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 .
Status of Claims
This is a first action on the merits in response to the application filed 20 March 2025. Claims 1-20 are pending and have been examined.
Priority
Receipt is acknowledged of certified copies of KR10-2024-0069627 filed 28 May 2024 papers required by 37 CFR 1.55.
Acknowledgment is made of applicant's claim for foreign priority based on an application KR10-2024-0043216 filed on 29 March 2024. It is noted, however, that applicant has not filed a certified copy of the application as required by 37 CFR 1.55. Further, as noted in the 29 August 2025 communication form the Office, the retrieval request under the priority document exchange program was unsuccessful. As a result priority based on this application is not entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 20 March 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of data gathering, applying business rules and data analysis, and generating an output, without significantly more. Independent claim 1 recites a process, independent claim 11 recites an apparatus, and independent claim 20 recites a product for generating a business process. Independent claims 1, 11. And 20 recite substantially similar limitations.
Taking independent claim 1 as representative, claim 1 recites the following limitations:
configuring a first prompt, based on input text comprising a description of the target business;
producing types and connection sequences of one or more unit elements constituting the business process using an artificial intelligence model, based on the first prompt; and
generating the business process corresponding to the target business, based on the types and connection sequences of the one or more unit elements.
Under Step 1, independent claims 1, 11, and 20 recite at least one step or act, including configuring a first prompt. Thus, the claims fall within one of the statutory categories of invention.
Under Step 2A Prong One, the recited limitations for configuring a first prompt, producing types and connection sequences of one or more unit elements, and generating the business process corresponding to the target business, illustrates a process that under the broadest reasonable interpretation cover performance of the limitations in the mind because the steps could be practically performed in the human mind, or by a human using pen and paper to design a business process for implementation or consideration by others. Per the Specification at [para. 0006-0007] the claimed inventive concept provides “a method that allows workers to more efficiently design the business process they want, and furthermore, to easily modify pre-designed business processes to generate business processes suitable for them.” Because the claim limitations are directed to a process for designing a business process, including modifying a pre-defined business process template, that could be performed by a worker manually or mentally with less efficiency, the claims fall within the mental processes grouping of abstract concepts. Therefore, the limitations recite an abstract idea of data gathering (text/user input), data analysis (configuring a first prompt, producing types and connection sequences), and outputting a result (generating the business process), and fall into the mental processes grouping of abstract concepts.
Per the Specification at paragraph [0072; Fig. 3] the inventive concept includes “a business process for performing a vacation recommendation,” per MPEP 2106.04(a)(2)(II) this concept falls under certain methods of organizing human activity because it involves managing personal behavior, and relationships or interactions between people. Therefore, the claims recite an abstract idea that falls under the mental processes and certain methods of organizing human activity groupings of abstract concepts.
Under Step 2A Prong Two the judicial exception of claim 1 is not integrated into a practical application. In particular the claims recite a computing device, and an artificial intelligence model for performing the recited steps. These elements are recited at a high level of generality and amount to no more than instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s Specification a [para. 0152] “The processor 10 may also be called a controller, a micro-controller, a micro-processor, a micro-computer, or the like.” Additionally, the claimed artificial intelligence model is broadly and generically claimed and described in the Specification, with an exemplary embodiment of a large language model (“such as a large language model (LLM)”; See Spec. at [0090, 0094]). The Specification does not provide additional details about the computer system that would distinguish it from any generic processing devices that communicate with one another in a network environment. Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h). Performance by computer of operations that previously were performed manually, albeit less efficiently, does not convert an abstract idea into eligible subject matter. The claimed computing device and artificial intelligence model are used a tools to implement the recited abstract idea, and not a technological improvement. 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.
Under Step 2B, the claim does 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 elements of a processor and artificial intelligence model amount to no more than mere instructions to apply the exception using a generic computer component or data processing technology, which cannot provide an inventive concept. See MPEP 2106.05.
Dependent claims 2-10 and 12-19 include the abstract ideas of the independent claims. The limitations of the dependent claims merely narrow the mental process by describing additional data analysis and processing steps. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly, independent claims 11 and 20 and the claims that depend therefrom are rejected as ineligible for patenting under 35 U.S.C. 101 based upon the same analysis applied to claim 1 above. Therefore claims 1 -20 are ineligible under 35 U.S.C. 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kishan et al. (US 2026/0099792) in view of Cuomo et al. (US 2025/0131187).
