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 .
Claims 1-20 are presented for examination.
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 to 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (that is, an abstract idea) without significantly more.
Claim 1, 8, 15 as drafted, recites a process that, under its broadest reasonable interpretation, covers steps that could be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitation “provide the first metadata structure to a metadata validation model to generate a first validation indicator indicating whether the first metadata structure is valid according to validation rules”, together with the further limitations of “generate a first dataset consistent with the first metadata structure”, “generate a first test record for the first metadata structure”, and “generate a first code sample associated with the first test record”, recites the abstract idea of a mental process. These limitations encompass a person evaluating a data description and forming corresponding test data, test criteria, and test instructions through observation, evaluation, judgment, and opinion, or even with the aid of pen and paper. Thus, these limitations fall within the “Mental Processes” grouping of abstract ideas.
This judicial exception is not integrated into a practical application. The claim recites the following additional elements: “a non-transitory, computer-readable storage medium”, “at least one data processor”, “a system”, “a metadata validation model”, “a data generation model”, “a test generation model”, “a code generation model”, “a first device”, “a second device”, and “a user interface”. These additional elements are merely instructions to implement the abstract idea on a computer, or merely use a generic computer or generic computer components as a tool to perform the abstract idea. See MPEP 2106.05(f). The claim further recites “retrieve, from a first database and using a first device, a first metadata structure”, “store the first dataset in the first database”, “retrieve, using a second device, the first dataset from the first database”, “generate a graphical representation ... for display on a user interface”, and “transmit the updated first code sample to the data transformation environment”, which do nothing more than add insignificant extra-solution activity to the judicial exception, such as data gathering, data storage, and outputting the results of the abstract idea. See MPEP 2106.05(g). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration into a practical application, the additional elements “a non-transitory, computer-readable storage medium”, “at least one data processor”, and the recited generative and validation models are generic computer components and instructions used as tools to perform the abstract idea. See MPEP 2106.05(f). As to the retrieving, storing, displaying, and transmitting steps, the courts have recognized that receiving or transmitting data over a network, storing and retrieving information in memory, and presenting the results of a computation are well-understood, routine, and conventional activities. See MPEP 2106.05(d). Accordingly, the additional elements, taken individually and as an ordered combination, do not provide an inventive concept. Thus, claim 1 is not patent eligible.
Claim 2, 9, 16 further recites “extract a first descriptor”, “generate an indication of an error associated with the first descriptor”, and “determine that the first validation indicator indicates that the first metadata structure is not valid”, which under the broadest reasonable interpretation recites a mental process that can be carried out through observation, evaluation, judgment, and opinion, or even with the aid of pen and paper, and therefore does not integrate the abstract idea into a practical application and does not amount to significantly more.
Claim 3, 10, 17 further recites “update the first descriptor based on the identification of the deficiency” and “update the first metadata structure to include the updated first descriptor”, which recites a mental process of evaluating and revising a description, and therefore does not integrate the abstract idea into a practical application and does not amount to significantly more.
Claim 4, 11, 18 further recites “generate a first data record ... consistent with a textual description of the first descriptor”, which recites a mental process of composing data consistent with a description, and therefore does not integrate the abstract idea into a practical application and does not amount to significantly more.
Claim 5, 12, 19 further recites “generate a first test condition ... including an indication of a criterion for validation of test data”, which recites a mental process of determining a validation criterion, and therefore does not integrate the abstract idea into a practical application and does not amount to significantly more.
Claim 6, 13, 20 recites “receive, from the first device, an indication of a scripting framework” and “retrieve a representation of a set of test conditions”, which add insignificant extra-solution data gathering (MPEP 2106.05(g)), and further recites generating the code sample using a generic “code generation model” used as a tool (MPEP 2106.05(f)). Accordingly, the claim does not integrate the abstract idea into a practical application and does not amount to significantly more.
