Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
This is a first Office Action in response to arguments filed on 2/26/2025. Claims 1-20 are pending and examined below.
Claim Objections
Claim 6 is objected to because of the following informalities: it contains the conjunction and/or. For the purposes of examination, this limitation is being interpreted to mean or. Appropriate correction is required.
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 an abstract idea without significantly more.
In step 1 of the analysis, claims 1-14 are directed to a method claims 15-17 are directed to a computer system (machine) and claim 18-20 are directed to a non-transitory computer readable storage medium.
In step 2A part 1 of the analysis, claim 1 recites a “mental process,” as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation.
Claim 1 recites
automatically generating…one or more suggestions
updating the graph schema
which, as drafted, are steps which can be performed in the human mind. A human can read bytes and parse and format them to generate a reply.
In step 2A part 2 of the analysis, claim 1 further teaches
receiving a graph schema
receiving a first user input
outputting the updated graph schema
which are insignificant extra-solution activities.
In step 2B of the analysis, the claims does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 2 recites
Iteratively receiving a graph schema
which is an insignificant extra-solution activity.
Claim 3 describes the computational model and recites
iteratively receiving the respective graph schema
before describing the iterative receiving which does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 4 and 5 describe the computational model, which do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 6 describes the joining test rule which does not include additional elements that are sufficient to amount to significantly more than the judicial exception and recites
automatically generating the one or more suggestions
computing…a respective score
identifying…a first set of edges to form a first suggestion
identifying…a second set of edges
which are insignificant extra-solution activities.
Claim 7 describes updating the graph schema, which does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 8 describes receiving a third user input, which does not include additional elements that are sufficient to amount to significantly more than the judicial exception and recites
automatically generating the graph schema
which is an insignificant extra-solution activity.
Claim 9 recites
receiving a user request
receiving the plurality of data tables
which is an insignificant extra-solution activity.
Claim 10 recites
displaying the one or more suggestions
which is an insignificant extra-solution activity.
Claim 11 recites
displaying…a graph-based representation
which is an insignificant extra-solution activity.
Claims 12-14 describe the schema generation method, which do not include additional elements that are sufficient to amount to significantly more than the judicial exception and recites.
Independent claims 15 and 18 are rejected under a similar rational as independent claim 1.
Dependent claims 16, 17, 19 and 20 are worded slightly differently, but rejected under the same rationale as claims 2 and 3.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 7-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Naufel (US Pub. 2024/0362208).
Regarding claim 1, Naufel teaches
A schema generation method, comprising: receiving a graph schema that includes vertices and edges associated with a plurality of data tables, the graph schema including at least a first vertex from a first data table of the plurality of data tables; (Fig. 2; Par. [0056] graph database #203 stores processed ETL data #253 (i.e. graph schema including vertices and edges) that can be used (i.e. received) for future queries)
automatically generating, based on the plurality of data tables and the graph schema, one or more suggestions using a computational model, wherein the one or more suggestions identify at least one of the group consisting of one or more vertices and one or more edges, the one or more suggestions including a respective suggestion that identifies a respective subset of the at least one of the group consisting of the one or more vertices and the one or more edges, the one or more vertices distinct from the first vertex; (Fig. 2; Par. [0054-56, 59] the AI-based natural language data query and visualization system uses a GPT model (i.e. computational model) for generating a query that is used for generating a graph visualization suggestion containing vertices and edges)
receiving a first user input, wherein the first user input is a selection of the respective suggestion from the one or more suggestions; (Par. [0059] new (i.e. first user input) or modified query is sent by user for updating the visualized query)
updating, based on the first user input, the graph schema; and (Par. [0059] new or modified query is sent by user for updating the visualized query)
outputting the updated graph schema. (Par. [0059] new or modified query is sent by user for updating the visualized (i.e. updated) query)
Regarding claim 2, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, including: iteratively receiving a respective graph schema, automatically generating a respective suggestion, receiving a respective first user input, and updating the respective graph schema for a plurality of iterations to expand vertices and edges associated with the respective graph schema, each iteration of the plurality of iterations corresponding to a respective updated graph schema, each edge linking two vertices. (Fig. 2; Par. [0054-56, 59] the AI-based natural language data query and visualization system uses a GPT model (i.e. computational model) for generating a query that is used for iteratively generating a graph visualization suggestion)
Regarding claim 3, Naufel teaches claim 2 as shown above, and further teaches
The schema generation method of claim 2, wherein: the computational model includes a plurality of models; and (Fig. 2; Par. [0134] GPT system (#200) selects from a plurality of appropriate models for the task )
iteratively receiving the respective graph schema, automatically generating the respective suggestion, receiving the respective first user input, and updating the respective graph schema for the plurality of iterations to expand vertices and edges associated with the respective graph schema includes: in each iteration: receiving a respective second user input for selecting a respective model from the plurality of models; and (Fig. 2; Par. [0054-56, 59] the AI-based natural language data query and visualization system uses a GPT model (i.e. computational model) for generating a query that is used for iteratively generating a graph visualization suggestion, including new or modified queries from a user)
in response to the respective second user input, automatically generating, based on the plurality of data tables and the respective graph schema, the respective suggestion using the respective model. (Par. [0059] new or modified query is sent by user for updating the visualized (i.e. updated) query)
Regarding claim 4, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, wherein the computational model includes a rule-based suggestion model corresponding to a predefined rule. (Par. [0433] the system is designed to support custom data transformation logic (i.e. predefined rules))
Regarding claim 5, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, wherein the computational model includes an intelligence model for executing large language models. (Fig. 2; Par. [0134] GPT system (i.e. intelligence model; #200) selects from a plurality of appropriate models for the task )
