DETAILED ACTION
Claims 1-20 are pending.
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 07/18/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim 11-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The following claim language is unclear:
As per claim 11, it recites “determining that the first architecture roadmap is not accepted to proceed to a recipient; generating a report indicating that the first architecture roadmap is not accepted to proceed to the recipient; and transmitting the report to a recipient.” It is unclear from the context of the claim whether the recipient is a single recipient or different recipients.
Claims 12-15 depend on claim 11 and fail to cure the deficiencies set forth above for claim 11.
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.
Claims 1, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2022/0197765 A1) in view of Brannon et al. (US 2020/0004938 A1).
Regarding claim 1, Zhang teaches the invention substantially as claimed including an apparatus (Fig. 5; [0048] computing device 500), comprising:
a memory (Fig. 5, system memory 504) operable to store:
a machine learning algorithm configured to analyze and structure data in accordance with one or more machine learning models ([0019] Thus, the MRs may be processed by extracting requirements utilizing NLP, or some other form of machine learning, and clustering the requirements. The requirement extraction may include extracting key words from the MR documents. A document term matrix may be created that ranks the extracted key words based on importance. [0027] A natural language processing (NLP) or other type of machine learning algorithm may be used for the requirement extraction. A document term matrix may be created that ranks the extracted key words based on importance. The requirements may then be clustered based upon the text extraction and the document term matrix. The requirements may be clustered into multiple clusters.); and
a processor communicatively coupled to the memory ([0049] In various embodiments, the computing device 500 may include at least one processing unit 502 and system memory 504.) and configured to:
receive a first response to a first request, wherein: the first response comprises a first plurality of input fields, and the first request is configured to request information associated with a communication device type ([0001] Mobile communication devices configured for operation in wireless communication networks, e.g., handset devices such as mobile phones, are often updated, fixed, patched, etc., by an original equipment manufacturer (OEM) once the mobile communication devices are deployed for use in a wireless communication network. For example, OEMs release hundreds of software updates for the mobile communication devices every year to add features, fix bugs, and improve security as well as stability. Such releases are generally referred to as maintenance releases (MRs) and need to be tested and certified by the operator of the wireless communication network before being released to the mobile communication devices. This is to help ensure that MRs do not impact the user experience.; [0002] There are generally 400 or more MRs released every year from OEMs. Additionally, there are 7000 or more candidate tests to run for each MR.; [0011] A natural language processing (NLP) or other type of machine learning algorithm may be used for the requirement extraction. The requirements may then be clustered based upon the text extraction.; [0019] As previously noted, the clusters may have various categories including, but not limited to, major fixes, security updates, display or screen attributes, gaming attributes, device type, technology protocol, brand of the device, etc.);
in response to receiving the first response to the first request, execute the machine learning algorithm to: evaluate the first plurality of input fields associated with the first response in accordance with the one or more machine learning models ([0011] A natural language processing (NLP) or other type of machine learning algorithm may be used for the requirement extraction. The requirements may then be clustered based upon the text extraction. The requirements may be clustered into multiple clusters. For example, in configurations, four clusters may be defined. A first cluster may relate to and include changes in MRs that relate to major fixes. A second cluster may include and relate to changes in MRs directed to security updates. A third cluster may relate to and include changes directed to a screen or display for devices, as well as for gaming. Finally, a fourth cluster may relate to a particular device, e.g., brand of the device.;);
in response to evaluating the first plurality of input fields associated with the first response, determine a first plurality of evaluation domains based on the first plurality of input fields, wherein: a first evaluation domain of the first plurality of evaluation domains referencing a first plurality of operational tasks to evaluate a first performance aspect of the communication device type ([0011]; [0019] As previously noted, the clusters may have various categories including, but not limited to, major fixes, security updates, display or screen attributes, gaming attributes, device type, technology protocol, brand of the device, etc.; [0029] The knowledge base 202 may take clusters of requirements 212 and perform requirement-test mapping 214 and map the clusters of requirements 212 to potential tests. This may provide one or more clusters of potential tests 216 for each of the clusters of requirements 212.); and
