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 .
Claims 1-20 are presented for examination.
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.
Claim 14 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Claim 14, the claim recites "the natural language question about the industrial system project." There is insufficient antecedent basis for this limitation in the claim. Claim 11, from which claim 14 depends, recites "a question about the industrial system project," but does not recite "a natural language question." It is therefore unclear whether "the natural language question" refers to the previously recited "question about the industrial system project" or introduces a further, separate question. For purposes of examination, Examiner interprets "the natural language question" as referring to the "question about the industrial system project" recited in claim 11.
Claim Rejections - 35 USC § 103
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 nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen (US 2024/0020096 A1) in view of Canedo (US 2022/0276628 A1), and further in view of Stump (US 2023/0058094 A1).
Regarding Claim 1, Chen (US 2024/0020096 A1) teaches
A system, comprising: a memory that stores executable components and one or more custom models; and a processor, operatively coupled to the memory, that executes the executable components (Para [0016], a system for generating computer code based on natural language input may include at least one memory storing instructions and at least one processor configured to execute the instructions to perform operations) [Examiner Comments: Chen's memory-and-processor system for generating computer code from natural language input reads on the claimed memory storing executable components and one or more custom models and the processor that executes the executable components.];
a user interface component configured to render an integrated development environment (IDE) interface and to receive, via interaction with the IDE interface, design input that defines aspects of an industrial system project (Para [0090], a user interface, as used herein, may refer to any means through which a user interacts with a software application or computer system ... a user interface may include a graphical user interface (GUI), a command line interface, a touch user interface, a voice user interface; Para [0078], Example tasks include code completion (e.g., suggesting code completions for developers as they write code; e.g., if a developer starts typing a line of code and then pauses, code snippets may be suggested to complete the task, which may save time and improve the accuracy of the code being written)) [Examiner Comments: Chen's user interface that receives natural language docstrings from a developer and displays generated code reads on the claimed user interface component configured to render an IDE interface and to receive design input.];
a project generation component configured to generate system project data based on the design input, the system project data comprising at least an industrial control program (Para [0005]-[0006], generating, using a trained machine-learning model and based on the docstring, one or more computer code samples configured to produce respective candidate results ... causing each of the one or more computer code samples to be executed) [Examiner Comments: Chen's generation of executable computer code samples from a natural language docstring reads on the claimed project generation component that produces system project data including a control program based on the design input.];
a generative artificial intelligence (AI) component configured to formulate and send a prompt to a generative AI model in response to receipt of a natural language input, submitted via the IDE interface, comprising at least one of a request to generate a portion of the industrial system project or a question about the industrial system project (Para [0005], receiving a docstring representing natural language text specifying a digital programming result; generating, using a trained machine-learning model and based on the docstring, one or more computer code samples) [Examiner Comments: Chen's receipt of a natural language docstring and its supply to a trained machine-learning model to generate code reads on the claimed generative AI component formulating and sending a prompt to a generative AI model in response to a natural language request.];
wherein the generative AI component formulates the prompt to obtain a response from the generative AI model comprising information used by the generative AI component to at least one of generate the portion of the industrial system project or generate a natural language answer to the question about the industrial system project (Para [0015], the trained machine learning model may be developed by applying training data comprising annotated computer code to a precursor model comprising a machine learning model trained on natural language prompts) [Examiner Comments: Chen's machine-learning model that returns computer code samples in response to natural language prompts reads on the claimed AI-model response containing information used by the generative AI component to generate the portion of the project or a natural language answer.];
the generative AI component formulates the prompt based on analysis of the natural language input ... and industry-specific information encoded in the one or more custom models (Para [0012], the trained machine learning model may be fine-tuned based on at least one of a public web source or software repository ... fine-tuned based on a set of training problems constructed from examples within the at least one public web source or software repository) [Examiner Comments: Chen's fine-tuning of the machine-learning model on domain-specific training data drawn from software repositories reads on formulating the prompt based on analysis of the natural language input and industry-specific information encoded in one or more custom models.].
Chen did not specifically teach
the industrial system project;
the industrial control program that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control program.
