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 .
Claim(s) 1-25 is/are pending and has/have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/28/2025 and 04/29/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code (see [0115]). Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Appropriate correction is required.
Claim Objections
Claims 18 and 23 are objected to because of the following informalities: the claims recite “a low-level planner” in the last respective limitations. The Examiner suggests amending the claim(s) to recite –the low-level planner-- in order to maintain clear antecedent basis. 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.
Claims 18-25 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 18 and 23 recite “the validator”. There is insufficient antecedent basis for this limitation in the claims.
Claims 19-22, 24, and 25, are rejected as being dependent upon a rejected base claim.
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.
Claims 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim(s) 1, 11, 15, 18, and 23, the limitation(s) of ((claims 1, 11, and 15) parsing, naming, and combining), (claims 18 and 23) tracing), generating a sequence of actions, parsing the sequence, generating feedback, generating a validated sequence, and executing the validated sequence, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind and/or with pen and paper but for the recitation of generic computer components. More specifically, the mental process of looking at a series of steps to accomplish a task, naming specific components of the steps, organizing the components, walking through the organization based on a requested task to identify specific steps for the task, writing down the required steps, reviewing the steps to identify any errors in the way they were written and determining how to fix the errors found, fixing the error, and following the final set of steps to perform the task. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or with pen and paper but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claim(s) recite(s) an abstract idea.
This judicial exception is not integrated into a practical application because the recitation of a computer program product, storage media, and processor in claims 11 and 23, and a system, processor, and storage media in claim 15, reads to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using [0058-74] in the specification. The recitation of execution within one or more web applications does not, by itself, integrate the abstract idea into a practical application or supply an inventive concept, as it is a use environment, not a technical improvement. The language functions as a field-of-use limitation or an environmental context, indicating where the steps happen, but not how the computer is improved. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) is/are directed to an abstract idea.
The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to parse, name, combine, trace, generate, parse, generate, generate, and execute, amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
With respect to claim(s) 2, 3, 9, 20, and 21, the claim(s) recite(s) characteristics of the UI flow, which reads on a human evaluating steps that have specific characteristics. No additional limitations are present.
With respect to claim(s) 4, the claim(s) recite(s) generating feedback regarding errors during operation, which reads on a human attempting to perform the steps, discovering an error in the process, and identifying a way to fix the error. No additional limitations are present.
With respect to claim(s) 5, 16, and 22, the claim(s) recite(s) determining meaningful names, which reads on a human using an understanding of the semantics of natural language to determine the names for the specific components of the steps. No additional limitations are present.
With respect to claim(s) 6, 14, and 17, the claim(s) recite(s) tracing a path, which reads on a human walking through the organization based on a requested task to identify specific steps for the task. No additional limitations are present.
With respect to claim(s) 7, the claim(s) recite(s) the utterance comprises natural language input, which reads on a human hearing a task request spoken in a human language by another person. No additional limitations are present.
With respect to claim(s) 8 and 19, the claim(s) recite(s) the agent/planner comprises a large language model, which reads on a human using a series of learned rules regarding human language for how to perform the process. No additional limitations are present.
With respect to claim(s) 10, the claim(s) recite(s) populated by demonstration, which reads on a human watching another person perform a task and recording the steps the other person took. No additional limitations are present.
With respect to claim(s) 12, 13, 24, and 25, the claim(s) recite(s) transferring/downloading instructions and (claims 13 and 25) metering and generating an invoice, which reads on a human retrieving written instructions, timing how long they take using the steps within the instructions, and calculating a bill for how long they used the instructions. The recitation of storage devices and processing systems read to generic computer components as per [0058-74] in the specification.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
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.
Claim(s) 1-7, 9, 11, 12, 14-18, 20-24, is/are rejected under 35 U.S.C. 103 as being unpatentable over Maseedu et al. (U.S. PG Pub No. 2020/0117584), hereinafter Maseedu, in view of Das et al. (U.S. PG Pub No. 2022/0121554), hereinafter Das.