a second evaluation domain of the first plurality of evaluation domains referencing a second plurality of operational tasks to evaluate a second performance aspect of the communication device type ([0019] As previously noted, the clusters may have various categories including, but not limited to, major fixes, security updates, display or screen attributes, gaming attributes, device type, technology protocol, brand of the device, etc.; [0029] The knowledge base 202 may take clusters of requirements 212 and perform requirement-test mapping 214 and map the clusters of requirements 212 to potential tests. This may provide one or more clusters of potential tests 216 for each of the clusters of requirements 212.);
determine a first priority order relating to the first plurality of operational tasks, determine a second priority order relating to the second plurality of operational tasks, the first priority order being greater than the second priority order ([0029] The knowledge base 202 may take clusters of requirements 212 and perform requirement-test mapping 214 and map the clusters of requirements 212 to potential tests. This may provide one or more clusters of potential tests 216 for each of the clusters of requirements 212. Additionally, the knowledge base 202 may perform a multi-criteria test prioritization 218. The prioritization may be based upon, for example, the test histories 208, the test reports 210, popularity of potential tests, etc. For example, the test histories 208 may include a failed test history with respect to previous changes and/or MRs.; [0031] Prioritized tests 230 for the new requirements cluster 228 may be retrieved from the prioritized test data base 218 that have been prioritized based upon multiple criteria.); and
generate a first architecture roadmap comprising the first plurality of operational tasks and the second plurality of operational tasks, the first architecture roadmap being a first plan to perform the first plurality of operational tasks and the second plurality of operational tasks over a first time period (Fig. 2, Step 236; [0033] Based upon one or more of the prioritized tests, the previous tests, the failed tests, the relevant tests, the technology protocol, the OEM and technology, a set of recommended tests 236 may be selected by the recommendation engine 224, e.g., the set of recommended tests 118 of FIG. 1. Based upon the total execution time of the set of recommended tests 236, a total execution time may be determined. If the total execution time exceeds a predetermined threshold, then the recommendation engine 224 may reduce the number of recommended tests in the set of recommended tests 236. The tests that are removed from the selected set of recommended tests 230 may be lower prioritized tests.; Claim 7 “wherein the actions further comprise: selecting the tests to be performed based at least in part on test execution time for each of the one or more tests”; ); and
transmit the first architecture roadmap to a plurality of reviewing entities ([0024] The recommended tests may then be provided to the OEM of the device and/or the operator of the wireless communication network, e.g., subject matter experts (SMEs). The OEM and/or the operator of the wireless communication network may determine tests to perform from the set of recommended tests, as well as any additional test(s) to perform not included in the set of recommended tests, to determine a set of tests to be performed. The set of tests may then be performed with respect to the proposed requirements/changes within the received MRs.; [0040]).
Zhang teaches receiving the maintenance releases (MRs) with the plurality of fields but does not explicitly teach that the receipt of the MRs are in response to a request.
However, Brannon teaches receive a first response to a first request ([0018] generating a privacy risk assessment questionnaire; transmitting the privacy risk assessment questionnaire to the particular vendor; and receiving privacy risk assessment questionnaire responses from the particular vendor.).
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 teachings of Brannon of submitting a questionnaire to a vendor such as Zhang’s OEM and receive a response (i.e., MRs) to evaluate vendor compliance. The modification would have been motivated by the desire of combining known techniques to yield predictable results.
Regarding claim 9, it is a method type claim having similar limitations as claim 1 above. Therefore, it is rejected under the same rationale above.
Regarding claim 16, it is a media/process type claim having similar limitations as claim 1 above. Therefore, it is rejected under the same rationale above.
Claims 2-8, 10-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2022/0197765 A1) in view of Brannon et al. (US 2020/0004938 A1) in further view of Rotta et al. (US 2019/0164100 A1).
