DETAILED ACTION
This Action is a response to the filing received 3 April 2024. Claims 1-20 are presented for examination.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 16 May 2025 is being considered by the examiner.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 8, 10-11 and 19-20 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Simoni, Diglio Antonio, U.S. 2018/0260309 A1 (“Simoni”).
Regarding claim 1, Simoni teaches: A server system for identifying test cases, the server system comprising: a memory storing a Natural Language Processing (NLP) pre-trained model, a network interface, and a processor, the processor operably coupled to the memory and the network interface (Simoni, e.g., FIG. 2, disclosing memory 30, processor(s) 28, and communication interface 32; see also, e.g., ¶¶34-36, wherein NLP is used to process received test scripts and/or test metadata; see also, e.g., ¶5, describing identifying and outputting a subset of a suite of test cases), the processor configured to:
obtain a group of test cases, each test case comprising a name, a description and one or more steps for testing (Simoni, e.g., ¶34, “test management computing device 12 obtains test scripts and/or test metadata for a test suite …” See also, e.g., ¶35, “test metadata can include descriptions of the test scripts or names or descriptions of steps included in the test scripts …”);
for each test case, process at least the description and the one or more steps using the NLP pre-trained model to output a vector of numerical values across n-number of dimensions (Simoni, e.g., ¶36, “test management computing device 12 optionally performs natural language processing step(s) on the test scripts and/or test metadata. The NPL steps can include feature extraction, stop words removal, stemming, or n-gram generation, for example, although other types and numbers of NLP step(s) can also be performed …” See also, e.g., ¶37, “test management computing device 12 generates a vector model based on a semantic vectorization of the test scripts and/or metadata …”);
compile a group of vectors corresponding to the group of test cases (Simoni, e.g., ¶37, “generate a two-dimensional term document matrix having columns corresponding to test scripts and rows corresponding to terms extracted from the test scripts and/or metadata …”);
apply a clustering process to the group of vectors to identify a subset of vectors from the group of vectors (Simoni, e.g., ¶39, “implements one or more cluster optimizations on the vector model generated in step 304 to identify a plurality of maximally separate and compact clusters of test scripts based on a similarity metric …” See also, e.g., ¶40, “determines … a subset of the test scripts that are candidates for facilitating reduction of the test suite …” Examiner’s note: the test scripts are represented as vectors input into the cluster optimization); and
output a subset of test cases corresponding to the subset of vectors (Simoni, e.g., ¶41, “outputs an indication of the subset of the test scripts that are candidates for facilitating reduction of the test suite … output via a graphical user interface (GUI) …”).
Claims 11 and 20 are rejected for the reasons given in the rejection of claim 1 above. Examiner notes that with respect to claim 11, Simon further teaches: A method for identifying test cases, the method executed in a computing environment comprising one or more processors and memory, wherein the memory stores at least a test application and a Natural Language Processing (NLP) pre-trained model (Simoni, e.g., FIG. 2, disclosing memory 30, processor(s) 28, and communication interface 32; see also, e.g., ¶¶34-36, wherein NLP is used to process received test scripts and/or test metadata; see also, e.g., ¶5, describing identifying and outputting a subset of a suite of test cases), and the method comprising: [[[the operations performed by the system of claim 1]]]; and with respect to claim 20, Simoni further teaches: A non-transitory computer readable medium storing computer executable instructions which, when executed by at least one computer processor, cause the at least one computer processor to carry out a method for identifying test cases, the non-transitory computer readable medium further comprising a test application and a Natural Language Processing (NLP) pre-trained model (Simoni, e.g., FIG. 2, disclosing memory 30, processor(s) 28, and communication interface 32; see also, e.g., ¶¶34-36, wherein NLP is used to process received test scripts and/or test metadata; see also, e.g., ¶5, describing identifying and outputting a subset of a suite of test cases), and the method comprising: [[[the operations performed by the system of claim 1]]].
