DETAILED ACTION
Notice of 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 .
Response to Amendment
Applicant’s Amendment and remarks dated 1/5/2026 have been considered. Claims 1-20 are pending.
Claim Objections. The objections to claims 3, 10, and 17 are withdrawn in view of Applicant’s amendments to such claims.
Response to Arguments
On page 10 of Applicant’s 1/5/2026 Amendment and remarks, Applicant asserts that paras. 0158-0170 and Figs. 4D and 4E provide written description support for the claim amendments.
The examiner agrees that the portions of the disclosure identified by Applicant provide sufficient written description support for the claim amendments.
On pages 10-11 of Applicant’s 1/5/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, Prong 1, Applicant argues that “none of the claim elements of the claims recite a mathematical concept, certain method of organizing human activity, or a mental process.”
The examiner respectfully disagrees. As explained in the office action with specific examples, each of the independent claims recite several mental processes, which Applicant has not specifically rebutted in any instance.
On pages 11-13 of Applicant’s 1/5/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 101, with respect to Step 2A, Prong 2, Applicant argues the “claims as a whole clearly improve upon computer-related technology.”
The examiner respectfully disagrees with respect to claims 1-6, 8-13, and 15-20, but as explained below, claims 7 and 14 are deemed subject matter eligible. MPEP 2106.04(d)(1) explains the following two considerations with respect to demonstrating an improvement to another technology or technical field:”
In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.
With regard to the first consideration, the examiner agrees that paras. 0049-0050 (together with paras. 0047-0048) of the instant specification describe improvements related to computer database technologies, particularly using natural language commands to more easily allow people to search databases, including databases that use SQL commands.
However, with regard to the second consideration, the examiner finds that claims 1-6, 8-13, and 15-20 do not reflect the improvement. As a whole, these claims claim the mental processes that a human can perform in order to develop synthetic utterances that can be used to train a machine learning model, which does not reflect the actual improvement to database technologies itself. Creating training data, by itself, is not an improvement to computer database technologies.
However, claims 7 and 14 go further and require accessing an utterance, inputting the utterance into the trained machine learning model, translating the utterance into a logical form, and actually executing said logical form as a query on a database to retrieve a result for the query. These limitations reflect the improvement to actual database searches and are therefore considered to be subject matter eligible.
The examiner further notes that Example 47, Claims 2 and 3 of the Subject Matter Eligibility Examples are also instructive. While claims 1-6, 8-13, and 15-20 are more similar to Claim 2 of Example 47, where training data is received and processed, an artificial neural network (ANN) is trained, and then the ANN is used to output data. In contrast, claims 7 and 14 are more similar to Claim 3 of Example 47, where additional steps (d)-(f) provide further specific remedial actions to prevent network intrusions, showing the improvement to actual network security. Here, claims 7 and 14 actually require an utterance to be translated and used to query a database, reflecting the improvement to database querying technologies.
On pages 13-14 of Applicant’s 1/5/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 102, Applicant argues that as amended, WU no longer anticipates claims 1, 7-8, and 14-15.
While the examiner does not agree with Applicant’s characterization of the WU reference, the examiner does agree that WU no longer anticipates the claims as amended. All rejections under 35 U.S.C. 102 are hereby withdrawn.
On page 14 of Applicant’s 1/5/2026 Amendment and remarks, with respect to the rejections under 35 U.S.C. 103, Applicant argues that the previous claim rejections should be withdrawn in view of the new claim amendments.
The examiner agrees that the new claim amendments overcome the prior rejections. All rejections under 35 U.S.C. 103 are hereby withdrawn.
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 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. As noted above, claims 7 and 14 are found to be subject matter eligible.
Regarding Step 1 of the Alice/Mayo framework, Claims 1-7 are directed to a method (a process), Claims 8-14 are directed to a system (a machine), and Claims 15-20 are directed to a computer program product tangibly embodied in one or more non-transitory machine readable-readable medium (an article of manufacture), which each fall within one of the four statutory categories of inventions.
