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 action is in response to application filed on 08/16/2024.
Claims 1-20 are pending.
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.
Claim(s) 1, 4-5, 8, 11-12, 15, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aditya et al. (20170090893 A1) hereinafter Aditya in further view of Mohan et al. (US 20230325298 A1) hereinafter Mohan in further view of Morton et al. (US 11776578 B2) hereinafter Morton in further view of Vaezi et al. (US 20250328652 A1) hereinafter Vaezi.
Regarding claim 1, Aditya discloses
A non-transitory, computer-readable storage medium comprising instructions recorded thereon, wherein the instructions, when executed by at least one processor of a system, cause the system to: (Aditya [0011] discloses a system storing instructions executed by one or more processors).
retrieve a first metadata structure associated with a data transformation environment, wherein the first metadata structure includes a first set of descriptors associated with record identifiers of the first metadata structure; (Aditya [0059]-[0061] discloses receiving a dataset from a data source, where the transformation representation module that receives one or more transformations developed in different programming platforms from the client device, receives metadata relating to the transformations and adds the transformations to the transformation library and sends it to the transformation pipeline module. Data obtained from metadata include user data, item data, interaction data from a third-party data source such as mining, tracking, or analytics service and scans for columns, column name, and column order/data types, thus demonstrating the records and descriptors associated with the metadata).
extract a set of descriptors associated with record identifiers of the first metadata structure; (Aditya [0059-[0062] discloses extracting column name, column type, basic statistics about the columns in the dataset, IDs, point weights, scoring weights, yield, group ID, etc.)
Aditya lacks explicitly
provide the set of descriptors and the associated record identifiers to a natural language generation model to generate a test dataset, wherein each record of the test dataset is consistent with a corresponding descriptor of the set of descriptors;
generate, for display on a user interface, a set of graphical representations corresponding to the test dataset;
receive an indication of a modification to a first graphical representation of the set of graphical representations;
determine a record associated with the first graphical representation of the set of graphical representations;
in response to receiving the indication of a modification to the first graphical representation, update the record corresponding to the first graphical representation of the set of graphical representations;
provide the updated test dataset to a code generation model to generate a code sample that enables dynamic testing of the data transformation environment using the updated test record.
Mohan teaches
provide the set of descriptors and the associated record identifiers to a natural language generation model to generate a test dataset, wherein each record of the test dataset is consistent with a corresponding descriptor of the set of descriptors; (Mohan [0047] and [0063] discloses a scanning agent using natural language processing to create a logical block, where after receiving a first resource requirement, the scanning agent then produces a first script and a first test dataset.)
It would have been obvious to one of ordinary skill in the art before the effective filling date of
the claimed invention to have modified Aditya to incorporate the teachings of Mohan to “provide the set of descriptors and the associated record identifiers to a natural language generation model to generate a test dataset, wherein each record of the test dataset is consistent with a corresponding descriptor of the set of descriptors” in order to quickly generate the test dataset by employing a NLP model, thus improving the system’s efficiency in testing.
Aditya in view of Mohan lacks
generate, for display on a user interface, a set of graphical representations corresponding to the test dataset;
receive an indication of a modification to a first graphical representation of the set of graphical representations;
determine a record associated with the first graphical representation of the set of graphical representations;
in response to receiving the indication of a modification to the first graphical representation, update the record corresponding to the first graphical representation of the set of graphical representations;
update the test dataset to include the updated record corresponding to the first graphical representation of the set of graphical representations; and
Morton teaches
generate, for display on a user interface, a set of graphical representations corresponding to the test dataset; (Morton column 2, lines 1-20 discloses presenting visual representations of identified elements in a user interface).
receive an indication of a modification to a first graphical representation of the set of graphical representations; (Morton column 2, lines 1-20 discloses a user interacting with the visual representations in order to modify metadata entries).
determine a record associated with the first graphical representation of the set of graphical representations; (Morton column 2, lines 1-20 discloses updating the metadata in response to user interactions with the visual representations, which include modifying metadata entries to include rendering code for use in rendering the replacement value as overlay content over the value of the particular element. In determining that the particular element is targeted for replacement includes determining that the particular element is included in a dictionary that maps original values to replacement values, thus showing how the targeted graphical representation has its original record determined based on dictionary that maps values).
in response to receiving the indication of a modification to the first graphical representation, update the record corresponding to the first graphical representation of the set of graphical representations; (Morton column 2, lines 1-20 discloses the original value being updated to the replacement value indicated by the user).
update the test dataset to include the updated record corresponding to the first graphical representation of the set of graphical representations; and (Morton column 2, lines 1-20 discloses the dictionary that contains the mapping of original values and updating the values with the replacement value).
It would have been obvious to one of ordinary skill in the art before the effective filling date of
the claimed invention to have modified Aditya in view of Mohan to incorporate Morton to “generate, for display on a user interface, a set of graphical representations corresponding to the test dataset;
receive an indication of a modification to a first graphical representation of the set of graphical representations;”, “determine a record associated with the first graphical representation of the set of graphical representations;”, “in response to receiving the indication of a modification to the first graphical representation, update the record corresponding to the first graphical representation of the set of graphical representations;” and “update the test dataset to include the updated record corresponding to the first graphical representation of the set of graphical representations” in order to allow user input to modify the testing requirements, allowing for more flexible/effective testing to be produced by giving the user more control over the test data.
