DETAILED ACTION
This action is in response to claims filed 13 November 2023 for application 18507630 filed 13 November 2023. Currently claims 1-20 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 6-7, 9 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Deo in view of Song et al. (RestGPT: Connecting Large Language Models with Real-World Applications via RESTful APIs).
Regarding claim 1, Deo discloses: A method comprising:
receiving, from a client device, a workflow request comprising a natural language description of an objective to accomplish using one or more digital content items stored in a content management system (“For example, a particular workflow might be configured to stimulate an AI entity with a question encountered in a workflow, and then to use all or portions of the AI entity's response as content for a generated document. In response to identification of a reason to stimulate the AI entity (e.g., a question being encountered in a workflow), any applicable metadata is gathered (operation 2) and the metadata is in turn provided to a prompt generator (operation 3). The prompt generator gathers context to be used in a prompt (operation 4).” [0042], natural language [0043], Fig 1 A cloud content management system Deo 20240362467);
segmenting the workflow request into a set of tasks for accomplishing the objective (Fig 1a and Fig 1b different paths (tasks) of a set of paths are used; actions in a workflow [0048]);
determining data from the one or more digital content items stored in the content management system via execution of one or more tasks from the set of tasks (Fig 1a content object context and metadata); and
generating, from the data determined from the one or more digital content items and using a large language model (Fig 1a content objects and generative AI system, LLM [0069]), a workflow output having a digital content (Fig 1a use the entity response to advance the flow, generated document).
Deo does not explicitly disclose: a computer instruction directing an external computing platform to use the digital content, the computer instruction being formatted in accordance with an application programming interface of the external computing platform such that the computer instruction is executable by the external computing platform and a result of executing the computer instruction includes content accessible by one or more client devices.
Song teaches: a computer instruction directing an external computing platform to use the digital content, the computer instruction being formatted in accordance with an application programming interface of the external computing platform such that the computer instruction is executable by the external computing platform and a result of executing the computer instruction includes content accessible by one or more client devices (“The workflow of RestGPT can be characterized as an iterative "plan and execution" loop, which can be described as follows. In the planning stage, the planner employs LLMs’ common-sense knowledge to conduct natural language planning to decompose the user query into a sub-task for the present step while the API selector thereafter chooses an appropriate API to solve the sub-task. Subsequently, the executor formulates the API call and parses the API response. The planner accepts the executor’s response and generates sub-task for the next step. Once the planner outputs a termination signal, RestGPT concludes the loop and produces the final execution outcome” p4 last ¶, Fig 2, Fig Spotify on the user’s external device is used to create a playlist accessible by the user device and potentially other devices).
Deo and Song are in the same field of endeavor of using LLMs and workflows in a content system. Deo discloses using an LLM for taking input and information/context about a content item and generating a post. Song teaches a system that takes client queries to break a task down into a workflow and return an instruction to be executed on the device. It would have been obvious to one of ordinary sill in the art before the effective filing date to modify the known LLM based content management system as disclosed by Deo with the known LLM workflow instruction execution as taught by Song to automatically create a desired execution outcome.
Regarding claim 2, Deo does not explicitly discloses, however Song teaches: The method of claim 1, wherein generating, the workflow output having the computer formatted in accordance with the application programming interface of the external computing platform comprises generating the workflow output having the computer instruction formatted in accordance with the application programming interface based on determining that the external computing platform is indicated within the workflow request (“The workflow of RestGPT can be characterized as an iterative "plan and execution" loop, which can be described as follows. In the planning stage, the planner employs LLMs’ common-sense knowledge to conduct natural language planning to decompose the user query into a sub-task for the present step while the API selector thereafter chooses an appropriate API to solve the sub-task. Subsequently, the executor formulates the API call and parses the API response. The planner accepts the executor’s response and generates sub-task for the next step. Once the planner outputs a termination signal, RestGPT concludes the loop and produces the final execution outcome” p4 last ¶, Fig 2, Fig Spotify on the user’s external device is used to create a playlist accessible by the user device and potentially other devices).