Regarding claim 2, Zhang teaches wherein the processor is further configured to: receive review feedback from the plurality of reviewing entities, the review feedback comprising a plurality of changes to the first architecture roadmap ([0024] The recommended tests may then be provided to the OEM of the device and/or the operator of the wireless communication network, e.g., subject matter experts (SMEs);
in response to approving the plurality of changes, determine a third evaluation domain of the first plurality of evaluation domains referencing a third plurality of operational tasks to evaluate a third performance aspect of the communication device type; determine a third priority order relating to the third plurality of operational tasks, the second priority order being greater than the third priority order; and update the first architecture roadmap to comprise the first plurality of operational tasks, the second plurality of operational tasks, and the third plurality of operational tasks (Fig. 2, Step 236; [0011]; [0019]; [0033] Based upon one or more of the prioritized tests, the previous tests, the failed tests, the relevant tests, the technology protocol, the OEM and technology, a set of recommended tests 236 may be selected by the recommendation engine 224, e.g., the set of recommended tests 118 of FIG. 1. Based upon the total execution time of the set of recommended tests 236, a total execution time may be determined. If the total execution time exceeds a predetermined threshold, then the recommendation engine 224 may reduce the number of recommended tests in the set of recommended tests 236. The tests that are removed from the selected set of recommended tests 230 may be lower prioritized tests.);
generate an award comprising an updated version of the first architecture roadmap, and transmit the award to a recipient ([0017] Once the set of recommended tests is finalized, the finalized set of recommended tests may be provided to the OEM for execution of the tests).
Zhang nor Brannon explicitly teach in response to receiving the review feedback from the plurality of reviewing entities, execute the machine learning algorithm to: evaluate the review feedback in accordance with the one or more machine learning models;
in response to evaluating the review feedback, accept the plurality of changes to the first architecture roadmap based on one or more rules and policies indicating that the first architecture roadmap is allowed to be updated.
However, Rotta teaches in response to receiving the review feedback from the plurality of reviewing entities, execute the machine learning algorithm to: evaluate the review feedback in accordance with the one or more machine learning models (Abstract: evaluating an IT change request system based on cognitive and machine learning technologies. The system includes a computing device having a change request evaluator based on a machine learning trained model and in digital communication with a server. A historical database stores change records and a definitions database stores a definition for words appearing in the change records. A change request is received at the change request evaluator, which finds a model change record by comparing the change request to the change records in the historical database and identifying definitions common to both the change request and the change record. A business mapping tool interfaces with the change request evaluator and determines a business impact of the model change record and associates the business impact with the change request. The change request evaluator approves or rejects the change request.);
in response to evaluating the review feedback, accept the plurality of changes to the first architecture roadmap based on one or more rules and policies indicating that the first architecture roadmap is allowed to be updated (Fig. 3; [0023] The aforementioned components of the system 100 are utilized to evaluate and approve IT change requests based on natural language processing and machine learning technologies.; [0028]).
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 teachings of Rotta with the teachings of Zhang and Brannon to allow for reviewers to make changes/modifications and approve change requests. The modification would have been motivated by the desire of making approval process consistent and less labor-intensive.
Regarding claim 3. Rotta teaches wherein the processor is further configured to:
receive review feedback from the plurality of reviewing entities, the review feedback comprising a plurality of changes to the first architecture roadmap (Fig. 4 Change Request 400);
in response to receiving the review feedback from the plurality of reviewing entities, execute the machine learning algorithm to:
evaluate the review feedback in accordance with the one or more machine learning models; in response to evaluating the review feedback, deny the plurality of changes to the first architecture roadmap based on one or more rules and policies ([0032] At the final step 414, the system 100 will compile all information identified and verified by the previous steps 400-412 and approve the change request, as appropriate. The information is compiled through machine learning policies and rules of the system 100 according to the previous steps 400-412. The information is leveraged from the components of the system 100 to evaluate the request based on the change approval policies in the change approval policies database 308 created by the machine learning model. Once the request is evaluated, the system 100 will either approve or reject the change request.); and in response to disapproving the plurality of changes, determine that the first architecture roadmap is not accepted to proceed to a recipient, generate a report indicating that the first architecture roadmap is not accepted to proceed to the recipient, and transmit the report to the recipient (Abstract; [0032] Once the request is evaluated, the system 100 will either approve or reject the change request.).
