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 .
Claim Interpretation
During patent examination, pending claims must be “given their broadest reasonable interpretation consistent with the specification.” MPEP 2111; See also, MPEP 2173.02. Limitations appearing in the specification but not recited in the claim are not read into the claim. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-551 (CCPA 1969). See also, In re Zletz, 893 F.2d 319, 321-22, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (“During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow”). The reason is simply that during patent prosecution when claims can be amended, ambiguities should be recognized, scope and breadth of language explored, and clarification imposed. An essential purpose of patent examination is to fashion claims that are precise, clear, correct, and unambiguous. Only in this way can uncertainties of claim scope be removed, as much as possible, during the administrative process.
The Examiner respectfully requests of the Applicant in preparing responses, to consider fully the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-5, 7-8, 10-14, 16-17, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2021/0333983 A1 to Singh et al. (hereinafter Singh).
With regards to claim 1, Singh discloses:
1. A computer system comprising at least one hardware processor (see, Fig. 38, and detailed description, including, the computing system 3800 includes one or more processing units 3802, 3804, para. 0049) configured to:
receive an encoding of a robotic process automation (RPA) activity (see, Summary, and detailed description, including, The program is further configured to cause the at least one processor to automatically generate an RPA workflow including the identified RPA process, para. 0005) and an encoding of a design-time target label (see, detailed description, including, RPA system 100 includes a designer 110 that allows a developer to design and implement workflows, para. 0019), wherein the RPA activity mimics a human interaction with a target element of a user interface (UI), and wherein the design-time target label comprises a text label attached to the target element within a design-time instance of the UI (SEE, Fig. 1, and detailed description, including, RPA system 100 includes a designer 110 that allows a developer to design and implement workflows. Designer 110 may provide a solution for application integration, as well as automating third-party applications, administrative Information Technology (IT) tasks, and business IT processes. Designer 110 may facilitate development of an automation project, which is a graphical representation of a business process. Simply put, designer 110 facilitates the development and deployment of workflows and robots, and concerning the many processes, is interpreted to possess a label, para. 0019);
in response, identify a runtime instance of the target element within a runtime instance of the UI exposed by the computer system, the runtime instance of the target element identified according to a similarity between a meaning of the design-time target label and a meaning of a label attached to the runtime instance of the target element (see, Fig. 6, and detailed description, including, identified processes may be listed for a user to peruse, and may be sorted by various factors including, but not limited to, an RPA score indicating how suitable a given process is to RPA (e.g., based on complexity of the automation, execution time, perceived benefit to key performance indicators such as revenue generated, revenue saved, time saved, etc.), process name, total recording time, the number of users who executed the process, process execution time (e.g., least or most time), etc. The process workflow may be displayed when a user clicks on a given process, including steps, parameters, and interconnections. In certain embodiments, only process activities that appear to be important from a clustering perspective may be used, para. 0060); and
in response to identifying the runtime instance of the target element, execute the RPA activity on the runtime instance of the target element (see, detailed description, including, suggested processes from AI layers 632 may be presented to an RPA engineer via a designer application 652 on a computing system 650. The RPA engineer can then review the workflow, make any desired changes, and then deploy the workflow via a robot to computing systems 602, 604, 606, or cause the robot to be deployed, para. 0062).