Regarding Claim 1, Kishan et al. discloses a method for generating a business process configured to include one or more unit elements to perform a given target business using a computing device, the method comprising: (During the design phase, the administrator 104 can utilize a workflow compiler 136 of the workflow assistant 130 to manage compilation of workflows provided by a workflow provider 126. As described above, a workflow can include one or more processes (e.g., respectively handled by one or more participants). Kishan et al. [para. 0021-0026]. … The ERP system 100 and any of the other systems described herein can be implemented in conjunction with any of the hardware components described herein, such as the computing systems described below (e.g., processing units, memory, and the like). … The method 200 can be performed, e.g., by the administrator 104 using the workflow assistant 130 and the workflow engine 140 of FIG. 1. Kishan et al. [para. 0034-0037; Fig. 1-3]);
configuring a first prompt, based on input text comprising a description of the target business; (A generative AI hub 120 can be used provide generative AI capabilities to the ERP system 100. Kishan et al. [para. 0020]. … the pre-processor 144 can prompt the LLM 124 with a prompt including a process object representing a selected process in the workflow. The prompt can instruct the LLM 124 to generate a response that includes a text description of the selected process based on the information contained in the process object. … The autonomous agent 160 can determine which process (also referred to as a “target process”) in the workflow needs to be involved to generate a proper response for the received user query. Kishan et al. [para. 0024-0031]);
producing types and connection sequences of one or more unit elements constituting the business process using an artificial intelligence model, based on the first prompt; (Prompts in LLMs can be input instructions that guide model behavior. Prompts can be textual cues, questions, or statements that users provide to elicit desired responses from the LLMs. Kishan et al. [para. 0058] … autonomous agent 160 can determine the selected task by prompting the LLM 124 with both the user query and the context prompt 150 (text description) corresponding to the target process. Kishan et al. [para. 0032]. … The flow of a process in BPMN is depicted through nodes and links between them. Nodes represent the various elements in the process, such as events, activities, and gateways, while links (typically depicted as arrows) indicate the flow or sequence in which these elements occur. Different processes within the workflow can be linked to one another through message flows, which allow communication and data exchange between separate pools or participants, ensuring coordination and continuity across the interconnected processes. Kishan et al. [para. 0089-0090, 121-0127]);
and generating the business process corresponding to the target business, based on the types and connection sequences of the one or more unit elements. (… a workflow can include one or more processes (e.g., respectively handled by one or more participants). A given process can include a plurality of tasks and links connecting the plurality of tasks. The links can define an operation sequence of the plurality of tasks. … . The set of nodes in the process object represent the tasks of the process and can be organized in a hierarchical relationship representing the operation sequence of the tasks. In some examples, the process object can be represented in a data exchange format, such as JavaScript Object Notation (JSON) or the like. … Compilation of a workflow includes generating a detailed text description for each process within the workflow. These descriptions provide a narrative of the tasks and their operation sequence, clarifying how each process is structured and functions within the overall workflow. Kishan et al. [para. 0021-0024]).
While Kishan et al. discloses a workflow compiler to manage compilation of workflows, wherein the set of nodes that represent tasks of the process can be organized in a hierarchical relationship (Kishan et al. [para. 0021-0024]), Cuomo et al. additionally discloses generating the business process corresponding to the target business, based on the types and connection sequences of the one or more unit elements. ( a method includes receiving a user prompt via a user interface. The user prompt is for a generative artificial intelligence system and is specified as natural language. Cuomo et al. [para. 0005-0017, 0033-0036]. … consider another example where user prompt 204 is “create a diagram for an order to cash process.” In this example, conditioner 104 matches user prompt 204 with the prompt template of “create a BPMN diagram for the {process} business process.” … , conditioned prompt 210 will be “Create a BPMN diagram for the ‘order to cash’ business process. Respond with the notation only. Assign each step a unique name or ID. Specify the type of task for each step, such as user task, system task, exclusive gateway, or end event. Specify the sequence and dependencies of each step using arrows or other connectors. Optionally, include any input or output parameters for each step.” … FIG. 5 illustrates an example BPMN diagram as generated by generative AI system 108 responsive to receiving the conditioned prompt 210. Cuomo et al. [para. 0096-0105; Fig. 1-2, 5]). It would have been obvious to one of ordinary skill in the art of generative AI workflow building to modify the process of Kishan et al. to include generating the business process corresponding to the target business, based on the types and connection sequences of the one or more unit elements as disclosed by Cuomo et al. to facilitate human-computer communication using Natural Language Processing (NLP) by allowing a generative AI system to transform natural language input, e.g., a prompt, from a user into precise, structured output such as machine-readable instructions and/or computer-readable instructions (Cuomo et al. [para. 0027], in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 2, Kishan et al. and Cuomo et al. combined disclose the method of wherein, in the configuring of the first prompt, the first prompt is configured using a system prompt configured to include information about a plurality of types of unit elements and a user prompt configured based on the input text. (… generative AI hub 120 can include an embedding model 122 and a large language model, or LLM 124.. Kishan et al. [para. 0020]. … the end user 102 can enter a user query (in natural language) related to a workflow through the UI 106. The autonomous agent 160 corresponding to the workflow can be activated in response to the user query. The activation of the autonomous agent 160 can instantiate an LLM graph 132. … The autonomous agent 160 can determine the selected task by prompting the LLM 124 with both the user query and the context prompt 150 (text description) corresponding to the target process. Kishan et al. [para. 0029-0032; Fig. 1, 4]).