Regarding claim 4, Zhang teaches wherein the processor is further configured to:
receive a second response to a second request, wherein: the second response comprises a second plurality of input fields ([0001]; [0002]; [0011]; [0019]); and
the second request is configured to request information associated with the communication device type ([0019]);
in response to evaluating the review feedback, determine a second plurality of evaluation domains based on the second plurality of input fields ([0024]; [0040]), wherein:
a third evaluation domain of the second plurality of evaluation domains referencing a third plurality of operational tasks to evaluate a third performance aspect of the communication device type ([0011]; [0019] As previously noted, the clusters may have various categories including, but not limited to, major fixes, security updates, display or screen attributes, gaming attributes, device type, technology protocol, brand of the device, etc.; [0029] The knowledge base 202 may take clusters of requirements 212 and perform requirement-test mapping 214 and map the clusters of requirements 212 to potential tests. This may provide one or more clusters of potential tests 216 for each of the clusters of requirements 212.); and
a fourth evaluation domain of the second plurality of evaluation domains referencing a fourth plurality of operational tasks to evaluate a fourth performance aspect of the communication device type ([0019] As previously noted, the clusters may have various categories including, but not limited to, major fixes, security updates, display or screen attributes, gaming attributes, device type, technology protocol, brand of the device, etc.; [0029] The knowledge base 202 may take clusters of requirements 212 and perform requirement-test mapping 214 and map the clusters of requirements 212 to potential tests. This may provide one or more clusters of potential tests 216 for each of the clusters of requirements 212.);
determine a third priority order relating to the third plurality of operational tasks; determine a fourth priority order relating to the fourth plurality of operational tasks, the third priority order being less than the second priority order and greater than the fourth priority order ([0029] The knowledge base 202 may take clusters of requirements 212 and perform requirement-test mapping 214 and map the clusters of requirements 212 to potential tests. This may provide one or more clusters of potential tests 216 for each of the clusters of requirements 212. Additionally, the knowledge base 202 may perform a multi-criteria test prioritization 218. The prioritization may be based upon, for example, the test histories 208, the test reports 210, popularity of potential tests, etc. For example, the test histories 208 may include a failed test history with respect to previous changes and/or MRs.; [0031] Prioritized tests 230 for the new requirements cluster 228 may be retrieved from the prioritized test data base 218 that have been prioritized based upon multiple criteria.); and
generate a second architecture roadmap comprising the third plurality of operational tasks and the fourth plurality of operational tasks, the second architecture roadmap being a second plan to perform the third plurality of operational tasks and the fourth plurality of operational tasks over a second time period (Fig. 2, Step 236; [0033] Based upon one or more of the prioritized tests, the previous tests, the failed tests, the relevant tests, the technology protocol, the OEM and technology, a set of recommended tests 236 may be selected by the recommendation engine 224, e.g., the set of recommended tests 118 of FIG. 1. Based upon the total execution time of the set of recommended tests 236, a total execution time may be determined. If the total execution time exceeds a predetermined threshold, then the recommendation engine 224 may reduce the number of recommended tests in the set of recommended tests 236. The tests that are removed from the selected set of recommended tests 230 may be lower prioritized tests.; Claim 7 “wherein the actions further comprise: selecting the tests to be performed based at least in part on test execution time for each of the one or more tests”); and
transmit the second architecture roadmap to the plurality of reviewing entities, wherein: a first review entity of the plurality of reviewing entities is configured to evaluate the third plurality of operational tasks in the second architecture roadmap and a second review entity of the plurality of reviewing entities is configured to evaluate the fourth plurality of operational tasks in the second architecture roadmap ([0024] The recommended tests may then be provided to the OEM of the device and/or the operator of the wireless communication network, e.g., subject matter experts (SMEs). The OEM and/or the operator of the wireless communication network may determine tests to perform from the set of recommended tests, as well as any additional test(s) to perform not included in the set of recommended tests, to determine a set of tests to be performed. The set of tests may then be performed with respect to the proposed requirements/changes within the received MRs.; [0040]).
In addition, Rotta teaches in response to receiving the review feedback from the plurality of reviewing entities, execute the machine learning algorithm to:
evaluate the review feedback in accordance with the one or more machine learning models (Abstract: evaluating an IT change request system based on cognitive and machine learning technologies. The system includes a computing device having a change request evaluator based on a machine learning trained model and in digital communication with a server. A historical database stores change records and a definitions database stores a definition for words appearing in the change records. A change request is received at the change request evaluator, which finds a model change record by comparing the change request to the change records in the historical database and identifying definitions common to both the change request and the change record. A business mapping tool interfaces with the change request evaluator and determines a business impact of the model change record and associates the business impact with the change request. The change request evaluator approves or rejects the change request.).