Claim 7, 14 further recites “update the first metadata structure according to the ... updated descriptor” and generating a second dataset, second test record, and second code sample, which recite the same mental process of evaluation and revision identified above, implemented with the generic models used as tools. Accordingly, the claim does not integrate the abstract idea into a practical application and does not amount to significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 14, 16-17, and 18 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Claim 14, the limitation “an identifier of a first record identifier of the first metadata structure” is indefinite because it is unclear whether “an identifier of a first record identifier” is a separate element from “a first record identifier,” or whether the two terms refer to the same element. This is inconsistent with the corresponding limitation of claim 7, which recites only “a first record identifier of the first metadata structure.” For purposes of examination, the limitation is interpreted as “a first record identifier of the first metadata structure.”
Regarding Claim 16, the limitation “determining that the validation indicator indicates that the first metadata structure is not valid” lacks proper antecedent basis. Claim 15, from which claim 16 depends, recites “a first validation indicator.” It is unclear whether “the validation indicator” refers to “the first validation indicator” of claim 15 or to a different indicator. For purposes of examination, “the validation indicator” is interpreted as “the first validation indicator.” Applicant is invited to amend for consistency with claims 2 and 9.
Dependent claim 17, is also rejected under 35 U.S.C. 112(b) as being indefinite for failing to cure the deficiencies of their independent claims.
Regarding Claim 18, the preamble recites “wherein generating the first metadata structure comprises”; however, claim 15, from which claim 18 depends, recites “retrieving ... a first metadata structure,” not generating it, so “generating the first metadata structure” lacks antecedent basis. Furthermore, the body of claim 18 recites “generating the first dataset including the first data record,” which is directed to generating the first dataset rather than the first metadata structure, rendering the claim internally inconsistent and indefinite. For purposes of examination, claim 18 is interpreted consistent with claims 4 and 11 as reciting “wherein generating the first dataset comprises.”
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4- 6, 8, 9, 11- 13, 15, 16, and 18- 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (US 11,720,478 B2) in view of Matcha (US 11,537,936 B2) further in view of Givoni (US 8,347,267 B2).
Regarding Claim 1, Kumar teaches:
A non-transitory, computer-readable storage medium comprising instructions recorded thereon, wherein the instructions when executed by at least one data processor of a system, cause the system to:
retrieve, from a first database and using a first device, 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; (Claim 1, "generating and training a text processing machine learning model for processing feature specifications from the one or more product specification documents, the text processing machine learning model is configured to extract contextual information from the one or more product specification documents") Examiner Comments: Kumar's product specification documents that characterize the software application under test, from which contextual information is extracted, read on the claimed first metadata structure and its set of descriptors that characterize the data transformation environment.
provide the first metadata structure to a metadata validation model to generate a first validation indicator indicating whether the first metadata structure is valid according to validation rules; (Claim 1, "machine learning classifiers to classify the blocks into user interface elements, text labels, and validation markers") Examiner Comments: Kumar's classification of specification content into validation markers reads on providing the metadata structure to a metadata validation model to generate an indicator of validity according to validation rules.
provide the first dataset to a test generation model to generate a first test record for the first metadata structure; (Claim 1, "generating and training a test cases machine learning model for generating test cases from the processed data") Examiner Comments: Kumar's test cases machine learning model that generates test cases from the processed data reads on providing the first dataset to a test generation model to generate a first test record.
provide the first test record for the first metadata structure to a code generation model to generate a first code sample associated with the first test record, wherein the first code sample includes code data for executing a test algorithm with respect to the first test record; (Claim 1, "generating automated test procedures from the test cases and the test data, wherein the automated test procedures are configured for validation testing of the software application during development and post-development") Examiner Comments: Kumar's generation of automated test procedures from the test cases reads on providing the test record to a code generation model to generate a code sample that includes code for executing a test algorithm.
Kumar did not specifically teach:
in response to determining that the first validation indicator indicates that the first metadata structure is valid according to the validation rules, provide the first metadata structure to a data generation model to generate a first dataset consistent with the first metadata structure; store the first dataset in the first database; retrieve, using a second device, the first dataset from the first database.