Regarding Claim 1
Step 2A, prong 1 (Is the claim directed to a law of nature, a natural phenomenon or an abstract idea).
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer (e.g., “computer-implemented” and “machine learning model”).
generating a set of abstract syntax trees for a first subset of logical forms of the plurality of logical forms, wherein generating the set of abstract syntax trees comprises, for each respective logical form of the first subset of logical forms, parsing the respective logical form into an abstract syntax tree and normalizing the abstract syntax tree; (under the broadest reasonable interpretation, this limitation can be performed mentally (or using pencil and paper), for example, a human can mentally (or using pencil and paper) review a logical form (such as a SQL query) that is part of a first subset of logical forms, and mentally imagine (or draw using pencil and paper) a corresponding abstract syntax tree, and then normalize such abstract syntax tree (e.g., by proofreading and using consistent terms))
generating a set of delexicalized logical forms for a second subset of logical forms of the plurality of logical forms, each delexicalized logical form of the set of delexicalized logical forms including a delexicalized version of a logical form of the plurality of logical forms, wherein the delexicalized version of the logical form comprises a non-terminal symbol that represents an element associated with the database schema information; (under the broadest reasonable interpretation, this limitation can be performed mentally (or using pencil and paper), for example, a human can mentally (or using pencil and paper) generate delexicalized logical forms, such as by taking a SQL query and replacing specific variable names to be associated table or column IDs, where the associated table or column IDs (such as T1 for table 1, or C1 for column 1) can be non-terminal symbols that represent elements analyzed from the database schema information)
generating a set of lexicalized logical forms by lexicalizing the set of delexicalized logical forms, wherein lexicalizing the set of delexicalized logical forms comprises replacing non-terminal symbols of the set of delexicalized logical forms with components sampled from one or more databases; (under the broadest reasonable interpretation, this limitation can be performed mentally (or using pencil and paper), for example, a human can mentally (or using pencil and paper) generate lexicalized logical forms, such as by taking a SQL query and replacing table or column IDs with replacement variable names from other databases)
generating a plurality of synthetic natural language utterances for the set of lexicalized logical forms, (under the broadest reasonable interpretation, this limitation can be performed mentally (or using pencil and paper), for example, a human can mentally (or using pencil and paper) generate a natural language question that corresponds to the SQL query, such as for the SQL query “Select name FROM employee ORDER_BY age ASC”, generating the natural language question “list the names of employees in ascending order of age” that describes what the SQL query does; the examiner notes that para. 0065 of the instant specification explains that an utterance can be a “text utterance” and therefore the broadest reasonable interpretation of “utterance” includes text utterances that are not spoken aloud)
generating synthetic training data by combining the set of lexicalized logical forms and the plurality of synthetic natural language utterances with the original training data; (under the broadest reasonable interpretation, this limitation can be performed mentally (or using pencil and paper), for example, a human can mentally (or using pencil and paper) combine the set of lexicalized logical forms and the synthetic natural utterances with the original training data to create a combined training set)
Step 2A, prong 2 (Does the claim recite additional elements that integrate the judicial exception into a practical application?).
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements (e.g., “computer-implemented” and “machine learning model”) which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “accessing original training data” limitation, such additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “the original training data including a plurality of utterances, a plurality of logical forms, and database schema information, each logical form of the plurality of logical forms corresponding to at least one utterance of the plurality of utterances” limitation, this limitation merely describes details of the data being processed, and therefore such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate a judicial exception into a practical application.
Regarding the “wherein generating the plurality of synthetic natural language utterances comprises using a trained tree-to-string model and a grammar to decode the set of abstract syntax trees into the plurality of synthetic natural language utterances” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of using a trained model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (a generic trained model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Regarding the “generating a machine learning model by training a machine learning model using the synthetic training data to translate utterances to logical forms” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of generic machine learning training. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (generic machine learning training). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not integrate the judicial exception into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?)
