DETAILED ACTION
This Office action is responsive to Request for Continued Examination filed on April 20th, 2026. Claims 1, 7-8 and 14-15 are amended, claims 1-20 are pending and have been examined.
Any objections/rejections not mentioned in this Office action have been withdrawn by the Examiner.
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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) is acknowledged. Accordingly, claims 1-20 have been afforded the benefit of the earlier filing date of Provisional Application 63/520877, filed August 21st, 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 1st, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendments and Arguments
Regarding rejections made under 35 U.S.C. 103, Applicant argues, “Luo describes an NL2SQL-to-NL2VIS synthesizer that generates visualizations based on SQL queries and edits NL queries based on the synthesized VIS. Luo, Abstract. Specifically, Luo utilizes a unified intermediate representation-abstract syntax trees (ASTs)-to bridge SQL and VIS queries. Id., Section 2.2. Luo generates VIS trees by adding or deleting nodes from a SQL tree. Id., Luo's output relies entirely on this mapped abstraction from an SQL tree. Luo, however, does not teach or suggest the claimed including a visualization clause into a natural language utterance and modifying an MRL logical form of the generating the first training data step. Additionally, Luo fails to disclose or teach instantiating a manipulation template with selected values of the generating the third training data step. Instead, Luo relies on mapping structural data operations. Accordingly, Applicant respectfully submits that Luo fails to disclose or suggest the features recited by Claims 1, 8, and 15.
Han describes generating a first, second, and third datasets based on corresponding database information (Han translation, pp. 2 and 3). However, Han's data augmentation methodology is limited to applying a ‘schema transformation operation’ to a database. Han defines schema transformation operations as changing a schema structure (e.g., joining tables, decomposing tables, adding tables, adding columns) or changing the names of schema elements.
Han does not teach or suggest generating a logical form based on an input MRL logical form and one or more construction rules of the generating the second training data step. Han describes structural database schema manipulation, not MRL logical form construction rules. Like Luo, Han also fails to teach or suggest instantiating a manipulation template. Accordingly, Applicant respectfully submits that Han fails to cure the deficiencies of Luo.
For at least these reasons, Applicant respectfully submits that Claims 1, 8, and 15 are patentable over Luo and Han,” ( starting on page 16 of Remarks).
Examiner respectfully disagrees. The amended claim language describe processing steps that may be interpreted to include the teachings of Luo, particularly inclusion of visualization clauses in natural language utterances and modification of logical forms. Similarly, Han may be referenced to teach processing steps that describe generating logical forms from input logical forms and training dataset augmentation using templates. These references in combination teach each element of the claimed invention, and that combination would be obvious to a person having ordinary skill in the art. Accordingly, the rejections under 35 U.S.C. 103 are maintained. Further details are provided below.
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, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over "Synthesizing Natural Language to Visualization (NL2VIS) Benchmarks from NL2SQL Benchmarks" by Yuyu Luo et al. (hereinafter, "Luo") in view of WIPO Publication 2023/128021 to Han et al. (hereinafter, "Han").
Regarding claims 1, 8 and 15, Luo teaches a method, computer-readable media and system comprising: accessing an original training dataset, a visualization query dataset, an incremental visualization dataset, and a manipulation visualization dataset (page 1, Abstract, "In this paper, we propose a NL2VIS synthesizer (NL2SQL-to-NL2VIS) that synthesizes NL2VIS benchmarks by piggybacking NL2SQL benchmarks.");
generating first training data by processing, in a first data manufacturing pipeline, first training examples in the original training dataset and the visualization query dataset, wherein the generating comprises including a visualization clause into a natural language utterance of a training example of the original training dataset and modifying a meaning representation language (MRL) logical form that corresponds to the natural language utterance (page 5, section 2.5 NL Edits: Generating NL Variants, "Each VIS
t
i
∈
T
'
V
is associated with a set of tree edits
Δ
i
, i.e., deletions
Δ
-
i
and insertions
Δ
+
i
. Next, we need to modify the NL of the corresponding SQL query to reflect these changes," and Example 5. "Assume that the VIS query is simply to visualize the above result using a pie chart, i.e., adding 'VISUALIZE pie' to the corresponding SQL tree. The NL edits will automatically revise the above NL for SQL to a NL for VIS as 'show the proportion about how many male and female faculties do we have?'"); and
training, using the augmented training dataset, a machine learning model to convert a natural language utterance into meaning representation language (MRL) logical form that includes one or more visualization actions (page 3, Step 2. NL Synthesis, "The purpose of having variants of NL specifications, which is a way of data augmentation [18], is to train a robust model.").
