DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This action is in response to the following communication: Non-provisional Application No. 18/750,964 filed on 06/21/2024.
3. Claims 1-17 are pending.
Claim 1 is an independent claims.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, is directed to that judicial exception, an abstract idea, as it has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Regarding claim 1, the limitations “identifying an intervention request that requires human input to complete the programming task” as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. These limitations encompass a human mind carrying out these functions through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, this limitation recites and falls within the “Mental Processes” grouping of abstract ideas under Prong 1.
Claim 1: Under Prong 2 Step 2A, the judicial exception is not integrated into a practical application. The additional elements “code repository” merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea, thus is not a practical application under Prong 2. The additional element “obtaining a set of project configuration”, “indexing original source code”, “advancing the programming task”, “writing the new source code”, “obtaining human input”, and “writing the intervention request” do nothing more than add insignificant extra solution activity to the judicial exception of merely gathering data. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application and the claim is therefore directed to the judicial exception. See MPEP 2106.05(f) and (g), respectively.
Claim 1: Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above in prong 2, the additional elements “code repository” merely recite instructions to implement an abstract idea on a generic computer, or merely uses a generic computer or computer components as a tool to perform the abstract idea, and the additional element “obtaining a set of project configuration”, “indexing original source code”, “advancing the programming task”, “writing the new source code”, “obtaining human input”, and “writing the intervention request” is merely gathering data which the courts have identified as well-understood, routine conventional activity. See for example Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362, MPEP 2106.05(d). Therefore, the additional elements do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims are not patent eligible under 35 USC 101.
Claim 1 recite further additional elements “code repository”. These additional elements are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or generic computer components. See MPEP 2106.05(f). Therefore, the additional elements recited in claim 1 does not integrate the judicial exception into a practical application under prong 2, nor amount to significantly more under step 2B.,
Regarding claims 2-17, the additional elements of “receiving a configuration”, “configuration information includes best practices”, “indexing source code”, “prompt comprises project configuration information”, “project configuration information is obtained”, “validating the new source code”, “manual testing by a human”, “automated testing the source code” , “tuning the machine learning model”, “machine learning model is a large language model”, and “prompt includes” is analyzed under Prong 2 as mere data gathering which does not integrate the judicial exception into a practical application, or amounts to significantly more under Step 2B for the reasons provided in the rejection of claim 1.
Claim Rejections - 35 USC § 103
6. 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 of this title, 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.
7. Claims 1-3, 5-12 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Sing et al., US 2021/0132913 (hereinafter Sing) in view of Wong, “Natural Language Generation and Understanding of Big Code for AI-Assisted Programming”, (hereinafter Wong), published June 1st, 2023.
In regards to claim 1, Sing teaches:
A method for programmatically generating source code using a machine learning model, the method comprising: (Abstract), see “obtaining one or more candidate code completion suggestions that match the identified context) and (p. 8, claim 9), see “each given one of the code samples is associated with a score that satisfies a predefined value, wherein the score is generated by a neural network model and is indicative of a quality of the given code sample”.
obtaining a set of project configuration information that defines a programming task (Abstract), see “identifying a context of the user input relative to a given computer programming language”.
indexing original source code in a code repository (p. 4, [0056]), see “this score may be included in an API response from the storage and indexing module 210 to the user device 220 to customize the order that suggestions are displayed to the user in the smart programming assistant 224”.
advancing the programming task by constructing a prompt to a machine learning model to obtain new source code as an output (Fig. 4), see “404 extract code samples for the programming language from one or more secondary web sources, 412 perform code data analytics to assign ranks to code components”, (p. 4, [0056]), see “he feedback information may include, for example, a user's most typed classes, favorite functions, and/or other statistics related to the use of smart programming assistant 224 by the user. The feedback information is provided as input to a ML model of the code analytics module 214, which generates a score (e.g., a popularity score) for one or more code components for the individual user. This score may be included in an API response from the storage and indexing module 210 to the user device 220 to customize the order that suggestions are displayed to the user in the smart programming assistant 224” and (p. 4, [0062]), see “in at least one example embodiment, the smart programming assistant 224 includes a back-feed logging mechanism per session for each user. This mechanism may be used to gather personal code documentation and/or code sample favorites (e.g., by monitoring user statistics). The back-feed logging mechanism may be implemented, at least in part, using a logging framework to gather such information. This information may be used as input to the ML model of code analytics module 214 to refine suggestions for each user over time”.
