Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responding to application papers dated 5/22/2024.
Claims 1-20 are pending in the application.
The information disclosure statement filed on 5/30/2024 has been considered.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required: “computer-readable” media is not described in the specification; the term, “storage media” is defined. The original claims do recite the computer-readable media, therefore it is recommended to change the term to “storage media” or incorporate the computer-readable media into the specification.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Specifically, claims 1-20 are directed to an abstract idea.
Per claim 1, the claim is directed to an idea of itself, mental processes that can be performed in the human mind, or by a human using a pen and paper. The step of extracting and generating an inference as drafted can be pure mental process because a human (developer) can traverse the parse tree to extract data and infer based on the extracted data manually using a pen and paper through observation, evaluation, judgment, opinion, Under Prong 1. A parse tree is a mere representation of syntactic structure of code or text where each node contains data, therefore, a human can manually read/extract the tree by walking/traversing the tree on paper. Under Prong 2, the additional limitations, the step of receiving a parse tree is a mere data gathering for the mental steps, hence an insignificant extra solution activity and the machine learning model is used as a mere generic tool, described at a high level of generality for applying or performing the abstract idea (inference) as a generic computing component and do not indicate any integration of the abstract idea into a practical application. See MPEP see MPEP 2106.05(f) /2106.05(h). Therefore, the additional limitations do not integrate the abstract idea into a practical application. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components or insignificant extra solution activities (e.g. processors, devices, program instructions), then it falls within the "Mental Processes" grouping of abstract ideas (2019 PEG step 2A, Prong 1: Abstract idea grouping? Yes, Mental Process). At most, the steps of providing, receiving and presenting the response are not found to include anything more than what is well-understood, routine, conventional activity in the field. In this case, it is noted that the claimed extra-solution of data gathering and outputting/displaying is acknowledged to be a well-understood, routine, conventional activity court recognized as WURC examples in MPEP 2106.05(d)(ll), for example, data gathering and retrieving, storing data, updating, transmitting, and displaying a result - Symantec, Versata Dev, Content extraction, Electric Power Group). Insignificant extra solution activities or mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Viewing the limitations individually and as a combination, the additional elements merely perform data gathering and perform the mental steps using generic computing components as tools without integrating the abstract idea into a practical application. For at least these reasons, claim 1 is not patent eligible.
Per claims 2-10, these claims are directed to the same idea itself as in claim 1, reciting details of data and the mental steps without adding any other additional element that is significantly more. Therefore, the claims are rejected for the same reasons as in claim 1.
Per claims 11-20, these claims are directed to the same idea itself as in claims 1-10, reciting details of data and the mental steps without adding any other additional element that is significantly more, other than the media recited at the preamble as a generic computing component. Therefore, the claims are rejected for the same reasons as in claims 1-10.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 4, 5, 7, 8, 11, 14, 15, 17, and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12026631. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent anticipates claims
Per claim 1: A method comprising: receiving a parse tree that contains a plurality of tree nodes, wherein each tree node of the plurality of tree nodes is associated with a respective production rule that generated the tree node; extracting an extracted plurality of sequences of production rules, wherein each sequence of production rules of the extracted plurality of sequences of production rules consists of respective production rules of a sequence of tree nodes in a respective tree path of the parse tree; and generating an inference, from a fixed-size encoding that represents the extracted plurality of sequences of production rules, by a machine learning (ML) model (Patent claim 1. A method comprising: receiving a parse tree that contains a plurality of tree nodes, wherein each tree node of the plurality of tree nodes is associated with a respective production rule that generated the tree node; extracting an extracted plurality of sequences of production rules having respective sequence lengths that satisfy a predefined length constraint that only accepts at least one fixed length that is greater than two, wherein each sequence of production rules of the extracted plurality of sequences of production rules consists of respective production rules of a sequence of tree nodes in a respective directed tree path of the parse tree having a path length that satisfies same said predefined length constraint that only accepts said at least one fixed length that is greater than two; and inferencing from the parse tree, by a machine learning (ML) model, based on the extracted plurality of sequences of production rules having respective sequence lengths that satisfy said predefined length constraint that only accepts said at least one fixed length that is greater than two).