However, Matcha teaches:
provide the first metadata structure to a data generation model to generate a first dataset consistent with the first metadata structure; store the first dataset in the first database; (Claim 1, "apply the query expression to the master data set to generate the test data set from the master data set ... store, in the memory, the test data set") Examiner Comments: Matcha's generation of a test data set from a master data set defined by columns (fields) and rows (entries), and storing the generated test data set in memory, reads on generating a first dataset consistent with the first metadata structure and storing it in the first database.
retrieve, using a second device, the first dataset from the first database; (Claim 1, "obtain, from the memory, the master data set and the query expression") Examiner Comments: Matcha's obtaining of the stored data set from memory reads on retrieving the first dataset from the first database using a second device.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the specification-driven validation of Kumar with the data set generation and storage of Matcha in order to automatically populate the test environment with data consistent with the validated specification, thereby reducing manual test data preparation and improving repeatability of validation, as suggested by Matcha (Abstract).
Kumar and Matcha did not specifically teach:
generate a graphical representation of the first metadata structure, the first test record, and the first code sample for display on a user interface; detect an indication of a modification to the graphical representation for display on the user interface; in response to detecting the indication of the modification, update the first code sample; and transmit the updated first code sample to the data transformation environment to enable dynamic testing of the data transformation environment using test records.
However, Givoni teaches:
generate a graphical representation of the first metadata structure, the first test record, and the first code sample for display on a user interface; (col. 8, ln 24-40, "In the Data Grid (Grid Visualization) 24, every column represents a variable of the object and the values that the user enters in the Grid column") Examiner Comments: Givoni's Data Grid visualization displaying test-case variables, values, and the generated script reads on generating a graphical representation of the metadata structure, test record, and code sample for display on a user interface.
detect an indication of a modification to the graphical representation for display on the user interface; in response to detecting the indication of the modification, update the first code sample; (Abstract, "A global change manager automates modifying in the data structure, data object attributes across multiple scripts") Examiner Comments: Givoni's global change manager, which modifies data object attributes entered by the user and propagates the change across the scripts, reads on detecting a modification to the graphical representation and updating the code sample in response.
transmit the updated first code sample to the data transformation environment to enable dynamic testing of the data transformation environment using test records. (Abstract, "The script is executed to apply each of the test cases to the AUT and receive responses") Examiner Comments: Givoni's execution of the script against the Application Under Test reads on transmitting the updated code sample to the data transformation environment to enable dynamic testing using test records.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the specification-driven test and code generation of Kumar and Matcha with the graphical test editing and script execution of Givoni in order to provide user oversight and control over the automatically generated tests before they are executed against the environment, thereby improving the accuracy and maintainability of the validation tests, as suggested by Givoni (Abstract).
Regarding Claim 2, Kumar, Matcha, and Givoni teach the none-transitory, computer-readable storage medium of claim 1.
Matcha further teaches
extract a first descriptor of the first set of descriptors, wherein the first descriptor is associated with a first record identifier of the first metadata structure; provide the first descriptor to the metadata validation model to generate an indication of an error associated with the first descriptor, wherein the indication of the error includes an identification of a deficiency associated with the first descriptor; (Matcha, Claim 1, "applying the ML pipeline results in either generation of a test ML model from the test data set or indication of an error in the test data set") Examiner Comments: Matcha's indication of an error in the data set reads on providing the first descriptor to the metadata validation model to generate an indication of an error identifying a deficiency associated with the first descriptor.
Kumar further teaches
in response to generating the indication of the error, determine that the first validation indicator indicates that the first metadata structure is not valid. (Kumar, Claim 1, "machine learning classifiers to classify the blocks into user interface elements, text labels, and validation markers") Examiner Comments: Kumar's validation markers, in combination with Matcha's error indication, read on determining that the validation indicator indicates the metadata structure is not valid.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the specification-driven validation of Kumar with the data set generation and storage of Matcha in order to automatically populate the test environment with data consistent with the validated specification, thereby reducing manual test data preparation and improving repeatability of validation, as suggested by Matcha (Abstract).
Regarding Claim 4, Kumar, Matcha, and Givoni teach the none-transitory, computer-readable storage medium claim 1.
Matcha further teaches
extract a first descriptor from the first metadata structure ...; provide the first descriptor to the data generation model to generate a first data record, wherein the first data record includes data consistent with a textual description of the first descriptor; and generate the first dataset including the first data record associated with the first descriptor. (Matcha, Claim 1, "apply the query expression to the master data set to generate the test data set from the master data set") Examiner Comments: Matcha's generation of a test data set of entries (records) consistent with the specified fields (descriptors) reads on generating a first data record consistent with a textual description of the first descriptor and generating the first dataset including that data record.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the specification-driven validation of Kumar with the data set generation and storage of Matcha in order to automatically populate the test environment with data consistent with the validated specification, thereby reducing manual test data preparation and improving repeatability of validation, as suggested by Matcha (Abstract).
