DETAILED ACTION
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 .
This action is in response to response filed on 6/19/2026. This action is Non-FINAL.
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 21 recites the limitation "the other completion of the issue report". There is insufficient antecedent basis for this limitation in the claim. The examiner believes claim 21 should be dependent on claim 19 not claim 15.
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.
Claims 1,4-8, 11-15 and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Alamir et al. (US 2022/0391803 A1) further in view of Li et al. (US 2021/0004432 A1) and Bahrami et al. (US 2020/0204431 A1).
As per claim 1 (Amended), Alamir et al. teaches the invention as claimed including, “A system that assists with writing issue reports that describe software engineering tasks and issue information, the system comprising:
one or more processors; and
a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to:
retrieve context data that establishes a context for a new software engineering task, in response to receiving one of an implicit request or an explicit request for a predicted completion of an issue report, the issue report describing the new software engineering task
transform the context data to be compatible with a data format used to train a machine-learning model to assist with performing software engineering tasks;”
Alamir et al. teaches each task has a description of the requirements, an estimate of complexity, an estimate of duration for completion, an assignee, and a priority (0088). Also see 0011 and 0072. The artificial intelligence for task management model 302 receives a description of the task that relates to a project that is upcoming or ongoing. The task description includes a description of the requirements for competing the task, an estimate of complexation, an estimated amount of time that is expected to be required for completing the task, an assignee, and a priority (0071). “predict, by the machine learning model, that the description of the new software engineering task in the issue report is incorrect;
predict, by the machine-learning model using the transformed context data, at least one of a deletion or replacement of the incorrect description of the new software engineering task in the issue report that needs to be incorporated in the to correct the incorrect description;”
Alamir et al. teaches the artificial intelligence for task management model 302 analyzes the received task description to determine whether there is any redundancy i.e., whether the newly received task is duplicative of an existing task. The task description may be checked for quality, i.e., whether any similar tasks have caused downstream issues that may be replicated by the newly received task. A result of the analysis of the task description may be provided in a notification message that is transmitted to a user from whom the task description was received (0072). Task analysis module performs analysis in order to determine whether a task should actually be completed, and also performs a quality check on each task. If multiple users create a task to fix one particular bug, there could be multiple tasks that refer to the same issue. If determined that a similar task exists, the user is warned that the task is similar to the one that has caused issues downstream, with a recommendation on how to improve the task. For example, the task analysis module may provide a recommendation to add more information to the task description (0089). Also see figure 5.
Alamir et al. teaches, A message is that includes a notification of the detected problem and a recommendation for adjusting the description of the first task in order to overcome the detected problem is transmitted to the user (0012). Also see 0020. The user can revise the task description in such a way that increases the likelihood of assignees understanding the requirement and therefore completing the task without sending back for clarification (0079). Also see figure 5. However, Alamir et al. does not explicitly appear to teach, “cause a selectable representation of the predicted at least one of the deletion or replacement of the incorrect description of the new software engineering task to be displayed on at least one graphical user interface; and
cause , in response to a selection of the displayed predicted at least one of the deletion or replacement of the incorrect description of the new software engineering task, the issue report to be displayed on the at least one graphical user interface, the displayed issue report incorporating the predicted at least one of the deletion or replacement of the incorrect description.
“
Li et al. teaches providing an identified replacement text segment for display to a user, receiving an input indicating a user’s selecting of the identified replacement test segment, and upon receiving the input, replacing the test segment in the document with the identified replacement test segment (0005). Also see paragraphs 0035-36 and figures 2D-2E.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Alamir et al. with Li et al. Both teach recommending a change to text. Alamir et al. teaches notifying the user of a change to be made and allowing the user to make the change, while Li et al. teaches displaying the change to the user to allow the user to select the change and automatically applying the change based on the selection. The steps of Li et al. would have been obvious to try, it achieves the same result as Alamir et al. by apply the change/revision to the task description of Alamir et al. and improves the system by making the change without the user having to manually enter it.
