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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05-02-2025 is in compliance
with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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:
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
nonstructural term having no specific structural meaning) for performing the claimed function;
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
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 preAIA
35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
The following language in Claim 29 is being interpreted under 35 U.S.C 112(f):
means for loading
means for swapping
means for generating
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA
35 U.S.C. 112, sixth paragraph, they 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-A IA 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 29 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second
paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 29 limitations “means for loading”, “means for generating”, “means for swapping” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The Specification ¶[0087], states "the various operations of methods described above may be performed by any suitable means capable of performing the corresponding functions. The means may include various hardware and/or software component(s) and/or module(s), including, but not limited to a circuit, an application specific integrated circuit (ASIC), or processor. Generally, where there are operations illustrated in figures, those operations may have corresponding counterpart means-plus-function components with similar numbering.". However, the fact that it “include various hardware and/or software component(s) and/or module(s), including, but not limited to a circuit, an application specific integrated circuit (ASIC), or processor” gives no information about the actual structure of the “means for loading”, “means for generating”, “means for swapping”. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Examiner is interpreting “means for loading”, “means for generating”, and “means for swapping” as a hardware part of the processing system.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already
implicitly or inherently discloses the corresponding structure, material, or acts and clearly links
them to the function so that one of ordinary skill in the art would recognize what structure,
material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.0l(o) and 2181.
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, 2, 4-12, 14-6, 18-26, 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (US 11,386,326 B2) in view of Lahiry et al. (“Dual Dictionary Compression for the Last Level Cache”).
Regarding claim 1, Lin explicitly discloses:
A processing system, comprising: at least one memory having executable instructions stored thereon; (Lin, Col. 3, Lines 20-22: “In another aspect, there is provided a computer program product including a non-transitory computer readable medium storing instructions.”)
one or more processors configured to execute the executable instructions to cause the processing system to: (Lin, Col. 3, Lines 22-23: “The instructions may cause operations may executed by at least one data processor.”)
load, into the first memory, a dictionary associated with at least a portion of a machine learning model; (Lin, Col. 2, Lines 47-53: “The method may include: transforming a trained machine learning model including by replacing at least one layer of the trained machine learning model with a dictionary matrix and a coefficient matrix, the dictionary matrix and the coefficient matrix formed by at least decomposing a weight matrix associated with the at least one layer of the trained machine learning model”)
load, into the first memory from the second memory, a first coefficient matrix associated with a first portion of the machine learning model; (Lin, Col. 2, Lines 47-53: “The method may include: transforming a trained machine learning model including by replacing at least one layer of the trained machine learning model with a dictionary matrix and a coefficient matrix, the dictionary matrix and the coefficient matrix formed by at least decomposing a weight matrix associated with the at least one layer of the trained machine learning model”)
generate a first intermediate output associated with the first portion of the machine learning model based on an input into the at least the portion of the machine learning model, the dictionary, and the first coefficient matrix; (Lin, Col. 9, Lines 55- : “For example, each layer of the machine learning model 100 including, for example, the input layer 110, the first intermediate layer 120a, the second intermediate layer 120b, and the output layer 130, may be associated with a weight matrix Wmxn. Each element included in the weight matrix. Each element included in the weight matrix Wmxn may correspond to one of a plurality of weights (e.g., w1, w2, w1) applied at a corresponding layer of the machine learning model 100. The weight matrix Wmxn may be decomposed, by subspace projection, into a coefficient matrix Czxn and a dictionary matrix Dmxz· The product of the dictionary matrix Dmxz and the coefficient matrix Czxn may provide a reduced-dimension representation DC of the weight matrix Wmxn in which l<<n. Each column in the weight matrix Wmxn may be reconstructed as a linear com-bination of one or more columns in the dictionary matrix Dmxz as determined by the coefficient matrix Czxn· ”)
generate a second intermediate output associated with the second portion of the machine learning model based on the input into the at least the portion of the machine learning model, the first intermediate output, the dictionary, and the second coefficient matrix. (Lin, Col. 9, Lines 55- : “For example, each layer of the machine learning model 100 including, for example, the input layer 110, the first intermediate layer 120a, the second intermediate layer 120b, and the output layer 130, may be associated with a weight matrix Wmxn. Each element included in the weight matrix. Each element included in the weight matrix Wmxn may correspond to one of a plurality of weights (e.g., w1, w2, w1) applied at a corresponding layer of the machine learning model 100. The weight matrix Wmxn may be decomposed, by subspace projection, into a coefficient matrix Czxn and a dictionary matrix Dmxz· The product of the dictionary matrix Dmxz and the coefficient matrix Czxn may provide a reduced-dimension representation DC of the weight matrix Wmxn in which l<<n. Each column in the weight matrix Wmxn may be reconstructed as a linear combination of one or more columns in the dictionary matrix Dmxz as determined by the coefficient matrix Czxn· ”)
Lin fails to teach:
a first memory;
a second memory; and
swap the first coefficient matrix out of and a second coefficient matrix into the second memory, the second coefficient matrix being associated with a second portion of the machine learning model; and
However, Lahiry explicitly teaches:
a first memory; (Lahiry, Pg. 355, Fig. 3:
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, Pg. 355, Col. 2: “Figure 3 presents a block diagram of our cache model. We provide a dictionary swapping unit between the last level cache and main memory. The on-chip dictionary holds 64 entries and the larger off-chip dictionary holds 128 entries”)
a second memory; and (Lahiry, Pg. 355, Fig. 3:
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, Pg. 355, Col. 2: “Figure 3 presents a block diagram of our cache model. We provide a dictionary swapping unit between the last level cache and main memory. The on-chip dictionary holds 64 entries and the larger off-chip dictionary holds 128 entries”)
swap the first coefficient matrix out of and a second coefficient matrix into the second memory, the second coefficient matrix being associated with a second portion of the machine learning model; and (Lahiry, Pg. 356, Col. 1: “We can then use multiple criteria to determine if it is worth transferring the data in its compressed form. If we meet the criteria, we perform a dictionary index swap operation to replace the on-chip dictionary indices with the indices in memory.”, Pg. 356, Col. 2: “The dictionary index swap operation must only be done when specific conditions are met during block eviction. First, we check to see if the compressed line references a valid dictionary entry. If there is no valid entry, then a swap is unnecessary, since the compressed line is statically compressed and can be transferred uncompressed. If the compressed line references a valid entry in the dictionary, we check the metadata to see if the entry already exists in the off-chip dictionary. If it does, we can perform the swap and transfer the updated compressed line to memory.”)