While Luo teaches a method wherein a logical form is generated from a given logical form (section 2.3, Tree Edits: Generating VIS Candidates, "Next, we discuss how to generate (candidate) VIS trees from one SQL tree. Intuitively, given one SQL tree, we can 'delete' any tree nodes or 'insert' any tree nodes, as long as we can produce a valid VIS tree rooted with 'Visualize Q' (Figure 5)… (Visualize.) We will add a Visualize subtree. Also, we need to ensure that the VIS type (e.g., bar, line) added can lead to a valid VIS. We follow the rule of thumb (see Table 1) of VIS w.r.t. attribute types from the data visualization community [38, 65], which are encoded as rules in our system."), Han may also be referenced to teach generating second training data by processing, in a second data manufacturing pipeline, second training examples in the incremental visualization dataset, wherein the generating comprises generating a logical form based on an input MRL logical form and one or more construction rules (page 5, "According to an exemplary embodiment of the present invention, the data augmentation engine 152 may generate new learning data through a database schema modification operation, rather than augmenting learning data including new natural language query data. That is, unlike the conventional training data augmentation method of augmenting training data based on new natural language query data, the data augmentation engine 152 according to an exemplary embodiment of the present invention extends the diversity of database schema to a given training dataset. A new type of SQL query that does not exist can be automatically created," and page 6, "Subsequently, the data augmentation engine 152 may determine a schema transformation operation f1() to be applied to the database D1 for data augmentation (step 210). In addition, the data augmentation engine 152 may generate a new database D11 by applying the schema transformation operation f1() to the database D1 (operation220).
The data augmentation engine 152 checks the SQL query synchronization operation corresponding to the schema transformation operation f1(), applies the identified SQL query synchronization operation to the SQL query (Q1), and creates a new database (D11). A SQL query (Q11) may be generated for (step 230).
The new training dataset (DS11) derived through this process includes the first natural language query (q1), the new database (D11), and the new SQL query (Q11). The data augmentation engine 152 may store the new training datasets (q1, D11, Q11) as a training dataset (DS11) added by augmentation (step 240).").
Luo or Han may be referenced to teach the broadest reasonable interpretation of “generating a logical form based on an input MRL logical form and one or more construction rules,” and the combination of both references demonstrates the obviousness of a data manufacturing pipeline that includes that processing as a means for creating an augmented training data set.
Luo does not explicitly teach “generating third training data by processing, in a third data manufacturing pipeline, third training examples in the manipulation visualization dataset, wherein the generating comprises instantiating a manipulation template of the manipulation visualization dataset with selected values,” or “augmenting the original training dataset by adding the first training data, second training data, and third training data to the original training dataset to generate an augmented training dataset,” however, Han teaches generating second training data by processing, in a second data manufacturing pipeline, second training examples in the incremental visualization dataset wherein processing the second training examples comprises synthesizing additional training examples from the second training examples (page 5, "According to an exemplary embodiment of the present invention, the data augmentation engine 152 may generate new learning data through a database schema modification operation, rather than augmenting learning data including new natural language query data. That is, unlike the conventional training data augmentation method of augmenting training data based on new natural language query data, the data augmentation engine 152 according to an exemplary embodiment of the present invention extends the diversity of database schema to a given training dataset. A new type of SQL query that does not exist can be automatically created.");
generating third training data by processing, in a third data manufacturing pipeline, third training examples in the manipulation visualization dataset, wherein processing the third training examples comprises synthesizing additional training examples from the third training examples (page 6, "As shown in FIG. 4, the above data augmentation operation is not terminated once, but is repeatedly performed for other schema transformation operations and for other initial training datasets, so that training data is converted into new training datasets. can create That is, after generating a new training dataset based on the first schema transformation operation f1(), the data augmentation engine 152 may generate a new training dataset based on the second schema transformation operation f2()."); and
augmenting the original training dataset by adding the first training data, second training data, and third training data to the original training dataset to generate an augmented training dataset (page 5, "The data augmentation engine 152 accepts a training dataset and augments the training dataset to increase its quantity. Accordingly, the augmented training dataset output by the data augmentation engine 152 includes the training dataset added by the data augmentation engine 152 in addition to the original training dataset stored in the training dataset table 156.).
