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 .
DETAILED ACTION
This action is in response to the application filed on 02/23/2024.
Claims 1-20 are pending.
Examiner’s Note
Please note that Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirely as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
1. A method comprising:
determining an optimal code block from a plurality of code blocks, wherein the plurality of code blocks each solve a predetermined problem, and wherein the optimal code block optimizes a coding efficiency;
pairing the optimal code block with suboptimal code blocks from the plurality of code blocks, wherein the pairing generates suboptimal-to-optimal code pairs; and
training a machine learning model with the suboptimal-to-optimal code pairs, wherein the training fine-tunes the machine learning model for inferring edits to input code blocks, and wherein the edits to the input code blocks optimize the input code blocks in terms of the coding efficiency.
Claim 1, this claim is within at least one of the four categories of patent eligible subject matter as it is directing to a method claim under Step 1.
Regarding claim 1, the limitations “determining an optimal code block from a plurality of code blocks, wherein the plurality of code blocks each solve a predetermined problem, and wherein the optimal code block optimizes a coding efficiency,” and “training a machine learning model with the suboptimal-to-optimal code pairs, wherein the training fine-tunes the machine learning model for inferring edits to input code blocks, and wherein the edits to the input code blocks optimize the input code blocks in terms of the coding efficiency” as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. For example, a person is capable of selecting a code block which solves a problem with the aid of pen and paper. In the same manner, a person is capable of training a model with the aid of pen and paper using the optimized code to solve the problems. Therefore, these limitations encompass a human mind carrying out the function through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and falls within the “Mental Processes” grouping of abstract ideas under Prong 1.
Under Prong 2, the additional elements “pairing the optimal code block with suboptimal code blocks from the plurality of code blocks, wherein the pairing generates suboptimal-to-optimal code pairs” merely links optimal code block with suboptimal code blocks which is the use of the judicial exception to a particular code block or field of use, thus does not integrate the judicial exception into a practical application. See MPEP 2106.05(h).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “pairing the optimal code block with suboptimal code blocks from the plurality of code blocks, wherein the pairing generates suboptimal-to-optimal code pairs” merely links optimal code block with suboptimal code blocks which is the use of the judicial exception to a particular code block or field of use, thus does not integrate the judicial exception into a practical application. See MPEP 2106.05(h). Accordingly, the claims are not patent eligible under 35 USC 101.
2. The method of claim 1, comprising: receiving, at the machine learning model, an input code block; inferring, by the machine learning model, edits to the input code block; and
outputting, by the machine learning model, an optimized code block based on the input code block.
Under prong 2, the limitations for this claim further recite an additional element “receiving, at the machine learning model, an input code block; inferring, by the machine learning model, edits to the input code block; and outputting, by the machine learning model, an optimized code block based on the input code block” which do nothing more than to add insignificant extra solution activity to the judicial exception of merely gathering (i.e., receiving or inferring) and outputting data. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “receiving, at the machine learning model, an input code block; inferring, by the machine learning model, edits to the input code block; and outputting, by the machine learning model, an optimized code block based on the input code block” which is insignificant extra solution activity which do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering (i.e., receiving or inferring) and outputting data. The courts have identified mere data gathering (i.e., receiving or inferring) and outputting data are well-understood, routine and conventional activity. See MPEP 2106.05(d).
3. The method of claim 2, wherein the input code block is received from an integrated development environment (IDE) program.
Under prong 2, the limitations for this claim further recite an additional element “wherein the input code block is received from an integrated development environment (IDE) program” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “wherein the input code block is received from an integrated development environment (IDE) program” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception. See MPEP 2106.05(f). The courts have identified mere data gathering (i.e., receiving) and outputting data are well-understood, routine and conventional activity. See MPEP 2106.05(d).
4. The method of claim 3, wherein the machine learning model is executed on a same device as the IDE program.
Under prong 2, the limitations for this claim further recite an additional element “wherein the machine learning model is executed on a same device as the IDE program” which is recited at a high-level of generality such that it amounts no more than mere instructions for executing some model on a same device as the IDE program which merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “wherein the machine learning model is executed on a same device as the IDE program” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception, thus, cannot amount to an inventive concept. See MPEP 2105.06(f).
5. The method of claim 3, wherein the machine learning model is executed on a remote server.
Under prong 2, the limitations for this claim further recite an additional element “wherein the machine learning model is executed on a remote server” which is recited at a high-level of generality such that it amounts no more than mere instructions to execute some training model on a server which merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “wherein the machine learning model is executed on a remote server” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception, thus, cannot amount to an inventive concept. See MPEP 2105.06(f).