Regarding claim 8, the rejection of claim 1 is incorporated, and Simoni further teaches: wherein the clustering process is a K-means clustering computation (Simoni, e.g., ¶39, “Exemplary cluster optimizations include … k-means …”).
Regarding claim 10, the rejection of claim 1 is incorporated, and Simon further teaches: wherein the memory further stores an Application Programming Interface configured to obtain the group of test cases from a development software module, and to return the subset of test cases to the development software module (Simoni, e.g., ¶34, “test management computing device 12 obtains test scripts … from the test database 16 hosted by [Application Lifecycle Management] device 14 …” See also, e.g., ¶46, “test management computing device 12 can store the converted remaining test scripts in place of, or in combination with (e.g., as test metadata), the remaining test scripts in the test database 16”).
Claim 19 is rejected for the additional reasons given in the rejection of claim 10 above.
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 2 and 12 are rejected under 35 U.S.C. § 103 as being unpatentable over Simoni in view of Gupta et al., U.S. 2019/0026653 A1 (“Gupta”).
Regarding claim 2, the rejection of claim 1 is incorporated, but Simoni does not more particularly teach obtaining a word vector for each word in the description and steps, computing a sum of the word vectors then dividing the sum by a number of words in the description and steps to obtain a resulting vector, and returning the vector. However, Gupta does teach: wherein the processor is configured to process at least the description and the steps of a given test case using the NLP pre-trained model by at least: obtaining a word vector for each word in the description and the one or more steps; computing a sum of the word vectors, then divide the sum by a number of words in the description and the one or more steps to obtain a resulting vector; and returning the resulting vector as the vector of the given test case (Gupta, e.g., claim 7, “calculating a text representation … by adding vectors for each word in the social media communication to obtain a sum vector and dividing the sum vector by a total number of words in the social media communication …”) for the purpose of grouping the content of social-media derived text representations into classifications (Gupta, e.g., ¶¶47-61).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for cluster-based reduction of a test suite as taught by Simoni to provide for obtaining a word vector for each word in the description and steps, computing a sum of the word vectors then dividing the sum by a number of words in the description and steps to obtain a resulting vector, and returning the vector because the disclosure of Gupta shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for generating text embeddings for further analysis to provide for obtaining a word vector for each word in the description and steps, computing a sum of the word vectors then dividing the sum by a number of words in the description and steps to obtain a resulting vector, and returning the vector for the purpose of grouping the content of social-media derived text representations into classifications (Gupta, Id.).
Claim 12 is rejected for the additional reasons given in the rejection of claim 2 above.
Claims 3 and 13 are rejected under 35 U.S.C. § 103 as being unpatentable over Simoni in view of Gupta and Audhkhasi et al., U.S. 2020/0251096 A1 (“Audhkhasi”).
Regarding claim 3, the rejection of claim 1 is incorporated, but Simoni in view of Gupta does not more particularly teach that if a new word in the test description and steps is not part of a vocabulary library of the NLP model, generating a unique random word vector corresponding to the new word and storing the word and vector in an OOV library. However, Audhkhasi does teach: wherein if a new word in the description and the one or more steps is not part of a vocabulary library of the NLP pre-trained model, then the processor is configured to: generate a unique random word vector corresponding to the new word and store the new word and the unique random word vector in an Out-Of-Vocabulary library in the NLP pre-trained model (Audhkhasi, e.g., ¶3, “method for learning Out-of-Vocabulary (OOV) words … receive a character sequence for a new OOV word … providing an Acoustic Word Embedding (AWE) vector as an output … inserting the OOV word weight into a listing of … word embeddings used by the ASR system to output recognized words from an input …”) for the purpose of maintaining an additional vocabulary for newly recognized words without requiring complete NLP model retraining (Audhkhasi, e.g., ¶¶2-7).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for cluster-based reduction of a test suite as taught by Simoni in view of Gupta to provide that if a new word in the test description and steps is not part of a vocabulary library of the NLP model, generating a unique random word vector corresponding to the new word and storing the word and vector in an OOV library because the disclosure of Audhkhasi shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for speech recognition to provide that if a new word in the test description and steps is not part of a vocabulary library of the NLP model, generating a unique random word vector corresponding to the new word and storing the word and vector in an OOV library for the purpose of maintaining an additional vocabulary for newly recognized words without requiring complete NLP model retraining (Audhkhasi, Id.).