With regards to claim 2, Singh discloses:
2. The computer system of claim 1, wherein identifying the runtime instance of the target element comprises:
selecting a candidate target element from the runtime instance of the UI (see, detailed description, incldung, AI layers 632 process the log data and identify one or more potential processes therein, para. 0059);
automatically determining a candidate label comprising another text label attached to the candidate target element within the runtime instance of the UI (see, detailed description, including, (e.g., based on complexity of the automation, execution time, perceived benefit to key performance indicators such as revenue generated, revenue saved, time saved, etc.), process name, total recording time, the number of users who executed the process, process execution time (e.g., least or most time), etc., para. 0060);
transmitting the design-time target label and candidate label to a semantic assessor module (see, detailed description, including, similarities between processes may be determined by a common beginning and end and some amount of statistical commonality in the steps taking in between. Commonality may be determined by entropy, minimization of a process detection objective function, etc. The objective function threshold may be set automatically in some embodiments, and this may be modified during training if processes that were identified as dissimilar by the system are indicated as being similar by a user, para. 0061);
receiving from the semantic assessor module a similarity measure quantifying a similarity between the meaning of the design-time target label and a meaning of the candidate label (see, as above, Commonality may be determined by entropy, minimization of a process detection objective function, etc., para. 0061); and
determining whether the runtime instance of the target element comprises the candidate target element according to the similarity measure (see, as above, The objective function threshold may be set automatically in some embodiments, and this may be modified during training if processes that were identified as dissimilar by the system are indicated as being similar by a user, para. 0061).
With regards to claim 3, Singh discloses:
3. The computer system of claim 2, wherein identifying the runtime instance of the target element further comprises:
selecting a second candidate target element from the runtime instance of the UI see, detailed description, incldung, AI layers 632 process the log data and identify one or more potential processes therein, para. 0059);;
automatically determining a second candidate label comprising yet another text label attached to the second candidate target element within the runtime instance of the UI (see, detailed description, including, (e.g., based on complexity of the automation, execution time, perceived benefit to key performance indicators such as revenue generated, revenue saved, time saved, etc.), process name, total recording time, the number of users who executed the process, process execution time (e.g., least or most time), etc., para. 0060);
transmitting the second candidate label to the semantic assessor module (see, detailed description, including, similarities between processes may be determined by a common beginning and end and some amount of statistical commonality in the steps taking in between. Commonality may be determined by entropy, minimization of a process detection objective function, etc. The objective function threshold may be set automatically in some embodiments, and this may be modified during training if processes that were identified as dissimilar by the system are indicated as being similar by a user, para. 0061);
receiving from the semantic assessor module a second similarity measure quantifying a similarity between the meaning of the design-time target label and a meaning of the second candidate label (see, as above, Commonality may be determined by entropy, minimization of a process detection objective function, etc., para. 0061); and
determining whether the runtime instance of the target element comprises the candidate target element further according to the second similarity measure (see, as above, The objective function threshold may be set automatically in some embodiments, and this may be modified during training if processes that were identified as dissimilar by the system are indicated as being similar by a user, para. 0061).
With regards to claim 4, Singh discloses:
4. The computer system of claim 2, wherein identifying the runtime instance of the target element comprises comparing the similarity measure to a pre-determined threshold and determining whether the runtime instance of the target element comprises the candidate target element according to a result of the comparison (see, as above, The objective function threshold may be set automatically in some embodiments, and this may be modified during training if processes that were identified as dissimilar by the system are indicated as being similar by a user, para. 0061).
With regards to claim 5, Singh discloses:
5. The computer system of claim 2, wherein the semantic assessor module is configured to employ a pre-trained generative language model (GLM) to determine the similarity measure (see, detailed description, including, Web application 232 is the visual layer of the server platform. In this embodiment, web application 232 uses Hypertext Markup Language (HTML) and JavaScript (JS). However, any desired markup languages, script languages, or any other formats may be used without deviating from the scope of the invention, para. 0035).
With regards to claim 7, Singh discloses:
7. The computer system of claim 1, wherein:
the target element comprises an input field of the UI (see, detailed description, including, The data that is logged may include, but is not limited to, which buttons were clicked, where a mouse was moved, the text that was entered in a field, that one window was minimized and another was opened, the application associated with a window, etc., para. 0058);
the RPA activity comprises filling out the input field (see, above, and the text that was entered in a field, para. 0058); and
the label attached to the runtime instance of the target element is determined according to a placeholder value of the input field, the placeholder value displayed by the runtime instance of the UI (see, above, detailed description, including, The data that is logged may include, but is not limited to, which buttons were clicked, where a mouse was moved, the text that was entered in a field, that one window was minimized and another was opened, the application associated with a window, etc., para. 0058).
