DETAILED ACTION
This action is in response to the filing of 9-4-2024. Claims 1-8 are pending and have been considered below:
Allowable Subject Matter
Claims 5-6 are 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.
Claim Interpretation
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: calculation unit, rate setting unit, rate calculation unit, contraction unit, relearning unit, parameter setting unit, confirmation unit. Accuracy confirmation unit, observation unit, reception unit in claims 1, 3, 5 and 6.
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.
The units are found in the DNN contraction device including the hardware of Figure 2:600.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-8 represent method, system and medium type claims. Therefore claims 1-8 are directed to either a process, machine, manufacture or composition of matter.
Regarding claims 1 and 8:
2A Prong 1:
an importance calculation unit that calculates an importance of a channel by using a weighting factor set for the channel of a multilayer neural network model having the channel including a plurality of neurons for each of an input layer, an intermediate layer, and an output layer; a target contraction rate setting unit that sets a target contraction rate for the entire multilayer neural network model;
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion- calculating importance can be mentally performed).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
each layer contraction rate calculation unit that calculates a contraction rate of each layer of the multilayer neural network model based on the importance and the target contraction rate; a contraction unit that contracts each layer in accordance with the contraction rate calculated by each layer contraction rate calculation unit to generate a post- contraction multilayer neural network model; and a relearning unit that performs relearning of the post- contraction multilayer neural network model.
(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
each layer contraction rate calculation unit that calculates a contraction rate of each layer of the multilayer neural network model based on the importance and the target contraction rate; a contraction unit that contracts each layer in accordance with the contraction rate calculated by each layer contraction rate calculation unit to generate a post- contraction multilayer neural network model; and a relearning unit that performs relearning of the post- contraction multilayer neural network model.
(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
Regarding claim 2:
2A Prong 1:
wherein the importance calculation unit calculates an importance of the channel at a preceding stage connected to the channel at a subsequent stage based on the importance of the channel at the subsequent stage and a weighting factor of the channel at the subsequent stage in order from a layer closer to the output layer, and the each layer contraction rate calculation unit calculates a target contraction rate of each layer with respect to the target contraction rate of the entire multilayer neural network model based on the importance in each layer of the importance calculated for each channel.
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion- calculating can be mentally performed).
2A Prong 2: This judicial exception is not integrated into a practical application.
No Additional elements:
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
No Additional elements:
Regarding claim 3:
2A Prong 1:
a parameter setting unit that sets the importance and number of arithmetic operations of each layer extracted from the multilayer neural network model as parameters, wherein the each layer contraction rate calculation unit calculates a target contraction rate of each layer based on the parameter and the target contraction rate in the entire multilayer neural network model.
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion- setting parameters and calculating can be mentally performed).
2A Prong 2: This judicial exception is not integrated into a practical application.
No Additional elements:
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
No Additional elements:
Regarding claim 4:
2A Prong 1:
No abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the parameter setting unit sets a contribution degree of the importance calculated for each of the channels and a contribution degree of the number of arithmetic operations of each of the layers as the parameter.
(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the parameter setting unit sets a contribution degree of the importance calculated for each of the channels and a contribution degree of the number of arithmetic operations of each of the layers as the parameter.
(Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
Regarding claim 5:
2A Prong 1:
an operation speed confirmation unit that confirms an operation speed from the relearned post-contraction multilayer neural network model; and a recognition accuracy confirmation unit that confirms recognition accuracy of the relearned post-contraction multilayer neural network model; wherein the parameter setting unit determines the contribution degree of the importance and the contribution degree of the number of arithmetic operations of each layer based on a comparison result between the confirmed operation speed and a target operation speed and a comparison result between the confirmed recognition accuracy and target recognition accuracy.
As drafted, under the broadest reasonable interpretation, the claim covers mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion-confirming speed and accuracy can be mentally performed).
2A Prong 2: This judicial exception is not integrated into a practical application.