Regarding claims 1, 11, and 15, Maseedu teaches
(claim 1) A computer-implemented method comprising (method [0015]):
(claim 11) A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising (the computer system includes a CPU for executing computer programs, and a memory for storing programs and data [0020]):
(claim 15) A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations comprising (the computer system includes a CPU for executing computer programs, and a memory for storing programs and data [0020]):
parsing a UI flow stored in a UI flow database to identify states, actions, and parameters of the actions (a data analysis engine evaluates data sets to compute or re-compute values for one or more parameters used by the other engines, i.e. parsing…to identify, such as the actionable steps, i.e. actions, of entities in different hierarchical levels including procedure groups and procedures stored in databases, i.e. parsing a UI flow stored in a UI flow database, such as data entities, contextual parameters, including a status like “on” or “off”, i.e. states, or other parameters such as weights, biases, adjustment factors, and scores, i.e. parameters of the actions [0024],[0031],[0039-42],[0045-7]);
naming each of the identified states, actions, and parameters of the actions according to a semantically discrete functionality of each (each of the procedure groups and procedures has a description, i.e. naming, that can be matched to a natural language command, i.e. according to a semantically discrete functionality of each, which can also include matching required parameter values for the procedures, i.e. each of the identified states, actions, and parameters of the actions [0031],[0037],[0039-42],[0045-7]);
combining the named states, actions, and parameters of the actions into a semantic user interface state-action space (UI SAS) (procedural steps are organized into procedures, which are further organized into procedure groups, i.e. combining…into a semantic user interface state-action space, that can be identified as having values, i.e. named states, actions, and parameters of the actions, matching a command based on matching the values of constructs of a command Fig. 3B,[0031],[0037],[0039-42],[0045-7]);
generating, via a planning agent, a sequence of actions from the semantic UI state-action space corresponding to an utterance from a user, wherein the utterance comprises an input intended to convey a requested task to be executed within one or more web applications (a spoken command can be received for performing a specific programming, such as “Validate connectivity with Evolved Node B (eNB) where software has been updated”, i.e. an utterance from a user…comprises an input intended to convey a requested task to be executed, where the command is processed to identify parameters associated to the command, and the system can use the results from the NLP processor to identify definitions, procedure groups, procedures, and procedural steps associated with the command, i.e. generating…a sequence of actions from the semantic UI state-action space corresponding to an utterance from a user, where a procedures modeling engine can identify optimum definitions, procedure groups, and procedures associated with the received natural language command, i.e. planning agent, where the command may be related to a procedure performed for the browser, such as chrome, firefox, IE, or Safari, i.e. executed within one or more web applications [0016],[0027-9],[0037-42],[0045]);
parsing, via a syntactic error parser, the action sequence for … errors (a validation engine, i.e. syntactic error parser, can identify syntax errors in the commands, and also examines the list of parameters required by the identified procedures, i.e. parsing…the action sequence, to determine if there is additional information needed to fulfill the list of parameters required by the identified procedures, i.e. parsing…the action sequence for errors [0042-3]);
generating, via the syntactic error parser, a feedback …regarding the … errors (the validation engine, i.e. error parser, can interact with the user by querying for missing information, i.e. generating…a feedback regarding the errors [0042-3]);
generating, via the planning agent, a validated action sequence based on the feedback (when the required information is present, such as the user providing the missing additional information after being queried, i.e. generating…a validated action sequence based on the feedback, the appropriate parameters are passed by the system to the procedure identified by the procedures modeling engine, i.e. via the planning agent [0039-43],[0045]); and
executing, via an execution agent, the validated action sequence to perform the requested task within the one or more web applications (the appropriate parameters are passed to the identified procedure, i.e. validated action sequence, where the system, i.e. via an execution agent, executes the steps of the identified procedure, i.e. executing…the validated action sequence, which may be a procedure within the browser, i.e. within the one or more web applications [0037-43],[0045]).
While Maseedu provides identifying syntax errors in a command, Maseedu does not specifically teach identifying syntax errors in an action sequence, and thus does not teach
parsing, via a syntactic error parser, the action sequence for syntax errors;
generating, via the syntactic error parser, a feedback to the planning agent regarding the syntax errors.
Das, however, teaches parsing, via a syntactic error parser, the action sequence for syntax errors (a parsing module, i.e. via a syntactic error parser, receives data segments that are code, i.e. the action sequence, and performs a syntax analysis to determine a syntax error, i.e. parsing…for syntax errors [0026-9]);
generating, via the syntactic error parser, a feedback to the planning agent regarding the syntax errors (the parsing module, i.e. via the syntactic error parser, forwards the determined syntax error, i.e. generating…a feedback…regarding the syntax errors, to the neural engine, i.e. planning agent, to be automatically rectified, i.e. generating a validated action sequence [0026-9],[0031]).
Maseedu and Das are analogous art because they are from a similar field of endeavor in automatically evaluating code. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the identifying syntax errors in a command teachings of Maseedu with the identification and automatic rectification of syntax errors in code as taught by Das. It would have been obvious to combine the references to enable parsing a syntax error to assist developers in continuous build and continuous integration pipelines, such as when an error is difficult to identify or resolve (Das [0002-4],[0026]).
