DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the response to this office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Specification
The application specification failed to provide antecedent basis for the claimed limitation “wherein the instructions when executed by the processor further cause the processor to: compare a token subsequence from the parse tree to a predefined token subsequence stored in a dictionary; and responsive to a determination that the token subsequence matches the predefined token subsequence: override the determination to execute the reduce action, and determine to execute a shift action” as recited in claims 4, 12, 18 and wherein the application specification read “In some embodiments, the threshold for comparing the probability that the next action is a reduce action is a learned threshold that is determined during a training phase. In some embodiments, the translator 250 may override the threshold based on one or more phrases stored in a dictionary. That is, the translator 250 may determine that a next action is a reduce action even if the probability that the next action is a reduce action is lower than the threshold value if a token subsequence matches a token subsequence stored in the dictionary. For example, the translator 250 may identify that a token subsequence “must rise” matches a token subsequence stored in the dictionary, and may induce a reduce action to insert a token $rose even if the probability that the next action is a reduce action is lower than the learned threshold value (para 49, USPGPub 20250068846 A1)”, i.e., “reduce action” is enforced or favored by overriding the threshold, even though the probability of “reduce action” being lower than the “threshold”, other than claimed “override the determination to execute the reduce action; and determine to execute a shift action” which has nothing to do with “threshold”. The claimed limitation above appears to be opposite or conflicting to the concept of the application specification cited above.
Appropriate correction is required.
Domestic Priority
Applicant' s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date 35 U.S.C. 120, 121, 365(c), or 386(c) or under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a), except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application No. 17526,687 and now patented as U.S. 12175191 B2, and provisional application 63/119,523 filed on November 30, 2020 fail to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) for one or more claims of this application: claim 4, 12, 18. Accordingly, claims 4, 12, 18 are not entitled to the benefit of the prior applications. For example, the parent applications fail to disclose the claimed “wherein the instructions when executed by the processor further cause the processor to: compare a token subsequence from the parse tree to a predefined token subsequence stored in a dictionary; and responsive to a determination that the token subsequence matches the predefined token subsequence : override the determination to execute the reduce action, and determine to execute a shift action” as recited in claims 4, 12, 18.
Claim Objections
Claims 17-20 are objected to because of the following informalities:
Claim 17 recites “each sentence of the identified one or more sentences specifying …”, which should be -- each sentence of the identified one or more translatable sentences specifying …--. Claims 18-20 are objected due to the dependencies to claim 17.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 17-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Claim 17 recites “applying the probabilistic shift-reduce schedule” and wherein “the probabilistic shift-reduce schedule” herein has an insufficient antecedent basis for the limitation in claim 17 and causes confusing because it is unclear what it is referred to and it is unclear what it is and thus, renders claim indefinite. Claims 18-20 are rejected due to the dependencies to claim 17.
Claim 19 further recites “for each natural language statement of the one or more natural language statements” and wherein “the one or more natural language statements” and “each natural language statement of the one or more natural language statements” have insufficient antecedent bases for the limitations in claim 19 and causes confusing because it is unclear what they are referred to and it is unclear what they are and thus, further renders claim indefinite.
Claim Rejections - 35 USC § 103
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 of this title, 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 1-3, 5-11, 13-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Poznanovic et al. (US 20040088666 A1, hereinafter Poznanovic) and in view of reference Bowman et al (“A Fast Unified Model for Parsing and Sentence Understanding”, hereinafter Bowman, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, August, 2016, pp1-11, IDS-C1).
