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 .
DETAILED ACTION
Status of Claim
1. Applicant's amendment dated 02/12/2026 responding to the Office Action 12/30/2025 provided in the rejection of claims 1-30.
2. Claims 1, 2, 10, 11, 13, 20, 21, 23, 28 and 29 have been amended and claims 6, 16 and 24 have been canceled.
3. Claims 1-5, 7-15, 17-23 and 25-30 are pending in the application, of which claims 1, 13 and 23 in independent form and which have been fully considered by the examiner.
Response to Amendments
4. (A) Regarding Abstract objection: Abstract objection has been withdrawn in view of Applicant’s amendments.
Examiner Notes
5. Examiner cites particular columns and line numbers 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 that, in preparing responses, the applicant 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 Objections
6. Claims 1-5, 7-15, 17-23 and 25-30 are objected to because of the following informalities:
Claim 1, line 15; claim 13, line 21 and claim 23, line 20 recite “thereof”; “thereof” should be removed or replaced.
Claim 23 recites the limitation “a computing system" in line 11. There is insufficient antecedent basis for this limitation in the claim.
Claims 11, 21 and 29, line 6 recite “a generative AI model configured to generate code, provide sematic associations between text on a screen, suggest expressions and code snippets for activities of the RPA workflow, generate a form from a natural language description or a drawing, generate a process from a drawing, or any combination thereof”. These limitations should be changed to -- a generative AI model configured to at least one of generate code, provide sematic associations between text on a screen, suggest expressions and code snippets for activities of the RPA workflow, generate a form from a natural language description or a drawing, generate a process from a drawing
Claims 2-5, 7-10, 12, 14-15, 17-20, 22, 25-28 and 30 depend on objected claims are also objected.
Appropriate correction is required.
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.
7. Claims 1-5, 7-15, 17-23 and 25-30 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.
Claims 1, 13 and 23 recite “that can be”, in lines 14, 20 and 19 respectively, appear to be vague. It renders the claim indefinite.
Claims 2-5, 7-12, 14-15, 17-22 and 25-30 depend on claims 1, 13 and 23 are also rejected under 112(b).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
8. Claims 1-5, 7-15, 17-23 and 25-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis specific to Claims 1 and 15 is being presented below.
Claims 1, 13 and 23:
Step 1 Analysis:
Claim 1 of the instant application is direct to product.
Claim 13 of the instant application is direct to system.
Claim 23 of the instant application is direct to method.
Thus, they are statutory categories.
Step 2 Analysis:
Claim 1 recites:
(a) provide source data to a cognitive artificial intelligence (AI) layer as input, the cognitive Al layer configured to process the input;
(b) receive an output from the cognitive Al layer, the output from the cognitive Al layer comprising automatically generated code;
(c) implement the automatically generated code in a robotic process automation (RPA) workflow,
(d) the output from the cognitive Al layer comprises at least one of one or more expressions and one or more code snippets,
(e) the implementing of the automatically generated code comprises updating the RPA workflow by adding the at least one of the one or more expressions and the one or more code snippets to one or more respective activities of the RPA workflow,
(f) the one or more expressions are syntactic entities in a programming language that can be evaluated to determine a value thereof and are a combination of one or more constants, variables, functions, and operators that a programming language interprets according to particular rules of precedence and of association and computes to produce another value,
(g) the one or more code snippets are regions of reusable source code, machine code, or text.
Step 2A -- Prong 1:
The claim 1 recites the limitation of:
(f) the one or more expressions are syntactic entities in a programming language that can be evaluated to determine a value thereof and are a combination of one or more constants, variables, functions, and operators that a programming language interprets according to particular rules of precedence and of association and computes to produce another value,
Limitation (f) limitation that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “evaluated” and “determine” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2 Analysis:
Claims 13 and 23 recite:
(a) provide source data to a cognitive artificial intelligence (AI) layer as input, the cognitive Al layer configured to process the input;
(b) receive an output from the cognitive Al layer, the output from the cognitive Al layer comprising automatically generated code;
(c) implement the automatically generated code in a robotic process automation (RPA) workflow,
(d) the source data comprises at least one of natural language text, a recording of a user's speech, a video recording of a user's actions on a computing system, a document, pseudocode for a desired task, a drawing of a desired user interface or form, a drawing of a process, process documentation or a process description, and an RPA workflow and the output from the cognitive Al layer comprises at least one of one or more expressions and one or more code snippets,
(e) the implementing of the automatically generated code comprises updating the RPA workflow by adding the at least one of the one or more expressions and the one or more code snippets to one or more respective activities of the RPA workflow,
(f) the one or more expressions are syntactic entities in a programming language that can be evaluated to determine a value thereof and are a combination of one or more constants, variables, functions, and operators that a programming language interprets according to particular rules of precedence and of association and computes to produce another value,
(g) the one or more code snippets are regions of reusable source code, machine code, or text.