Regarding claims 18 and 23, Maseedu teaches
(claim 18) A computer-implemented method comprising (method [0015]):
(claim 23) A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising (the computer system includes a CPU for executing computer programs, and a memory for storing programs and data [0020]):
tracing, via a high-level planner, a route through a user interface state-action space (UI SAS) corresponding to a user-provided utterance that conveys a requested task to be executed (a spoken command can be received for performing a specific programming, such as “Validate connectivity with Evolved Node B (eNB) where software has been updated”, i.e. a user-provided utterance that conveys a requested task to be executed, where the command is processed to identify parameters associated to the command, and the system can use the results from the NLP processor to identify definitions, procedure groups, procedures, and procedural steps associated with the command, where a procedures modeling engine, i.e. via a high-level planner, can identify optimum definitions, procedure groups, and procedures associated with the received natural language command, i.e. tracing…a route through a user interface state-action space (UI SAS) [0016],[0027-9],[0037-42],[0045]),
wherein the UI SAS comprises a model representing a plurality of relevant states of one or more web applications and a corresponding plurality of actions that can be made from the plurality of states (a data analysis engine evaluates data sets to compute or re-compute values for one or more parameters used by the other engines, such as the actionable steps, i.e. a corresponding plurality of actions, of entities in different hierarchical levels including procedure groups and procedures stored in databases, i.e. the UI SAS comprises a model, such as data entities, contextual parameters, including a status like “on” or “off”, i.e. states, or other parameters such as weights, biases, adjustment factors, and scores, i.e. representing a plurality of relevant states of one or more … applications…actions that can be made from the plurality of states, and where the procedures may be performed for a browser, i.e. one or more web applications [0024],[0031],[0037-42],[0045-7]);
generating, via the high-level planner, an action sequence for a low-level planner comprising a sequence of states of the plurality of states and a corresponding sequence of actions of the plurality of actions (a spoken command can be received for performing a specific programming, where the command is processed to identify parameters associated to the command, where a procedures modeling engine, i.e. via the high-level planner, can identify optimum definitions, procedure groups, and procedures associated with the received natural language command, i.e. generating an action sequence…comprising…a corresponding sequence of actions of the plurality of actions, for further execution by the automation system, i.e. low-level planner, where the procedures have required parameters, such as “ON” or “OFF” that are also passed to the identified procedure, i.e. comprising a sequence of states of the plurality of states [0016],[0027-9],[0037-42],[0045])
generating, via the validator, a validated action sequence by parsing the action sequence for … discrepancies within the sequence of states and the corresponding sequence of actions (a validation engine, i.e. syntactic error parser, can identify syntax errors in the commands, and also examines the list of parameters required by the identified procedures, i.e. parsing the action sequence for…discrepancies, to determine if there is additional information needed to fulfill the list of parameters required by the identified procedures, such as the parameters “ON” or “OFF”, i.e. discrepancies within the sequence of states and the corresponding sequence of actions, where the validation engine can interact with the user by querying for missing information, and when the required information is present, such as the user providing the missing additional information after being queried, i.e. generating…a validated action sequence, the appropriate parameters are passed by the system to the procedure identified by the procedures modeling engine [0039-43],[0045]); and
executing, via a low-level planner, the validated action sequence to perform the requested task within one or more web applications (the appropriate parameters are passed to the identified procedure, i.e. validated action sequence, where the system, i.e. via a low-level planner, executes the steps of the identified procedure, i.e. executing…the validated action sequence, which may be a procedure within the browser, i.e. within the one or more web applications [0037-43],[0045]).
While Maseedu provides identifying syntax errors in a command, Maseedu does not specifically teach identifying syntax errors in an action sequence, and thus does not teach
generating, via the validator, a validated action sequence by parsing the action sequence for syntactic discrepancies within the sequence of states and the corresponding sequence of actions.
Das, however, teaches generating, via the validator, a validated action sequence by parsing the action sequence for syntactic discrepancies within the sequence of states and the corresponding sequence of actions (a parsing module, i.e. via a validator, receives data segments that are code, i.e. action sequence, and performs a syntax analysis to determine a syntax error, i.e. parsing the action sequence for syntactic discrepancies, where the prediction module of the neural engine may predict a correct set of action plans, i.e. discrepancies within the corresponding sequence of actions, and where an error may cause a compiler to land in an unknown state, making it difficult to understand the next state for the compiler, i.e. discrepancies within the sequence of states [0003],[0026-9],[0031],[0034]).