Regarding claim 6, Deo discloses: The method of claim 1, further comprising sending the workflow output having the computer instruction to the external computing platform for execution (Fig 1a, “In this particular embodiment the interactions with the AI entity are agnostic to the particular mechanism used. As shown, a generic instance of an input interface 122 is provided. Similarly, a generic instance of an output interface 126 is provided to be able to transport any number of responses (e.g., response 121) from the AI entity back to the CCM, whereafter the CCM parses the response and uses aspects of the response to advance the subject workflow (operation 6).” [0044]).
Regarding claim 7, Deo discloses: The method of claim 1, wherein determining the data from the one or more digital content items stored in the content management system via execution of the one or more tasks comprises at least one of extracting data segments from content of the one or more digital content items or generating derived data segments based on the content of the one or more digital content items (“FIG. 5A shows several example metadata extraction techniques that are used in systems that use responses from an AI entity to automate workflow processing. As an option, one or more variations of metadata extraction techniques 5A00 or any aspect thereof may be implemented in the context of the architecture and functionality of the embodiments described herein and/or in any environment.” [0076]).
Regarding claim 9, Deo discloses: The method of claim 1, wherein determining the data from the one or more digital content items stored in the content management system via execution of the one or more tasks comprises generating, using a text summarization model, a text summary of a text file stored in the content management system (“Although the foregoing disclosure relates substantially to the shown illustrative examples, there are many situations where information returned by the AI entity can be used in a document. Moreover an AI entity can be prompted in a manner that requires/requests the AI entity to produce responses in a prosaic form. For example, an AI entity can be prompted to provide a “summary” of the human readable portions of the content object. Additionally or alternatively, an AI entity can be prompted to identify specific words, phrases, or other aspects of a content object using highlighting, bolding, etc.” [0095]).
Regarding claim 11, Deo discloses: A system comprising:
at least one processor (Fig 7a); and
a non-transitory computer-readable medium comprising instructions that, when executed by the at least one processor (Fig 7a), cause the system to:
receive, from a client device, a workflow request comprising a natural language description of an objective to accomplish using one or more machine learning models and one or more digital content items stored in a content management system (“For example, a particular workflow might be configured to stimulate an AI entity with a question encountered in a workflow, and then to use all or portions of the AI entity's response as content for a generated document. In response to identification of a reason to stimulate the AI entity (e.g., a question being encountered in a workflow), any applicable metadata is gathered (operation 2) and the metadata is in turn provided to a prompt generator (operation 3). The prompt generator gathers context to be used in a prompt (operation 4).” [0042], natural language [0043], Fig 1 A cloud content management system);
generate, from the workflow request, a plurality of tasks that, when completed, accomplish the objective of the workflow request (Fig 1a and Fig 1b different paths (tasks) of a set of paths are used; actions in a workflow [0048]);
determine data from the one or more digital content items stored in the content management system via execution of the plurality of tasks (Fig 1a content object context and metadata); and
generate, from the data determined from the one or more digital content items and using a large language model, a workflow output having digital content (Fig 1 a content objects and generative AI system, use the entity response to advance the flow, generated document; LLM [0069]).
Deo does not explicitly disclose: a computer instruction directing an external computing platform to use the digital content, the computer instruction being formatted in accordance with an application programming interface of the external computing platform such that the computer instruction is executable by the external computing platform and a result of executing the computer instruction includes content accessible by one or more client devices.
Song teaches: a computer instruction directing an external computing platform to use the digital content, the computer instruction being formatted in accordance with an application programming interface of the external computing platform such that the computer instruction is executable by the external computing platform and a result of executing the computer instruction includes content accessible by one or more client devices (“The workflow of RestGPT can be characterized as an iterative "plan and execution" loop, which can be described as follows. In the planning stage, the planner employs LLMs’ common-sense knowledge to conduct natural language planning to decompose the user query into a sub-task for the present step while the API selector thereafter chooses an appropriate API to solve the sub-task. Subsequently, the executor formulates the API call and parses the API response. The planner accepts the executor’s response and generates sub-task for the next step. Once the planner outputs a termination signal, RestGPT concludes the loop and produces the final execution outcome” p4 last ¶, Fig 2, Fig Spotify on the user’s external device is used to create a playlist accessible by the user device and potentially other devices).
Regarding claim 12, Deo discloses: The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to:
provide, to the large language model, one or more prompts corresponding to the plurality of tasks for the workflow request; and generate, using the large language model, one or more instructions for completing the plurality of tasks based on the one or more prompts (Fig 1a provide generated prompt to and receive response from generative model).