Regarding Claim 3, Kishan et al. and Cuomo et al. combined disclose the method, wherein the system prompt comprises: first information about the types and characteristics of the unit elements; (… end user first enters an initial user query 910 “Please guide me through the buying process.” Joule directs the user query 910 to the autonomous agent created for the “4AI-SAP Ariba Buying” workflow described above. The autonomous agent first instantiates an LLM graph (e.g., entering the _Start_node 810 of FIG. 8), then determines a target process of the workflow that is most relevant to the user query 910, e.g., based on measuring similarities between the query vector embedding generated from the user query 910 and process vector embeddings generated from context prompts (text descriptions) of the seven processes depicted in FIGS. 5-7. Kishan et al. [para. 0122]);
second information about rules for configuring the business process, based on the one or more unit elements; (… the end user enters another user query 930 “I want to buy Microsoft 256 GB i5 8 GB Laptop Platinum.” Similarly, Joule directs the user query 910 to the autonomous agent created for the “4AI-SAP Ariba Buying” workflow, and the autonomous agent instantiates an LLM graph (e.g., entering the _Start_node 810 of FIG. 8). In this case, the autonomous agent also determines that the first process 500 is a target process which is most relevant to the user query 930. Kishan et al. [para. 0123]);
Kishan et al. fails to explicitly disclose and third information about a format of a response generated in the artificial intelligence model. Cuomo et al. discloses this limitation. (Examples of structured output may include, but are not limited to, data specified in a particular programming language, in a particular markup language, or other computer notation, data organized into a data structure such as an array or vector, or data having another predetermined organization or formatting. Cuomo et al. [para. 0026]. … an example of user prompt 204 may be “create a workflow to process insurance claims” and “generate a Business Process Model and Notation (BPMN) diagram that outlines the steps required to complete the workflow.” In this case, user 202 is requesting that generative AI system 108 create a structured output specified in BPMN format that defines a workflow for processing insurance claims. The structured output, having a specific format and syntax, may then be supplied to a downstream computer-based system. Cuomo et al. [para. 0036]). It would have been obvious to one of ordinary skill in the art of generative AI workflow building to modify the process of Kishan et al. to include third information about a format of a response generated in the artificial intelligence model as disclosed by Cuomo et al. to facilitate human-computer communication using Natural Language Processing (NLP) by allowing a generative AI system to transform natural language input, e.g., a prompt, from a user into precise, structured output such as machine-readable instructions and/or computer-readable instructions (Cuomo et al. [para. 0027], in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 4, Kishan et al. and Cuomo et al. combined disclose the method, wherein the user prompt comprises: fourth information obtained by, in case of generating the business process by modifying a pre-generated second business process, converting the second business process into a data format capable of being processed by the artificial intelligence model; and fifth information configured based on the input text. Cuomo et al. discloses this limitation. (Based on the feedback from the evaluation system, the conditioning instruction(s) of the prompt class(es) may be modified. Cuomo et al. [para. 0030-0033]. … user prompt 204 may be “create a workflow to process insurance claims” and “generate a Business Process Model and Notation (BPMN) diagram that outlines the steps required to complete the workflow.”… The structured output, having a specific format and syntax, may then be supplied to a downstream computer-based system. Cuomo et al. [para. 0036] … conditioning instructions and the prompt template are specific to the particular type of structured output that is to be generated by generative AI system 108 and, as such, may be specific to the selected prompt class. The conditioning instructions may be continually enhanced through the feedback mechanisms described herein. Cuomo et al. [para. 0045-0046, 0086]. … user prompt 204 may relate to data mapping and/or transformation functions. In the example below, data is to be transformed from a first format to a second and different format. User 202 may wish to create a data mapping that is precisely structured and directly usable in a downstream system. An example of conditioned prompt 210 that may be submitted to generative AI system 108 is shown below in Example 6. Cuomo et al. [para. 0126-0139; Fig. 4]). It would have been obvious to one of ordinary skill in the art of generative AI workflow building to modify the process of Kishan et al. to include the user prompt comprises: fourth information obtained by, in case of generating the business process by modifying a pre-generated second business process, converting the second business process into a data format capable of being processed by the artificial intelligence model; and fifth information configured based on the input text as disclosed by Cuomo et al. to ensure that the generated prompts meet the desired criteria, resulting in precise, contextually appropriate, and machine-consumable output for downstream systems or applications (Cuomo et al. [para. 0118]), in a manner that would have yielded predictable results at the relevant time.
