DETAILED ACTION
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 response to the amendments filed on 03/25/2026.
Claims 1-11 and 13-30 are currently pending for examination. Claims 1, 3, 8, 9, 11, 13-15, 19, 20, 22-24, 27, 28 and 30 have been amended. Claim 12 is cancelled.
No new IDS has been filed.
Examiner’s Note
Applicants are suggested to include figures 2 and 4 with related text in the claims to provide the application in a better position for an allowance.
Response to Arguments
The previous objections to claims 8, 9, 19, 20, 27 and 28 have been withdrawn in response to the applicant’s amendments/remarks.
Regarding the previous 112(b) rejections, the applicant has amended the claims and argued, in page 14 of the remarks, that “… submits that the rejections has been overcome and … rejection be withdrawn”. However, the applicant’s amendments cause the new rejections stated in the 112 rejections section below.
Regarding the 102 rejections, the applicant amended claim 1 to include the limitations of previous claims 11 and 12, and has, in pages 14-16 of the remarks, argued that “… relied on fig. 7 and paragraphs [0092] and [0094] of Iyer et al. … be noted that Iyer et al. pertains to training/using AI models to automatically supplement and/or complete for RPA workflows during development by a user. This augments RPA workflow development, but is not RPA workflow repair …”.
The applicant’s these arguments are not persuasive.
As the applicant noted, Iyer teaches that “… During training, various labeled data (in this case, images) are fed through neural network 600. Successful identifications strengthen weights for inputs to neurons, whereas unsuccessful identifications weaken them. A cost function, such as mean square error (MSE) or gradient descent, may be used to punish predictions that are slightly wrong much less than predictions that are very wrong. If the performance of the AI/MI model is not improving after a certain number of training iterations, a data scientist may modify the reward function, provide indications of where non-identified graphical elements are, provide corrections of misidentified graphical elements, etc. – see par. 0092 of Iyer. Moreover, Iyer, in par. 0094, further teaches that “… training data with known output is passed through the neural network and error is computed with a cost function from known target output, which gives the error for backpropagation. Error is computed at the output, and this error is transformed into corrections for network weights that will minimize the error …”.
In other words, Iyer clearly teaches detecting one or more issues (e.g., the unsuccessful identifications of the inputs to the neural network, not improving the performance of the AI/MI model after the certain number of training interactions or the error at the output in the training, etc.) in the RPA workflow during the development/training, and automatically repair (e.g., providing corrections of misidentified graphical elements, transforming the error into corrections for network weights, etc.) the RPA workflow, wherein the automatic repair comprises modifying activity parameters (e.g., modifying the weight parameters, etc.) …”. Please note that the claim does not limit “detecting an issue in the RPA workflow” NOT to be during the RPA workflow development and Iyer’s teaching of one of the automatic repair limitations (e.g., modifying activity parameters) is enough to reject the claim under the 102. See the 102 rejections section below for detail.
The applicants’ arguments regarding the claims 14, 23 and dependent claims for the limitations of claim 1 described above is not persuasive and the response for the argument is similar to the response stated above for the claim 1.
Thus, the applicants’ arguments are not persuasive. Please see amended rejections below for the amended claims. This action is 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.
Claims 6, 7, 17, 18 and 23-30 are rejected under 35 U.S.C. 112(b) as failing to set forth the subject matter which the inventor or a joint inventor regard as their invention.
Claims 6, 7, 17, 18 recite “… suggest a task …” “… suggested next task …”; however, it is not clear whether the term, “a task”, is the same as “a task” included in the claim 1 or 14.
Claim 23 recites “a task” in different locations (see lines 19, 24, etc.). However, it is not clear whether they are the same or not. Claims 24-30 depend from the claim 23, and are analyzed and rejected accordingly.
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 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 1-11 and 13-30 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Iyer et al. (US 2022/0075605 A1).
Please note that teaching of one of the limitations of the reference is enough to reject the claimed limitations with the term, “at least one”, a number of limitations with “or” – see the limitations of claim 1.