Regarding claim 13, Deo discloses: The system of claim 12, further comprising instructions that, when executed by the at least one processor, cause the system to generate, using the large language model, the one or more instructions for completing the plurality of tasks by generating at least one instruction for using a machine learning model or an application programming interface of the content management system in determining the data from the one or more digital content items (“The figure is being presented to illustrate one possible way to represent a workflow. More specifically, the presented CMS workflow representation 115 has an entry point capability (e.g., depicted by flow entry point 116), a decision-making capability (e.g., depicted by switch 139), and a plurality of preconfigured actions (e.g., depicted by preconfigured action 131.sub.1 and by preconfigured action 131.sub.2). In this particular representation, the workflow is invoked by an event (e.g., flow event 107), however there can be any other or different signaling (e.g., an API call) that results in invocation of a workflow at any particular location of the flow. More particularly, a workflow, or a portion of a workflow can be invoked at any location of the flow. In some cases, such a location may coincide with a workflow entry point, or such a location may coincide with a workflow decision, or such a location may coincide with a workflow action.” [0047]).
Regarding claim 14, Deo discloses: The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to generate the workflow output having the digital content and the computer instruction for using the digital content by generating, using the large language model, the digital content and an instruction to the external computing platform to generate viewable content that includes the digital content and is accessible by client devices via the external computing platform (“In this particular embodiment the interactions with the AI entity are agnostic to the particular mechanism used. As shown, a generic instance of an input interface 122 is provided. Similarly, a generic instance of an output interface 126 is provided to be able to transport any number of responses (e.g., response 121) from the AI entity back to the CCM, whereafter the CCM parses the response and uses aspects of the response to advance the subject workflow (operation 6).” [0044], Fig 1a output interface).
Regarding claim 15, Deo does not explicitly disclose, however Song teaches: The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to generate the workflow output having the digital content and the computer instruction formatted in accordance with the application programming interface of the external computing platform by generating the workflow output having the computer instruction formatted in accordance with the application programming interface based on determining that the external computing platform is indicated within the workflow request (“The workflow of RestGPT can be characterized as an iterative "plan and execution" loop, which can be described as follows. In the planning stage, the planner employs LLMs’ common-sense knowledge to conduct natural language planning to decompose the user query into a sub-task for the present step while the API selector thereafter chooses an appropriate API to solve the sub-task. Subsequently, the executor formulates the API call and parses the API response. The planner accepts the executor’s response and generates sub-task for the next step. Once the planner outputs a termination signal, RestGPT concludes the loop and produces the final execution outcome” p4 last ¶, Fig 2, Fig Spotify on the user’s external device is used to create a playlist accessible by the user device and potentially other devices).
Regarding claim 16, Deo discloses: The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to provide the workflow output having the digital content and the computer instruction to the external computing platform (Fig 1a, “In this particular embodiment the interactions with the AI entity are agnostic to the particular mechanism used. As shown, a generic instance of an input interface 122 is provided. Similarly, a generic instance of an output interface 126 is provided to be able to transport any number of responses (e.g., response 121) from the AI entity back to the CCM, whereafter the CCM parses the response and uses aspects of the response to advance the subject workflow (operation 6).” [0044]).
Regarding claim 17, Deo discloses: A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to:
provide, for display within a graphical user interface of a client device, an interactive option for entering natural language text (Fig 1a input question, “Such context might be in the form of additional metadata and/or corresponding metadata values (e.g., as extracted by the shown metadata extractor 106), and/or such context might be in the form of natural language representation of an event or event sequence, and/or such context might be in the form of natural language representation of the actual contents of content objects 104. Given such context, the prompt generator provides a generated prompt to the AI entity (operation 5). In this particular example, the generated prompt is composed of natural language text of an inquiry (e.g., question 117), together with context (e.g., context 119) pertaining to the inquiry. As is known in the art, a prompt can be provided to an AI entity using a hypertext transport protocol (HTTP), or a prompt can be provided to an AI entity using application programming interfaces (APIs).” [0043], “A document template configuration user interface such as is depicted in FIG. 6B serves to aid the user in identifying information that the user deems to be particularly useful for interacting with an AI entity. For example, the shown document template configuration user interface suggests different corpora of information, specifically, information to be derived from the contents of a content object of the CMS or information to be derived from an AI entity.” [0091]);
receive, via the interactive option displayed in the graphical user interface, a workflow request comprising a natural language description of an objective to accomplish using one or more digital content items stored in a content management system (Fig 1a input question);
generate, from data determined from the one or more digital content items and using a large language model, a workflow output having digital content (Fig 1a content objects and generative AI system, LLM [0069]); and
provide, for display within the graphical user interface of the client device in response to execution of the computer instruction by the external computing platform, viewable content that is generated by the external computing platform using the digital content or a link to the viewable content (Fig 1 use the entity response to advance the flow, generated document).