Maseedu and Das are analogous art because they are from a similar field of endeavor in automatically evaluating code. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the identifying syntax errors in a command teachings of Maseedu with the identification and automatic rectification of syntax errors in code as taught by Das. It would have been obvious to combine the references to enable parsing a syntax error to assist developers in continuous build and continuous integration pipelines, such as when an error is difficult to identify or resolve (Das [0002-4],[0026]).
Regarding claim 2, Maseedu in view of Das teaches claim 1, and Maseedu further teaches
the UI flow corresponds to a task to be executed within the one or more web applications (the procedures defined using procedural steps, i.e. UI flow, area are a series of actions to be performed on a specific resource, such as a browser, i.e. corresponds to a task to be executed within the one or more web applications [0037-41]).
Regarding claims 3, 20, and 21, Maseedu in view of Das teaches claims 1, 18, and 20, and Maseedu further teaches
(claim 3) each of the actions comprises a plurality of primitive UI interactions that complete a semantically discrete purpose within the task, terminating in a boundary UI event/(claim 20) the plurality of relevant states and the corresponding plurality of actions within the UI SAS are structured as a plurality of subtasks based on a semantically discrete purpose of the plurality of relevant states and the corresponding plurality of actions/(claim 21) each subtask in the plurality of subtasks terminates in a boundary UI event (each procedure is defined using procedural steps, i.e. each of the actions comprises a plurality of primitive UI interactions/the plurality of relevant states and the corresponding plurality of actions within the UI SAS are structured as a plurality of subtasks, where each step has a specific intended result, i.e. interactions that complete a semantically discrete purpose within the task (of the plurality of relevant states and the corresponding plurality of actions), and there is a final step in each procedure, and each step has an intended result, i.e. terminating in a boundary UI event [0031-42]).
Regarding claim 4, Maseedu in view of Das teaches claim 1, and Das further teaches
generating, via the execution agent, the feedback to the planning agent regarding execution errors generated in operation (the data may be forwarded to a compiler for executing the data segments, where the errors associated with the data segments may be detected by the compiler, i.e. via the execution agent…regarding execution errors generated in operation, where the errors are sent to a data repository for a deep learning model to predict a solution, i.e. generating the feedback to the planning agent [0044-5]).
Where the motivation to combine is the same as previously presented.
Regarding claims 5, 16, and 22, Maseedu in view of Das teaches claims 1, 15, and 20, and Maseedu further teaches
(claims 5 and 16) determining a meaningful names for the identified states, actions, and parameters of the actions via a semantic analysis of a semantically discrete purpose of blocks of primitive UI interactions/(claim 22) each subtask in the plurality of subtasks are assigned meaningful names determined via a semantic analysis of the semantically discrete purpose of the plurality of states and the corresponding plurality of actions (each of the procedure groups, procedures, steps, and variables has a description and/or name, i.e. meaningful names, that can be matched to a natural language command, i.e. semantically discrete purpose of blocks of primitive UI interactions/the semantically discrete purpose of the plurality of states and the corresponding plurality of actions, through NLP processing of the commands to identify subjects, predicates, actions, context, and conditions, where the procedures modeling engine identifies optimum definitions and values, and data analysis engine provides new definitions and values, where definitions can be determined by a trained NLP engine Figs. 6, 7A,[0027][0031],[0037-42],[0044-7]).
Regarding claims 6, 14, and 17, Maseedu in view of Das teaches claims 1, 11, and 15, and Maseedu further teaches
tracing, via the planning agent, a path through the semantic user interface state-action space (UI SAS) (a spoken command can be received for performing a specific programming, such as “Validate connectivity with Evolved Node B (eNB) where software has been updated”, where the command is processed to identify parameters associated to the command, and the system can use the results from the NLP processor to identify definitions, procedure groups, procedures, and procedural steps associated with the command, where a procedures modeling engine, i.e. via the planning agent, can identify optimum definitions, procedure groups, and procedures associated with the received natural language command, i.e. tracing…a path through the semantic user interface state-action space (UI SAS) [0016],[0027-9],[0037-42],[0045]).
Regarding claim 7, Maseedu in view of Das teaches claim 1, and Maseedu further teaches
the utterance from the user comprises a natural language input (a spoken command can be received for performing a specific programming, such as “Validate connectivity with Evolved Node B (eNB) where software has been updated”, i.e. natural language input [0016],[0027-9],[0039]).
Regarding claim 9, Maseedu in view of Das teaches claim 1, and Maseedu further teaches
the flow database is populated by UI flows consisting of sequences of primitive UI interactions that implement end to end an intent of a user utterance (procedure groups, i.e. UI flows, and procedures with their procedure steps, i.e. consisting of sequences of primitive UI interactions, are stored in a database, i.e. flow database, where each procedure step goes through a series of actions to perform the command spoken by the user, i.e. that implement end to end an intent of a user utterance [0016],[0024],[0037-42]).