As per claim 1, Iyer teaches one or more non-transitory computer-readable media storing one or more computer programs [see fig. 5, paras. 0068, 0069], the one or more computer programs configured to cause at least one processor to:
provide information pertaining to a robotic process automation (RPA) workflow developed in an RPA designer application comprising one or more activities to one or more artificial intelligence (Al)/ machine learning (ML) models, the one or more AI/ML models trained to suggest a next activity, suggest a next sequence of activities, suggest modifications to parameters of at least one of the one or more activities in the RPA workflow, or any combination thereof, based on content of the one or more activities in the RPA workflow [abstract; fig. 3; par. 0005, lines 1-13; par. 0006, lines 1-14; par. 0063, lines 1-11 of Iyer teaches to provide information pertaining to a robotic process automation (RPA) workflow developed in an RPA designer application comprising one or more activities to one or more artificial intelligence (Al)/ machine learning (ML) models, the one or more AI/ML models trained to suggest a next activity, suggest a next sequence of activities, suggest modifications to parameters of at least one of the one or more activities in the RPA workflow, or any combination thereof, based on content of the one or more activities in the RPA workflow];
receive an output from the one or more AI/ML models comprising the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or any combination thereof [fig. 7; par. 0005, lines 1-13; par. 0006, lines 1-14 of Iyer teaches to receive an output from the one or more AI/ML models comprising the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or any combination thereof];
detect one or more issues in the RPA workflow; and automatically repair the RPA workflow, wherein the automatic repair comprises modifying activity parameters, replacing an activity with another activity suitable for a task, adding one or more additional activities an activity work, or any combination thereof [fig. 7; par. 0092, lines 1-12; par. 0094, lines 1-8 of Iyer teaches to detect one or more issues in the RPA workflow; and automatically repair (e.g., corrections of misidentified graphical elements) the RPA workflow, wherein the automatic repair comprises modifying activity parameters (e.g., modifying the reward function), replacing an activity with another activity suitable for a task, adding one or more additional activities (e.g., transforming into corrections) an activity work, or any combination thereof].
In other words, Iyer clearly teaches detecting one or more issues (e.g., the unsuccessful identifications of the inputs to the neural network, not improving the performance of the AI/MI model after the certain number of training interactions, the error at the output in the training, etc.) in the RPA workflow during the development/training, and automatically repair (e.g., providing corrections of misidentified graphical elements, transforming the error into corrections for network weights, etc.) the RPA workflow, wherein the automatic repair comprises modifying activity parameters (e.g., modifying the weight parameters, etc.).
As per claim 2, Iyer teaches the one or more non-transitory computer-readable media of claim 1.
Iyer further teaches wherein the one or more AI/ML models are configured to provide a confidence score for the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or the combination thereof [par. 0030, lines 1-24; par. 0035, lines 1-23 of Iyer teaches wherein the one or more AI/ML models are configured to provide a confidence score for the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or the combination thereof].
As per claim 3, Iyer teaches the one or more non-transitory computer-readable media of claim 1.
Iyer further teaches wherein the automatic modification of the RPA workflow to incorporate the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or the combination thereof is performed responsive to a confidence score exceeding an automatic insertion threshold [par. 0030, lines 1-24; par. 0035, lines 1-23 of Iyer teaches wherein the automatic modification of the RPA workflow to incorporate the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or the combination thereof is performed responsive to a confidence score exceeding an automatic insertion threshold].
As per claim 4, Iyer teaches the one or more non-transitory computer-readable media of claim 1.
Iyer further teaches wherein the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or the combination thereof is based on a context of the RPA workflow in a current state of development [par. 0005, lines 1-13; par. 0030, lines 1-24; par. 0035, lines 1-23; par. 0104, lines 1-17 of Iyer teaches wherein the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or the combination thereof is based on a context (e.g., the confidence score, etc.) of the RPA workflow in a current state of development].
As per claim 5, Iyer teaches the one or more non-transitory computer-readable media of claim 1.
Iyer further teaches wherein at least one of the one or more AI/ML models are trained using context based on RPA workflows that have been developed before [par. 0035, lines 1-23; par. 0038, lines 1-29 of Iyer teaches wherein at least one of the one or more AI/ML models are trained using context based on RPA workflows that have been developed before].
As per claim 6, Iyer teaches the one or more non-transitory computer-readable media of claim 1.