Deo does not explicitly disclose: a computer instruction directing an external computing platform to use the digital content, the computer instruction being formatted in accordance with an application programming interface of the external computing platform such that the computer instruction is executable by the external computing platform and a result of executing the computer instruction includes content accessible by one or more client devices.
Song teaches: a computer instruction directing an external computing platform to use the digital content, the computer instruction being formatted in accordance with an application programming interface of the external computing platform such that the computer instruction is executable by the external computing platform and a result of executing the computer instruction includes content accessible by one or more client devices (“The workflow of RestGPT can be characterized as an iterative "plan and execution" loop, which can be described as follows. In the planning stage, the planner employs LLMs’ common-sense knowledge to conduct natural language planning to decompose the user query into a sub-task for the present step while the API selector thereafter chooses an appropriate API to solve the sub-task. Subsequently, the executor formulates the API call and parses the API response. The planner accepts the executor’s response and generates sub-task for the next step. Once the planner outputs a termination signal, RestGPT concludes the loop and produces the final execution outcome” p4 last ¶, Fig 2, Fig Spotify on the user’s external device is used to create a playlist accessible by the user device and potentially other devices).
Regarding claim 18, Deo discloses: The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to generate the workflow output having the digital content from the data determined from the one or more digital content items and using the large language model by generating the digital content of the workflow output using the large language model based on data segments extracted or derived from content of the one or more digital content items stored in the content management system without providing the one or more digital content items to the large language model (Fig 1a context and metadata of the content are used).
Regarding claim 19, Deo discloses: The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to:
generate, in response to receiving the workflow request, a plurality of tasks for completing the objective of the workflow request (Fig 1a content object context and metadata); and
determine the data from the one or more digital content items stored in the content management system by executing the plurality of tasks using the large language model (Fig 1).
Regarding claim 20, Deo discloses: The non-transitory computer-readable medium of claim 19, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to determine the data from the one or more digital content items stored in the content management system using one or more machine learning models of the content management system (Fig 1a, “Policy enforcement agents run continuously (e.g., in the background) so as to aid in enforcing security and compliance policies. Certain policy enforcement agents are configured to deal with items such as content object retention schedules, achievement of time-oriented governance requirements, and establishment and maintenance of trust controls (e.g., smart access control exceptions). Further, certain policy enforcement agents apply machine learning techniques to deal with items such as dynamic threat detection.” [0124]).
Claim(s) 3-5 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deo in view of Song and further in view of Zhou et al. (Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce).
Regarding claim 3, Deo discloses:
The method of claim 1, wherein:
receiving, from the client device, the workflow request comprising the objective to accomplish using the one or more digital content items stored in the content management system comprises receiving, from the client device, the workflow request comprising the objective to post … for a product or service associated with the one or more digital content items stored in the content management system (“For example, given a content object an AI entity can be asked to isolate one or more specific items of information. Strictly as examples, given an SEC filing, the AI entity can be asked to isolate quarterly results, policy descriptions and/or policy violations, etc. In fact, an AI entity can be asked to format content in a particular manner. For example, the AI entity might be asked to reformat an AI-generated summary into a blog post.” [0096]).
Deo doesn’t explicitly disclose; however, Zhou teaches:
ecommerce listing for a product or service (“The objective of our methodology is to fine-tune a large language model for generating product descriptions that enhance both user engagement and click-through rates. The model fine-tuning consists of two major components: language model likelihood and CTR optimization.” P3 §3.2 ¶2);
generating, from the data determined from the one or more digital content items and using the large language model, the workflow output having the computer instruction that is executable by the external computing platform comprises generating, from the data and using the large language model, listing content and at least one computer instruction for creating the ecommerce listing on an ecommerce platform using the listing content (“By employing our methodology, e-commerce platforms can enhance product listings en masse, improving overall platform attractiveness and customer engagement.” P6 §4.8 ¶2).