Claim 1: Poznanovic teaches a non-transitory computer readable storage medium (title and abstract, ln 1-11, computer usable medium, para 14) configured to store instructions (storing computer readable program code, para 14), the instructions when executed by a processor (executed on a hybrid reconfigurable hardware-instruction processor computer, para 3) cause the processor (executing functions, para 14):
receive a specification for a design of an electronic circuit (receiving high level language HLL source code at step 302 in fig. 3, e.g., C, C++, FORTRAIN, COBOL, PASCAL, Java, etc., para 53-54, or well-defined interface specification representing control flow information, para 107, and for FPGA instantiated design, para 298, and for a hybrid reconfigurable hardware-instruction processor computer, para 3);
identify one or more natural language statements of the specification (e.g., HLL in fig. 5, English words or phrases: variable “parameter”, subroutine “initialize (a,b,c)”, logic “if … else”, loop “do i=1, n”, etc. an example in fig. 5, ), each natural language statement of the identified one or more natural language statements specifying a condition to be satisfied for the design of the electronic circuit (e.g., logic “if … else”, loop “do i=1, n”, and assignment “=” for relationship between “parameters”, etc., as the specified condition to be satisfied in fig. 5);
for each natural language statement of the one or more natural language statements:
receive a parse tree for the natural language statement (dataflow graph DFG in fig. 16 and the left portions of fig. 20-21, respectively, given for further operations such as fig. 17, and right portions of figs. 20-21, respectively, and the DFG or CFG-DFG generated from steps 202, 204 in fig. 2);
retrieve a token from the parse tree (from top as input to bottom as output, a token as each of nodes in figs. 16-17 and 20-21, and extracted from source files, for further parsing, para 103),
determine whether to execute a reduce action on the token based on a given token of a respective parse tree (token or node LDA nodes in the parse tree fig. 16, pass-by-reference parameters or copy-and-restore rather than reference, i.e., parameter ref or local ref in fig. 14, para 216 or ), and responsive to a determination to execute the reduce action (implemented by multi-adaptive processors MAPs compiler, para 216):
determine whether the retrieved token from the parse tree is a replace token (LDA replaced by a concept of copy-and-restore, rather than by reference, para 216 or parameter feed node ARGAR corresponding to subroutine call with passing parameters in the HLL file, para 226); and
replace the retrieved token from the parse tree with a semantic concept extracted from the identified one or more natural language statements responsive to a determination that the retrieved token from the parse tree is the replace token (replacing the LDA by a wire from the top node ACON to the lower node LDKR and the LDA tokens or nodes beneath the node ACON removed and replaced with wires as in fig. 17, and based on the semantic concept of the source file, C or C++ programming code, with parameter reference, i.e., global parameter reference, and local reference indicating address constants ACONs in fig. 14, para 216 or replacing linked list of ARGAR nodes or tokens and each representing an argument to the same subroutine call in middle portion of fig. 20, and based on subroutine or function call concept in the HLL file, para 226).
However, Poznanovic does not explicitly teach wherein a machine learning model trained to determine a probability of executing the reduce action is used for disclosed determination of whether to execute the reduce action on the token.
Bowman teaches an analogous field of endeavor by disclosing a method (title and abstract, ln 9-24 and fig. 2) and wherein a probability of executing the reduce action (
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in a model represented by formula 5, a chance of transition a that is either reduce or shift of token operated from buffer to stack, session 3.4 Parsing: Predicting transitions, p.1469) based on a given token of a respective parse tree (tokens in the buffer from top to bottom in form of tree nodes held in buffer such as TreeRNNs, session Why a tree-sequence hybrid, p.1469) is disclosed and wherein the probability is determined by using a machine learning mode trained to determine the probability of executing the reduce action or shift action on the given token of a respective parse tree (the model mimics the decisions of an external parser and the result is feedback to the model as input for training, and after trailing, the higher probability for SHIFT or for REDUCE transition is taken and the corresponding transition, either SHIFT or REDUCE, is executed, session 3.4 Parsing: Predicting transitions, p.1469) for benefits of obtaining higher parsing performance (by effectively reducing ambiguity with less complexity, session Why a tree-sequence hybrid, p.1469 and by improving the operability of parsing tree in a batch and speedup computation specifically for large neural network SPINN, abstract).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have applied the machine learning model and wherein the machine learning mode is trained to determine the probability of executing the reduce action and used for determining whether to execute the reduce action on the token, as taught by Bowman, to determining whether to execute the reduce action on the token based on the given token of the respective parse tree implemented by the instructions stored in the non-transitory computer readable storage medium, as taught by Poznanovic, for the benefits discussed above.
Claim 9 recited a method and has been analyzed and rejected according to claim 1 above (method steps implemented by the processor on the instructions stored in the non-transitory computer readable storage medium of claim 1).
Claim 17: has been analyzed and rejected according to claim 1 above and the combination of Poznanovic and Bowman further teaches identifying one or more translatable sentences of the specification (Poznanovic, variable “parameter”, subroutine “initialize (a,b,c)”, logic “if … else”, loop “do i=1, n”, etc. as translatable sentences in HLL in fig. 5) and the probabilistic shift-reduce schedule (
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in the model of formula 5, and the discussion in claim 1 above, and applied in shift-reduce parser, session 3.2 Composition and representation, p.1468).