Luo and Han are considered analogous because they are each concerned with machine learning model training, including natural language and structured data queries. 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 modified Luo with the teachings of Han for the purpose of training a robust model as prompted by Luo, and improvements to model performance would have been predictable.
Regarding claims 2, 9 and 16, Luo teaches (ii) each example in the visualization query dataset comprises a natural language utterance, a system programming language corresponding to the natural language utterance, a visualization type presented in the natural language utterance, and a schema (page 5, section 2.5, "For insertions such as grouping, aggregation, Order, and VIS types, we use NL extracted from Tableau’s Ask Data [50] and NL4DV2 as rules to enrich the text.");and (iv) the manipulation visualization dataset comprises one or more manipulation templates (page 12, Training Data Generation, "DBPal [63] augments training data based on a set of pre-defined (NL, SQL) templates.").
Luo does not explicitly teach “each example in the original training dataset comprises a natural language utterance, a MRL logical form corresponding to the natural language utterance, and a schema,” or “the incremental visualization dataset comprises one or more data annotation and incremental natural language templates,” however, Han teaches (i) each example in the original training dataset comprises a natural language utterance, a MRL logical form corresponding to the natural language utterance, and a schema (page 5, "The data augmentation engine 152 accepts a training dataset and augments the training dataset to increase its quantity… In an exemplary embodiment, the training dataset may consist of a set of natural language queries, databases, and SQL queries. The natural language query and the SQL query may be stored in the learning dataset table 156 together with database information."),and (iii) the incremental visualization dataset comprises one or more data annotation and incremental natural language templates (page 2, "According to the template-based method, synthetic data is created using a template of a predefined natural language query and SQL pair. The reference to the data in the database is empty as a slot in the template.").
Luo and Han are considered analogous because they are each concerned with machine learning model training. 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 modified Luo with the teachings of Han for the purpose of expanding dataset coverage. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Regarding claims 3, 10 and 17, Luo teaches (c) selecting a visualization type for the example based on constraints of the MRL logical form and popularity scores associated to each visualization type (page 4, section 2.3, "We will add a Visualize subtree. Also, we need to ensure that the VIS type (e.g., bar, line) added can lead to a valid VIS. We follow the rule of thumb (see Table 1) of VIS w.r.t. attribute types from the data visualization community [38, 65], which are encoded as rules in our system," and page 6, VIS and NL Queries, "Bar charts and its variants take a huge percentage matches real-world cases. As shown by Beagle [5] and SEEDB [56], bars (and histograms) are the most popular VIS types.");(d) adding a visualization clause to the natural language utterance associated with the example using a visualization clause template and the visualization type selected for the example to generate a visualization creation utterance, wherein the visualization clause includes a visualization action for the visualization type (page 2, section 2.1, "It takes a (NL, SQL) pair (nQ ,Q) as the input, and returns as output a set of (NL, VIS) pairs: {(n11, t1), … , (n1k, t1), … ,(nm1, tm), … ,(nmk ,tm)}".);(e) modifying, based on the visualization labeled schema and the visualization type selected for the example, the MRL logical form associated with the example to generate a visualization creation MRL logical form that corresponds to the visualization creation utterance, wherein the visualization creation MRL logical form comprises one or more visualization-related entities and a visualization clause that includes the visualization action for the visualization type (page 4, section 2.3, "Next, we discuss how to generate (candidate) VIS trees from one SQL tree. Intuitively, given one SQL tree, we can ‘delete’ any tree nodes or ‘insert’ any tree nodes, as long as we can produce a valid VIS tree rooted with ‘Visualize Q’ (Figure 5).");(f) assembling the visualization labeled schema, the visualization creation utterance, and the visualization creation MRL logical form to generate a new visualization example (page 4, section 2.3, "After different combinations of insertions above, we will obtain candidate VIS set TV.").