However, Canedo (US 2022/0276628 A1) teaches
the industrial system project and an industrial control program that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control program (Abstract, Applications of artificial intelligence (AI) in industrial automation have focused mainly on the runtime phase due to the availability of large volumes of data from sensors. Methods, systems, and apparatus that can use machine learning or artificial intelligence (AI) to complete automation engineering tasks are described herein.) [Examiner Comments: Canedo expressly directs machine-learning and AI techniques to the industrial automation engineering domain and to the completion of automation engineering tasks, which reads on applying an AI-based code-generation system to the specific context of industrial control programs that execute on industrial controllers to monitor and control industrial automation systems.].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chen into Canedo in order to apply the well-known benefits of generative-AI-assisted code authoring, namely faster development, reduced syntax errors, and accessibility for non-expert programmers, to the domain-specific task of authoring industrial control code, thereby accelerating industrial automation engineering and reducing the expertise barrier for developing control programs, consistent with Canedo’s stated goal of using AI to complete automation engineering tasks
Chen and Canedo did not specifically teach
the generative AI component is further configured to perform contextual analysis on the industrial system project to determine an industrial vertical for which the industrial system project is being developed, the industrial vertical comprising at least one of automotive, pharmaceutical, food and drug, oil and gas, mining, textiles, marine, die casting, or power generation; and the generative AI component formulates the prompt based on a result of the contextual analysis.
However, Stump (US 2023/0058094 A1) teaches
the generative AI component is further configured to perform contextual analysis on the industrial system project to determine an industrial vertical for which the industrial system project is being developed, the industrial vertical comprising at least one of automotive, pharmaceutical, food and drug, oil and gas, mining, textiles, marine, die casting, or power generation (Para [0066], project generation component 206 can select and apply a subset of guardrail templates 506 determined to be relevant to the project currently being developed, based on a determination of such aspects as the industrial vertical to which the project relates, the type of industrial application being programmed; Para [0068], code modules 508 and/or automation objects 222 can be categorized according to one or more of an industrial vertical (e.g., automotive, food and drug, oil and gas, textiles, marine, pharmaceutical, etc.)) Examiner Comments: Stump's industrial IDE that determines, based on analysis of the project currently being developed, the industrial vertical to which the project relates, selected from a group that expressly includes automotive, food and drug, oil and gas, textiles, marine, and pharmaceutical, reads on performing contextual analysis on the industrial system project to determine an industrial vertical drawn from the recited group.;
the generative AI component formulates the prompt based on analysis of the natural language input, a result of the contextual analysis, and industry-specific information encoded in the one or more custom models (Para [0065], IDE system 202 can customize design feedback 518, including programming recommendations, recommendations of predefined code modules 508 or visualizations 510, error and syntax highlighting, in accordance with the type of industrial system being developed and any applicable in-house design standards; Para [0066], During development, project generation component 206 can select and apply a subset of guardrail templates 506 determined to be relevant to the project currently being developed, based on a determination of such aspects as the industrial vertical to which the project relates) Examiner Comments: Stump's tailoring of its design feedback, programming recommendations, and selected code modules to the project based on the determined industrial vertical reads on formulating the output based on a result of the contextual analysis, and in combination with Chen's formulation of a prompt to a generative AI model based on the natural language input and industry-specific information, teaches formulating the prompt based on analysis of the natural language input, a result of the contextual analysis, and industry-specific information encoded in the one or more custom models.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the contextual industrial-vertical determination of Stump into the generative-AI-assisted industrial code authoring of Chen and Canedo in order to tailor the generated control code, recommendations, and design feedback to the particular standards, best practices, and design constraints of the industrial vertical for which the project is being developed, thereby improving the relevance and standards-compliance of the generated output and reducing engineering rework, consistent with Stump’s stated goal of customizing design feedback in accordance with the type of industrial system being developed.
Regarding Claim 2, Chen, Canedo and Stump teach
The system of Claim 1.