Claim 13 is rejected for the additional reasons given in the rejection of claim 3 above.
Claims 4 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over Simoni in view of Vangala et al., U.S. 2010/0287534 A1 (“Vangala”).
Regarding claim 4, the rejection of claim 1 is incorporated, but Simoni does not more particularly teach that the subset of vectors is a predetermined number stored in memory. However, Vangala does teach: wherein the subset of vectors is a predetermined number stored in the memory (Vangala, e.g., ¶146, “algorithm 1150 for clustering … initially establishes a unique cluster for each test case … clustering is performed while the optimal criterion for clustering can still be met, the number of clusters is not a predetermined cluster threshold size, e.g., k …”) for the purpose of associating comparable test cases, identifying redundant test cases, and forming a test case subset that can effectively test an application under constraints (Vangala, e.g., Abs., ¶¶5-8).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for cluster-based reduction of a test suite as taught by Simoni to provide that the subset of vectors is a predetermined number stored in memory because the disclosure of Vangala shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for test case analysis and clustering to provide that the subset of vectors is a predetermined number stored in memory for the purpose of associating comparable test cases, identifying redundant test cases, and forming a test case subset that can effectively test an application under constraints (Vangala, Id.).
Claim 14 is rejected for the additional reasons given in the rejection of claim 4 above.
Claims 5-7 and 15-17 are rejected under 35 U.S.C. § 103 as being unpatentable over Simoni in view of Wiggers et al., U.S. 2015/0331787 A1 (“Wiggers”).
Regarding claim 5, the rejection of claim 1 is incorporated, but Simoni does not more particularly teach a GUI including an element operable to receive a desired number of test cases, which is inputted into the clustering process to determine the subset of vectors, a quantity thereof matching the desired number of test cases. However, Wiggers does teach: wherein the memory also stores a graphical user interface (GUI) that includes a GUI element operable to receive a desired number of test cases, and the desired number of test cases is inputted into the clustering process to determine the subset of vectors, where a number of the subset of vectors matches the desired number of test cases (Wiggers, e.g., ¶¶36-37, “determine when to re-cluster the functions 120 based on the number of the concrete test cases 160 and the coverage information … when the number of the concrete test cases 160 is more than a particular test case number … particular test case number may be determined based on … input from a user …”) for the purpose of grouping similar software functions into clusters in order to identify or generate a smaller number of test cases for the application (Wiggers, e.g., ¶¶20-40).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for cluster-based reduction of a test suite as taught by Simoni to provide for a GUI including an element operable to receive a desired number of test cases, which is inputted into the clustering process to determine the subset of vectors, a quantity thereof matching the desired number of test cases because the disclosure of Wiggers shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for software testing and verification to provide for a GUI including an element operable to receive a desired number of test cases, which is inputted into the clustering process to determine the subset of vectors, a quantity thereof matching the desired number of test cases for the purpose of grouping similar software functions into clusters in order to identify or generate a smaller number of test cases for the application (Wiggers, Id.).
Regarding claim 6, the rejection of claim 1 is incorporated, but Simoni does not more particularly teach a GUI including a first element operable to receive a file comprising the group of test cases and a second element operable to receive a desired number of test cases. However, Wiggers does teach: wherein the memory also stores a GUI that includes a first GUI element operable to receive a file that comprises the group of test cases, and a second GUI element to operable to receive a desired number of test cases (Wiggers, e.g., ¶¶36-37, “determine when to re-cluster the functions 120 based on the number of the concrete test cases 160 and the coverage information … when the number of the concrete test cases 160 is more than a particular test case number … particular test case number may be determined based on … input from a user …”) for the purpose of grouping similar software functions into clusters in order to identify or generate a smaller number of test cases for the application (Wiggers, e.g., ¶¶20-40).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for cluster-based reduction of a test suite as taught by Simoni to provide for a GUI including a first element operable to receive a file comprising the group of test cases and a second element operable to receive a desired number of test cases because the disclosure of Wiggers shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for software testing and verification to provide for a GUI including a first element operable to receive a file comprising the group of test cases and a second element operable to receive a desired number of test cases for the purpose of grouping similar software functions into clusters in order to identify or generate a smaller number of test cases for the application (Wiggers, Id.).