Deo, Song and Zhou are in the same field of endeavor of using LLMs. Deo discloses using an LLM for taking input and information/context about a content item and generating a post. Song teaches a system that takes client queries to break a task down into a workflow and return an instruction to be executed on the device. Zhou discloses an ecommerce system that uses LLMs to generate listing information for content items. It would have been obvious to one of ordinary sill in the art before the effective filing date to modify the known LLM based content management system as disclosed by Deo and Song with the known LLM ecommerce posting as taught by Zhou to improve platform attractiveness and customer engagement.
Regarding claim 4, Deo discloses: The method of claim 3,
wherein receiving, from the client device, the workflow request comprises receiving, via a graphical user interface of the client device, user input for the workflow request (“In this particular embodiment the interactions with the AI entity are agnostic to the particular mechanism used. As shown, a generic instance of an input interface 122 is provided. Similarly, a generic instance of an output interface 126 is provided to be able to transport any number of responses (e.g., response 121) from the AI entity back to the CCM, whereafter the CCM parses the response and uses aspects of the response to advance the subject workflow (operation 6).” [0044]); and
further comprising providing, for display within the graphical user interface of the client device in response to receiving the user input for the workflow request [0044].
Deo doesn’t explicitly disclose; however, Zhou teaches: a link to the ecommerce listing on the ecommerce platform (“By employing our methodology, e-commerce platforms can enhance product listings en masse, improving overall platform attractiveness and customer engagement.” P6 §4.8 ¶2).
Regarding claim 5, Deo discloses: The method of claim 1, wherein generating the workflow output from the data determined from the one or more digital content items using the large language model comprises generating the workflow output from the data determined from the one or more digital content items using … large language model that is external to the content management system while preventing access to the one or more digital content items stored in the content management system by the third-party large language model (Fig 1a).
Deo doesn’t explicitly disclose; however, Zhou teaches: a third-party (Fig 1 LLAMA 2.0).
Regarding claim 10, Deo discloses: The method of claim 1, wherein generating the workflow output using the large language model comprises generating the workflow output using a pre-trained large language model having (Fig 1a, LLM [0069]).
Deo doesn’t explicitly disclose; however, Zhou teaches:
parameters learned based on an application programming interface of the external computing platform (“We train the model on a dataset of authentic product descriptions from Walmart, one of the largest eCommerce platforms. The model is then finetuned for domain-specific language features and eCommerce nuances to enhance its utility in sales and user engagement.” abstract).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deo in view of Song and further in view of Sollami et al. (US 20230128686 A1).
Regarding claim 8, Deo discloses: The method of claim 1, wherein determining the data from the one or more digital content items stored in the content management system via execution of the one or more tasks comprises generating… (Fig 1a).
Deo does not explicitly disclose; however, Sollami teaches: using an image description model, an image description for a digital image stored in the content management system (“A quality level of the text description may be determined based on metrics for user interactions with a website for a product depicted in the image that includes the text description of the image generated by inputting the image and metadata for the image to the description generating model. The description generating model may be adjusted based on the determined quality level of the text description. The text description generated by the description generating model from an input image may be placed on a webpage of a website that may sell the product depicted in the image, for example, a webpage of a ecommerce website that may be used to purchase the product.” [0025]).
Deo, Song and Sollami are in the same field of endeavor of using LLMs. Deo discloses using an LLM for taking input and information/context about a content item and generating a post. Song teaches a system that takes client queries to break a task down into a workflow and return an instruction to be executed on the device. Sollami teaches an image description model for items in an ecommerce system. It would have been obvious to one of ordinary sill in the art before the effective filing date to modify the known LLM based content management system as disclosed by Deo and Song with the known image description model for ecommerce as taught by Zhou to yield predictable results of automating product listings.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trujillo can be reached at (571)-272-3677. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ERIC NILSSON/ Primary Examiner, Art Unit 2151
Prosecution Insights