Regarding claim 7, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, wherein: updating the graph schema includes adding the respective subset of the at least one of the group consisting of the one or more vertices and the one or more edges into the graph schema. (Par. [0119, 125] any external data (e.g. tables) can be added )
Regarding claim 8, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, including: receiving a third user input for identifying an initial vertex from the plurality of data tables; and (Fig. 2; Par. [0054-56, 59] the AI-based natural language data query and visualization system uses a GPT model for generating a query that is used for iteratively generating a graph visualization suggestion)
in response to the third user input, automatically generating the graph schema including the initial vertex. (Fig. 2; Par. [0054-56, 59] the AI-based natural language data query and visualization system uses a GPT model (i.e. computational model) for generating a query that is used for iteratively generating a graph visualization suggestion, including new or modified queries from a user)
Regarding claim 9, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, including: receiving a user request; and (Fig. 2; Par. [0054-56, 59] the AI-based natural language data query and visualization system uses a GPT model (i.e. computational model) for generating a query that is used for iteratively generating a graph visualization suggestion, including new or modified queries from a user)
in response to the user request, receiving the plurality of data tables. (Fig. 2; Par. [0054-56, 59] the AI-based natural language data query and visualization system uses a GPT model (i.e. computational model) for generating a query that is used for iteratively generating a graph visualization suggestion, including new or modified queries from a user)
Regarding claim 10, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, including: displaying the one or more suggestions via a graphical interface. (Par. [0148] user interface is fully integrated with the GPT system, including for visualizing data)
Regarding claim 11, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, including: displaying, based on at least one of the graph schema or the updated graph schema, a graph-based representation via a graphical interface. (Par. [0148] user interface is fully integrated with the GPT system, including for visualizing data)
Regarding claim 12, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, wherein the plurality of data tables are relational data tables. (Par. [0099] data sources include relational databases)
Regarding claim 13, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, wherein the plurality of data tables are stored in at least one of the group consisting of relational database, data warehouse, and data lake. (Par. [0099] data sources include relational databases)
Regarding claim 14, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 1, wherein the receiving the graph schema, the automatically generating the one or more suggestions, the receiving the first user input, the updating the graph schema, and the outputting the updated graph schema are performed via an application or a user interface. (Par. [0148] user interface is fully integrated with the GPT system, including for visualizing data and getting user input)
Regarding claim 15, while worded differently, is rejected under the same rationale as claim 1.
Naufel further teaches one or more processors; and memory (Fig. 1 #104-5)
Regarding claim 16, while worded differently, is rejected under the same rationale as claim 2.
Regarding claim 17, while worded differently, is rejected under the same rationale as claim 3.
Regarding claim 18, while worded differently, is rejected under the same rationale as claim 15.
Regarding claim 19, while worded differently, is rejected under the same rationale as claim 2.
Regarding claim 20, while worded differently, is rejected under the same rationale as claim 3.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Naufel (US Pub. 2024/0362208) in view of Agrawal (US Pub. 2023/0112250).
Regarding claim 6, Naufel teaches claim 1 as shown above, and further teaches
The schema generation method of claim 4, wherein: the predefined rule includes a joining test rule; and (Par. [0433] the system is designed to support custom data transformation logic (i.e. predefined rules), including joins)
automatically generating the one or more suggestions using the computational model includes: identifying a second data table from the plurality of data tables, the second data table distinct from the first data table; (Fig. 2; Par. [0054-56, 59] the AI-based natural language data query and visualization system uses a GPT model (i.e. computational model) for generating a query that is used for iteratively (i.e. second table, third table, etc.) generating a graph visualization suggestion)
Naufel does not explicitly teach
computing, for each column of the second data table and based on the joining test rule, a respective score;
in accordance with a determination that the second data table includes two or more columns having scores that meet a threshold level: identifying, based on at least the second data table, a first set of edges to form a first suggestion; and
in accordance with a determination that the second data table includes one or more columns having scores that meet the threshold level: identifying, based on at least the second data table, a second set of edges and/or a first set of vertices to form a second suggestion, the first set of vertices distinct from the first vertex.
However, from the same field, Agrawal teaches
computing, for each column of the second data table and based on the joining test rule, a respective score; (Fig. 4; Par. [0226] final join candidates are given a join score and are joined or not joined based on a score threshold)
in accordance with a determination that the second data table includes two or more columns having scores that meet a threshold level: identifying, based on at least the second data table, a first set of edges to form a first suggestion; and (Fig. 4; Par. [0226-232] final join candidates are given a join score and are joined or not joined based on a score threshold, and then presented to user for selection)
in accordance with a determination that the second data table includes one or more columns having scores that meet the threshold level: identifying, based on at least the second data table, a second set of edges and/or a first set of vertices to form a second suggestion, the first set of vertices distinct from the first vertex. (Fig. 4; Par. [0226] final join candidates are given a join score and are joined or not joined based on a score threshold)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine the join scoring of Agrawal into the graph generation method of Naufel. The motivation for this combination would have been to improve the execution speed of the systems and methods by permitting access to data more quickly as explained in Agrawal (Par. [0034]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Alberg et al. (US Pub. 2017/0116228) teaches automatic inference of a cube schema.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to J MITCHELL CURRAN whose telephone number is (469)295-9081. The examiner can normally be reached M-F 8:00am - 5:00pm.
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/J MITCHELL CURRAN/Examiner, Art Unit 2169
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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