Chen further teaches
wherein the industry-specific information encoded in the one or more custom models comprises at least one of libraries of control program instructions, libraries of add-on instructions, libraries of control program samples, libraries of user-defined data types (UDTs), libraries of product manuals for industrial devices or software platforms, specification data for industrial devices, training data, information defining industrial standards, design standards for respective different types of industrial control applications, design standards for respective different industrial verticals, knowledge of industrial best practices, control design rules, or industrial domain-specific language (DSL) syntax data (Para [0069], the trained machine learning model may be fine-tuned based on ... packages (e.g., software components such as applications, libraries, drivers, and plugins), metadata (e.g., information associated with each package, such as version numbers, descriptions, dependencies, and other data relating to a particular software component), or text (e.g., blog entries, comments, descriptions) ... stored in a software repository) [Examiner Comments: Chen's fine-tuning training data includes libraries of software components and associated metadata and text, which reads on at least the claimed libraries of control program instructions, libraries of control program samples, and training data recited in the Markush-style list.].
Regarding Claim 3, Chen, Canedo, and Stump teach
The system of Claim 1.
Stump further teaches
wherein the generative AI component is further configured to generate at least one of the portion of the industrial system project or the natural language answer based on the result of the contextual analysis (Para [0069], project generation component 206 can infer a programmer's current programming task or design goal based on programmatic input being provided by the programmer ... and determine, based on this task or goal, whether one of the pre-defined code modules 508 or automation objects 222 may be appropriately added to the control program being developed to achieve the inferred task or goal; Para [0066], project generation component 206 can select and apply a subset of guardrail templates 506 determined to be relevant to the project currently being developed, based on a determination of such aspects as the industrial vertical to which the project relates) [Examiner Comments: Stump's generation of recommended code modules, automation objects, and design feedback selected on the basis of the determined industrial vertical and inferred design goal reads on generating at least one of the portion of the industrial system project or the natural language answer based on the result of the contextual analysis.].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the contextual industrial-vertical determination of Stump into the generative-AI-assisted industrial code authoring of Chen and Canedo in order to tailor the generated control code, recommendations, and design feedback to the particular standards, best practices, and design constraints of the industrial vertical for which the project is being developed, thereby improving the relevance and standards-compliance of the generated output and reducing engineering rework, consistent with Stump’s stated goal of customizing design feedback in accordance with the type of industrial system being developed.
Regarding Claim 4, Chen, Canedo and Stump teach
The system of Claim 1.
Chen further teaches
wherein the question about the industrial system project comprises a request for a recommended design tool that is supported by the system and that is capable of performing a design function described by the natural language input, and the natural language answer to the question comprises a natural language description of the recommended design tool (Para [0078], intelligent coding assistants (e.g., building intelligent coding assistants that can help developers perform complex coding tasks; Para [0047], Practical application examples of the present disclosure may include converting comments into computer code, providing predictive code suggestions based on user comments, auto-filling computer code (e.g., repetitive code, routine coding tasks), suggesting alternative code based on user comments, and identifying redundant or unnecessary code) [Examiner Comments: Chen's intelligent coding assistant that responds to a user's natural language comments or questions with recommended code and tool suggestions reads on the claimed question requesting a recommended design tool and the natural language description of the recommended tool.].
Regarding Claim 5, Chen, Canedo and Stump teach
The system of Claim 1.
Chen further teaches
wherein the user interface component is configured to, in response to receipt of a user interaction at a location on a workspace canvas area of the IDE interface in which the industrial control program is being displayed, render an in-line chat window as an overlay on the workspace canvas area, and receive the natural language input via interaction with the in-line chat window (Para [0078], intelligent coding assistants that can help developers perform complex coding tasks ... suggesting code completions for developers as they write code; e.g., if a developer starts typing a line of code and then pauses, code snippets may be suggested to complete the task) [Examiner Comments: Chen's intelligent coding assistant that provides in-context, in-line code suggestions to a developer at the point of code authoring reads on the claimed in-line chat window rendered as an overlay on the workspace canvas area at the location of user interaction.].
Regarding Claim 6, Chen, Canedo and Stump teach
The system of Claim 1.