Regarding Claim 5, Kishan et al. and Cuomo et al. combined disclose the method, wherein the fourth information comprises information about one or more unit elements constituting the second business process. Cuomo et al. discloses this limitation. ( A data transformation mapping prompt class capable of causing generative AI system 108 to generate structured data transformation mappings as a response. Different prompt classes may be created to generate data transformation mappings between different data format and/or schemas. Cuomo et al. [para. 0059] … user prompt 204 may relate to data mapping and/or transformation functions. In the example below, data is to be transformed from a first format to a second and different format. User 202 may wish to create a data mapping that is precisely structured and directly usable in a downstream system. An example of conditioned prompt 210 that may be submitted to generative AI system 108 is shown below in Example 6. Cuomo et al. [para. 0126-0127; Fig. 4]). It would have been obvious to one of ordinary skill in the art of generative AI workflow building to modify the process of Kishan et al. to include the fourth information comprises information about one or more unit elements constituting the second business process as disclosed by Cuomo et al. to ensure that the generated prompts meet the desired criteria, resulting in precise, contextually appropriate, and machine-consumable output for downstream systems or applications (Cuomo et al. [para. 0118]), in a manner that would have yielded predictable results at the relevant time.
which is converted into a JSON type. (… the process object can be represented in a data exchange format, such as JavaScript Object Notation (JSON) or the like. Kishan et al. [para. 0023, 0095] … the parser can convert the fourth process 630 (“Catalog.Next”) into the following JSON object. Kishan et al. [para. 0101-0103] ).
Regarding Claim 6, Kishan et al. and Cuomo et al. combined disclose the method, in the configuring of the first prompt, the first prompt is generated by reflecting, together with the input text, input information specifying connection positions to which one or more unit elements are to be added in the second business process, and wherein, in the generating of the business process, the business process is generated by adding one or more unit elements to the connection positions. (After invoking the API to execute the specific task, the autonomous agent can return to the Agent node 820 to determine whether additional tasks need to be executed for the user query. For instance, to respond to the user query, multiple tasks of the target process may need to be executed in a specific order, and each task may require invocation of a corresponding API. For each task, the autonomous agent can identify the corresponding API (e.g., by prompting the LLM 830), determine what action to take at the gateway node 840, and continue this process of decision-making and execution until all required tasks are completed. Kishan et al. [para. 0115-0117, 0121-0124]).
Regarding Claim 7, Kishan et al. and Cuomo et al. combined disclose the method, wherein, in the producing of the types and connection sequences of the unit elements, a response generated in the artificial intelligence model comprises a list of one or more unit elements constituting the business process. (… a given process can include a plurality of tasks and links connecting the plurality of tasks. The links define an operation sequence of the plurality of tasks. … method can generate text descriptions (e.g., context prompts 150) of the one or more processes using an LLM (e.g., the LLM 124). … . The set of nodes represent the tasks of the process and are organized in a hierarchical relationship representing the operation sequence of the tasks. In some examples, generating a text description of a selected process includes prompting the LLM with a prompt including a process object representing the selected process. Kishan et al. [para. 0038-0040]).
Regarding Claim 8, Kishan et al. and Cuomo et al. combined disclose the method, wherein the list of one or more unit elements comprises information about the types of respective unit elements and the connection sequences of respective unit elements. (… a given process can include a plurality of tasks and links connecting the plurality of tasks. The links define an operation sequence of the plurality of tasks. … method can generate text descriptions (e.g., context prompts 150) of the one or more processes using an LLM (e.g., the LLM 124). … . The set of nodes represent the tasks of the process and are organized in a hierarchical relationship representing the operation sequence of the tasks. In some examples, generating a text description of a selected process includes prompting the LLM with a prompt including a process object representing the selected process. Kishan et al. [para. 0038-0040]).