4. The method of Claim 1 wherein the extracted plurality of sequences of production rules consists of at least one selected from a group consisting of: sequences of production rules having at least a predefined minimum length that is greater than two, sequences of production rules having at most a predefined maximum length, and sequences of production rules having a same predefined length 3. The method of claim 1 wherein: the method further comprises counting a respective frequency of each distinct sequence of production rules in said extracted plurality of sequences of production rules; said ML model inferencing is further based on said frequencies of said distinct sequences of production rules in said extracted plurality of sequences of production rules (Patent claim 1. A method comprising: receiving a parse tree that contains a plurality of tree nodes, wherein each tree node of the plurality of tree nodes is associated with a respective production rule that generated the tree node; extracting an extracted plurality of sequences of production rules having respective sequence lengths that satisfy a predefined length constraint that only accepts at least one fixed length that is greater than two, wherein each sequence of production rules of the extracted plurality of sequences of production rules consists of respective production rules of a sequence of tree nodes in a respective directed tree path of the parse tree having a path length that satisfies same said predefined length constraint that only accepts said at least one fixed length that is greater than two; and inferencing from the parse tree, by a machine learning (ML) model, based on the extracted plurality of sequences of production rules having respective sequence lengths that satisfy said predefined length constraint that only accepts said at least one fixed length that is greater than two).
5. The method of Claim 1 wherein the extracted plurality of sequences of production rules contains duplicate sequences of production rules. (Patent claim 3. The method of claim 1 wherein: the method further comprises counting a respective frequency of each distinct sequence of production rules in said extracted plurality of sequences of production rules; said ML model inferencing is further based on said frequencies of said distinct sequences of production rules in said extracted plurality of sequences of production rules; Note that the frequency counting is duplicate counting).
7. The method of Claim 1 wherein the extracted plurality of sequences of production rules contains sequences of production rules of different lengths (patent claim 7. The method of claim 6 wherein said ML model inferencing based on the extracted plurality of sequences of production rules comprises inferencing based on the plurality of sequences, having said sequence lengths that satisfy said predefined length constraint that only accepts said at least one fixed length that is greater than two, of production rules of said parse tree of said logic statement selected from a group consisting of: a database query, a structured query language (SQL) statement, a scripting language statement, and a statement of a general-purpose programing language).
8. The method of Claim 1 wherein the extracted plurality of sequences of production rules does not contain a terminal production rule (patent claim 8. The method of claim 1 wherein said extracting said extracted plurality of sequences of production rules of sequences of tree nodes in directed tree paths excludes tree paths that contain tree leaves; Note that the leaf nodes are terminal production rules).
Per claims 11, 14, 15, 17 and 18, patent claims 11-20 correspond to the instant claims 11, 14, 15, 17 and 18.
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.
Claims 1, 2, 4, 6, 8, 10-12, 14, 16, 18, and 20 are rejected under 35 U.S.C. 102(a)(1)] as being anticipated by Kusner et al. (“Grammar Variational Autoencoder,” arXiv, 3/2017).