Regarding claims 12 and 24, Maseedu in view of Das teaches claims 11 and 23, and
the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system (the computer system includes a CPU for executing computer programs, and a memory for storing programs and data, i.e. the stored program instructions are stored in a computer readable storage device in a data processing system, where the system can include one or more networked devices that send and receive data and have access to one or more databases, including receiving and coordinating fulfillment of client requests, i.e. the stored program instructions are transferred over a network from a remote data processing system [0020-5]).
Claim(s) 8 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maseedu, in view of Das, and further in view of Jain et al. (U.S. PG Pub No. 2025/0131185), hereinafter Jain.
Regarding claims 8 and 19, Maseedu in view of Das teaches claims 1 and 18.
While Maseedu in view of Das provides using machine learning models, Maseedu in view of Das does not specifically teach the use of a large language model, and thus does not teach
((claim 8) the planning agent/(claim 19) high-level planner) comprises a large language model.
Jain, however, teaches ((claim 8) the planning agent/(claim 19) high-level planner) comprises a large language model (an LLM is used to generate a navigation workflow comprising a sequence of actions for a task-request from a user Figs. 2 and 3,[0061]).
Maseedu, Das, and Jain are analogous art because they are from a similar field of endeavor in automatically evaluating code. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the using machine learning models teachings of Maseedu, as modified by Das, with the use of an LLM to generate scripts for navigation workflows as taught by Jain. It would have been obvious to combine the references to enable the automation of repetitive processes to enhance operational efficiency, minimize costs and errors, and improve overall customer experience (Jain [0031]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maseedu, in view of Das, and further in view of Barello et al. (U.S. PG Pub No. 2026/0104933), hereinafter Barello.
Regarding claim 10, Maseedu in view of Das teaches claim 1.
While Maseedu in view of Das provides identifying procedures that perform actions in response to a user command, Maseedu in view of Das does not specifically teach capturing UI events to populate a database, and thus does not teach
the flow database is populated via program by demonstration to capture UI events.
Barello, however, teaches the flow database is populated via program by demonstration to capture UI events (Task capture automatically documents attended processes as users work, i.e. program by demonstration to capture UI events, such as recording user actions and automatically generating a comprehensive workflow diagram including the details about each step, i.e. the flow database is populated [0041]).
Maseedu, Das, and Barello are analogous art because they are from a similar field of endeavor in automatically evaluating code. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the identifying procedures that perform actions in response to a user command teachings of Maseedu, as modified by Das, with the capture of user actions to generate a workflow diagram as taught by Barello. It would have been obvious to combine the references to simplify the requirements gathering process for both subject matter experts explaining a process and Center of Excellence (CoE) members providing production-grade automations (Barello [0041]).
Claim(s) 13 and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Maseedu, in view of Das, and further in view of Seelemann, II, et al. (U.S. PG Pub No. 2021/0326045), hereinafter Seelemann, II.
Regarding claims 13 and 25, Maseedu in view of Das teaches claims 11 and 23.
While Maseedu in view of Das provides networked devices that can send and receive information, Maseedu in view of Das does not specifically teach the downloading of instructions in response to a request, metering use of the program, and generating an invoice, and thus does not teach
the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising:
program instructions to meter use of the program instructions associated with the request; and
program instructions to generate an invoice based on the metered use.
Seelemann, II, however, teaches the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system (the instructions can be downloaded from a storage medium to an external computer via a network for use on a remote computer [0110-1], claim 12), further comprising:
program instructions to meter use of the program instructions associated with the request (use of the systems can be metered [0110-1],[0116], claim 12); and
program instructions to generate an invoice based on the metered use (billing for use of the systems can be performed after metering use and allocating expenses [0110-1],[0116], claim 12).
Maseedu, Das, and Seelemann, II, are analogous art because they are from a similar field of endeavor in the automatic handling of data on distributed systems. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the networked devices that can send and receive information teachings of Maseedu, as modified by Das, with the downloading of instructions and the metering of and billing for usage of the systems as taught by Seelemann, II. It would have been obvious to combine the references to enable delivery of the processes as part of a service engagement with a client (Seelemann, II [0116]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICOLE A K SCHMIEDER whose telephone number is (571)270-1474. The examiner can normally be reached 8:00 - 5:00 M-F.
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/NICOLE A K SCHMIEDER/Primary Examiner, Art Unit 2659
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