Sing doesn’t explicitly teach:
writing the new source code to the code repository.
However, Wong teaches such use: (p. 2, 1st para.), see “by considering source code as a series of tokens and leveraging the inherent patterns and structures within vast code repositories, NLP techniques can be developed to enhance AI-assisted programming tasks, including code generation, code completion, code refinement, code summarization, defect detection, and clone detection”.
identifying an intervention request that requires human input to complete the programming task.
However, Wong teaches such use: (p. 12, 1st para.), see “additionally, prompt-based learning can be used in a semi-supervised or unsupervised manner, where the prompt provides a small amount of supervision to the language model, further reducing the necessary amount of task-specific data” and (p. 13, 3rd para.), see “the opportunity to integrate tools and LLMs enhances and streamlines the software development process. By incorporating LLMs into integrated tools as cloud virtual service providers [201, 202]), developers can leverage the power of NLP to automate repetitive tasks, improve code quality and readability, and increase efficiency in software development”.
obtaining human input for the intervention request from a human using digital communications as an outcome; and writing the intervention request and outcome to an intervention database.
However, Wong teaches such use: (p. 12, 1st para.), see “to reduce the computational expense of training LLMs, researchers and developers can employ various techniques, such as training on subsets of the data [173, 174]), optimizing the hyperparameters [175]), and leveraging transfer learning to reuse the knowledge learned from previous tasks. These techniques can help to speed up the training process and reduce the amount of required computing resources. Instead of training the LLMs continuously, some works focus on using prompt-learning [176, 177] and human feedback [178–182] to improve performance of the LLMs” and (p. 14, 2nd para.), see “additionally, prompt-based learning can be used in a semi-supervised or unsupervised manner, where the prompt provides a small amount of supervision to the language model, further reducing the necessary amount of task-specific data”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
In regards to claim 2, Sing teaches:
receiving a configuration of a digital coworker and adding details to the project configuration based upon the configuration of the digital coworker (p. 3, [0035]), see “accordingly, at least one embodiment of the invention includes providing an artificial intelligence based virtual programming assistant. For example, the virtual programming assistant may provide ranked code completions and context aware language documentation to a user via a user interface. Additionally, specific code samples from various sources may be provided in real time within the same context, for example, alongside the user's IDE or code editor. The virtual programming assistant supports internal sources (e.g., private code repositories or documentation of a user's organization) and/or external sources (such as, for example, Stack Overflow® and GitHub®”.
In regards to claim 3, Sing teaches:
the project configuration information includes best practices, repo locations, and documentation locations (p. 4, [0058]), see “the metrics may include quality metrics pertaining to degree in which a given code sample complies with best coding practices, an execution time associated with the given code sample, and security risks associated with the given code sample, for example), (p. 3, [0049]), see the code extractor 206 extracts code samples from one or more web sources 208, wherein each of the web sources 208 correspond to external sources or internal sources (e.g., private code repositories). In at least one example embodiment, the code extractor 206 extracts the data using publicly available APIs” and (p. 3, [0051]), see “the storage system 212 identifies the data that correspond to code documentation and transforms it into logically segmented key value pairs, wherein each library class is stored in a separate file”.
In regards to claim 5, Sing doesn’t explicitly teach:
indexing source code in a code repository comprises inputting the source code to a machine learning model.
However, Wong teaches such use: (p. 5, 1st para.), see “similar to other neural networks and raw text, language models cannot process source code directly, so the first step of the standard pipeline is to convert the code inputs into numbers of which the model can make sense. To do this, a tokenizer can be used to split the input into code syntax keyword, variables, or symbols (similar to punctuation) that are called tokens. Each token is mapped to an integer in the next step. These tokens typically correspond to words, punctuation marks, or other meaningful elements of the text. Tokenization is an important step in many NLP tasks, as it allows machine learning algorithms to process and analyze text in a more efficient and meaningful way”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
In regards to claim 6, Sing teaches:
indexing source code in a code repository comprises organizing classes and methods in an abstract syntax tree (AST) (p. 4, [0057]), see “the storage and indexing module 210 also obtains information regarding the languages, classes, functions, and libraries that each piece of documentation pertains to, from the collection and aggregation module 200” and (p. 3, 0039]), see “traverse each page link-by-link to identify targets to be extracted while storing the targets in a tree type structure”.
In regards to claim 7, Sing doesn’t explicitly teach:
the prompt comprises project configuration information and intervention information.
