DETAILED ACTION
Claims 1-20 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner’s Notes
Examiner has cited particular columns and line numbers, paragraph numbers, or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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) 1-4, 8-11 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al. (US-PGPUB-NO: 2023/0274167 A1) hereinafter Zheng, in further view of Hou et al. (US-PGPUB-NO: 2025/0383978 A1) hereinafter Hou.
As per claim 1, Zheng teaches a method for testing a set of tasks on a transactional system, comprising: receiving a model of a set of tasks, the set of tasks occurring between and among a plurality of entities which transact on a transactional system, the set of tasks further including an undefined task whose execution is undefined with existing configuration information (see Zhen paragraph [0065], “FIG. 2 is a schematic diagram of an architecture of a task learning system according to an embodiment of this application. As shown in FIG. 2, the task learning system 200 may include a knowledge base module 210 and a task processing apparatus 220. The task processing apparatus 220 may generate an inference model by using data (knowledge) stored in the knowledge base module 210, and perform, by using the inference model, inference on an input sample provided by a user. Certainly, when an inference task corresponding to the input sample belongs to a known task, the task processing apparatus 220 may perform inference on the input sample by using an existing task model in the knowledge base module 210”); processing the model of the set of tasks to identify the undefined task (see Zheng paragraph [0079], “In a task determining example, the task determining module 222 may determine, based on a difference between the target task attribute and the task attribute in the task information, that the inference task corresponding to the input sample is an unknown task”); querying for configuration information for the undefined task; and receiving the configuration information for the undefined task (see Zheng paragraph [0079], “Specifically, when the task determining module 222 determines that the difference between the target task attribute and the task attribute stored in the knowledge base module 210 is small, the input sample and a training sample that is used for model training and that corresponds to the task attribute have similar task attributes, so that a task model trained by using the training sample is also generally applicable to inference on the input sample”).
Zheng does not explicitly teach generating a first test for the undefined task, comprising: initiating the first test for the undefined task on the transactional system, the test based on the received configuration information; receiving a trace of the first test for the undefined task; determining whether the first test for the undefined task has properly executed based at least in part on the trace; when the first test for the undefined task is determined to not have properly executed, designing a second test for the undefined task, comprising: querying for new configuration information for the undefined task; and receiving the new configuration information for the undefined task; and initiating the second test for the undefined task on the transactional system, the second test based on the new configuration information. However, Hou teaches generating a first test for the undefined task (see Hou paragraph [0171], “At block 915, the processing logic may generate a test configuration object using the internal parameter format data”), comprising: initiating the first test for the undefined task on the transactional system, the test based on the received configuration information (see Hou paragraph [0077], “The request to generate the test run specification, based on the requested testing modality, may be forwarded to one or more interfaces to one or more testing modalities including: (i) an interface to a particular test scenario simulator, (ii) a plugin interface to one or more of an external SIL simulator or an external HIL simulator, or (iii) a plugin interface to an automated track testing job queue. These one or more interfaces may perform further formatting from the internal parameter format (e.g., intermediate scenario language) into the test specification. The further formatting may include one or more of: HIL rig specifications, track testing specifications, SL specifications, or the like”); receiving a trace of the first test for the undefined task (see Hou paragraph [0077], “These interfaces (e.g., plugin interfaces) may submit the request to a corresponding testing modality (e.g., a SIL testing modality for a SIL test specification, a HIL testing modality for a HIL test specification, a tracking testing modality for a track testing specification, or the like) via available web application protocol interfaces (APIs) (e.g., representational state transfer (REST), hypertext transfer protocol (HTTP), or the like)”); determining whether the first test for the undefined task has properly executed based at least in part on the trace (see Hou paragraph [0179], “At block 1015, the processing logic may compute a parametric metric based on the one or more asynchronous test sample results, where the parametric metric includes one or more of a parametric performance metric or a parametric coverage metric”); when the first test for the undefined task is determined to not have properly executed, designing a second test for the undefined task (see Hou paragraph [0180], “At block 1020, the processing logic may adjust a second test sample batch based on the parametric metric”), comprising: querying for new configuration information for the undefined task; and receiving the new configuration information for the undefined task; and initiating the second test for the undefined task on the transactional system, the second test based on the new configuration information (see Hou paragraph [0181], “The processor may be further configured to execute instructions to cause the device to perform one or more of: determine the one or more asynchronous test sample results based on a parametric metric that includes one or more of a parametric performance metric or a parametric coverage metric; determine the one or more asynchronous test sample results based on real world data; terminate the first sample batch based on the parametric metric; convert the plurality of first test samples from an internal parameter format to a testing modality format; convert the one or more asynchronous test sample results to the internal parameter format from the testing modality format; adjust the second test sample batch based on test adjustment configuration parameters including one or more of: a learning rate decay parameter, a sample size parameter, a hyper parameter, an algorithm selection parameter, or the like. The plurality of first test sample results may be generated using one or more different testing modalities. Alternatively, or in addition, one or more subsequent test sample batches may be adjusted based on one or more previous test sample batches until a metric (e.g., a convergence metric, a confidence metric, a coverage metric, a sampling metric, or the like) has been achieved”).