6. The method of claim 1, wherein the coding efficiency is carbon efficiency.
The limitations “wherein the coding efficiency is carbon efficiency” is an additional mental process under prong 1.
7. The method of claim 1, comprising: executing each code block in the plurality of code blocks; analyzing metrics generated for each code block during execution; and ranking each code block based on the metrics, wherein the optimal code block is a highest-ranking code block.
The limitations “analyzing metrics generated for each code block during execution” is an additional mental process under prong 1.
Under prong 2, the limitations for this claim further recite an additional element “executing each code block in the plurality of code blocks” merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f). For the additional elements “ranking each code block based on the metrics, wherein the optimal code block is a highest-ranking code block” do nothing more than to add insignificant extra solution activity to the judicial exception of merely analyzing and storing data for automation. See MPEP § 2106.05(h).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “executing each code block in the plurality of code blocks” merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f). For the additional elements “ranking each code block based on the metrics, wherein the optimal code block is a highest-ranking code block” which is insignificant extra solution activity which do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering (i.e., receiving) and ranking data. The courts have identified mere data gathering (i.e., receiving or inferring) and ranking data are well-understood, routine and conventional activity. See MPEP 2106.05(d).
8. A system comprising at least one computer including a processor and a memory, wherein the at least one computer is configured to:
determine an optimal code block from a plurality of code blocks, wherein the plurality of code blocks each solve a predetermined problem, and wherein the optimal code block optimizes a coding efficiency;
pair the optimal code block with suboptimal code blocks from the plurality of code blocks, wherein the pairing generates suboptimal-to-optimal code pairs; and
train a machine learning model with the suboptimal-to-optimal code pairs, wherein the training fine-tunes the machine learning model for inferring edits to input code blocks, and wherein the edits to the input code blocks optimize the input code blocks in terms of the coding efficiency.
Claim 8, this claim is within at least one of the four categories of patent eligible subject matter as it is directing to a system claim under Step 1.
Regarding claim 8, the limitations “train a machine learning model with the suboptimal-to-optimal code pairs, wherein the training fine-tunes the machine learning model for inferring edits to input code blocks, and wherein the edits to the input code blocks optimize the input code blocks in terms of the coding efficiency,” and “train a machine learning model with the suboptimal-to-optimal code pairs, wherein the training fine-tunes the machine learning model for inferring edits to input code blocks, and wherein the edits to the input code blocks optimize the input code blocks in terms of the coding efficiency” as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process.
For example, a person is capable of selecting a code block which solves a problem with the aid of pen and paper. In the same manner, a person is capable of training a model with the aid of pen and paper using the optimized code to solve the problems. Therefore, these limitations encompass a human mind carrying out the function through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and falls within the “Mental Processes” grouping of abstract ideas under Prong 1.
Under Prong 2, the additional elements “pair the optimal code block with suboptimal code blocks from the plurality of code blocks, wherein the pairing generates suboptimal-to-optimal code pairs” merely links optimal code block with suboptimal code blocks which is the use of the judicial exception to a particular code block or field of use, thus does not integrate the judicial exception into a practical application. See MPEP 2106.05(h).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “pair the optimal code block with suboptimal code blocks from the plurality of code blocks, wherein the pairing generates suboptimal-to-optimal code pairs” merely links optimal code block with suboptimal code blocks which is the use of the judicial exception to a particular code block or field of use, thus does not integrate the judicial exception into a practical application. See MPEP 2106.05(h). Accordingly, the claims are not patent eligible under 35 USC 101.
9. The system of claim 8, wherein the at least one computer is configured to: receive, at the machine learning model, an input code block; infer, by the machine learning model, edits to the input code block; and output, by the machine learning model, an optimized code block based on the input code block.
Under prong 2, the limitations for this claim further recite an additional element “receiving, at the machine learning model, an input code block; inferring, by the machine learning model, edits to the input code block; and outputting, by the machine learning model, an optimized code block based on the input code block” which do nothing more than to add insignificant extra solution activity to the judicial exception of merely gathering (i.e., receiving or inferring) and outputting data. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “receiving, at the machine learning model, an input code block; inferring, by the machine learning model, edits to the input code block; and outputting, by the machine learning model, an optimized code block based on the input code block” which is insignificant extra solution activity which do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering (i.e., receiving or inferring) and outputting data. The courts have identified mere data gathering (i.e., receiving or inferring) and outputting data are well-understood, routine and conventional activity. See MPEP 2106.05(d).