Regarding Claim 5, Kumar, Matcha, and Givoni teach the none-transitory, computer-readable storage medium of claim 1.
Kumar further teaches
provide the first dataset to the test generation model to generate a first test condition, wherein the first test condition includes an indication of a criterion for validation of test data; and generate the first test record including a representation of the first test condition. (Kumar, Claim 1, "machine learning classifiers to classify the blocks into user interface elements, text labels, and validation markers") Examiner Comments: Kumar's validation markers generated by the test cases model read on a test condition that includes a criterion for validation of test data, which is included in the first test record.
Regarding Claim 6, Kumar, Matcha, and Givoni teach the none-transitory, computer-readable storage medium of claim 1.
Givoni further teaches
receive, from the first device, an indication of a scripting framework; retrieve a representation of a set of test conditions associated with the first test record; and provide the representation of the set of test conditions and the indication of the scripting framework to the code generation model to generate the first code sample, wherein the first code sample enables execution of a test algorithm for testing the set of test conditions of the first test record with respect to the first dataset. (Givoni, Abstract, "automated test script generation with fully parameterized scripts") Examiner Comments: Givoni's generation of parameterized test scripts implementing the enumerated test cases reads on providing the set of test conditions and an indication of a scripting framework to a code generation model to generate a code sample that enables execution of a test algorithm.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the specification-driven test and code generation of Kumar and Matcha with the graphical test editing and script execution of Givoni in order to provide user oversight and control over the automatically generated tests before they are executed against the environment, thereby improving the accuracy and maintainability of the validation tests, as suggested by Givoni (Abstract).
Regarding Claim 8, is a system claim corresponding to the medium claim above (Claim 1) and, therefore, is rejected for the same reasons set forth in the rejection of claim 1.
Regarding Claim 9, is a system claim corresponding to the medium claim above (Claim 2) and, therefore, is rejected for the same reasons set forth in the rejection of claim 2.
Regarding Claim 11, is a system claim corresponding to the medium claim above (Claim 4) and, therefore, is rejected for the same reasons set forth in the rejection of claim 4.
Regarding Claim 12, is a system claim corresponding to the medium claim above (Claim 5) and, therefore, is rejected for the same reasons set forth in the rejection of claim 5.
Regarding Claim 13, is a system claim corresponding to the medium claim above (Claim 6) and, therefore, is rejected for the same reasons set forth in the rejection of claim 6.
Regarding Claim 15, is a method claim corresponding to the medium claim above (Claim 1) and, therefore, is rejected for the same reasons set forth in the rejection of claim 1.
Regarding Claim 16, is a method claim corresponding to the medium claim above (Claim 2) and, therefore, is rejected for the same reasons set forth in the rejection of claim 2.
Regarding Claim 18, is a method claim corresponding to the medium claim above (Claim 4) and, therefore, is rejected for the same reasons set forth in the rejection of claim 4.
Regarding Claim 19, is a method claim corresponding to the medium claim above (Claim 5) and, therefore, is rejected for the same reasons set forth in the rejection of claim 5.
Regarding Claim 20, is a method claim corresponding to the medium claim above (Claim 6) and, therefore, is rejected for the same reasons set forth in the rejection of claim 6.
Claims 3, 7, 10, 14, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (US 11,720,478 B2) in view of Matcha (US 11,537,936 B2) further in view of Givoni (US 8,347,267 B2) further in view of Nicotera (US 11,657,292 B1).
Regarding Claim 3, Kumar, Matcha, and Givoni teach the none-transitory, computer-readable storage medium of claim 2.
Kumar, Matcha, and Givoni did not specifically teach
in response to determining that the first validation indicator indicates that the first metadata structure is not valid, update the first descriptor based on the identification of the deficiency; update the first metadata structure to include the updated first descriptor ...; and provide the updated first metadata structure to the data generation model to generate the first dataset consistent with the updated first metadata structure.