Zheng and Hou are analogous art because they are in the same field of endeavors of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the claimed invention effective filing date to modify Zheng’s teaching of task learning system and related devices with Hou’s teaching of simulation, testing and validations to incorporate a testing results to determine whether continued testing needs to be performed to an unknown task.
As per claim 2, Zheng modified with Hou teaches wherein the undefined task is a first undefined task, further comprising: when the first test is determined to have properly executed, processing the model of the set of tasks to identify a second undefined task whose execution is undefined with existing configuration information; generating a second test for the second undefined task, comprising: querying for configuration information for the second undefined task; and receiving the configuration information for the second undefined task; initiating the second test for the second undefined task on the transactional system based on the configuration information for the second undefined task; receiving a trace of the second test; and determining whether the second test has properly executed based at least in part on trace of the second test (see Hou paragraph [0181], “The processor may be further configured to execute instructions to cause the device to perform one or more of: determine the one or more asynchronous test sample results based on a parametric metric that includes one or more of a parametric performance metric or a parametric coverage metric; determine the one or more asynchronous test sample results based on real world data; terminate the first sample batch based on the parametric metric; convert the plurality of first test samples from an internal parameter format to a testing modality format; convert the one or more asynchronous test sample results to the internal parameter format from the testing modality format; adjust the second test sample batch based on test adjustment configuration parameters including one or more of: a learning rate decay parameter, a sample size parameter, a hyper parameter, an algorithm selection parameter, or the like. The plurality of first test sample results may be generated using one or more different testing modalities. Alternatively, or in addition, one or more subsequent test sample batches may be adjusted based on one or more previous test sample batches until a metric (e.g., a convergence metric, a confidence metric, a coverage metric, a sampling metric, or the like) has been achieved”).
As per claim 3, Zheng modified with Hou teaches further comprising: regenerating the first test for the first undefined task, comprising: querying for updated configuration information for the first undefined task; and receiving the updated configuration information for the first undefined task; reinitiating the first test of the first undefined task, the first test further based on the updated configuration information (see Hou paragraph [0087], “The operations on the sampling algorithm and the parameter space may be updated asynchronously using parallelized assumptions. That is, when a scenario is being enqueued, an updated prior may be generated based on the collected data to enqueue subsequent scenarios. The scenario may include data that has been collected asynchronously using an internal parameter format. The asynchronously collected data may include one or more of simulated data or real-world data.”).
As per claim 4, Zheng modified with Hou teaches wherein: receiving a trace of the first test for the undefined task comprises: receiving trace information comprising test result information (see Hou paragraph [0194], “At block 1210, the processing logic may identify one or more asynchronous test sample results of the plurality of first test samples before the first test sample batch is complete”), and; designing the second test for the undefined task, further comprises: determining the new configuration information for the undefined task based at least in part on the test result information (see Hou paragraph [0196], “At block 1220, the processing logic may adjust a second test sample batch based on the one or more asynchronous test sample results”).