No Additional elements:
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
No Additional elements:
Regarding claim 6:
2A Prong 1:
No abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
a travel situation observation unit that observes a travel situation of a vehicle using the post-contraction multilayer neural network model and outputs an observation result of the travel situation to a server that stores a plurality of multilayer neural network models; and a model reception unit that receives the multilayer neural network model suitable for the travel situation selected from the server based on the observation result of the travel situation, and sets the multilayer neural network model received from the server as the multilayer neural network model to be read by the importance calculation unit. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
a travel situation observation unit that observes a travel situation of a vehicle using the post-contraction multilayer neural network model and outputs an observation result of the travel situation to a server that stores a plurality of multilayer neural network models; and a model reception unit that receives the multilayer neural network model suitable for the travel situation selected from the server based on the observation result of the travel situation, and sets the multilayer neural network model received from the server as the multilayer neural network model to be read by the importance calculation unit. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
Regarding claim 7:
2A Prong 1:
No abstract ideas
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
wherein the multilayer neural network model is a deep neural network model. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
wherein the multilayer neural network model is a deep neural network model. (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).
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 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.
Claims 1-4 and 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (“Zhang” 20210397965 A1) in view of A et al. (“A” 20200311552 A1).
Claim 1: Zhang discloses an arithmetic device comprising: an importance calculation unit that calculates an importance of a channel by using a weighting factor set for the channel of a multilayer neural network model having the channel including a plurality of neurons for each of an input layer, an intermediate layer, and an output layer (Paragraphs 74, 86 and 94; weighting used in determining importance);
a target contraction rate setting unit that sets a target contraction rate for the entire multilayer neural network model (Paragraphs 281 and 309; target compression rate analyzed);
each layer contraction rate calculation unit that calculates a contraction rate of each layer of the multilayer neural network model based on the importance and the target contraction rate(Paragraphs 74, 86, 94 and 115; weighting used in determining importance);
a contraction unit that contracts each layer in accordance with the contraction rate calculated by each layer contraction rate calculation unit to generate a post-contraction multilayer neural network model (Paragraphs 74, 86 and 94 115; weighting used in determining importance ad perform pruning/contraction);
and a relearning unit that performs relearning of the post- contraction multilayer neural network model (Paragraph 94; tuning of model).
A is also incorporated to more explicitly disclose a channel (abstract). A also provides a compression functionality based on importance of channels (Paragraphs 95, 115 and 132), while further disclosing a target compression rate (Paragraphs 19-21 and 160), in addition to layer compression functionality (Paragraph 153) and further provides a relearning (tuning) of the model (Paragraph 22-23).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to apply a known technique to a known device ready for improvement and incorporate the target compression specifically to channels for enhanced capability in Zhang. One would have been motivated to provide the functionality as a more precise method of determining importance within a channel for improved optimization of the model.
Claim 2: Zhang and A disclose an arithmetic device according to claim 1, wherein the importance calculation unit calculates an importance of the channel at a preceding stage connected to the channel at a subsequent stage based on the importance of the channel at the subsequent stage and a weighting factor of the channel at the subsequent stage in order from a layer closer to the output layer, and the each layer contraction rate calculation unit calculates a target contraction rate of each layer with respect to the target contraction rate of the entire multilayer neural network model based on the importance in each layer of the importance calculated for each channel (Zhang: Paragraphs 114-116; each importance calculated with different methods i.e. summation and A: Paragraphs 102, 113, 161; subsequent determination for compression of each layer).
Claim 3: Zhang and A disclose an arithmetic device according to claim 2, further comprising, a parameter setting unit that sets the importance and number of arithmetic operations of each layer extracted from the multilayer neural network model as parameters, wherein the each layer contraction rate calculation unit calculates a target contraction rate of each layer based on the parameter and the target contraction rate in the entire multilayer neural network model (Zhang: Paragraphs 281 and 309; target compression rate analyzed and A: Paragraphs 102, 113, 161; compression of each layer).
Claim 4: Zhang and A disclose an arithmetic device according to claim 3, wherein the parameter setting unit sets a contribution degree of the importance calculated for each of the channels and a contribution degree of the number of arithmetic operations of each of the layers as the parameter (Zhang: Paragraphs 74, 86 and 94 115; weighting used in determining importance ad perform pruning/contraction and A: Paragraphs 230-231 and 316; contribution of channel)
Claim 7: Zhang and A disclose an arithmetic device according to claim 2, wherein the multilayer neural network model is a deep neural network model (Zhang: Paragraphs 75 and 160; convolutional and recurrent NN and A: Paragraph 84; DNN).
Claim 8 is similar in scope to claim 1 and therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
20200082268 A1
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/SHERROD L KEATON/ Primary Examiner, Art Unit 2148
9-16-2026
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