10. The system of claim 9, wherein the input code block is received from an integrated development environment (IDE) program.
Under prong 2, the limitations for this claim further recite an additional element “wherein the input code block is received from an integrated development environment (IDE) program” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “wherein the input code block is received from an integrated development environment (IDE) program” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception. See MPEP 2106.05(f). The courts have identified mere data gathering (i.e., receiving) and outputting data are well-understood, routine and conventional activity. See MPEP 2106.05(d).
11. The system of claim 10, wherein the machine learning model is executed on a same device as the IDE program.
Under prong 2, the limitations for this claim further recite an additional element “wherein the machine learning model is executed on a same device as the IDE program” which is recited at a high-level of generality such that it amounts no more than mere instructions for executing some model on a same device as the IDE program which merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “wherein the machine learning model is executed on a same device as the IDE program” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception, thus, cannot amount to an inventive concept. See MPEP 2105.06(f).
12. The system of claim 10, wherein the machine learning model is executed on a remote server.
Under prong 2, the limitations for this claim further recite an additional element “wherein the machine learning model is executed on a remote server” which is recited at a high-level of generality such that it amounts no more than mere instructions to execute some training model on a server which merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “wherein the machine learning model is executed on a remote server” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception, thus, cannot amount to an inventive concept. See MPEP 2105.06(f).
13. The system of claim 8, wherein the coding efficiency is carbon efficiency.
The limitations “wherein the coding efficiency is carbon efficiency” is an additional mental process under prong 1.
14. The system of claim 8, comprising: executing each code block in the plurality of code blocks; analyzing metrics generated for each code block during execution; and ranking each code block based on the metrics, wherein the optimal code block is a highest-ranking code block.
The limitations “analyzing metrics generated for each code block during execution” is an additional mental process under prong 1.
Under prong 2, the limitations for this claim further recite an additional element “executing each code block in the plurality of code blocks” merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f). For the additional elements “ranking each code block based on the metrics, wherein the optimal code block is a highest-ranking code block” do nothing more than to add insignificant extra solution activity to the judicial exception of merely analyzing and storing data for automation. See MPEP § 2106.05(h).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “executing each code block in the plurality of code blocks” merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f). For the additional elements “ranking each code block based on the metrics, wherein the optimal code block is a highest-ranking code block” which is insignificant extra solution activity which do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering (i.e., receiving) and ranking data. The courts have identified mere data gathering (i.e., receiving or inferring) and ranking data are well-understood, routine and conventional activity. See MPEP 2106.05(d).
15. A non-transitory computer readable storage medium, including instructions stored thereon, which instructions, when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:
determining an optimal code block from a plurality of code blocks, wherein the plurality of code blocks each solve a predetermined problem, and wherein the optimal code block optimizes a coding efficiency;
pairing the optimal code block with suboptimal code blocks from the plurality of code blocks, wherein the pairing generates suboptimal-to-optimal code pairs; and
training a machine learning model with the suboptimal-to-optimal code pairs, wherein the training fine-tunes the machine learning model for inferring edits to input code blocks, and wherein the edits to the input code blocks optimize the input code blocks in terms of the coding efficiency.
Claim 15, this claim is within at least one of the four categories of patent eligible subject matter as it is directing to a non-transitory computer readable storage medium claim under Step 1.
Regarding claim 15, the limitations “train a machine learning model with the suboptimal-to-optimal code pairs, wherein the training fine-tunes the machine learning model for inferring edits to input code blocks, and wherein the edits to the input code blocks optimize the input code blocks in terms of the coding efficiency,” and “train a machine learning model with the suboptimal-to-optimal code pairs, wherein the training fine-tunes the machine learning model for inferring edits to input code blocks, and wherein the edits to the input code blocks optimize the input code blocks in terms of the coding efficiency” as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. For example, a person is capable of selecting a code block which solves a problem with the aid of pen and paper. In the same manner, a person is capable of training a model with the aid of pen and paper using the optimized code to solve the problems. Therefore, these limitations encompass a human mind carrying out the function through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, these limitations recite and falls within the “Mental Processes” grouping of abstract ideas under Prong 1.