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements (e.g., “computer-implemented” and “machine learning model”) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “accessing original training data” limitation, as discussed above, the additional element of a data gathering step is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “the original training data including a plurality of utterances, a plurality of logical forms, and database schema information, each logical form of the plurality of logical forms corresponding to at least one utterance of the plurality of utterances” limitation, such limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use as explained above, which does not amount to significantly more than the judicial exception. MPEP 2106.05(h).
Regarding the “wherein generating the plurality of synthetic natural language utterances comprises using a trained tree-to-string model and a grammar to decode the set of abstract syntax trees into the plurality of synthetic natural language utterances” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding the “generating a machine learning model by training a machine learning model using the synthetic training data to translate utterances to logical forms” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Accordingly, at Step 2B, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not amount to significantly more than the judicial exception.
Regarding Claim 2
Step 2A, Prong 1
wherein normalizing the respective abstract syntax tree comprises performing a binarizing operation on the abstract syntax tree, performing unary wrapping operation on the abstract syntax tree, or adding one or more deleting nodes to the abstract syntax tree. (under the broadest reasonable interpretation, this limitation can be performed mentally (or using pencil and paper), for example, a human can mentally (or using pencil and paper) review an abstract syntax tree and mentally (or using pencil and paper) normalize elements, such as by re-ordering the tree into a binary tree format, applying unary headers to nodes (as shown in Fig. 4E, process 4600), or identifying certain nodes as “_DELETE_” as shown in Fig. 4E, process 4700)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 3
Step 2A, Prong 2
Regarding the “wherein at least one delexicalized logical form of the set of delexicalized logical forms is generated automatically using a machine-learning model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception. In particular, the claim only recites the additional element of a generic machine learning model. This additional element is recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (using a generic machine learning model). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)).
Step 2B
Regarding the “wherein at least one delexicalized logical form of the set of delexicalized logical forms is generated automatically using a machine-learning model” limitation, such limitation is recited at a high-level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, because the limitation merely provides instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not add significantly more than the judicial exception. (See MPEP 2106.05(f)).
Regarding Claim 4
Step 2A, Prong 1
wherein generating the set of lexicalized logical forms comprises analyzing each delexicalized logical form of the set of delexicalized logical forms to identify one or more constraints in the delexicalized logical form and sampling one or more components ... based on the one or more constraints. (under the broadest reasonable interpretation, this limitation can be performed mentally (or using pencil and paper), for example, a human can mentally (or using pencil and paper) generate delexicalized logical forms, such as by taking a SQL query and identifying specific query constraints and replacing specific variable names to be associated table or column IDs, and then by reviewing the names for table/column IDs in a separate database to identify replacement samples)
Step 2A, Prong 2
Regarding the “of the one or more databases” limitation, such additional element of a data storage step is recited at a high level of generality and amounts to extra-solution activity of storing data, i.e. post-solution activity of data storage for use in the claimed process (see MPEP 2106.05(g)).
Step 2B
Regarding the “of the one or more databases” limitation, as discussed above, the additional element of a data storage step is recited at a high level of generality and amounts to extra-solution activity of storing data, i.e. post-solution activity of storing data after or during use in the claimed process. The courts have found limitations directed to storing information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), "electronic record keeping," and "storing and retrieving information in memory").
Regarding Claim 5
Step 2A, Prong 1
wherein the generating the plurality of synthetic natural language utterances comprises translating, by the tree-to-string model, each lexicalized logical form of the set of lexicalized logical forms into an utterance. (under the broadest reasonable interpretation, this limitation can be performed mentally (or using pencil and paper), for example, a human can mentally (or using pencil and paper) use a set of mental or written rules for how to convert a tree to a string (e.g., depth first traversal starting from the left) to translate a SQL query (corresponding to recited “lexicalized logical form”) to a corresponding natural language question that corresponds to the generated SQL query)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 6
Step 2A, Prong 1
wherein the generating the plurality of synthetic natural language utterances comprises reordering each abstract syntax tree of the set of abstract syntax trees to result in reordered abstract syntax trees and decoding each of the reordered abstract syntax trees into an utterance. (under the broadest reasonable interpretation, this limitation can be performed mentally (or using pencil and paper), for example, a human can mentally (or using pencil and paper) reorder an abstract syntax tree (e.g., change it to be a depth-first traversal), and then decode such abstract syntax tree to an associated SQL query, and then can generate a corresponding natural language question that corresponds to the generated SQL query)
Regarding Step 2A, Prong 2, the claim does not include any additional elements that integrate the judicial exception into a practical application and regarding Step 2B, there are no additional elements recited that amount to significantly more than the judicial exception.