1. A method comprising:
receiving a parse tree that contains a plurality of tree nodes, wherein each tree node of the plurality of tree nodes is associated with a respective production rule that generated the tree node (Kusner, see at least abstract, the key observation that frequently, discrete data can be represented as a parse tree from a context-free grammar. We propose a variational autoencoder which encodes and decodes directly to and from these parse trees, ensuring the generated outputs are always valid; page 1, right col., Given a grammar, every valid discrete object can be described as a parse tree from the grammar; Note that each internal node in the parse tree corresponds to exactly one production rule from the CFG that expanded it);
extracting an extracted plurality of sequences of production rules, wherein each sequence of production rules of the extracted plurality of sequences of production rules consists of respective production rules of a sequence of tree nodes in a respective tree path of the parse tree (Kusner, see at least page 3, left col., We decompose this tree into a sequence of production rules by performing a pre-order traversal on the branches of the parse tree going from left-to-right, shown in box 4. We convert these rules into 1-hot indicator vectors, where each dimension corresponds to a rule in the SMILES grammar; Note that the parse tree is obtained using CFG and decomposed into a sequence of productions through a pre-order traversal on the tree where the traversal visits each node in a directed root to leaf path order, producing the sequence of production rules along the tree paths);
generating an inference, from a fixed-size encoding that represents the extracted plurality of sequences of production rules, by a machine learning (ML) model (Kusner, see at least page 3, left col., To encode this molecule into a continuous latent representation we begin by using the SMILES grammar to parse this string into a parse tree … We convert these rules into 1-hot indicator vectors, where each dimension corresponds to a rule in the SMILES grammar, box 5 . Letting K denote the total number of production rules in the entire grammar, and T(X) the number of productions applied in total to generate the output string for X, the collection of 1-hot vectors can be written as a T(X)×K matrix X. We use a deep convolutional neural network to map this collection of 1-hot vectors X to a continuous la tent vector z The architecture of the encoding network is described in the supplementary material; page 3, right col., we define a fixed binary mask vector; page 11, we encode the data as sequences of one-hot vectors and apply a series of one-dimensional convolutions to the sequence data … followed by fully-connected layers that predict the mean and variance parameters of a Gaussian distribution qφ(z|x); Note that the data is encoded as sequences of 1-hot vectors, then passed through an encoder and the sequence is flattened into a vector and passed through the fully connected layer producing the mean and variance. Here, the fully connected layer output is the fixed-size vector z which is a fixed-dimensional encoding of the production rule sequence- encoding of the sequences via CNN and fully connected layer and the ML infers from that encoding).
2. The method of Claim 1 wherein the fixed-size encoding that represents the extracted plurality of sequences of production rules consists of at least one selected from a group consisting of: a lossy encoding of the parse tree, a plurality of Booleans, and a plurality of non-negative integers (Kusner, see at least page 2, we encode the data as sequences of one-hot vectors and apply a series of one-dimensional convolutions to the sequence data; page 3, left col., To encode this molecule into a continuous latent representation we begin by using the SMILES grammar to parse this string into a parse tree …1-hot vectors X to a continuous latent vector z; Note that one-hot corresponds to Boolean because each position in a vector is 0 or 1 indicating which production rule applies. Also, one-hot is lossy because the one-hot sequence is compressed through the CNN layers into a fixed-size latent vector z).
4. The method of Claim 1 wherein the extracted plurality of sequences of production rules consists of at least one selected from a group consisting of: sequences of production rules having at least a predefined minimum length that is greater than two, sequences of production rules having at most a predefined maximum length, and sequences of production rules having a same predefined length (Kusner, see at least page 5, left col., We limit the length of every selected string to have at most 15 production rules. Given this dataset we train both the CVAE and GVAE to learn a latent space of arithmetic ex pressions. We propose to perform optimization in this la tent space of expressions to find an expression that best fits a fixed dataset. A common measure of best fit is the test MSE between the predictions made by a selected expression and the true data. In the generated expressions, the presence of exponential functions can result in very large MSE values. For this reason, we use as target variable log(1 + MSE) instead of MSE; fig. 2 and associated texts, max length; Note that the maximum sequence length to be fixed is required in advance for the CNN to operate on a fixed-size input).
6. The method of Claim 1 wherein the extracted plurality of sequences of production rules does not contain at least one selected from a group consisting of: a particular sequence of production rules that the parse tree contains and a production rule that the parse tree contains (Kusner, see at least Kusner, see at least the sequence of production rules are converted into one-hot vectors in which each dimension corresponds to one production in the grammar, therefore, only one single flat sequence of all production rules from the entire parse tree via the pre-order traversal is extracted, there is no sliding window, no subpath extraction of varying lengths along the tree paths).