However, Nicotera teaches:
in response to determining that the first validation indicator indicates that the first metadata structure is not valid, update the first descriptor based on the identification of the deficiency; update the first metadata structure to include the updated first descriptor ...; and provide the updated first metadata structure to the data generation model to generate the first dataset consistent with the updated first metadata structure. (Nicotera, Abstract, "The analytic server may regenerate new labeled data based on the feedback of the LD and D2. The analytic server may train a dataset generator by iteratively performing these steps for refinement until the regenerated candidate examples reach a pass rate threshold") Examiner Comments: Nicotera's iterative refinement, in which data is regenerated based on feedback identifying deficiencies until a quality threshold is met, reads on updating the descriptor based on the identified deficiency and regenerating the dataset from the updated metadata structure.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the iterative refinement of Nicotera with the combination of Kumar, Matcha, and Givoni so that, upon determining that a metadata structure or its data is not valid, the descriptor is updated based on the identified deficiency and a consistent data set is regenerated from the updated structure. This is the application of a known problem-solving technique (Nicotera's iterative, feedback-driven regeneration) to improve a similar system in the same field, and it produces the predictable result of automatically resolving detected deficiencies and improving the quality and reliability of the generated test data without manual intervention, as expressly recognized by Nicotera (Abstract).
Regarding Claim 7, Kumar, Matcha, and Givoni teach the none-transitory, computer-readable storage medium of claim 1.
Givoni further teaches
determine that the indication of the modification includes (1) a first record identifier of the first metadata structure and (2) an updated descriptor associated with the first record identifier; update the first metadata structure according to the first record identifier and the updated descriptor; (Givoni, Abstract, "A global change manager automates modifying in the data structure, data object attributes across multiple scripts") Examiner Comments: Givoni's global change manager that modifies a data object attribute (updated descriptor) identified by the user reads on determining that the modification includes a record identifier and an updated descriptor and updating the metadata structure accordingly.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the specification-driven test and code generation of Kumar and Matcha with the graphical test editing and script execution of Givoni in order to provide user oversight and control over the automatically generated tests before they are executed against the environment, thereby improving the accuracy and maintainability of the validation tests, as suggested by Givoni (Abstract).
Nicotera further teaches
provide the updated first metadata structure to the data generation model to generate a second dataset consistent with the updated first metadata structure; provide the second dataset to the test generation model to generate a second test record; provide the second test record to the code generation model to generate a second code sample associated with the second test record; and update the first code sample to include the second code sample. (Nicotera, Abstract, "The analytic server may regenerate new labeled data based on the feedback ... by iteratively performing these steps for refinement") Examiner Comments: Nicotera's regeneration of data based on feedback reads on regenerating a second dataset, second test record, and second code sample from the updated metadata structure and updating the first code sample to include the second code sample.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the iterative refinement of Nicotera with the combination of Kumar, Matcha, and Givoni so that, upon determining that a metadata structure or its data is not valid, the descriptor is updated based on the identified deficiency and a consistent data set is regenerated from the updated structure. This is the application of a known problem-solving technique (Nicotera's iterative, feedback-driven regeneration) to improve a similar system in the same field, and it produces the predictable result of automatically resolving detected deficiencies and improving the quality and reliability of the generated test data without manual intervention, as expressly recognized by Nicotera (Abstract).
Regarding Claim 10, is a system claim corresponding to the medium claim above (Claim 3) and, therefore, is rejected for the same reasons set forth in the rejection of claim 3.
Regarding Claim 14, is a system claim corresponding to the medium claim above (Claim 7) and, therefore, is rejected for the same reasons set forth in the rejection of claim 7.
Regarding Claim 17, is a method claim corresponding to the medium claim above (Claim 3) and, therefore, is rejected for the same reasons set forth in the rejection of claim 3.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMIR SOLTANZADEH whose telephone number is (571)272-3451. The examiner can normally be reached M-F, 9am - 5pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wei Mui can be reached at (571) 272-3708. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AMIR SOLTANZADEH/Examiner, Art Unit 2191
/WEI Y MUI/Supervisory Patent Examiner, Art Unit 2191
Prosecution Insights