Chen further teaches
wherein the generative AI component is configured to formulate and send the prompt in response to determining that the response from the generative AI model will cause the portion of the industrial system project or the natural language answer to have a probability of satisfying the request or the question represented by the natural language input that exceeds a defined threshold (Para [0012], identifying at least one of the computer code samples may further be based on a mean-log probability; Para [0073], A higher mean-log probability may indicate that the machine learning model is assigning higher probabilities to correct output (e.g., correct computer code samples), thereby suggesting better performance) [Examiner Comments: Chen's mean-log-probability-based identification of computer code samples reads on the claimed threshold-based determination that the AI response will satisfy the request with a probability that exceeds a defined threshold.].
Regarding Claim 7, Chen, Canedo and Stump teach
The system of Claim 1.
Chen further teaches
wherein the request to generate the portion of the industrial system project comprises a request to create at least a portion of the industrial control program (Para [0047], generate code snippets, functions, and even entire programs, based on natural language descriptions, in several programming languages) [Examiner Comments: Chen's generation of code snippets, functions, or entire programs based on natural language descriptions reads on the claimed request to create a portion of the industrial control program.].
Chen did not specifically teach
a controller definition representing an industrial controller, or a binding definition that assigns the industrial control program to the controller definition.
However, Canedo teaches
a controller definition representing an industrial controller, or a binding definition that assigns the industrial control program to the controller definition (Abstract, Methods, systems, and apparatus that can use machine learning or artificial intelligence (AI) to complete automation engineering tasks are described herein.) [Examiner Comments: Canedo's AI-based completion of automation engineering tasks encompasses the conventional industrial engineering tasks of defining industrial controllers and binding control programs to those controller definitions, which reads on the claimed controller definition and binding definition.].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chen into Canedo in order to extend the generative-AI code authoring of Chen to the controller-definition and program-to-controller binding operations of industrial engineering, providing a complete end-to-end AI-assisted workflow from natural-language specification to deployable-and-bound industrial control code.
Regarding Claim 8, Chen, Canedo and Stump teach
The system of Claim 1.
Chen further teaches
wherein the generative AI component is configured to formulate the prompt further based on analysis of selected subsets of the industry-specific information determined to be relevant to the natural language input (Para [0011], the trained machine learning model may be fine-tuned based on a set of training problems constructed from examples within the at least one public web source or software repository) [Examiner Comments: Chen's fine-tuning on selected subsets of training data constructed from relevant examples in the software repository reads on the claimed formulation of the prompt further based on selected subsets of the industry-specific information determined to be relevant to the natural language input.].
Regarding Claim 9, Chen, Canedo and Stump teach
The system of Claim 1.
Chen further teaches
wherein the natural language input comprises a request to add one or more instances of a specified project element to the industrial system project, the specified project element comprising at least one of a control code instruction, an add-on instruction, a data tag, or a device definition, and the generative AI component is configured to, in response to receipt of the natural language input, add the one or more instances of the specified project element to the industrial system project (Para [0047], converting comments into computer code, providing predictive code suggestions based on user comments, auto-filling computer code (e.g., repetitive code, routine coding tasks), suggesting alternative code based on user comments) [Examiner Comments: Chen's auto-filling of code based on user comments and addition of code snippets to a project reads on adding one or more instances of a specified project element such as a control code instruction or a data tag to the industrial system project in response to the natural language input.].
Regarding Claim 10, Chen, Canedo and Stump teach
The system of Claim 1.
Chen further teaches
wherein the generative AI component is further configured to, in response to determining, based on analysis of the natural language input, that additional natural language input will yield a response to the natural language input having a probability of satisfying the natural language query that exceeds a threshold, generate a first natural language response that prompts for the additional natural language input and render the first natural language response via the user interface component; and in response to receipt, via the user interface component, of a natural language answer to the first natural language response, generate a second natural language response to the natural language input based on analysis of the industrial system project, the natural language answer, the industry knowledge encoded in the one or more custom models, and a response prompted from a generative AI model (Para [0003], conventional methods and systems lack an ability to continually learn and improve through interaction with users, feedback, and/or self-generated data) [Examiner Comments: Chen expressly identifies continuous, iterative interaction with the user as an improvement of the disclosed system, which reads on the claimed multi-turn dialogue pattern of prompting the user for additional information and then generating a second response based on the user's answer together with the custom-model knowledge.].