As per claims 8-11, these are the system claims to method claims 1-4, respectively. Therefore, they are rejected for the same reasons as above.
As per claims 15-18, these are the computer program product stored in a non-transitory computer readable medium (see Zheng paragraph [0222], “These computer program instructions may alternatively be stored in a computer-readable memory that can indicate the computer or the another programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate an artifact that includes an instruction apparatus. The instruction apparatus implements specific functions in one or more processes in the flowcharts and/or in one or more blocks in the block diagrams”) claim to method claims 1-4, respectively. Therefore, they are rejected for the same reasons as above.
Claim(s) 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng (US-PGPUB-NO: 2023/0274167 A1) and Hou (US-PGPUB-NO: 2025/0383978 A1), in further view of Vartha et al. (US-PAT-NO: 10,216,508 B1) hereinafter Vartha.
As per claim 5, Zheng modified with Hou do not explicitly teach wherein the configuration information comprises a content template and a content rule. However, Vartha teaches wherein the configuration information comprises a content template and a content rule (see Vartha [column 16,lines 3-15], “In some embodiments, the templates may, collectively, reflect the hierarchical nature of the configuration information stored in configuration database 120. For example, some indexing keys or filtering criteria, and corresponding user interface elements, may be common across all templates, while other keys and/or filtering criteria may be specific to certain client domains or to client content for a specific client domain that is of a particular data types. The templates may also define rules, based on the client-domain-specific configuration information, for determining the target service or repository to which service requests submitted by users in certain client domains are to be routed for fulfillment”).
Zheng, Hou and Vartha are analogous art because they are in the same field of endeavors of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the claimed invention effective filing date to modify Zheng’s teaching of task learning system and related devices and Hou’s teaching of simulation, testing and validations with Vartha’s teaching of software installation and management and implementing and operating configurable service platform to incorporate having a template and rules with configuration information in order to easily detect proper testing.
As per claim 12, this is the system claim to method claim 5. Therefore, it is rejected for the same reasons as above.
Claim(s) 6, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng (US-PGPUB-NO: 2023/0274167 A1) and Hou (US-PGPUB-NO: 2025/0383978 A1), in further view of Yuan et al. (US-PAT-NO: 11,599,813 B1) hereinafter Yuan.
As per claim 6, Zheng modified with Hou do not explicitly teach wherein querying for configuration information for the undefined task comprises: sending questions to a configuration interface; and receiving the configuration information for the undefined task comprises: receiving answers to the questions from the configuration interface. However, Yuan teaches wherein querying for configuration information for the undefined task comprises: sending questions to a configuration interface; and receiving the configuration information for the undefined task comprises: receiving answers to the questions from the configuration interface (see Yuan [column 4, lines 48-55], “One or more users such as user 10 may be associated with a user account with a provider network 190. The user interface 30 may display or present a set of prompts or questions, and the user's answers to those questions may determine the presentation of additional prompts as well as the selection of one or more workflow templates and configuration of one or more workflows from the selected template(s). The prompts may represent solicitations of user input. For example, the system 100 may determine an initial prompt for presentation via the user interface 30. The initial prompt may ask the user 10 to select a high-level process for use of a machine learning model, e.g., model training, real-time inference, batch inference, monitoring, and so on. The initial prompt may list a set of such processes from which the user 10 can make a selection. Based on a response to this prompt from the user 10, the workflow builder 110 may select one of the workflow templates 111 corresponding to the high-level process indicated by the user input. In one embodiment, a user 10 may select multiple workflow templates to represent an end-to-end machine learning lifecycle, such as one workflow template for ad-hoc training, one workflow template for scheduled production batch inferencing, and another workflow template for model performance monitoring”).
Zheng, Hou and Yuan are analogous art because they are in the same field of endeavors of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the claimed invention effective filing date to modify Zheng’s teaching of task learning system and related devices and Hou’s teaching of simulation, testing and validations with Yuan’s teaching of interactive workflow generation for machine learning lifecycle management to incorporate having an interface in which prompts can be given to receive answers to configuration information.