Alamir et al. does not explicitly appear to teach “transform the context data to be compatible with a data format used to train a machine-learning model to assist with performing software engineering tasks;”
Bahrami et al. teaches, a ticket interface that is configured to receive information from the user and generate a ticket inquiry. The inquiry may include data and information related to an issue of the computing system (context). A REST API enables a ticket interface to communicate with the resolution system. The REST API reformats (transforms) the information provided by the user in a particular format that is acceptable by the resolution system (0038). Bahrami et al. teaches, after the models are trained and tested (machine learning), the resolution system may then address new ticket inquiries (context data for task) that are submitted from the computer system. The resolution system may retrieve (implicit/explicit request) a ticket inquiry (context data) from the computer system and use the models to determine a problem statement representative of an issue of the ticket inquiry. The resolution system may then predict a solution to the issue (0017). Also see 0046, 0066-0067and figure 2.
It would have been obvious to one of ordinary skills in the art before the effective filing date to modify Alamire et al. with Bahrami et al. Alamir et al. teaches artificial intelligence for task management model 302 analyzes the received task description to determine whether there is any redundancy i.e., whether the newly received task is duplicative of an existing task (0072). Bahrami et al. teaches a resolution system that uses trained model to predict a solution to a issue. Bahrami et al. teaches a REST API reformats (transforms) the information provided by the user in a particular format that is acceptable by the resolution system (0038). This will allow the received task description of Alamir et al. if received in a different format to be reformatted into a data formation that is acceptable by the artificial intelligence therefore making sure the artificial intelligence receives the correct data in order to process the received data correctly.
As per claim4 (currently amended), Alamir et al. further teaches, “The system of claim 1, wherein the plurality of instructions further causes the one or more processors to enable clarification of the updating of the
Alamir et al. teaches, A message is that includes a notification of the detected problem and a recommendation for adjusting the description of the first task in order to overcome the detected problem is transmitted to the user (0012). Also see 0020. The user can revise the task description in such a way that increases the likelihood of assignees understanding the requirement and therefore completing the task without sending back for clarification (0079). Also see figure 5. Tasks can be related to code that results in a bug (0083).
As per claim 5 (Currently Amended), Alamir et al. further teaches, “They system of claim 1, wherein the plurality of instructions further causes the one or more processors to predict, by the machine-learning model, another completion of the
Alamir et al. teaches, A message is that includes a notification of the detected problem and a recommendation for adjusting the description of the first task in order to overcome the detected problem is transmitted to the user (0012). Also see 0020. The user can revise the task description in such a way that increases the likelihood of assignees understanding the requirement and therefore completing the task without sending back for clarification (0079). As can be seen in figure 5, the recommend to the user 4 feeds back to 1 and tasks are analyzed again in step 2. This will allow the task to be corrected and then analyzed again for further correction.
As per claim 6 (Currently Amended), Alamir et al. further teaches, “The system of claim 5, wherein the plurality of instructions further causes the one or more processors to output the other completion of the
Alamir et al. teaches, A message is that includes a notification of the detected problem and a recommendation for adjusting the description of the first task in order to overcome the detected problem is transmitted to the user (0012). Also see 0020. The user can revise the task description in such a way that increases the likelihood of assignees understanding the requirement and therefore completing the task without sending back for clarification (0079). As can be seen in figure 5, the recommend to the user 4 feeds back to 1 and tasks are analyzed again in step 2. This will allow the task to be corrected and then analyzed again for further correction and a notification sent as shown above.
As per claim 7, Alamir et al. further teaches, The system of claim 1, wherein the issue information comprises at least one of whether an issue is reproducible, a requirement to reproduce the issue, or a current priority associated with the issue.”
Alamir et al. teaches a description of a task that relates to a project. The task description include a description of the requirements (s) and a priority (0071).
As per claims 8, 4-15 and 18-21¸ contain similar limitations to claims 1 and 4-7 and are therefore rejected for similar reasons.
Response to Arguments
Applicant's arguments filed 5/21/2026 have been fully considered but they are moot due to amendments. Please see above rejections.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK A GOORAY whose telephone number is (571)270-7805. The examiner can normally be reached Monday - Friday 10:00am - 6:00pm.
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/MARK A GOORAY/ Examiner, Art Unit 2199
/LEWIS A BULLOCK JR/ Supervisory Patent Examiner, Art Unit 2199
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