Regarding Claim 11, Chen (US 2024/0020096 A1) teaches
A method, comprising: rendering, by a system comprising a processor, an integrated development environment (IDE) interface; receiving, by the system via interaction with the IDE interface, design input that defines aspects of an industrial system project (Para [0057], A user interface, as used herein, may refer to any means through which a user interacts with a software application or computer system. For example, a user interface may include a graphical user interface (GUI), a command line interface, a touch user interface, a voice user interface, or a virtual reality user interface) [Examiner Comments: Chen's rendering of a user interface that receives natural language input from a developer reads on the claimed rendering of an IDE interface and receiving of design input.];
generating, by the system, system project data based on the design input, the system project data comprising at least an industrial control program (Para [0081], generating, using a trained machine-learning model and based on the docstring, one or more computer code samples configured to produce respective candidate results) [Examiner Comments: Chen's generation of executable computer code samples based on the docstring reads on the claimed generation of system project data including a control program based on the design input.];
generating, by the system, a prompt directed to a generative artificial intelligence (AI) model in response to receiving, via interaction with the IDE interface, a natural language input comprising at least one of a request to generate a portion of the industrial system project or a question about the industrial system project (Para [0005], receiving a docstring representing natural language text specifying a digital programming result; generating, using a trained machine-learning model and based on the docstring, one or more computer code samples) [Examiner Comments: Chen's supply of the natural language docstring to the trained machine-learning model to generate code reads on the claimed generation of a prompt directed to a generative AI model in response to a natural language input.];
wherein the generating of the prompt comprises generating the prompt to obtain a response from the generative AI model comprising information used by the system to at least one of generate the portion of the industrial system project or generate a natural language answer to the question, and the generating of the prompt further comprises generating the prompt based on analysis of the natural language input ... and industry-specific information encoded in one or more custom models (Para [0011], the trained machine learning model may be fine-tuned based on at least one of a public web source or software repository ... fine-tuned based on a set of training problems constructed from examples within the at least one public web source or software repository) [Examiner Comments: Chen's fine-tuning of the machine-learning model on domain-specific training data drawn from software repositories reads on generating the prompt based on the natural language input and industry-specific information encoded in one or more custom models.].
Chen did not specifically teach
the industrial system project and the industrial control program that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control program.
However, Canedo (US 2022/0276628 A1) teaches
an industrial system project and an industrial control program that, in response to execution on an industrial controller, causes the industrial controller to monitor and control an industrial automation system in accordance with the industrial control program (Abstract, Applications of artificial intelligence (AI) in industrial automation have focused mainly on the runtime phase due to the availability of large volumes of data from sensors. Methods, systems, and apparatus that can use machine learning or artificial intelligence (AI) to complete automation engineering tasks are described herein.) [Examiner Comments: Canedo expressly directs machine-learning and AI techniques to the industrial automation engineering domain, which reads on applying the generative-AI method to an industrial control program that executes on an industrial controller to monitor and control an industrial automation system.].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Chen into Canedo in order to apply the well-known benefits of generative-AI-assisted code authoring, namely faster development, reduced syntax errors, and accessibility for non-expert programmers, to the domain-specific task of authoring industrial control code, thereby accelerating industrial automation engineering and reducing the expertise barrier for developing control programs, consistent with Canedo’s stated goal of using AI to complete automation engineering tasks.
Chen and Canedo did not specifically teach
performing, by the system, contextual analysis on the industrial system project to determine an industrial vertical for which the industrial system project is being developed, the industrial vertical comprising at least one of automotive, pharmaceutical, food and drug, oil and gas, mining, textiles, marine, die casting, or power generation; and generating the prompt based on the industrial vertical determined by the contextual analysis.