Step 2A -- Prong 1:
The claims 13 and 23 recite the limitations of:
(f) the one or more expressions are syntactic entities in a programming language that can be evaluated to determine a value thereof and are a combination of one or more constants, variables, functions, and operators that a programming language interprets according to particular rules of precedence and of association and computes to produce another value,
Limitation (f) limitation that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “evaluated” and “determine” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 1 recites the additional limitations of “A non-transitory computer-readable medium storing a computer program, the computer program configured to cause at least one processor”. The limitation of “A non-transitory computer-readable medium”, “at least one processor” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. The limitation “a cognitive artificial intelligence (AI) layer” recited as tools perform abstract idea. The claim 13 recites the additional limitations of “One or more computing systems”, “memory” and “at least one processor”. The limitation of “One or more computing systems”, “memory” and “at least one processor” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. The limitation “a cognitive artificial intelligence (AI) layer” recited as tools perform abstract idea. The claim 23 recites the additional limitations of “A computer-implemented”, “a computing system”. The limitation of “A computer-implemented”, “a computing system” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. The limitation “a cognitive artificial intelligence (AI) layer” recited as tools perform abstract idea. Additionally, limitations (a), (c), (d) and (g) are merely insignificant extra solution activity of gathering data and outputting data. Limitations (b) and (e) perform as well-understood, routine and conventional activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
As explained with respect to Step 2A Prong Two, the additional elements in the claim are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The same analysis applies here in 2B, i.e., simply adding extra-solution activity or well-understood, routine and conventional activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B since the courts have identified functions such as gathering, displaying, updating, transmitting/receiving and storing/uploading data as well- understood, routine, conventional activity. See MPEP 2106.05(d) and See MPEP 2106.05(g) . Therefore, claims are ineligible.
Dependent claims
Additionally, claim 2 recites “wherein the source data comprises at least one of natural language text, a recording of a user's speech, a video recording of a user's actions on a computing system, a document, pseudocode for a task, a drawing of a more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 2 is ineligible.
Additionally, claims 3 and 14 recite “wherein the computer program comprises the cognitive Al layer” is merely insignificant extra solution activity of collecting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 3 and 14 are ineligible.
Additionally, claim 4 recites “wherein the computer program is an RPA designer application” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 4 is ineligible.
Additionally, claims 5 and 15 recite “wherein the output from the cognitive Al layer comprises the RPA workflow” is merely insignificant extra solution activity of outputting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 5 and 15 are ineligible.
Additionally, claims 7, 17 and 25 recite “wherein the output from the cognitive Al layer comprises one or more configuration changes for the RPA workflow and the implementing of the automatically generated code comprises updating the RPA workflow by changing one or more respective activities of the RPA workflow to include the one or more configuration changes” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 7, 17 and 25 are ineligible.
Additionally, claims 9, 19 and 27 recite “wherein the computer program is further configured to cause the at least one processor to: prompt a user regarding the output from the cognitive Al layer; receive an indication that the user does not accept the output from the cognitive Al layer; receive one or more modifications to the RPA workflow from the user; and modify the RPA workflow to include the output from the cognitive Al layer with the modifications from the user” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 9, 19 and 27 are ineligible.
Additionally, claims 10, 20 and 28 recite “wherein the computer program is further configured to cause the at least one processor to: prompt a user regarding the output from the cognitive Al layer; receive an indication that the user does not accept the output from the cognitive Al layer; and fail the automatic code generation and send data pertaining to the failure for retraining one or more Al /machine learning (ML) models of the cognitive Al layer” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 10 and 20 ineligible.
Additionally, claims 11, 21 and 29 recite “wherein the cognitive Al layer comprises: a generative AI model configured to generate code, provide sematic associations between text on a screen, suggest expressions and code snippets for activities of the RPA workflow, generate a form from a natural language description or a drawing, generate a process from a drawing, or any combination thereof; and one or more other AI_/ machine learning (ML) models configured to use output from the generative AI model to provide automatic code generation functionality” is merely insignificant extra solution activity of outputting data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 11 and 21 are ineligible.
Additionally, claims 12, 22 and 30 recites “wherein the computer program is further configured to cause the at least one processor to: add security and/or compliance rules to the RPA workflow for compliance with laws and/or policies” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more that the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claims 12, 22 and 30 are ineligible.
Allowable Subject Matter
9. Claims 1, 13 and 23 would be allowable if rewritten or amended to overcome the claim objection, the rejection(s) under 35 U.S.C 112(b) and the rejections under 101, set for the in this office action.
