Xie further discloses determining the target input data arrangement type of the current operator according to the plurality of data arrangement types corresponding to the plurality of pieces of input data and the plurality of memories occupied by the plurality of pieces of input data comprises: accumulating memories occupied by input data with the same data arrangement type among the plurality of pieces of input data respectively; and determining a data arrangement type corresponding to a maximum memory accumulation result as the target input data arrangement type of the current operator (these features are not required since they are not part of the BRI of claims based on claim 7).
As to claim 9, Xie further discloses the data-arrangement-sensitive operator comprises at least one of following operators: an operator that has an explicit requirement on a data arrangement type of input data; an operator that has no explicit requirement on the data arrangement type of the input data, but the input data contains a mask, an index, and a tensor related to an arrangement order; and an operator whose input data dimension is different from an output dimension, or an operator that depends on an adjacent relationship of dimensions (see [0089]).
As to claim 10, Xie further discloses the data-arrangement-insensitive operator comprises at least one of following operators: an operator with the same computation logic for all input elements; and an operator that operates on a specified dimension and has no dependency on the adjacent relationship of the dimensions, or an addition operator, a subtraction operator, a multiplication operator, or a division operator (see [0089]).
As to claim 11, Xie discloses a neural network computing device (FIG. 2), comprising:
an obtaining unit (see [0101]) configured to obtain a piece of first input data of a current operator and a data arrangement type of the first input data (FIG. 12, input data in NCHW format);
a determining unit (see [0101]) configured to determine a target input data arrangement type of the current operator (FIG. 12 and [0125], data in NHWC format of the Cuda core);
an adjusting unit (see [0101]) configured to adjust the first input data according to the target input data arrangement type of the current operator to obtain a piece of second input data, wherein a data arrangement type of the second input data is the same as the target input data arrangement type of the current operator (FIG. 12 and [0125], T operator converts data from NCHW format to NHWC format); and
a computing unit (see [0101]) configured to compute based on the second input data and the current operator to obtain an output result, which is a piece of input data of a downstream operator of the current operator or a piece of output data of the neural network (FIG. 12 and [0126], output data).
As to claims 12, 14, 16-20, claims 12, 14, 16-20 recite the same features as those recited in claims 2, 4, 6-10, respectively, and are therefore rejected for the same reasons as used above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOUBACAR ABDOU TCHOUSSOU whose telephone number is (571)272-7625. The examiner can normally be reached M-F 8am-4pm.
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/BOUBACAR ABDOU TCHOUSSOU/Primary Examiner, Art Unit 2482
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