Claim 2: the combination of Poznanovic and Bowman further teaches, according to claim 1 above, wherein the instructions to determine whether to execute the reduce action on the token using the machine learning model cause the processor to:
determine to execute the reduce action responsive to a determination that the probability of executing the reduce action is greater than a threshold value (Bowman, the higher probability is assigned to a transition, either shift or reduce, i.e., probability for shift action is a threshold if the probability for reduce action is higher compared to the probability for shift action, and vise verse, or
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corresponding to shift action and
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corresponding to reduce action, as threshold value, of an external parser respectively applied for training transition decision function formula 5, session 3.4 Parsing: Predicting transition, p.1469).
Claim 3: the combination of Poznanovic and Bowman further teaches, according to claim 2 above, wherein the threshold value is a learned threshold determined during a training phase for the machine learning model (or
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corresponding to shift action and
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corresponding to reduce action, as threshold value, of an external parser respectively applied for training transition decision function formula 5, by feedback the decisions to the decision function formula 5 and discussed in claim 2 above, 3.4 Parsing: Predicting transition, p.1469).
Claim 5: the combination of Poznanovic and Bowman further teaches, according to claim 1 above, wherein the instructions when executed by the processor further cause the processor to: for each natural language statement of the one or more natural language statements: retrieve a next token from the parse tree responsive to a determination to execute a shift action (Poznanovic, from LDKR to KADD or to KSUB and then to STKR as shift action in fig. 17, and Bowman, at shift transition, retrieving word “sat” from the buffer to the stack at last step in fig. 2a and similarly, retrieving word “the” at the step i=1 in response to the shift transition in fig. 2b, etc.).
Claim 6: the combination of Poznanovic and Bowman further teaches, according to claim 1 above, wherein the instructions when executed by the processor further cause the processor to select the semantic concept based on a position of the replace token within the parse tree (Poznanovic, the node LDA to be removed is positioned underneath node ACON in fig. 16the function call with different parameters while linked list position in fig. 20).
Claim 7: the combination of Poznanovic and Bowman further teaches, according to claim 1 above, wherein the semantic concept is translated to a target language (Poznanovic, a op code with a language in figs. 16-17, 20-21 and being different from HLL of fig. 5) using a set of replacement rules (Poznanovic, LDA is from beneath any ACONs, and LDA nodes are treated as copy-and-restore, other than by reference, para 216).
Claim 8: the combination of Poznanovic and Bowman further teaches, according to claim 1 above, wherein the instructions when executed by the processor further cause the processor to:
execute a terminate action responsive to a determination to execute a terminate action (Poznanovic, responsive to a “return” and “end” in a subroutine in HLL, e.g., fig. 5, and corresponding action of nodes ST_SCALAR in fig. 26B and for loop in fig. 26A, para 246); and
append an end of code token (Poznanovic, TERMINATION node as token in fig. 26B) to a translation of the natural language statement (Pozananovic, at end of the loop after LOOP_VALID node in fig. 26B), wherein the translation is a code generated for verifying that the condition specified in the natural language statement is satisfied for the design of the electronic circuit (Pozananovic, validating the loop end of fig. 26A and shift to the next TERMINATION node in fig. 26B, para 221).
Claim 10 has been analyzed and rejected according to claims 9, 2 above.
Claim 11 has been analyzed and rejected according to claims 10, 3 above.
Claim 13 has been analyzed and rejected according to claims 9, 5 above.
Claim 14 has been analyzed and rejected according to claims 9, 6 above.
Claim 15 has been analyzed and rejected according to claims 9, 7 above.
Claim 16 has been analyzed and rejected according to claims 9, 8 above.
Claim 19 has been analyzed and rejected according to claims 17, 5 above.
Claim 20 has been analyzed and rejected according to claims 17, 6 above.
The prior art Goyal et al. (US 20170315984 A1) made of record and not relied upon is considered pertinent to applicant's disclosure because Goyal above disclosed hardware-based programmable text analysis according to rest-shift tree structure and operations, which is part of the disclosures disclosed by the applicant.
Examiner Comment
Claims 4, 12, 18 are objected because the subject matter claimed in claims 4, 12, 18 is not supported by the application specification, and it appears to be conflicting to the disclosure of the application specification, as described in specification objection as set forth above. Therefore, at this point, a prior art rejection for the claims would not be considered proper, as it would have to be based on mere assumptions and considerable speculation about the scope of the claims, see MPEP 2173.06. It is noted that the objection of claims 4, 12, 18 without application of prior art rejection is not an indication that the instantly amendment claims are patentable. A prior art search has been performed by the Office, and recorded in attached PTO-892 form.
Conclusion
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/LESHUI ZHANG/
Primary Examiner,
Art Unit 2695
Prosecution Insights