However, Stump (US 2023/0058094 A1) teaches
performing, by the system, contextual analysis on the industrial system project to determine an industrial vertical for which the industrial system project is being developed, the industrial vertical comprising at least one of automotive, pharmaceutical, food and drug, oil and gas, mining, textiles, marine, die casting, or power generation (Para [0066], project generation component 206 can select and apply a subset of guardrail templates 506 determined to be relevant to the project currently being developed, based on a determination of such aspects as the industrial vertical to which the project relates, the type of industrial application being programmed; Para [0068], code modules 508 and/or automation objects 222 can be categorized according to one or more of an industrial vertical (e.g., automotive, food and drug, oil and gas, textiles, marine, pharmaceutical, etc.)) [Examiner Comments: Stump's industrial IDE that determines, based on analysis of the project currently being developed, the industrial vertical to which the project relates, selected from a group that expressly includes automotive, food and drug, oil and gas, textiles, marine, and pharmaceutical, reads on performing contextual analysis on the industrial system project to determine an industrial vertical drawn from the recited group.];
the generating of the prompt further comprises generating the prompt based on analysis of the natural language input, the industrial vertical determined by the contextual analysis, and industry-specific information encoded in one or more custom models (Para [0066], During development, project generation component 206 can select and apply a subset of guardrail templates 506 determined to be relevant to the project currently being developed, based on a determination of such aspects as the industrial vertical to which the project relates) [Examiner Comments: Stump's tailoring of its programming recommendations and selected code modules to the project based on the determined industrial vertical, in combination with Chen's formulation of a prompt to a generative AI model based on the natural language input and industry-specific information, reads on generating the prompt based on the natural language input, the industrial vertical determined by the contextual analysis, and industry-specific information encoded in one or more custom models.].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the contextual industrial-vertical determination of Stump into the generative-AI-assisted industrial code authoring method of Chen and Canedo in order to tailor the generated control code, recommendations, and design feedback to the particular standards, best practices, and design constraints of the industrial vertical for which the project is being developed, thereby improving the relevance and standards-compliance of the generated output and reducing engineering rework, consistent with Stump’s stated goal of customizing design feedback in accordance with the type of industrial system being developed.
Regarding Claim 12, Chen, Canedo, and Stump teach
The method of Claim 11.
The additional limitation of Claim 12 is rejected for the same reasons set forth with respect to Claim 2.
Regarding Claim 13, Chen, Canedo, and Stump teach
The method of Claim 11.
The additional limitation of Claim 13 is rejected for the same reasons set forth with respect to Claim 3.
Regarding Claim 14, Chen, Canedo, and Stump teach
The method of Claim 11.
The additional limitation of Claim 14 is rejected for the same reasons set forth with respect to Claim 4.
Regarding Claim 15, Chen, Canedo, and Stump teach
The method of Claim 11.
The additional limitation of Claim 15 is rejected for the same reasons set forth with respect to Claim 5.
Regarding Claim 16, Chen, Canedo, and Stump teach
The method of Claim 11.
The additional limitation of Claim 16 is rejected for the same reasons set forth with respect to Claim 6.
Regarding Claim 17, Chen, Canedo, and Stump teach
The method of Claim 11.
The additional limitation of Claim 17 is rejected for the same reasons set forth with respect to Claim 7.
Regarding Claim 18, Chen, Canedo, and Stump teach
The method of Claim 11.
The additional limitation of Claim 18 is rejected for the same reasons set forth with respect to Claim 8.
Regarding Claim 19, is a non-transitory computer-readable medium claim corresponding to the system claim above (Claim 1) and, therefore, is rejected for the same reasons set forth in the rejection of claim 1.
Regarding Claim 20, Chen, Canedo, and Stump teach
The non-transitory computer-readable medium of Claim 19.
The additional limitation of Claim 20 is rejected for the same reasons set forth with respect to Claim 2.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the arguments do not apply to the previous cited sections of the references used in the previous office action. The current office action is now citing additional references to address the newly added claimed limitations.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMIR SOLTANZADEH whose telephone number is (571)272-3451. The examiner can normally be reached M-F, 9am - 5pm ET.
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/AMIR SOLTANZADEH/Examiner, Art Unit 2191
/WEI Y MUI/ Supervisory Patent Examiner, Art Unit 2191
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