Claims 2-5, 7-12, 14-15, 17-22 and 25-30 would be allowable if rewritten or amended to overcome the claim objection, the rejection(s) under 35 U.S.C 112(b) and the rejections under 101, set for the in this office action.
The cited references:
Touati et al. (US Pub. No. 2023/0393832 A1 – herein after Touati). Touati discloses the cognitive model processes the extracted code information to evaluate the snippet – See paragraph [0091-0092]. generating one or more hierarchical structure of conditional statements based on a control flow analysis and (ii) performing a data flow-based analysis in the hierarchical structure of conditional statements. The hierarchical structure defines parent or entry conditional statements and child conditional statements dependent on the parent or entry conditional statements – See paragraph [0144]). Automatically updating workflows, code, software configurations, and automatically updating various services – See paragraph [0207], to drive the full functionality of object-oriented operations, the general purpose utility classes must be added – See paragraph [0124]. The method performs necessary updates on any software system that may be updated or otherwise altered or changed as a result of the determination that one or more of the connected software systems has been removed, updated or otherwise changed, or that one or more new software system has been added to the integrated software systems – See paragraphs [0169-0170]). Touati does not discloses the one or more expressions are syntactic entities in a programming language that can be evaluated to determine a value thereof and are a combination of one or more constants, variables, functions, and operators that a programming language interprets according to particular rules of precedence and of association and computes to produce another value, and the one or more code snippets are regions of reusable source code, machine code, or text as recited in claims 1, 13 and 23.
Joshi et al. (US Patent No. 11954458 B2 – herein after Joshi). Joshi discloses provides a method of translating decision or other business logic to facilitate migration between BRM (business rules management) systems. The method includes a first step of receiving, at a rules translation system, a first code in a first file format representing a first business rule. A second step includes classifying, at the rules translation system and using decision or other business logic, the first code as falling under a first rule category type, and a third step includes extracting, at the rules translation system, a first text from the first code – See col. 2, lines 6-15. the decision logic framework can allow business decision makers to evaluate an operation of different decision logic management systems to allow the decision makers to select a BRMS that best suits the needs of the organization (e.g., ease of use, flexibility, cost, business infrastructure requirements, and the like). In other words, because the proposed systems can convert a business rule from any format to a standard format, when a BRMS is to be updated or replaced, the business can elect to switch vendors supporting the underlying BRMS without impacting the business units' operation – See col. 7, lines 26-35. Joshi does not disclose the claimed invention.
Dalli et al. (US Pub. No. 2022/0398460 A1 – herein after Dalli). Dalli discloses an embodiment may evaluate rules in the form of IF-conditions in order to activate one or more partitions. The output of Ci(X) may be binary. It may be noted that the partitions may be static or dynamic, and may be discovered either through an external partitioning process such as AutoXAI or through a connected neural network. It may also be noted that INNs may also function with only one partition. For example, for all values of X, C.sub.i(X) may always be one (1). This is equivalent to having zero partitions. In this exemplary case, there is no need to apply a partitioning method to find suitable partitions – See paragraph [0078]. The coefficients θ of an explainable architecture x within a behavioral model BM may be modified to enforce specific rules. Rule enforcement may also be activated by a conditional constraint located in BM.sub.c, where BM.sub.c∈{c.sub.1, . . . , c.sub.n}. Activation may fire an event e, where event e may activate a trigger t where such rule enforcement is passed using a feedback action to an explainable model or causal architecture – See paragraph [0080]. Dalli does not disclose the claimed invention.
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dalli et al. (US Pub. No. 2022/0067520 A1 – herein after Dalli). Dalli discloses verifying the behavior of machine learning models to assure the safety of such systems meets the required specifications and adapt such architecture according to the execution sequences of the behavioral model. An embodiment may enable conditions in a behavioral model to be integrated in the execution sequence of behavioral modeling in order to monitor the probability likelihoods of certain paths in a system – See Abstract and specification for more details.
Bakshi et al. (US Pub. No. 2022/0107817 A1) discloses executing an application on a computing system may be determined by computational optimization based on configuration parameter values and/or monitored performance metrics associated with executing the application on the computing system. Configuration parameter values associated with executing the application on the computing system may be updated based on monitored performance metrics associated with executing the application – See Abstract and specification for more details.
Mathur et al. (US Pub. No. 2021/0224644 A1) discloses emphasizes incorporating the strengths of artificial intelligence (AI) in the automation space for software deployments and testing. In some instances, combining techniques of robotic process automation (RPA) along with machine learning (ML) techniques and cognitive intelligence provides a suitable approach for executions in simplified manner, with fewer resources spend and encompassing a large scale on-demand and cloud enterprise applications and platform environments – See paragraphs [0005-0006].
11. 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.
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/MONGBAO NGUYEN/ Examiner, Art Unit 2192
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