Aditya in view of Mohan in further view of Morton lacks
provide the updated test dataset to a code generation model to generate a code sample that enables dynamic testing of the data transformation environment using the updated test record.
Vaezi teaches
provide the updated test dataset to a code generation model to generate a code sample that enables dynamic testing of the data transformation environment using the updated test record. (Vaezi [0005]-[0007] and abstract discloses after receiving user input and modification request, the first code sample in the first container to generate a first modified code sample. This modified code sample is used for testing to validate its operation and to identify vulnerabilities. Further in [0049]-[0051] discloses the input may include dataset such as a test dataset).
It would have been obvious to one of ordinary skill in the art before the effective filling date of
the claimed invention to have modified Aditya in view of Mohan in further view of Mortan to “provide the updated test dataset to a code generation model to generate a code sample that enables dynamic testing of the data transformation environment using the updated test record” in order to generate the test code sample by using the modified record, thus having a more effective test.
Regarding claim 4, it’s directed to a non-transitory having similar limitations cited in claim 1. Thus, claim 4 is also rejected under the same rationale as cited in rejection of claim 1 above.
Regarding claim 5, it’s directed to a non-transitory having similar limitations cited in claim 1. Thus, claim 5 is also rejected under the same rationale as cited in rejection of claim 1 above.
Regarding claim 8, it’s directed to a system having similar limitations cited in claim 1. Thus, claim 8 is also rejected under the same rationale as cited in rejection of claim 1 above.
Regarding claim 11, it’s directed to a system having similar limitations cited in claim 1. Thus, claim 11 is also rejected under the same rationale as cited in rejection of claim 1 above.
Regarding claim 12, it’s directed to a system having similar limitations cited in claim 1. Thus, claim 12 is also rejected under the same rationale as cited in rejection of claim 1 above.
Regarding claim 15, it’s directed to a method having similar limitations cited in claim 1. Thus, claim 15 is also rejected under the same rationale as cited in rejection of claim 1 above.
Regarding claim 18, it’s directed to a method having similar limitations cited in claim 1. Thus, claim 18 is also rejected under the same rationale as cited in rejection of claim 1 above.
Regarding claim 18, it’s directed to a method having similar limitations cited in claim 1. Thus, claim 18 is also rejected under the same rationale as cited in rejection of claim 1 above.
Claim(s) 2-3, 9-10, and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aditya et al. (20170090893 A1) hereinafter Aditya in further view of Mohan et al. (US 20230325298 A1) hereinafter Mohan in further view of Morton et al. (US 11776578 B2) hereinafter Morton in further view of Vaezi et al. (US 20250328652 A1) hereinafter Vaezi in further view of James et al. (US 11392578 B1) hereinafter James.
Regarding claim 2, Aditya in view of Mohan in further view of Morton in further view of Vaezi discloses
The non-transitory, computer-readable storage medium of claim 1
The combination lacks
wherein the instructions for generating the test dataset cause the system to: provide the set of descriptors and the associated record identifiers to the natural language generation model to generate a set of values and an associated set of fields; and
generate a data structure including the set of values and the associated set of fields, wherein the data structure comprises links between each value of the set of values and a corresponding field of the associated set of fields.
James teaches
wherein the instructions for generating the test dataset cause the system to: provide the set of descriptors and the associated record identifiers to the natural language generation model to generate a set of values and an associated set of fields; and (Column 12, lines 55-67 and column 3, lines 1-3 discloses generated data record that include a collection of field-value pairs, where each pair stores a particular item of performance data in associated with a field for the item. While James does not explicitly state natural language model, it would be obvious in combination with the previous references to have the previously stated natural language model to perform these functions).
generate a data structure including the set of values and the associated set of fields, wherein the data structure comprises links between each value of the set of values and a corresponding field of the associated set of fields. (James Column 8, lines 21-50 discloses how the field and the field value are linked together, where a field name has an associated field value and are stored in a data store).
It would have been obvious to one of ordinary skill in the art before the effective filling date of
the claimed invention to have modified Aditya in view of Mohan in further view of Morton in further view of Vaezi to incorporate the teachings of James to “wherein the instructions for generating the test dataset cause the system to: provide the set of descriptors and the associated record identifiers to the natural language generation model to generate a set of values and an associated set of fields” and “generate a data structure including the set of values and the associated set of fields, wherein the data structure comprises links between each value of the set of values and a corresponding field of the associated set of fields” in order to have more information the user can then modify resulting in accurate/flexible tests.