8. The method of Claim 1 wherein the extracted plurality of sequences of production rules does not contain a terminal production rule (Kusner, see at least page 3, left col., We convert these rules into 1-hot indicator vectors, where each dimension corresponds to a rule in the SMILES grammar; page 3, right col., for every non-terminal α we define a fixed binary mask vector; Note that is a negative limitation that is a mere design choice for feature extraction fed to an LM model as extracting only production rules whose right-hand side contains no terminal symbol).
10. The method of Claim 1 wherein a size of the fixed-size encoding that represents the extracted plurality of sequences of production rules does not depend on at least one selected from a group consisting of: a count of the extracted plurality of sequences of production rules and a count of the plurality of tree nodes (Kusner, see at least page 3, left col., We convert these rules into 1-hot indicator vectors, where each dimension corresponds to a rule in the SMILES grammar … Letting K denote the total number of production rules in the entire grammar, and T(X) the number of pro ductions applied in total to generate the output string for X, the collection of 1-hot vectors can be written as a T(X)×K matrix X. We use a deep convolutional neural network to map this collection of 1-hot vectors X to a continuous la tent vector z The architecture of the encoding network is described in the supplementary material; page 3, right col., for every non-terminal α we define a fixed binary mask vector; Note that the width of 1-hot vector is the total number of rules in the grammar and the maximum number of the rule is fixed, therefore, the size does not dependent on the count of rules or nodes).
Per claims 11, 12, 14, 16, 18 and 20, they are the media versions of claims 1, 2, 4, 6, 8, and 10, and are rejected for the same reasons set forth in connection with the rejection of claims 1, 2, 4, 6, 8, and 10 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.
Claims 3, 5, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kusner in view of Mohajer et al. (US10896671, hereafter Mohajer).
Per claims 3 and 13,
Kusner does not explicitly teach wherein a count of the plurality of non-negative integers is less than at least one selected from a group consisting of: a count of the extracted plurality of sequences of production rules and a count of the plurality of tree nodes. Mohajer teaches wherein a count of the plurality of non-negative integers is less than at least one selected from a group consisting of: a count of the extracted plurality of sequences of production rules and a count of the plurality of tree nodes (Mohajer, see fig.2-4 and associated texts, Based on collecting all eligible candidate rules for generalization, the bag of rules 400 may first accumulate histogram or statistics, such as the frequency counts of identical rules; the frequency counts of rules with a shared name trigger; the frequency counts of rules that share and entire trigger (name and context) but not the action parts of the rules. In some embodiments, building a bag of rules 400 may simultaneously build corresponding partitions of the set of rules, such as, say, partitions by a shared name trigger, or by a shared context trigger. All the data serve as preliminaries for rule generalization … rule promotion module 335 processes the rules found in the bag of rules 400 in order to extract some generic rules. Adding a rule to global rules 330 (or contextual rules 340) is called “promoting” the rule to global status (or contextual status). Rule promotion module 335 operates differently in different embodiments; its operation is subject to numerous variations. For instance, it is possible to assign to every rule an initial generalization score based on its relative frequency of occurrence in the bag of rules 400. Stated simply, the more frequent a rule is (among generalizable user-defined rules)).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have combined Kusner’s a variational autoencoder system with Mohajer’s count-based bag of rules to modify Kusner to combine the duplicate sequence extraction as taught by Mohajer, with a reasonable expectation of success, since they are analogous art because they are from the same field of endeavor related to data extraction or machine learning. Combining Mohajer’s functionality with that of Kusner results in a system that allows utilization of the bag of rules. The modification would be obvious because one having ordinary skill in the art would be motivated to make this combination to improve computation producing a fixed-size lossless encoding without neural overhead with counting frequency of structural patterns (Mohajer, see fig.2-4 and associated texts, Based on collecting all eligible candidate rules for generalization, the bag of rules 400 may first accumulate histogram or statistics, such as the frequency counts of identical rules; the frequency counts of rules with a shared name trigger; the frequency counts of rules that share and entire trigger (name and context) but not the action parts of the rules. In some embodiments, building a bag of rules 400 may simultaneously build corresponding partitions of the set of rules, such as, say, partitions by a shared name trigger, or by a shared context trigger. All the data serve as preliminaries for rule generalization … rule promotion module 335 processes the rules found in the bag of rules 400 in order to extract some generic rules. Adding a rule to global rules 330 (or contextual rules 340) is called “promoting” the rule to global status (or contextual status). Rule promotion module 335 operates differently in different embodiments; its operation is subject to numerous variations. For instance, it is possible to assign to every rule an initial generalization score based on its relative frequency of occurrence in the bag of rules 400. Stated simply, the more frequent a rule is (among generalizable user-defined rules)).