Regarding Claim 8
Step 2A, Prong 1
Claim 8 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“one or more processors”, “non-transitory computer-readable media”, “instructions”, and “machine learning model”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 8 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“one or more processors”, “non-transitory computer-readable media”, “instructions”, and “machine learning model”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 8 recites a system that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 8. While claim 8 recites additional generic computing components (“one or more processors”, “non-transitory computer-readable media”, “instructions”, and “machine learning model”), such additional generic computing components do not change the analysis under Step 2B.
Claim 9 depends from claim 8 and corresponds to the method of claim 2 and is therefore rejected for the same reasons explained above with respect to claims 2 and 8.
Claim 10 depends from claim 8 and corresponds to the method of claim 3 and is therefore rejected for the same reasons explained above with respect to claims 3 and 8.
Claim 11 depends from claim 8 and corresponds to the method of claim 4 and is therefore rejected for the same reasons explained above with respect to claims 4 and 8.
Claim 12 depends from claim 8 and corresponds to the method of claim 5 and is therefore rejected for the same reasons explained above with respect to claims 5 and 8.
Claim 13 depends from claim 8 and corresponds to the method of claim 6 and is therefore rejected for the same reasons explained above with respect to claims 6 and 8.
Regarding Claim 15
Step 2A, Prong 1
Claim 15 recites a computer-program product tangibly embodied in one or more non-transitory machine-readable media that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 1 with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“data processors”, “non-transitory machine-readable media”, “instructions”, and “machine learning model”), such additional generic computing components do not change the analysis under Step 2A, Prong 1.
Step 2A, Prong 2
Claim 15 recites a computer-program product tangibly embodied in one or more non-transitory machine-readable media that corresponds to the method of claim 1, and therefore the analysis under Step 2A, Prong 2 with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“data processors”, “non-transitory machine-readable media”, “instructions”, and “machine learning model”), such additional generic computing components do not change the analysis under Step 2A, Prong 2.
Step 2B
Claim 15 recites a computer-program product tangibly embodied in one or more non-transitory machine-readable media that corresponds to the method of claim 1, and therefore the analysis under Step 2B with respect to claim 1 also applies to this claim 15. While claim 15 recites additional generic computing components (“data processors”, “non-transitory machine-readable media”, “instructions”, and “machine learning model”), such additional generic computing components do not change the analysis under Step 2B.
Claim 16 depends from claim 15 and corresponds to the method of claim 2 and is therefore rejected for the same reasons explained above with respect to claims 2 and 15.
Claim 17 depends from claim 15 and corresponds to the method of claim 3 and is therefore rejected for the same reasons explained above with respect to claims 3 and 15.
Claim 18 depends from claim 15 and corresponds to the method of claim 4 and is therefore rejected for the same reasons explained above with respect to claims 4 and 15.
Claim 19 depends from claim 15 and corresponds to the method of claim 5 and is therefore rejected for the same reasons explained above with respect to claims 5 and 15.
Claim 20 depends from claim 15 and corresponds to the method of claim 6 and is therefore rejected for the same reasons explained above with respect to claims 6 and 15.
Allowable Subject Matter
Claims 1-6, 8-13, and 15-20 would be allowed, provided that the rejections under 35 U.S.C. 101 are overcome.