Regarding Claim 9, Kishan et al. and Cuomo et al. combined disclose the method, wherein, in the generating of the business process, the business process is generated by sequentially configuring and adding each unit element, based on the types and connection sequences of the one or more unit elements. (… a given process can include a plurality of tasks and links connecting the plurality of tasks. The links define an operation sequence of the plurality of tasks. … method can generate text descriptions (e.g., context prompts 150) of the one or more processes using an LLM (e.g., the LLM 124). … . The set of nodes represent the tasks of the process and are organized in a hierarchical relationship representing the operation sequence of the tasks. In some examples, generating a text description of a selected process includes prompting the LLM with a prompt including a process object representing the selected process. Kishan et al. [para. 0038-0040]).
Regarding Claim 10, Kishan et al. and Cuomo et al. combined disclose the method, further comprising automatically performing the target business using the generated business process. (… the autonomous agent 160 can autonomously execute the selected task (e.g., by calling the corresponding API) without user input. Kishan et al. [para. 0033, 0055]).
Regarding Claims 11-19, claims 11-19 recite substantially similar limitations to those of claims 1-9 respectively and are therefore rejected based upon the same prior art combination, reasoning, and rationale. Claims 11-19 are directed to an apparatus for generating a business process configured to include one or more unit elements to perform a given target business, the apparatus comprising: a processor; and a memory, wherein the memory comprises instructions configured to, when executed by the processor, cause the apparatus to implement specific operations, which is disclosed by Kishan et al. [para. 0129]: the computing system 1000 includes one or more processing units 1010, 1015 and memory 1020, 1025. In FIG. 10, this basic configuration 1030 is included within a dashed line. The processing units 1010, 1015 can execute computer-executable instructions, such as for implementing the features described in the examples herein.
Regarding Claim 20, claim 20 recites substantially similar limitations to those of claim 1 and is therefore rejected based upon the same prior art, reasoning, and rationale. Claim 20 is directed to a computer-readable storage medium storing instructions configured to, when executed by a processor, cause an apparatus, comprising the processor and generating a business process configured to include one or more unit elements to perform a given target business, to implement specific operations, which is disclosed by Kishan et al. [para. 0134-0136]: The innovations can be described in the context of computer-executable instructions, such as those included in program modules, being executed in a computing system on a target real or virtual processor (e.g., which is ultimately executed on one or more hardware processors).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Deo et al. (US 2024/0362467) - a content management system to implement workflow processes wherein the content management system (CMS) exposes instances of stored content objects to a plurality of user devices through an electronic interface. Further systems and subsystem are established for identifying metadata maintained by the CMS for the stored content objects, and for identifying a generative AI entity (GAIE) to interact with the CMS.
Farseez et al. (US 2025/0045802) – automated analysis and generation of marketing or advertising content, e.g., using large language models (LLMs). The apparatus extracts specific insights from input advertisements, such as needs served, brand personas, products advertised, target audiences, tone, and topical categories. These insights are summarized in the formats commonly used in digital marketing, including brand evaluations, comparative analyses of campaigns, possible future advertising content examples, and examples of user personas together with the imaginary persona stories supporting them. The apparatus may leverage multi-modal prompt engineering to have LLM identify key features of advertisements, generalize analyses, present examples, and generate customer personas, stories, marketing content examples.
Tremblay et al. (US 2022/0343250) – A custom code action may be selected from a list of actions. A new custom code action may be created that may be added to the previously created workflow based on the selection. A custom instruction code may be received for a customized action associated with the new custom code action. The previously created workflow may be executed based on an occurrence of the one or more events. The new custom code action may be triggered as part of the execution of the previously created workflow.
Qazvinian et al. (US 2025/0013963) – the system: receives a prompt related to people analytics from a client device associated with a user; generates an embedding representation of the received prompt using a generative AI system incorporating one or more generative AI models; performs a similarity search using the generated embedding representation to identify similar prompts that have been submitted before; obtains an executable expression for responding to the received prompt; executes the executable expression using a data warehouse comprising one or more data sources to obtain a response to the received prompt; determines a type of response based on the nature of the received prompt; generates a response output based on the determined type and the response to the received prompt; and provides the response output to the client device associated with the user.
Roper Jr et al. (US 2025/0217114) - method includes receiving a user request indicative of a digital task involving an input digital model, and retrieving a corresponding input digital model file. Then, determining characteristic attributes of the input digital model, where the characteristic attributes include digital artifacts generated from the input digital model file. Then, selecting from a collection of templates, using a machine learning (ML) engine, a template matching the characteristic attributes of the input digital model. T Finally, the method includes generating the sharable script that implements the digital task, based on the selected template.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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