Regarding claim 7, the rejection of claim 6 is incorporated, and Wiggers further teaches: wherein the processor is configured to automatically determine a total number of test cases in the group of test cases, and displays the total number of test cases in the GUI, and the processor confirms that the desired number of test cases is less than the total number of testcases (Wiggers, e.g., ¶¶36-37, “determine when to re-cluster the functions 120 based on the number of the concrete test cases 160 and the coverage information … when the number of the concrete test cases 160 is more than a particular test case number … particular test case number may be determined based on … input from a user …”).
Claims 15-17 are rejected for the additional reasons given in the rejections of claims 5-7 above.
Claims 9 and 18 are rejected under 35 U.S.C. § 103 as being unpatentable over Simoni in view of Pustovit et al., U.S. 2013/0275946 A1 (“Pustovit”).
Regarding claim 9, the rejection of claim 1 is incorporated, but Simoni does not more particularly teach that the group of test cases is formatted as a matrix including name, description and one or more steps for each test case. However, Pustovit does teach: wherein the group of test cases is formatted as a matrix of three columns, comprising the name, the description and the one or more steps, and each row in the matrix is a software test case (Pustovit, e.g., ¶30, “test case description data file may include a pre-formatted table including … name of a particular test case … description of the test case … one or more ‘test step’ …”) for the purpose of generating and executing a set of tests against an application, information regarding the tests being comprehensively stored in a data set (Pustovit, e.g., ¶¶29-39).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and method for cluster-based reduction of a test suite as taught by Simoni to provide that the group of test cases is formatted as a matrix including name, description and one or more steps for each test case because the disclosure of Pustovit shows that it was known to those of ordinary skill in the pertinent art to improve a system and method for test development process automation to provide that the group of test cases is formatted as a matrix including name, description and one or more steps for each test case for the purpose of generating and executing a set of tests against an application, information regarding the tests being comprehensively stored in a data set (Pustovit, Id.).
Claim 18 is rejected for the additional reasons given in the rejection of claim 9 above.
Conclusion
Examiner has identified particular references contained in the prior art of record within the body of this action for the convenience of Applicant. Although the citations made are representative of the teachings in the art and are applied to the specific limitations within the enumerated claims, the teaching of the cited art as a whole is not limited to the cited passages. Other passages and figures may apply. Applicant, in preparing the response, should consider fully the entire reference as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art and/or disclosed by Examiner.
Examiner respectfully requests that, in response to this Office Action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
When responding to this Office Action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections. See 37 C.F.R. 1.111(c).
Examiner interviews are available via telephone and video conferencing using a USPTO-supplied web-based collaboration tool. Applicant is encouraged to submit an Automated Interview Request (AIR) which may be done via https://www.uspto.gov/patent/uspto-automated-interview-request-air-form, or may contact Examiner directly via the methods below.
Any inquiry concerning this communication or earlier communication from Examiner should be directed to Andrew M. Lyons, whose telephone number is (571) 270-3529, and whose fax number is (571) 270-4529. The examiner can normally be reached Monday to Friday from 10:00 AM to 6:00 PM ET. If attempts to reach Examiner by telephone are unsuccessful, Examiner’s supervisor, Wei Mui, can be reached at (571) 272-3708. Information regarding the status of an application may be obtained from the Patent Center system. For more information about the Patent Center system, see https://www.uspto.gov/patents/apply/patent-center. If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call (800) 786-9199 (in USA or Canada) or (571) 272-1000.
/Andrew M. Lyons/Primary Examiner, Art Unit 2191