As per claim 13, this is the system claim to method claim 6. Therefore, it is rejected for the same reasons as above.
As per claim 19, this is the computer program product stored in a non-transitory computer readable medium (see Zheng paragraph [0222], “These computer program instructions may alternatively be stored in a computer-readable memory that can indicate the computer or the another programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate an artifact that includes an instruction apparatus. The instruction apparatus implements specific functions in one or more processes in the flowcharts and/or in one or more blocks in the block diagrams”) claim to method claim 6. Therefore, it is rejected for the same reasons as above.
Claim(s) 7, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng (US-PGPUB-NO: 2023/0274167 A1) and Hou (US-PGPUB-NO: 2025/0383978 A1), in further view of Crisan (US-PAT-NO: 6,944,759 B1).
As per claim 7, Zheng modified with Hou do not explicitly teach wherein: the first undefined task occurs between a first entity and a second entity of the plurality of entities, the first entity having a set of pre-existing configuration information for executing pre-existing tasks on the transactional system and the second entity having a set of pre-existing configuration information for executing pre-existing tasks on the transactional system; querying for configuration information for the undefined task is based on at least one of: the set of pre-existing configuration information of the first entity, and the set of pre-existing configuration information of the second entity; and designing the first test for the undefined task, further comprises: determining recommended information; and determining the configuration information for the undefined task based at least in part on the recommended information and on background information related to at least one of: the first entity, the second entity, and the transactional system. However, Crisan teaches wherein: the first undefined task occurs between a first entity and a second entity of the plurality of entities, the first entity having a set of pre-existing configuration information for executing pre-existing tasks on the transactional system and the second entity having a set of pre-existing configuration information for executing pre-existing tasks on the transactional system (see Crisan [column 3, lines 44-50], “Referring now to FIG. 2, the preferred embodiment of the automatic configuration analysis and improvement mechanism includes a configuration management module 202 which interacts with a knowledge base 204. The configuration management module 202 generally analyzes a computer system's current configuration of hardware and software components 200 using the knowledge base 204 and recommends an improved configuration 206 to the user”); querying for configuration information for the undefined task is based on at least one of: the set of pre-existing configuration information of the first entity, and the set of pre-existing configuration information of the second entity; and designing the first test for the undefined task, further comprises: determining recommended information; and determining the configuration information for the undefined task based at least in part on the recommended information and on background information related to at least one of: the first entity, the second entity, and the transactional system (see Crisan [column 3, lines 51-59], “The recommended configuration 206 may include such recommendations as different versions of the software the user already uses or new software altogether that the user should use. The recommended configuration generally will be one that will improve the performance of the computer system. Improved performance includes operating faster, experiencing fewer problems, and/or experiencing less severe problems such as system lock-ups and crashes”).
Zheng, Hou and Crisan are analogous art because they are in the same field of endeavors of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the claimed invention effective filing date to modify Zheng’s teaching of task learning system and related devices and Hou’s teaching of simulation, testing and validations with Crisan’s teaching of automatically managing the configuration of a computer system to incorporate using past historical information in order to recommend configurations based on those historical data.
As per claim 14, this is the system claim to method claim 7. Therefore, it is rejected for the same reasons as above.
As per claim 20, this is the computer program product stored in a non-transitory computer readable medium (see Zheng paragraph [0222], “These computer program instructions may alternatively be stored in a computer-readable memory that can indicate the computer or the another programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate an artifact that includes an instruction apparatus. The instruction apparatus implements specific functions in one or more processes in the flowcharts and/or in one or more blocks in the block diagrams”) claim to method claim 7. Therefore, it is rejected for the same reasons as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Caulfield (US-PGPUB-NO: 2025/0328784 A1) teaches automatic system for new event identification using large language models.
Wu et al. (US-PGPUB-NO: 2021/0287082 A1) teaches utilizing machine learning to perform a merger and optimization operation.
Michelsen (US-PGPUB-NO: 2015/0205708 A1) teaches service modeling and virtualization.
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/LENIN PAULINO/Examiner, Art Unit 2197
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