With regards to claim 8, Singh discloses:
8. The computer system of claim 1, wherein the target element comprises an item selected from a set consisting of a button of the UI, a menu item of the UI, and a hyperlinked element of the UI, and wherein the RPA activity comprises clicking or tapping the item (see, detailed description, including, The data that is logged may include, but is not limited to, which buttons were clicked, where a mouse was moved, the text that was entered in a field, that one window was minimized and another was opened, the application associated with a window, etc., para. 0058).
With regard to claim 10, claim 10 (a method claim) recites substantially similar limitations to claim 1 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 11, claim 11 (a method claim) recites substantially similar limitations to claim 2 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 12, claim 12 (a method claim) recites substantially similar limitations to claim 3 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 13, claim 13 (a method claim) recites substantially similar limitations to claim 4 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 14, claim 14 (a method claim) recites substantially similar limitations to claim 5 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 16, claim 16 (a method claim) recites substantially similar limitations to claim 7 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 17, claim 17 (a method claim) recites substantially similar limitations to claim 8 (a system claim) and is therefore rejected using the same art and rationale set forth above.
With regard to claim 19, claim 19 (a non-transitory computer-readable medium claim) recites substantially similar limitations to claim 1 (a system claim) (with the addition of at least one processor, see, para. 0048, and a memory, item 3806, para. 0049)) and is therefore rejected using the same art and rationale set forth above.
Allowable Subject Matter
Claims 6, 9, 15, and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
For convenience: claims 6, 9, 15, and 18 are provided below:
6. The computer system of claim 5, wherein determining the similarity measure comprises:
employing the GLM to determine a first embedding vector of the design-time target label and a second embedding vector of the candidate label; and
determining the similarity measure according to a distance between the first and second embedding vectors.
9. The computer system of claim 1, wherein:
the target element comprises a hyperlinked element of the UI (see, detailed description, including, The process workflow may be displayed when a user clicks on a given process, including steps, parameters, and interconnections, para. 0060); and
the at least one hardware processor is configured to determine the label attached to the runtime instance of the target element according to an alternative text or tooltip displayed by the runtime instance of the UI when hovering over the runtime instance of the target element.
15. The method of claim 14, wherein determining the similarity measure comprises:
employing the GLM to determine a first embedding vector of the design-time target label and a second embedding vector of the candidate label; and
determining the similarity measure according to a distance between the first and second embedding vectors.
18. The method of claim 10, wherein:
the target element comprises a hyperlinked element of the UI; and
the method comprises employing the at least one hardware processor to determine the label attached to the runtime instance of the target element according to an alternative text or tooltip displayed by the runtime instance of the UI when hovering over the runtime instance of the target element.
A sampling of the prior art made of record and not relied upon and considered pertinent to Applicants’ disclosure includes: U.S. Patent No. 12,248,285 B2 to Ripa et al. (hereinafter Ripa) that discusses Automatic data transfer between a source and a target using semantic artificial intelligence (AI) for robotic process automation (RPA) is disclosed. A user may be provided with the option of selecting a source and a target and indicating through an intuitive user interface that he or she would like to copy data from the source to the destination, regardless of format. This may be done at design time or at run time. For instance, the source and/or target may be a web page, a graphical user interface (GUI) of an application, an image, a file explorer, a spreadsheet, a relational database, a flat file source, any other suitable format, or any combination thereof. The source and the target may have different formats. The source, target, or both may not necessarily be visible to the use.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM D. TITCOMB whose telephone number is (571)270-5190. The examiner can normally be reached 9:30 AM - 6:30 PM (M-F).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen C. Hong can be reached at 571-272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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WILLIAM D. TITCOMB
Primary Examiner
Art Unit 2178
/WILLIAM D TITCOMB/Primary Examiner, Art Unit 2178 6-18-2026
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