Per claims and 5 and 15:
Kusner does not explicitly teach wherein the extracted plurality of sequences of production rules contains duplicate sequences of production rules. Mohajer teaches wherein the extracted plurality of sequences of production rules contains duplicate sequences of production rule (Mohajer, see fig.2-4 and associated texts, Based on collecting all eligible candidate rules for generalization, the bag of rules 400 may first accumulate histogram or statistics, such as the frequency counts of identical rules; the frequency counts of rules with a shared name trigger; the frequency counts of rules that share and entire trigger (name and context) but not the action parts of the rules. In some embodiments, building a bag of rules 400 may simultaneously build corresponding partitions of the set of rules, such as, say, partitions by a shared name trigger, or by a shared context trigger. All the data serve as preliminaries for rule generalization … rule promotion module 335 processes the rules found in the bag of rules 400 in order to extract some generic rules. Adding a rule to global rules 330 (or contextual rules 340) is called “promoting” the rule to global status (or contextual status). Rule promotion module 335 operates differently in different embodiments; its operation is subject to numerous variations. For instance, it is possible to assign to every rule an initial generalization score based on its relative frequency of occurrence in the bag of rules 400. Stated simply, the more frequent a rule is (among generalizable user-defined rules)). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have combined Kusner’s a variational autoencoder system with Mohajer’s count-based bag of rules to modify Kusner to combine the duplicate sequence extraction as taught by Mohajer, with a reasonable expectation of success, since they are analogous art because they are from the same field of endeavor related to data extraction or machine learning. Combining Mohajer’s functionality with that of Kusner results in a system that allows utilization of the bag of rules for frequency count (duplicate sequences). The modification would be obvious because one having ordinary skill in the art would be motivated to make this combination to improve computation producing a fixed-size lossless encoding without neural overhead with counting frequency of identical rules (Mohajer, see fig.2-4 and associated texts, Based on collecting all eligible candidate rules for generalization, the bag of rules 400 may first accumulate histogram or statistics, such as the frequency counts of identical rules; the frequency counts of rules with a shared name trigger; the frequency counts of rules that share and entire trigger (name and context) but not the action parts of the rules. In some embodiments, building a bag of rules 400 may simultaneously build corresponding partitions of the set of rules, such as, say, partitions by a shared name trigger, or by a shared context trigger. All the data serve as preliminaries for rule generalization … rule promotion module 335 processes the rules found in the bag of rules 400 in order to extract some generic rules. Adding a rule to global rules 330 (or contextual rules 340) is called “promoting” the rule to global status (or contextual status). Rule promotion module 335 operates differently in different embodiments; its operation is subject to numerous variations. For instance, it is possible to assign to every rule an initial generalization score based on its relative frequency of occurrence in the bag of rules 400. Stated simply, the more frequent a rule is (among generalizable user-defined rules)).
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kusner in view of Yan et al. (US20170262761, hereafter Yan).