Claims 7 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Independent claims 1, 8, and 15 would be considered allowable, provided that the rejections under 35 U.S.C. 101 are overcome, because none of the references of record either alone or in combination fairly disclose or suggest the combination of limitations specified in the independent claims, including at least:
wherein the delexicalized version of the logical form comprises a non-terminal symbol that represents an element associated with the database schema information;
wherein lexicalizing the set of delexicalized logical forms comprises replacing non-terminal symbols of the set of delexicalized logical forms with components sampled from one or more databases;
generating a plurality of synthetic natural language utterances for the set of lexicalized logical forms, wherein generating the plurality of synthetic natural language utterances comprises using a trained tree-to-string model and a grammar to decode the set of abstract syntax trees into the plurality of synthetic natural language utterances;
The closest prior art of record discloses:
Wu, Kun, et al. "Data augmentation with hierarchical SQL-to-question generation for cross-domain text-to-SQL parsing." arXiv preprint arXiv:2103.02227 (Oct. 26, 2021), hereinafter referenced as WU, discloses “a simple and resource-cheap data augmentation framework for cross-domain text-to- SQL parsing with no human intervention.” (p. 2, section 1). As shown in Fig. 2B, WU teaches using an abstract syntax tree grammar (ASTG) to generate sketch trees (corresponding to recited “abstract syntax trees”) which can be used to create a pattern such as “SELECT C FROM T ORDER_BY C ASC”, which corresponds to the recited “delexicalized logical form”, which corresponds to an SQL query (corresponding to the “plurality of logical forms”), where as shown in Fig. 2 in red, the variables “C” and “T” stand for columns and tables, which correspond to the recited “non-terminal symbols”. (p. 2, section 1, and p. 3, section 2.1)
US 20180373986 A1, hereinafter referenced as RAINWATER, discloses refactoring and normalizing abstract syntax trees. (para. 0017).
US 20200134020 A1, hereinafter referenced as RABINOVICH, discloses using a first subset of data to generate positive training data, and a second subset of data to generate negative positive data. (para. 0026). This teaches that different subsets of the same training data can be separately used to derive separate types of training data, that are combined in a consolidated training set.
Mayer, Mikaël, et al. "Polynomial-time proactive synthesis of tree-to-string functions from examples." arXiv preprint arXiv:1701.04288 (2017), hereinafter referenced as MAYER, discloses an algorithm for learning text-to-string transducers (corresponding to recited “trained tree-to-string model”). (p. 13, section 6). The trees are represented by a context-free grammar and include non-terminal symbols. (p. 4, section 2).
However, the examiner has found that the distinct feature of the Applicant's claimed invention over the prior art is the explicit claiming of the aforementioned limitations in combination with all the other limitations as specified in independent claims 1, 8, and 15. Moreover, the examiner finds that one of ordinary skill in the art would not have been motivated to combine the prior art of record in the precise manner recited in independent claims 1, 8, and 15 without the hindsight aid of Applicant’s disclosure. Therefore, because the prior art of record does not anticipate nor make obvious the limitations recited in independent claims 1, 8, and 15, such claims would be allowed if the rejections under 35 U.S.C. 101 are overcome.
Dependent claims 2-6, 9-13, and 16-20 would be allowed because they depend from an allowable independent base claim, provided that the rejections under 35 U.S.C. 101 are overcome.
Dependent claims 7 and 14 would be allowed if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20050234701 A1 (Graehl) discloses tree-to-string transducers. (paras. 0050-0051).
Flanigan, Jeffrey, et al. "Generation from abstract meaning representation using tree transducers." Proceedings of the 2016 conference of the north american chapter of the association for computational linguistics: Human language technologies. 2016. Discloses that “many grammar-based approaches can be formulated as weighted tree-to-string transducers.” (p. 738, section 10).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C LEE whose telephone number is (571)272-4933. The examiner can normally be reached M-F 12:00 pm - 8:00 pm ET.
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/MICHAEL C. LEE/Examiner, Art Unit 2128
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