Under Prong 2, the additional elements “A non-transitory computer readable storage medium, including instructions stored thereon, which instructions, when read and executed by one or more computer processors,” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components, See MPEP 2106.05(f). For the additional elements “pair the optimal code block with suboptimal code blocks from the plurality of code blocks, wherein the pairing generates suboptimal-to-optimal code pairs” merely links optimal code block with suboptimal code blocks which is the use of the judicial exception to a particular code block or field of use, thus does not integrate the judicial exception into a practical application. See MPEP 2106.05(h).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements “A non-transitory computer readable storage medium, including instructions stored thereon, which instructions, when read and executed by one or more computer processors,” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception. See MPEP 2106.05(f). For the additional elements of “pair the optimal code block with suboptimal code blocks from the plurality of code blocks, wherein the pairing generates suboptimal-to-optimal code pairs” merely links optimal code block with suboptimal code blocks which is the use of the judicial exception to a particular code block or field of use, thus does not integrate the judicial exception into a practical application. See MPEP 2106.05(h). Accordingly, the claims are not patent eligible under 35 USC 101.
16. The non-transitory computer readable storage medium of claim 15, comprising: receiving, at the machine learning model, an input code block; inferring, by the machine learning model, edits to the input code block; and outputting, by the machine learning model, an optimized code block based on the input code block.
Under prong 2, the limitations for this claim further recite an additional element “receiving, at the machine learning model, an input code block; inferring, by the machine learning model, edits to the input code block; and outputting, by the machine learning model, an optimized code block based on the input code block” which do nothing more than to add insignificant extra solution activity to the judicial exception of merely gathering (i.e., receiving or inferring) and outputting data. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “receiving, at the machine learning model, an input code block; inferring, by the machine learning model, edits to the input code block; and outputting, by the machine learning model, an optimized code block based on the input code block” which is insignificant extra solution activity which do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering (i.e., receiving or inferring) and outputting data. The courts have identified mere data gathering (i.e., receiving or inferring) and outputting data are well-understood, routine and conventional activity. See MPEP 2106.05(d).
17. The non-transitory computer readable storage medium of claim 16, wherein the input code block is received from an integrated development environment (IDE) program.
Under prong 2, the limitations for this claim further recite an additional element “wherein the input code block is received from an integrated development environment (IDE) program” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “wherein the input code block is received from an integrated development environment (IDE) program” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception. See MPEP 2106.05(f). The courts have identified mere data gathering (i.e., receiving) and outputting data are well-understood, routine and conventional activity. See MPEP 2106.05(d).
18. The non-transitory computer readable storage medium of claim 17, wherein the machine learning model is executed on a remote server.
Under prong 2, the limitations for this claim further recite an additional element “wherein the machine learning model is executed on a remote server” which is recited at a high-level of generality such that it amounts no more than mere instructions to execute some training model on a server which merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “wherein the machine learning model is executed on a remote server” amount to no more than mere instructions, or generic computer and/or computer components to carry out the exception, thus, cannot amount to an inventive concept. See MPEP 2105.06(f).
19. The non-transitory computer readable storage medium of claim 15, wherein the coding efficiency is carbon efficiency.
The limitations “wherein the coding efficiency is carbon efficiency” is an additional mental process under prong 1.
20. The non-transitory computer readable storage medium of claim 15, comprising:
executing each code block in the plurality of code blocks; analyzing metrics generated for each code block during execution; and ranking each code block based on the metrics, wherein the optimal code block is a highest-ranking code block.
The limitations “analyzing metrics generated for each code block during execution” is an additional mental process under prong 1.
Under prong 2, the limitations for this claim further recite an additional element “executing each code block in the plurality of code blocks” merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f). For the additional elements “ranking each code block based on the metrics, wherein the optimal code block is a highest-ranking code block” do nothing more than to add insignificant extra solution activity to the judicial exception of merely analyzing and storing data for automation. See MPEP § 2106.05(h).
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above that recited an additional element “executing each code block in the plurality of code blocks” merely using generic computing equipment to execute/run the software tools to perform the abstract idea. See MPEP 2106.05(f). For the additional elements “ranking each code block based on the metrics, wherein the optimal code block is a highest-ranking code block” which is insignificant extra solution activity which do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering (i.e., receiving) and ranking data. The courts have identified mere data gathering (i.e., receiving or inferring) and ranking data are well-understood, routine and conventional activity. See MPEP 2106.05(d).
Claims 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 15 directed to non-transitory computer readable storage medium. As recited, “non-transitory computer readable storage medium, including instructions stored thereon, which instructions, when read and executed by one or more computer processor,” however, the medium is reasonably interpreted as entirely software per se. The instruction as recited are software instructions and therefore, the claim is claiming software instructions/commands and hence the claim is non-statutory. Therefore, the claims constitute computer programs representing computer software per se.