Per claims 7 and 17:
Kusner does not explicitly teach wherein the extracted plurality of sequences of production rules contains sequences of production rules of different lengths. Yan teaches wherein the extracted plurality of sequences of production rules contains sequences of production rules of different lengths (Yan, see at least 0011] determining the rule is repeated using subsequence patterns of a different length; [0021] the rule extractor is configured to repeat computing a plurality of difference values for each foreground sequence of a plurality of the foreground sequences and repeat determining the rule using subsequence patterns of a different length; [0123] how often the same respective subsequence pattern corresponding to the largest difference value is present in the background sequences). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have combined Kusner’s a variational autoencoder system with Yan’s extraction of sequences of different lengths to modify Kusner to combine the extraction as taught by Yan, with a reasonable expectation of success, since they are analogous art because they are from the same field of endeavor related to data extraction or machine learning. Combining Yan’s functionality with that of Kusner results in a system that allows duplicate sequence extraction. The modification would be obvious because one having ordinary skill in the art would be motivated to make this combination to capture different levels of structural context (Yan, see at least 0011] determining the rule is repeated using subsequence patterns of a different length; [0021] the rule extractor is configured to repeat computing a plurality of difference values for each foreground sequence of a plurality of the foreground sequences and repeat determining the rule using subsequence patterns of a different length; [0123] how often the same respective subsequence pattern corresponding to the largest difference value is present in the background sequences).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kusner in view of Beyer et al. (US 8015564, hereafter Beyer).
Per claim 9:
Kusner does not explicitly teach wherein the fixed-size encoding that represents the extracted plurality of sequences of production rules contains at least one selected from a group consisting of: a count of the plurality of tree nodes, a count of leaf nodes in the plurality of tree nodes, a length of a longest tree path in the parse tree, a count of tree nodes or tree paths in the parse tree that are excluded from the extracted plurality of sequences of production rules, a count of distinct production rules in the parse tree, a count of distinct production rules that are in both of the parse tree and the extracted plurality of sequences of production rules, and a count of distinct production rules that are in the parse tree and not in the extracted plurality of sequences of production rules. Beyer teaches wherein the fixed-size encoding that represents the extracted plurality of sequences of production rules contains a length of a longest tree path in the parse tree (fig. 4 and associated texts, the dispatcher selects the longest critical path first rule upon the number of completed jobs in the queue reaching an upper threshold; claim 13. The method of claim 12 wherein the system status reaches a remaining processing time equal to or less than a longest critical path length of remaining jobs plus a buffer time period and the selection rule returns a longest critical path first dispatching rule as the second dispatching rule). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have combined Kusner’s a variational autoencoder system with Beyer’s longest tree path length to modify Kusner to combine the extraction as taught by Beyer, with a reasonable expectation of success, since they are analogous art because they are from the same field of endeavor related to data extraction or machine learning. Combining Beyer’s functionality with that of Kusner results in a system that allows capturing the depth of the tree. The modification would be obvious because one having ordinary skill in the art would be motivated to make this combination to capture the full structural depth of the parse tree for maximum information extraction (fig. 4 and associated texts, the dispatcher selects the longest critical path first rule upon the number of completed jobs in the queue reaching an upper threshold; claim 13. The method of claim 12 wherein the system status reaches a remaining processing time equal to or less than a longest critical path length of remaining jobs plus a buffer time period and the selection rule returns a longest critical path first dispatching rule as the second dispatching rule).
Per claim 19, it is the media version of claim 9, and is rejected for the same reasons set forth in connection with the rejection of claim 9 above.
Examiner’s Note
The Examiner has pointed out particular references contained in the prior art of record within the body of this action 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. Applicant, in preparing the response, should consider fully the entire reference 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Jiang et al., (“DECKARD: Scalable and Accurate Tree-Based Detection of Code Clones,”2007, IEEE) is related to detecting code clones based on a characterization of subtrees with numerical vectors;
Zhang, et al. (“A Novel Neural Source Code Representation based on Abstract Syntax Tree,” 2019) is related to an AST-based Neural Network (ASTNN) for source code representation;
Wang et al., (“Sparse Coding for N-Gram Feature Extraction and Training for File Fragment Classification,” 2018) is related to N-gram feature extraction;
Yin et al. (“A Syntactic Neural Model for General-Purpose Code Generation”) is related to a grammar model to capture syntax as prior knowledge;
Alon et al., (“A General Path-Based Representation for Predicting Program Properties,” 2018) is related to a general path-based representation for learning from programs.
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/INSUN KANG/Primary Examiner, Art Unit 2193
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