Luo does not explicitly teach “accessing an example from the original training dataset,” “adding, to the schema associated with the example, one or more visualization-related entities and schema-linking relations that link the one or more visualization-related entities to one or more entities within the schema to generate a visualization labeled schema,” or “repeating steps (a) and (c) - (f) for a random or predefined number of examples in the original training dataset to generate a visualization training dataset, the visualization training dataset comprising the new visualization examples,” however, Han teaches (a) accessing an example from the original training dataset (page 6, "Referring to FIGS. 3 and 4 , the data augmentation engine 152 first accepts an initial training dataset (step 200).");(b) adding, to the schema associated with the example, one or more visualization-related entities and schema-linking relations that link the one or more visualization-related entities to one or more entities within the schema to generate a visualization labeled schema (page 6, "Subsequently, the data augmentation engine 152 may determine a schema transformation operation f1() to be applied to the database D1 for data augmentation (step 210). In addition, the data augmentation engine 152 may generate a new database D11 by applying the schema transformation operation f1() to the database D1 (operation 220).”)and repeating steps (a) and (c) - (f) for a random or predefined number of examples in the original training dataset to generate a visualization training dataset, the visualization training dataset comprising the new visualization examples (page 6, "As shown in FIG. 4, the above data augmentation operation is not terminated once, but is repeatedly performed for other schema transformation operations and for other initial training datasets, so that training data is converted into new training datasets.").
Luo and Han are considered analogous because they are each concerned with machine learning model training. 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 modified Luo with the teachings of Han for the purpose of expanding dataset coverage. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Regarding claims 4, 11 and 18, Luo teaches (a) accessing an example from the visualization query dataset, wherein the natural language utterance associated with the example comprises a visualization clause that includes a visualization action for the visualization type (page 3, Example 2, "Figure 4 gives a real example from a Flight dataset, where the original (NL, SQL) pair (nQ, Q) from the Spider [68] NL2SQL benchmark is given as input." Figure 4 shows the utterance nQ containing a visualization action.);
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(b) converting the system programming language into MRL logical form corresponding to the natural language utterance (page 3, section 2.2, "An ideal grammar to bridge SQL and VIS queries is desired to be: (1) uniform: it can represent both SQL and VIS queries; (2) language-agnostic: it can be converted to either an SQL query or a VIS query with a specific language (e.g., Vega-Lite); and (3) extensible: it can be extended to support other SQL queries or more visualization types. One choice is based on Abstract Syntax Tree (AST). In particular, we extend SemQL [21], which was used for NL2SQL, to further support NL2VIS. The extended grammar is shown in Figure 5.");(c) adding, to the schema, one or more visualization-related entities and schema-linking relations that link the one or more visualization-related entities to one or more entities within the schema to generate a visualization labeled schema (page 3, Step 1. VIS Synthesis, "Tree edits may modify the SQL tree tQ by adding how to visualize (i.e., the vis type) and some vis related data operations (e.g., grouping and binning), as well as deleting some nodes (e.g., an SQL query may select more attributes than needed for VIS).");(d) modifying, based on the visualization labeled schema and the visualization type presented in the natural language utterance, the MRL logical form to generate a visualization creation MRL logical form that corresponds to the natural language utterance, wherein the visualization creation MRL logical form comprises one or more visualization-related entities and a visualization clause that includes the visualization action for the visualization type (page 4, section 2.3, "Next, we discuss how to generate (candidate) VIS trees from one SQL tree. Intuitively, given one SQL tree, we can ‘delete’ any tree nodes or ‘insert’ any tree nodes, as long as we can produce a valid VIS tree rooted with ‘Visualize Q’ (Figure 5).");(e) assembling the visualization labeled schema, the natural language utterance, and the visualization creation MRL logical form to generate a new visualization example (page 4, section 2.3, "After different combinations of insertions above, we will obtain candidate VIS set TV.").