Regarding claim 5, Zhang teaches wherein the processor is further configured to:
receive first review feedback from the first review entity, the first review feedback approving the third plurality of operational tasks in the second architecture roadmap, receive second review feedback from the second review entity, the second review feedback approving the fourth plurality of operational tasks in the second architecture roadmap ([0024] The recommended tests may then be provided to the OEM of the device and/or the operator of the wireless communication network, e.g., subject matter experts (SMEs); Fig. 2, Step 236; [0011]; [0019]; The OEM and/or the operator of the wireless communication network may determine tests to perform from the set of recommended tests, as well as any additional test(s) to perform not included in the set of recommended tests, to determine a set of tests to be performed. The set of tests may then be performed with respect to the proposed requirements/changes within the received MRs. If the tests pass, then the received MRs may be certified and the changes in the received MRs may be certified and implemented in devices used within the wireless communication network. In configurations, a predetermined threshold number of tests, e.g., 70 percent, 80 percent, etc., that pass may be deemed sufficient for implementation of the changes within the MR.; [0033] Based upon one or more of the prioritized tests, the previous tests, the failed tests, the relevant tests, the technology protocol, the OEM and technology, a set of recommended tests 236 may be selected by the recommendation engine 224, e.g., the set of recommended tests 118 of FIG. 1. Based upon the total execution time of the set of recommended tests 236, a total execution time may be determined. If the total execution time exceeds a predetermined threshold, then the recommendation engine 224 may reduce the number of recommended tests in the set of recommended tests 236. The tests that are removed from the selected set of recommended tests 230 may be lower prioritized tests.), generate an award comprising the second architecture roadmap; and transmit the award to the recipient ([0017] Once the set of recommended tests is finalized, the finalized set of recommended tests may be provided to the OEM for execution of the tests).
Regarding claim 6, it recites similar limitations as claim 2 above but recited for different evaluation domains. Zhang as cited for claim 2 encompasses a plurality of different clusters of tasks and apply to the language of claim 6. Accordingly, it is rejected under the same rationale.
Regarding claim 7, Zhang teaches wherein: the award comprises a timeline indicating a third time period to perform the third plurality of operational tasks, a fourth time period to perform the fourth plurality of operational tasks, and a fifth time period to perform the fifth plurality of operational tasks, and the second time period comprises the third time period, the fourth time period, and the fifth time period (Claim 7 “wherein the actions further comprise: selecting the tests to be performed based at least in part on test execution time for each of the one or more tests.”).
Regarding claim 8, Zhang teaches wherein: the third time period, the fourth time period, and the fifth time period do not overlap with one another (Claim 7).
Regarding claim 10, it is a method type claim having similar limitations as claim 2 above. Therefore, it is rejected under the same rationale above.
Regarding claim 11, it is a method type claim having similar limitations as claim 3 above. Therefore, it is rejected under the same rationale above.
Regarding claim 12, it is a method type claim having similar limitations as claim 4 above. Therefore, it is rejected under the same rationale above.
Regarding claim 13, it is a method type claim having similar limitations as claim 5 above. Therefore, it is rejected under the same rationale above.
Regarding claim 14, it is a method type claim having similar limitations as claim 6 above. Therefore, it is rejected under the same rationale above.
Regarding claim 15, it is a method type claim having similar limitations as claim 4 above. Therefore, it is rejected under the same rationale above.
Regarding claim 17, it is a media/process type claim having similar limitations as claim 2 above. Therefore, it is rejected under the same rationale above.
Regarding claim 18, it is a media/process type claim having similar limitations as claim 3 above. Therefore, it is rejected under the same rationale above.
Regarding claim 19, it is a media/process type claim having similar limitations as claim 4 above. Therefore, it is rejected under the same rationale above.
Regarding claim 20, it is a media/process type claim having similar limitations as claim 5 above. Therefore, it is rejected under the same rationale above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE A CHU JOY-DAVILA whose telephone number is (571)270-0692. The examiner can normally be reached Monday-Friday, 6:00am-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aimee J Li can be reached at (571)272-4169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORGE A CHU JOY-DAVILA/Primary Examiner, Art Unit 2195
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