However, Wong teaches such use: (p. 11, last para.), see “the prompt can be a simple sentence or a full paragraph, depending on the complexity of the task and the amount of information needed to guide the LLMs. One of the main advantages of prompt-based learning is its flexibility and ease of use. It allows users to quickly fine-tune pre-trained language models for specific tasks without requiring a large amount of task-specific data. Additionally, prompt based learning can be used in a semi-supervised or unsupervised manner, where the prompt provides a small amount of supervision to the language model, further reducing the necessary amount of task-specific data”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
In regards to claim 8, Sing teaches:
the project configuration information is obtained using REST API (representational state transfer (REST) application programming interface (API)) (p. 4, [0061]), see “in at least one embodiment, the smart programming assistant 224 regularly makes use of representational state transfer (REST) web service calls to an API Gateway for fetching real time suggestions from the storage and indexing module 210”.
In regards to claim 9, Sing doesn’t explicitly teach::
validating the new source code.
However, Wong teaches such use: (p. 12, last para.), see “code validation and testing involve thorough validation and testing of the generated code before integrating it with real-world systems to identify and fix any security issues. Data sanitization and validation ensure that the training data are free from malicious code or sources”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
In regards to claim 10, Sing doesn’t explicitly teach::
validating comprises manual testing by a human within a development environment.
However, Wong teaches such use: (p. 10, 2nd para.), see “clones can be classified into four types [129, 130]), with types 1–3 being syntactic clones that differ in minor ways, while type 4 clones, known as semantic clones, are difficult to detect since they have different syntax but the same semantics and, thus, require manual validation”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
In regards to claim 11, Sing doesn’t explicitly teach:
validating comprises automated testing the source code.
However, Wong teaches such use: (p. 13, 3rd para.), see “the opportunity to integrate tools and LLMs enhances and streamlines the software development process. By incorporating LLMs into integrated tools as cloud virtual service providers [201, 202]), developers can leverage the power of NLP to automate repetitive tasks, improve code quality and readability, and increase efficiency in software development”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
In regards to claim 12, Sing doesn’t explicitly teach:
fine tuning the machine learning model based on at least one intervention outcome.
However, Wong teaches such use: (p. 11, last para.), see “it allows users to quickly fine-tune pre-trained language models for specific tasks without requiring a large amount of task-specific data. Additionally, prompt based learning can be used in a semi-supervised or unsupervised manner, where the prompt provides a small amount of supervision to the language model, further reducing the necessary amount of task-specific data”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
In regards to claim 14, Sing doesn’t explicitly teach:
the prompt includes a list of best practices.
However, Wong teaches such use: (p. 12, 2nd para.), see “leveraging LLMs in AI-assisted programming tasks has enormous potential to improve software development efficiency and reduce the time and effort required to write code manually. However, several challenges need to be addressed to ensure the performance and effectiveness of LLMs. One of the primary concerns is the quality of the generated code or documentation [35]), which can be impacted by the accuracy and robustness of the LLMs... Therefore, it is critical to ensure that the generated code meets the desired specifications and adheres to coding standards and best practices”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
In regards to claim 15, Sing doesn’t explicitly teach:
the prompt includes a list of code standards.
However, Wong teaches such use: (p. 12, 2nd para.), see “leveraging LLMs in AI-assisted programming tasks has enormous potential to improve software development efficiency and reduce the time and effort required to write code manually. However, several challenges need to be addressed to ensure the performance and effectiveness of LLMs. One of the primary concerns is the quality of the generated code or documentation [35]), which can be impacted by the accuracy and robustness of the LLMs... Therefore, it is critical to ensure that the generated code meets the desired specifications and adheres to coding standards and best practices”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
In regards to claim 16, Sing doesn’t explicitly teach:
the prompt includes a list of past interventions and outcomes.
However, Wong teaches such use: (p. 6, 1st para.), see “decoder-only models, also known as autoregressive models, are a type of neural network architecture used in natural language processing tasks such as GPT-2 [58]), GPT-3 [59]), GPT-J [60]), Reformer [61]), and GPT-Neo [62]), which use the decoder to predict the next token output given all previous tokens. They rely solely on a decoder network to generate output”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
In regards to claim 17, Sing doesn’t explicitly teach:
the machine learning model is a large language model (LLM).