The combination of Lin and Lahiry are analogous art because they are in the same field of training time series data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, having the teachings of Lin and Lahiry before them, to modify the teachings of Lin to include the teachings of Lahiry to improve bandwidth by transferring the data in compressed form instead of decompressing on eviction and to help with data consistency.
Regarding claim 2, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein a sum of a size of the dictionary and a size of the first coefficient matrix has an upper bound equal to a size associated with the first memory. (Lin, Col. 1, Lines 50-53: “and a product of the dictionary matrix and the coefficient matrix comprising a reduced-dimension representation of the weight matrix associated with the at least one layer of the trained machine learning model”, Col. 2, Lines 31-37: “A first layer of the trained machine learning model is associated with a first threshold value and a second layer of the trained machine learning model may be associated with a second threshold value. The first threshold value and/or the second threshold value may be adjusted based at least on one or more resource constraints associated with the client”)
Regarding claim 4, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein the first coefficient matrix is associated with a layer of the machine learning model. (Lin, Col. 1, Lines 47-50: “the dictionary matrix and the coefficient matrix formed by at least decomposing a weight matrix associated with the at least one layer of the trained machine learning mode”)
Regarding claim 5, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein the first memory comprises a memory collocated with at least one of the one or more processors and wherein the second memory comprises a memory remote from the at least one of the one or more processors. (Lahiry, Pg. 355, Fig. 3:
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, Pg. 355, Col. 2: “Figure 3 presents a block diagram of our cache model. We provide a dictionary swapping unit between the last level cache and main memory. The on-chip dictionary holds 64 entries and the larger off-chip dictionary holds 128 entries”)
Regarding claim 6, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein the dictionary comprises an overcomplete dictionary. (Lin, Col. 9, Lines 44-54: “The length of the dictionary matrix ( e.g., the quantity of columns included in the dictionary matrix) may determine the resources required to update the transformed machine learning model 100" at the client 320. Accordingly, transforming the trained machine learning model 100' into the transformed machine learning model 100" may include decomposing the weight matrix associated with at least one layer of the trained machine learning model 100' into a dictionary matrix whose length does not exceed the resource constraint 420 of the client 320.”)
Regarding claim 7, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein the dictionary comprises a plurality of sub-dictionaries, each respective sub-dictionary being associated with a respective portion of the machine learning model. (Lin, Col. 6, Lines 31-35: “the updates to the transformed machine learning model may be limited to the dictionary matrix of that single layer and to portions ( e.g., rows) of the dictionary matrix corresponding to new categories of data”)
Regarding claim 8, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein the dictionary comprises a column-normalized dictionary, and (Lin, Col. 10, Lines 37-40: “Table 2 below depicts an example of an adaptive projection algorithm for iteratively decomposing the weight matrix Wmxn into the coefficient matrix Czxn and the dictionary matrix Dmxz·”, Table 2:
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)
wherein generating the first intermediate output is further based on a normalization scaling factor associated with the column-normalized dictionary. (Lin, Col. 6, Lines 57-60: “each of the first intermediate layer 120a and/or the second intermediate layer 120b may be implemented as a core computation layer, normalization layer, pooling layer, non-linear layer, and/or the like.”)