Luo does not explicitly teach “repeating steps (a), (b), (d) and (e) for a random or predefined number of examples in the visualization query dataset to generate a visualization training dataset, the visualization training dataset comprising the new visualization examples,” however, Han teaches (f) repeating steps (a), (b), (d) and (e) for a random or predefined number of examples in the visualization query dataset to generate a visualization training dataset, the visualization training dataset comprising the new visualization examples (page 6, "As shown in FIG. 4, the above data augmentation operation is not terminated once, but is repeatedly performed for other schema transformation operations and for other initial training datasets, so that training data is converted into new training datasets.").
Luo and Han are considered analogous because they are each concerned with machine learning model training. 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 modified Luo with the teachings of Han for the purpose of expanding dataset coverage. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Regarding claims 5, 12 and 19, Luo teaches (b) adding, to the schema, one or more visualization-related entities and schema-linking relations that link the one or more visualization-related entities to one or more entities within the schema to generate a visualization labeled schema (page 3, Step 1. VIS Synthesis, "Tree edits may modify the SQL tree tQ by adding how to visualize (i.e., the vis type) and some vis related data operations (e.g., grouping and binning), as well as deleting some nodes (e.g., an SQL query may select more attributes than needed for VIS).");(c) composing, based on the incremental natural language template, the base utterance, and the incremental use-case type, a visualization example utterance that comprises a visualization action for the incremental use-case type (page 3, Step 2. NL Synthesis, "Given the input (nQ, tQ), each good VIS query ti, and the tree operations Δi that convert tQ into ti , it will revise nQ to reflect the change of Δi , and get variants of NL specifications. The purpose of having variants of NL specifications, which is a way of data augmentation [18], is to train a robust model.");(d) constructing, based on the input MRL logical form and a set of MRL logical form construction rules defined for the incremental use-case type, a visualization incremental MRL logical form (page 4, Candidate VIS Generation, "Given an SQL tree tQ, we first perform different deletions on tQ to get a set I of intermediate SQL trees {tI1,…, tIl}. For each intermediate SQL tree tIi ∈ I, we then make insertions to get a set of VIS trees TV = {t1,…, tn}.");(e) assembling the visualization labeled schema, the visualization example utterance, and the visualization incremental MRL logical form to generate a new visualization example (page 4, section 2.3, "After different combinations of insertions above, we will obtain candidate VIS set TV.").
Luo does not explicitly teach “accessing an incremental natural language template and data annotation from the incremental visualization dataset, wherein the incremental natural language template comprises a library of different text to be used for an incremental use-case type to be added to a visualization incremental utterance, and wherein the data annotation comprises a base utterance, an input MRL logical form, an incremental use-case type to be used in the visualization example utterance, and a schema,” or “repeating steps (a) and (c) - (e) for a random or predefined number of examples to generate a visualization training dataset, the visualization training dataset comprising the new visualization examples,” however, Han teaches (a) accessing an incremental natural language template and data annotation from the incremental visualization dataset, wherein the incremental natural language template comprises a library of different text to be used for an incremental use-case type to be added to a visualization incremental utterance, and wherein the data annotation comprises a base utterance, an input MRL logical form, an incremental use-case type to be used in the visualization example utterance, and a schema (page 2, "According to the template-based method, synthetic data is created using a template of a predefined natural language query and SQL pair. The reference to the data in the database is empty as a slot in the template. Given a database, data is created by randomly selecting data from the database and filling the corresponding slots.")and (f) repeating steps (a) and (c) - (e) for a random or predefined number of examples to generate a visualization training dataset, the visualization training dataset comprising the new visualization examples (page 6, "As shown in FIG. 4, the above data augmentation operation is not terminated once, but is repeatedly performed for other schema transformation operations and for other initial training datasets, so that training data is converted into new training datasets.").