Claims 16-20, the claims do not remedy claim 15 and therefore are directed to non-statutory subject matter for the same reason(s) as noted above.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1-5, 7-12, 14-18 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by USPN 20250094146 to Vijayaraghavan et al.
Per claim 1:
Vijayaraghavan discloses:
1. A method comprising:
determining an optimal code block from a plurality of code blocks (Paragraph [0022] “a detection output 145 that characterizes one or more defects in the computer code snippet”), wherein the plurality of code blocks each solve a predetermined problem, and wherein the optimal code block optimizes a coding efficiency (Paragraph [0022] “code snippet that negatively impact a dynamic energy efficiency of the computer code snippet upon execution by a computing device”);
pairing the optimal code block with suboptimal code blocks from the plurality of code blocks, wherein the pairing generates suboptimal-to-optimal code pairs (Paragraph [0027] “code embedding feature vector 135A maps the code data 110 to a vector space… the code data 110 within the context of computer code snippets… intermediate vector to generate the code embedding feature vector 135A”); and
training a machine learning model with the suboptimal-to-optimal code pairs (Paragraph [0028] “embedding the sub-tokens with a sequence of symbols, e.g., by associating the sub-tokens with a pre-trained natural language entity-relationship knowledge graph”), wherein the training fine-tunes the machine learning model for inferring edits to input code blocks (Paragraph [0038] “embedding models 240 are configured to process each training example 235 to generate respective embedding feature vectors 245. The code defect generation model 260A is configured to generate synthetic code defects (that would negatively impact the dynamic energy efficiencies of code executions) for code snippets”), and wherein the edits to the input code blocks optimize the input code blocks in terms of the coding efficiency (Paragraph [0028] “applying a bidirectional Long Short-Term Memory (LSTM) to the symbol sequence to generate an intermediate vector, and (i) performing average polling to the intermediate vector to generate the rule embedding feature vector 135B. In particular, the bidirectional LSTM network included in the machine-learning model 130B can have a higher drop rate (e.g., ˜35%)”).
Per claim 2:
Vijayaraghavan discloses:
2. The method of claim 1, comprising:
receiving, at the machine learning model, an input code block (Paragraph [0025] “first embedding machine-learning model 130A is configured to process an input generated from the code data 110 to generate a code embedding feature vector 135A”);
inferring, by the machine learning model, edits to the input code block (Paragraph [0025] “an input generated from the rule data 120 to generate a rule embedding feature vector 135B”); and
outputting, by the machine learning model, an optimized code block based on the input code block (Paragraph [0035] “code optimization engine 160 is configured to determine updates to the computer code snippet for improving the energy efficiency of the computer code snippet based on the detection output 145”).
Per claim 3:
Vijayaraghavan discloses:
3. The method of claim 2, wherein the input code block is received from an integrated development environment (IDE) program (Paragraph [0021] “environment includes a user device 102 operated by a user 105, e.g., a computer program developer. The system 100 can be a system implemented as computer programs on one or more computers in one or more locations”).
Per claim 4:
Vijayaraghavan discloses:
4. The method of claim 3, wherein the machine learning model is executed on a same device as the IDE program (Fig. 2 and Paragraph [0036] “a model training system 200 for performing training of a machine-learning model, e.g., the GANs model 260… system implemented as computer programs on one or more computers in one or more locations”).
Per claim 5:
Vijayaraghavan discloses:
5. The method of claim 3, wherein the machine learning model is executed on a remote server (Paragraph [0021] “system 100 can be a system implemented as computer programs on one or more computers in one or more locations”; Paragraph [0066] “client and server are generally remote from each other and typically interact through a communication network”).
Per claim 7:
Vijayaraghavan discloses:
7. The method of claim 1, comprising:
executing each code block in the plurality of code blocks (Paragraph [0031] “efficiency evaluation engine 150 is configured to determine a metric that measures a comprehensive energy efficiency of the code snippet”);
analyzing metrics generated for each code block during execution (Paragraph [0034] “determines the comprehensive energy efficiency metric based on (i) the dynamic energy efficiency score, (ii) the static energy efficiency score, and (iii) the assessment data”); and
ranking each code block based on the metrics (Paragraph [0032] “the efficiency evaluation engine 150 computes a dynamic energy efficiency score for the code snippet based on the detection output 145”), wherein the optimal code block is a highest-ranking code block (Paragraph [0029] “a priority of a detected code defect (e.g., “critical”, “normal”, “low”, or a numerical score specifying the priority). In general, a detected code defect with a higher priority (e.g., a “critical” priority) refers to a defect that has a more pronounced negative impact on the dynamic energy efficiency of the computer code snippet”).