Regarding claim 9, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein the dictionary comprises a low-rank decomposition of one or more matrices defining the at least the portion of the machine learning model. (Lin, Col. 2, Lines 53-57: “a product of the dictionary matrix and the coefficient matrix comprising a reduced-dimension representation of the weight matrix associated with the at least one layer of the trained machine learning model;”)
Regarding claim 10, the combination of Lin and Lahiry discloses all the limitation of claim 9 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein the low-rank decomposition of the one or more matrices defining the at least the portion of the machine learning model comprises a singular value decomposition of the one or more matrices defining the at least the portion of the machine learning model. (Lin, Col. 10, Lines 6-12: “the reduced-dimension representation DC of the weight matrix Wmxn may be associated with a decomposition error threshold β, which may correspond to a maximum tolerable difference between the weight matrix Wmxn and the reduced-dimension representation DC of the weight matrix Wmxn based on the coefficient matrix Czxn and the dictionary matrix Dmxz (e.g., |W-DC| < β”)
Regarding claim 11, the combination of Lin and Lahiry discloses all the limitation of claim 9 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein the dictionary comprises a sparse decomposition of the low-rank decomposition of the one or more matrices defining the at least the portion of the machine learning model. (Lin, Col. 2, Lines 53-57: “a product of the dictionary matrix and the coefficient matrix comprising a reduced-dimension representation of the weight matrix associated with the at least one layer of the trained machine learning model;”)
Regarding claim 12, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein a matrix product of the dictionary and the first coefficient matrix comprises a weight matrix associated with the first portion of the machine learning model. (Lin, Col. 9, Lines 55-67: “each layer of the machine learning model 100 including, for example, the input layer 110, the first intermediate layer 120a, the second intermediate layer 120b, and the output layer 130, may be associated with a weight matrix Wmxn·… The product of the dictionary matrix Dmxz and the coefficient matrix Czxn may provide a reduced-dimension representation DC of the weight matrix Wmxn in which l<<n.”)
Regarding claim 14, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry further teaches:
wherein the first memory comprises static random-access memory (SRAM) and wherein the second memory comprises dynamic random-access memory (DRAM). (Lahiry, Pg. 353, Col. 2, ¶[2]: “These dictionaries entries can be static or dynamic.”, Pg. 355, Fig. 3:
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, Pg. 355, Col. 2: “Figure 3 presents a block diagram of our cache model. We provide a dictionary swapping unit between the last level cache and main memory. The on-chip dictionary holds 64 entries and the larger off-chip dictionary holds 128 entries)
Regarding to independent claim 15, 29 and 30, they are rejected under the same rationale with independent claim 1 as they are analogous claims.
Regarding to dependent claim 16, it is rejected under the same rationale with dependent claim 2 as they are analogous claims.
Regarding to dependent claim 18, it is rejected under the same rationale with dependent claim 4 as they are analogous claims.
Regarding to dependent claim 19, it is rejected under the same rationale with dependent claim 5 as they are analogous claims.
Regarding to dependent claim 20, it is rejected under the same rationale with dependent claim 6 as they are analogous claims.
Regarding to dependent claim 21, it is rejected under the same rationale with dependent claim 7 as they are analogous claims.
Regarding to dependent claim 22, it is rejected under the same rationale with dependent claim 8 as they are analogous claims.
Regarding to dependent claim 23, it is rejected under the same rationale with dependent claim 9 as they are analogous claims.
Regarding to dependent claim 24, it is rejected under the same rationale with dependent claim 10 as they are analogous claims.
Regarding to dependent claim 25, it is rejected under the same rationale with dependent claim 11 as they are analogous claims.
Regarding to dependent claim 26, it is rejected under the same rationale with dependent claim 12 as they are analogous claims.
Regarding to dependent claim 28, it is rejected under the same rationale with dependent claim 14 as they are analogous claims.
Claim(s) 3, 13, 17 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. (US 11,386,326 B2) in view of Lahiry et al. (“Dual Dictionary Compression for the Last Level Cache”) and further in view of Lou et al. (US 2023/0104491 A1)
Regarding claim 3, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry fails to teach:
wherein the first coefficient matrix is associated with an attention head in the machine learning model.
However, Lou explicitly teaches:
wherein the first coefficient matrix is associated with an attention head in the machine learning model. (Lou, ¶[0008]: “The method also includes performing a linear projection of the input in each of the plurality of encoder blocks using the attention dictionary, the index matrix associated with the respective encoder block, and the coefficient matrix associated with the respective encoder block.”)
The combination of Lin, Lahiry and Lou are analogous art because they are in the same field of training time series data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, having the teachings of Lin, Lahiry and Lou before them, to modify the teachings of Lin and Lihary to include the teachings of Lou to improve bandwidth by transferring the data in compressed form instead of decompressing on eviction and to help with data consistency.
Regarding claim 13, the combination of Lin and Lahiry discloses all the limitation of claim 1 (as shown in the rejections above).
Lin in view of Lahiry fails to teach:
wherein the first coefficient matrix comprises a sparse coefficient matrix.
However, Lou explicitly teaches:
wherein the first coefficient matrix comprises a sparse coefficient matrix. (Lou, ¶[0084]: “generating a sparse coefficient matrix for each encoder block from the index matrix and the coefficient matrix associated with the encoder block,”)
Regarding dependent claim 17, it is rejected under the same rationale with claim 3 because they are analogous claims.
Regarding dependent claim 27, it is rejected under the same rationale with claim 13 because they are analogous claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY TRAN whose telephone number is (571)270-0693. The examiner can normally be reached Monday - Friday 7:30 am - 5:00 pm EST.
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/AMY TRAN/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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