However, Wong teaches such use: (p. 13, 3rd para.), see “the opportunity to integrate tools and LLMs enhances and streamlines the software development process. By incorporating LLMs into integrated tools as cloud virtual service providers [201, 202]), developers can leverage the power of NLP to automate repetitive tasks, improve code quality and readability, and increase efficiency in software development) and (p. 2, 4th para.), see additionally, the latest developments in AI assisted programming using transformer-based LLMs trained on Big Code are explored, and both the generation and comprehension aspects are discussed. The review concludes with the open challenges and opportunities in AI-assisted programming. This review paper highlights the unique contributions of this review in comparison to existing reviews”.
Sing and Wong are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing and Wong before him or her, to modify the system of Sing to include the teachings of Wong, as a system for AI assisted code generation, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to more accurately anticipate potential issues throughout the software development life cycle, as suggested by Wong (p. 12, 1st para., p. 15, conclusions).
8. Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sing in view of Wong in view of Bahrami et al., U.S. Patent No. 11,609,748 (hereinafter Bahrami).
In regards to claims 1 and 7 the rejections above are incorporated accordingly.
In regards to claim 4, Sing and Wong, in particular Sing doesn’t explicitly teach:
indexing source code in a code repository comprises vectorizing classes and methods within the source code.
However, Bahrami teaches such use (column 6, lines 36-49), see “the augmented programming language corpus may include a vector set including the description-code vectors. In some embodiments, the description-code vectors included in the augmented programming language corpus may be considered positively classified examples for training the machine learning model such that the machine learning model is trained to consider the code description paired to the section of source code in a positively classified description-code vector to be a valid relation. In other words, the machine learning model may be taught that a given positively classified description-code vector describes one example of a correct association of a natural language code description, represented by the code comment, and a section of source code” and (column 11, lines 19-24), see “at block 520, the natural language search query may be mapped to a search vector. Mapping the natural language search query to the search vector may include preparing the natural language search query for vectorization”.
Sing, Wong and Bahrami are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing, Wong and Bahrami before him or her, to modify the system of Sing and Wong, in particular Sing to include the teachings of Bahrami, as a system for semantic code search, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to provide helpful or accurate source-code recommendations based on user queries as suggested by Bahrami (column 6, lines 36-49, column 14, last para.).
In regards to claim 13, Sing and Wong, in particular Sing doesn’t explicitly teach:
the prompt includes a list of locations for code packages.
However, Bahrami teaches such use: (column 3, lines 45-54), see “FIG. 1 is a diagram of an example system 100 related to generating an augmented programming language corpus and performing a source-code search based on the augmented programming language corpus and a natural language search query. The system 100 may include a source-code parser 120 and a machine learning model 130. The source-code parser 120 may obtain one or more source-code packages 110 and/or source code 115 and output source-code sections 122, code comments 124, and/or source-code metadata 126”.
Sing, Wong and Bahrami are analogous art because they are from the same field of endeavor, code generation.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teaching of Sing, Wong and Bahrami before him or her, to modify the system of Sing and Wong, in particular Sing to include the teachings of Bahrami, as a system for semantic code search, and accordingly it would enhance the system of Sing, which is focused on a smart programming assistant, because that would provide Sing with the ability to provide helpful or accurate source-code recommendations based on user queries as suggested by Bahrami (column 6, lines 36-49, column 14, last para.).
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Application Publications
Smith 11,442,702 teaches A system and method may provide assistance to programmers during programming to reduce the number of routine tasks that must be performed. In some aspects, the system may suggest one or more code snippets that comprise code completions. The code snippets may be single or multi-token. In some aspects, the system may provide predictive editing or predictive navigation, where the system may predict edits or navigation actions based on a programmer's actions. In some aspects, the system is based on machine learning methods and is trained on past actions of programmers in a code editor.
Gillman 20240028312 teaches A method for automatically generating and executing computer code includes receiving, by a machine learning engine, a user-specified data set and a user-specified task. The machine learning engine analyzes at least one characteristic of the user-specified data set and at least one characteristic of the user-specified task and generates at least one machine learning model for processing the user-specified data set. The machine learning model generates a first output by processing the user-specified data set. The machine learning engine receives a natural language description of a user-requested data transformation task for execution with a subset of the first output and directs a large language model to identify an archetype of the user-requested data transformation task.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evral Bodden whose telephone number is 571-272-3455. The examiner can normally be reached on Monday to Friday from 9am to 5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cha Do, can be reached at telephone number 571-272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automatedinterview-request-air-form.
If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/EVRAL E BODDEN/Primary Examiner, Art Unit 2193
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