Claims 8-12 and 14 is/are the apparatus/system claim corresponding to method claims 1-5 and 7 respectively, and rejected under the same rational set forth in connection with the rejection of claims 1-5 and 7 respectively, as noted above.
Claims 15-18 and 20 is/are the medium/product claim corresponding to method claims 1-5 and 7 respectively, and rejected under the same rational set forth in connection with the rejection of claims 1-5 and 7 respectively, as noted above.
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) 6, 13 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over USPN 20250094146 to Vijayaraghavan et al. in view of USPN 20220398095 to Weldemariam et al.
Per claim 6:
The rejection of claim 1 is incorporated and further, VIJAYARAGHAVAN does not explicitly discloses wherein the coding efficiency is carbon efficiency.
However, Weldemariam discloses in an analogous computer system wherein the coding efficiency is carbon efficiency (Paragraph [0010] “dynamically predicting a carbon footprint associated with a plurality of code datasets and automatically optimizing the plurality of code datasets based on the predicted carbon footprint for data migration”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the method of wherein the coding efficiency is carbon efficiency as taught by Weldemariam into the method of evaluating and improving the energy efficiency of computer code as taught by VIJAYARAGHAVAN. The modification would be obvious because of one of ordinary skill in the art would be motivated to add/incorporate the features of wherein the coding efficiency is carbon efficiency to provide an efficient technique of having carbon efficiency code so as to have optimized code during data migration and data transformation to avoid further damage our environment as suggested by Weldemariam (paragraph [0001-0004]).
Claim 13 is/are the apparatus/system claim corresponding to method claim 6 and rejected under the same rational set forth in connection with the rejection of claim 6 as noted above.
Claim 19 is/are the medium/product claim corresponding to method claim 6 and rejected under the same rational set forth in connection with the rejection of claim 6 as noted above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Related cited arts:
Liu, Yizhi, et al. "Optimizing {CNN} model inference on {CPUs}." 2019 USENIX Annual Technical Conference (USENIX ATC 19). 2019. pp. 1025-1040
Ojijo, Mourice O., and Olabisi E. Falowo. "A survey on slice admission control strategies and optimization schemes in 5G network." Ieee Access 8 (2020): pp. 14977-14990.
Ballé, Johannes, et al. "Nonlinear transform coding." IEEE Journal of Selected Topics in Signal Processing 15.2 (2020): pp. 339-353.
USPN 9087195 disclosed are systems, methods and computer program products for efficient and reliable analysis, optimization and detection of obfuscated malware. One disclosed example method for malware detection includes loading an executable software code on a computer system and disassembling the software code into an assembly language or other low-level programming language. The method then proceeds to simplifying complex assembly instructions and constructing a data flow model of the simplified software code. The dependencies and interrelations of code elements of the data flow model are analyzed to identify obfuscated software codes therein. The identified obfuscated codes are then optimized. Based on the results of optimization, determination is made whether the software code is malicious and/or whether further antimalware analysis of the optimized software code is necessary.
USPN 20140114637 discloses a method for a discrete event simulation model of a system utilizing a just-in-time compilation for one or more code blocks associated with an event in one or more discrete event simulation models is disclosed. The method comprises the steps of determining the event in a discrete event simulation model according to a kind of event, retrieving the code block associated with the event, compiling the code block into an object file using a compiler, linking the object file with a predetermined function in a simulation library, compiling the object file and the predetermined function into a customized dynamic link library, loading the customized dynamic link library (DLL) within a discrete event simulation program execution and linking the customized DLL to a simulation program. The method allows user entered logic to be executed in high speed by integrating a just-in-time compiler embedded into the simulation model to allow dynamic generation of high speed code blocks within one or more simulations.
USPN 20170147292 discloses a system is provided in which a human annotation, undertaken for direct implementation of parallelization measures, is used for training an adaptive automatic classification method, which is then applied automatically to code blocks to be analyzed, wherein further suitable patterns obtained by human review from the automatically analyzed code blocks may then be used in turn for continuous improvement of the adaptive automatic classification method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Satish Rampuria whose telephone number is 571-272-3732. The examiner can normally be reached on Monday-Friday from 8:30 AM to 5:00 PM.
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/Satish Rampuria/Primary Examiner, Art Unit 2193