Luo and Han are considered analogous because they are each concerned with machine learning model training. 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 modified Luo with the teachings of Han for the purpose of expanding dataset coverage. Given that all the claimed elements were known in the prior art, one skilled in the art could have combined the elements by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Regarding claims 6, 13 and 20, Luo teaches (a) accessing a manipulation template from the manipulation visualization dataset, wherein the manipulation template comprises a natural language utterance definition and a corresponding MRL logical form definition for a visualization manipulation use-case (page 12, Training Data Generation, "DBPal [63] augments training data based on a set of pre-defined (NL, SQL ) templates.");(b) composing, using the manipulation template, a new visualization example comprising a visualization example utterance and a corresponding visualization manipulation MRL logical form (page 5, section 2.5, "Each VIS ti ∈ T'V is associated with a set of tree edits Δi , i.e., deletions Δ-i and insertions Δ+i . Next, we need to modify the NL of the corresponding SQL query to reflect these changes.");(c) repeating steps (a) and (b) for a random or predefined number of examples to generate a visualization training dataset, the visualization training dataset comprising the new visualization examples (page 4, Candidate VIS Generation, "For each intermediate SQL tree tIi ∈ I, we then make insertions to get a set of VIS trees TV = {t1, … ,tn}.").
Regarding claims 7 and 14, Luo teaches synthesizing additional training examples from the first training examples further comprises, after adding, to the schema associated with the example, determining whether the example is suitable for augmentation based on analysis of the MRL logical form using a set of filtering rules (page 3, Step 1. VIS Synthesis, "In order to ensure that each VIS query w.r.t. a VIS tree in TV is ‘good’, e.g., a bar chart with several hundred bars is not readable thus is bad. Hence, we need to filter ‘bad’ charts, while only keeping good charts as T'V= {t1, . . . ,tm }."), and only performing (c)-(f) when the example is determined to be suitable for augmentation (page 4, section 2.4, "By doing so, we prune bad visualizations from the candidate VIS set TV and get a set of good VIS set T'V."), and wherein the determination of whether the example is suitable for augmentation is performed for each example in the original training dataset that is accessed in accordance with (g) and (a) (page 3, Step 1. VIS Synthesis, "In order to ensure that each VIS query w.r.t. a VIS tree in TV is ‘good’, e.g., a bar chart with several hundred bars is not readable thus is bad. Hence, we need to filter ‘bad’ charts, while only keeping good charts as T'V= {t1, . . . ,tm }.").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
U.S. Patent 11,687,544 to Kelly et al.
U.S. Patent 12,182,147 to Merchant et al.
U.S. Patent Application Publication 2018/0293508 to Ko et al.
U.S. Patent Application Publication 2020/0226212 to Tan et al.
U.S. Patent Application Publication 2022/0138621 to Patil et al.
U.S. Patent Application Publication 2022/0164540 to Setlur et al.
U.S. Patent Application Publication 2022/0405314 to Du et al.
U.S. Patent Application Publication 2023/0185834 to Arthur et al.
U.S. Patent Application Publication 2024/0061833 to Tangari et al.
U.S. Patent Application Publication 2024/0134846 to Datt et al.
U.S. Patent Application Publication 2024/0386215 to Eisenschlos et al.
U.S. Patent Application Publication 2024/0394249 to Cunningham et al.
“Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks” by Dibia and Demiralp.
“Empowering Natural Language to Visualization Neural Translation Using Synthesized Benchmarks” by Luo et al.
“NL2VIZ: Natural Language to Visualization via Constrained Syntax-Guided Synthesis” by Wu et al.
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/SEAN THOMAS SMITH/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
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