Iyer further teaches to suggest a task to perform next for the RPA workflow based on output from at least one of the one or more AI/ML models [par. 0005, lines 1-13; par. 0030, lines 1-24; par. 0035, lines 1-23; par. 0038, lines 1-29 of Iyer teaches to suggest a task (e.g., modifying or adding) to perform next for the RPA workflow based on output from at least one of the one or more AI/ML models].
As per claim 7, Iyer teaches the one or more non-transitory computer-readable media of claim 6.
Iyer further teaches to automatically perform the suggested next task when a confidence score associated with the suggested next task exceeds an automatic performance threshold [par. 0030, lines 1-24; par. 0035, lines 1-23; par. 0038, lines 1-29 of Iyer teaches to automatically perform the suggested next task (e.g., modifying or adding) when a confidence score associated with the suggested next task exceeds an automatic performance threshold].
As per claim 8, Iyer teaches the one or more non-transitory computer-readable media of claim 1.
Iyer further teaches wherein the RPA workflow pertains to a user interface (UI) automation, and at least one of the one or more AI/ML models is trained using information pertaining to applications, version of the applications, screens of the applications, and graphical elements of the screens from an object repository [figs. 1, 7, 8A, 10; par. 0041, lines 1-11; par. 0047, lines 1-19; par. 0063, lines 1-28; par. 0064, lines 1-11 of Iyer teaches wherein the RP A workflow pertains to a UI automation (see fig. 8A), and at least one of the one or more AI/ML models is trained using information pertaining to applications, version of the applications, screens of the applications, and graphical elements of the screens from an object repository].
As per claim 9, Iyer teaches the one or more non-transitory computer-readable media of claim 1.
Iyer further teaches wherein the RP A workflow pertains to a user interface (UI) automation, and at least one of the one or more AI/ML models is trained using one or more graphs comprising relationships between applications, version of the applications, screens of the applications, and graphical elements of the screens from an object repository [figs. 1, 7, 8A, 10; par. 0041, lines 1-11; par. 0047, lines 1-19; par. 0063, lines 1-28; par. 0064, lines 1-11 of Iyer teaches wherein the RP A workflow pertains to a UI automation, and at least one of the one or more AI/ML models is trained using one or more graphs comprising relationships between applications, version of the applications, screens of the applications, and graphical elements of the screens from an object repository].
As per claim 10, Iyer teaches the one or more non-transitory computer-readable media of claim 9.
Iyer further teaches wherein at the least one of the one or more AI/ML models trained using the one or more graphs is trained to recognize ontological associations from the one or more graphs [par. 0084, lines 1-15; par. 0102, lines 1-13 of Iyer teaches wherein at the least one of the one or more AI/ML models trained using the one or more graphs is trained to recognize ontological associations from the one or more graphs].
As per claim 11, Iyer teaches the one or more non-transitory computer-readable media of claim 1.
Iyer further teaches to notify a user of the RPA designer application of the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or the combination thereof, or automatically modify the RPA workflow to incorporate the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or the combination thereof, into the RPA workflow [figs. 7, 9; par. 0005, lines 1-13; par. 0006, lines 1-14; par. 0029, lines 1-13; par. 0035, lines 1-12 of Iyer teaches to notify a user of the RPA designer application of the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or the combination thereof, or automatically modify the RPA workflow to incorporate (e.g., to add) the suggested next activity, the suggested next sequence of activities, the suggested modifications to the parameters, or the combination thereof, into the RPA workflow].
As per claim 13, Iyer teaches the one or more non-transitory computer-readable media of claim 1.
Iyer further teaches wherein the automatic repair of the RPA workflow is performed iteratively during development of the RPA workflow [par. 0100, lines 1-12; par. 0109, lines 1-14 of Iyer teaches wherein the automatic repair of the RPA workflow is performed iteratively during development of the RPA workflow].
Claims 14-22 are system claims that correspond to the media claims “1 and 4”, 3 and 5-11, respectively and are analyzed and rejected accordingly.
Claims 23-30 are method claims that correspond to the media claims “1, 6 and 7”, 3-5 and 8-11, respectively and are analyzed and rejected accordingly.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAUNG T LWIN whose telephone number is (571)270-7845. The examiner can normally be reached on Monday - Friday 10:00 am - 6:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Farid Homayounmehr can be reached on 571-272-3739. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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.
/MAUNG T LWIN/Primary Examiner, Art Unit 2495
Prosecution Insights