Regarding claim 3, the combination discloses
The non-transitory, computer-readable storage medium of claim 2
The combination lacks
wherein the instructions for generating the set of values and the associated set of fields cause the system to: determine a first field associated with the set of descriptors, wherein the first field is associated with a user identifier, an address, a user account value, or a demographic metric.
determine, using the set of descriptors, a first value corresponding to the first field; and
determine, using the set of descriptors, a first value corresponding to the first field; and
James teaches
wherein the instructions for generating the set of values and the associated set of fields cause the system to: determine a first field associated with the set of descriptors, wherein the first field is associated with a user identifier, an address, a user account value, or a demographic metric; (James column 8, lines 21-51 discloses field name “UserID” with a corresponding field value)
determine, using the set of descriptors, a first value corresponding to the first field; and (James column 8, lines 21-51 discloses field name “UserID” with a corresponding field value may cause the system to field-search the machine data of events to identify events having that field-value pair. For example, field name “UserID” with a corresponding field value of “12345”, thus demonstrating a value corresponding to field).
store the first value and the first field within the data structure for the test dataset. (James column 8, lines 21-26 disclose the system stores the events in a data store, where the events stored are field-searchable, i.e., field/value pairs).
It would have been obvious to one of ordinary skill in the art before the effective filling date of
the claimed invention to have modified Aditya in view of Mohan in further view of Morton in further view of Vaezi to incorporate the teachings of James to “wherein the instructions for generating the set of values and the associated set of fields cause the system to: determine a first field associated with the set of descriptors, wherein the first field is associated with a user identifier, an address, a user account value, or a demographic metric”, “determine, using the set of descriptors, a first value corresponding to the first field”, “determine, using the set of descriptors, a first value corresponding to the first field;” and “store the first value and the first field within the data structure for the test dataset.” in order to different user data and thus allow for better organization of data per user account.
Regarding claim 9, it’s directed to a system having similar limitations cited in claim 2. Thus, claim 9 is also rejected under the same rationale as cited in rejection of claim 2 above.
Regarding claim 10, it’s directed to a system having similar limitations cited in claim 3. Thus, claim 10 is also rejected under the same rationale as cited in rejection of claim 3 above.
Regarding claim 16, it’s directed to a method having similar limitations cited in claim 2. Thus, claim 16 is also rejected under the same rationale as cited in rejection of claim 2 above.
Regarding claim 17, it’s directed to a method having similar limitations cited in claim 3. Thus, claim 17 is also rejected under the same rationale as cited in rejection of claim 3 above.
Claim(s) 6-7, 13-14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aditya et al. (20170090893 A1) hereinafter Aditya in further view of Mohan et al. (US 20230325298 A1) hereinafter Mohan in further view of Morton et al. (US 11776578 B2) hereinafter Morton in further view of Vaezi et al. (US 20250328652 A1) hereinafter Vaezi in further view of Leizerovich et al. (US 9734214 B2) hereinafter Leizerovich.
Regarding claim 6, Aditya in view of Mohan in further view of Morton in further view of Vaezi discloses
The non-transitory, computer-readable storage medium of claim 1.
provide the updated test dataset to the code generation model to cause the code generation model to generate the code sample, (Vaezi [0005]-[0007] and abstract discloses after receiving user input and modification request, the first code sample in the first container to generate a first modified code sample. This modified code sample is used for testing to validate its operation and to identify vulnerabilities. Further in [0049]-[0051] discloses the input may include dataset such as a test dataset).
The combination lacks explicitly
and scripting framework identifier
receive, from a user device, a scripting framework identifier; and
wherein the code sample is consistent with a scripting framework associated with the scripting framework identifier.
Leizerovich teaches
receive, from a user device, a scripting framework identifier; and (Leizerovich column 5, lines 7-21 discloses the test scripts used are SQL, which based on the specification on [0076] is an example of scripting framework. Column 3, lines 33-45 disclose that engine receives this input from user through the GUI).
wherein the code sample is consistent with a scripting framework associated with the scripting framework identifier. (Leizerovich column 6, lines 39-63 discloses the test execution engine generates the test scripts such as SQL scripts from the test data, thus the sample is consistent with a scripting framework).
It would have been obvious to one of ordinary skill in the art before the effective filling date of
the claimed invention to have modified Aditya in view of Mohan in further view of Morton in further view of Vaezi to incorporate the teachings of Leizerovich to “receive, from a user device, a scripting framework identifier” and “wherein the code sample is consistent with a scripting framework associated with the scripting framework identifier” in order to provide the code generation model an expected coding format and thus enhances flexible code generation.
Regarding claim 7, it’s directed to a non-transitory medium having similar limitations cited in claim 1. Thus, claim 7 is also rejected under the same rationale as cited in rejection of claim 1 above.
Regarding claim 13, it’s directed to a system having similar limitations cited in claim 6. Thus, claim 13 is also rejected under the same rationale as cited in rejection of claim 6 above.
Regarding claim 14, it’s directed to a system having similar limitations cited in claim 7. Thus, claim 14 is also rejected under the same rationale as cited in rejection of claim 7 above.
Regarding claim 20, it’s directed to a method having similar limitations cited in claim 6. Thus, claim 20 is also rejected under the same rationale as cited in rejection of claim 6 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER J SALLEY whose telephone number is (571)272-6355. The examiner can normally be reached Mon-Fri, 7:30am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do can be reached at (571) 272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER J SALLEY/Examiner, Art Unit 2193
/Chat C Do/ Supervisory Patent Examiner, Art Unit 2193
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