DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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 therefore, subject to the conditions and requirements of this title.
Claim(s) 1-20 rejected under 35 U.S.C. 101 because
Regarding claim 1 and analogous claim 8, 15:
Step 1 (whether a claim is to a statutory category):
Yes, the claim is within the four statutory categories (a process, machine, manufacture, or composition of matter). Claim 1 recites a method, therefore, falls within a process category.
Step 2A Prong 1 (whether a claim is directed to a judicial exception):
Yes, “receiving, via a communication interface, a natural language processing (NLP) task request comprising a user input from a user application;” wherein receiving a request describes a mental process (observation) See MPEP 2106.04(a)(2)(III). And, “generating one or more prompts based on the NLP task request;” wherein based on a request generate a prompt describes a mental process (observation, evaluation) See MPEP § 2106.04(a)(2)(III).
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “transforming, by a data gateway, the one or more prompts into a normalized API request;” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). And, “transmitting, by the data gateway, the normalized API request to an external vendor server hosting one or more neural network based NLP models,” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). And, “wherein the normalized API request is translated to a vendor-specific request for generating a vendor-specific response by the one or more neural network based NLP models, and wherein the vendor-specific response is translated to a normalized API response; and” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). And, “receiving, by the data gateway, the normalized response from the external vendor server” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, it describes an additional element as “apply it” or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 2 and analogous claim 9, 16:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “wherein the normalized API request comprises a parameter specifying a specific vendor and/or a specific neural network based NLP model” describes additional elements that integrate the judicial exception into a practical application with the words "apply it" (or an equivalent), such as mere (i.e., selecting a particular data source or type of data to be manipulated) to implement an abstract idea on a computer (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, it does not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Regarding claim 3 and analogous claim 10, 17:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “wherein the data gateway takes a form of a web application” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g) and that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
Step 2B (Inventive concept):
No, it describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 4 and analogous claim 11, 18:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “incorporating the one or more prompts into a payload of the normalized API request” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B (Inventive concept):
No, it does not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Regarding claim 5 and analogous claim 12, 19:
Further modifies the abstract idea of claim 1.
Step 2A Prong 1 (whether a claim is directed to a judicial exception):
Yes, “searching a database of user-specified prompt templates based on the NLP task;” wherein looking at data for prompt templated based on the task is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment). See MPEP § 2106.04(a)(2)(III). And, “retrieving, from a database of context features, one or more context vectors based on the NLP task request; and” wherein finding context vectors in a database based on the task is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “populating one or more user-specified prompt templates with the retrieved one or more context vectors” is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g)
Step 2B (Inventive concept):
No, it does not add significantly more since the intended practical application is well-understood, routine, and conventional and stated at a generic level (i.e. “apply it”, see MPEP 2106.05(f)).
Regarding claim 6 and analogous claim 13, 20:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “transmitting, via the data gateway to the external vendor server, a request to return a list of neural network based NLP models” describes an additional element as “apply it”, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Step 2B (Inventive concept):
No, it describes an additional element as “apply it” or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Regarding claim 7 and analogous claim 14:
Further modifies the abstract idea of claim 1.
Step 2A Prong 2 (evaluate whether the claim recites additional elements that integrate the exception into a practical application):
No, “wherein the customized generative Al infrastructure comprises a user-interactive chatbot user interface application that interactively receives one or more user utterances, and” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). And, “presents one or more responses generated by a neural network based NLP model through a secure connection” is an additional element that amounts to adding insignificant extra-solution activity (i.e., post solution activity) to the judicial exception. See MPEP §§ 2106.04(d), 2106.05(g).
Step 2B (Inventive concept):
No, it describes an additional element as “apply it” or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Asefi et al,. (US11481559B1) in view of Ganteaume et al., (US11256548B2), further in view of Reza et al., (US20230237277A1).
Regarding claim 1 and analogous claim 8, 15:
Asefi teaches:
A method of building a customized generative artificial intelligence (Al) application at an enterprise server, the method comprising: receiving, via a communication interface, a natural language processing (NLP) task request comprising a user input from a user application; (Col 2, lines 15-18, “The artificial intelligence natural language processing platform discussed herein provides training modules that connect to chatbot component application programming interfaces (APIs)”…Col 3, lines 35-36 “The conversation gateway 135 may receive a first user input including a first text string from a chatbot session (i.e., wherein nlp task under the broadest reasonable interpretation (BRI) is interpreted as first user input text to be processed)”…Col 3, lines 13-17, “For example, the user may be chatting with a chatbot via a chat user interface presented in webpage provided by the interactive channel provider 115. The interactive channel provider 115 may provide chatbot services from the AI NLP platform service 120.”)
transmitting, by the data gateway, the normalized API request to an external vendor server hosting one or more neural network based NLP models, (Col 3-4, lines 53, “the natural language processor may be a third-party NLP [external vendor]. The third-party NLP connector 145 may act as a conduit between the NLP engine 140 and a variety of third-party NLP services (not shown in FIG. 1). The third-party NLP services may provide NLP services to the NLP engine 140 that may be used in evaluating received text. The third-party NLP connector 145 may include a variety of API mechanisms that may be used to transmit data [transmitting] between the NLP engine 140 and the third-party NLP services. Functions provided by the third-party NLP connector 145 may include connectivity, data translation, data configuration, data masking, etc. For example, the NLP engine 140 may determine a domain for the received text and may transmit a request to a third-party NLP service for that domain via the third-party NLP connector 145.”)
wherein the normalized API request is translated to a vendor-specific request (Col 3, lines 53-65, “The third-party NLP connector 145 may include a variety of API mechanisms that may be used to transmit data between the NLP engine 140 and the third-party NLP services. Functions provided by the third-party NLP connector 145 may include connectivity, data translation, data configuration, data masking, etc. For example, the NLP engine 140 may determine a domain for the received text and may transmit a request to a third-party NLP service for that domain via the third-party NLP connector”)
receiving, by the data gateway, the normalized response from the external vendor server (Col 3, lines 54, “The third-party NLP connector 145 may act as a conduit between the NLP engine 140 and a variety of third-party NLP services (not shown in FIG. 1). The third-party NLP services may provide NLP services to the NLP engine 140 that may be used in evaluating received text. The third-party NLP connector 145 may include a variety of API mechanisms that may be used to transmit data between the NLP engine 140 and the third-party NLP services.”)
Asefi does not explicitly teach:
generating one or more prompts based on the NLP task request;
transforming, by a data gateway, the one or more prompts into a normalized API request;
for generating a vendor-specific response by the one or more neural network based NLP models, and
wherein the vendor-specific response is translated to a normalized API response; and
Ganteaume teaches:
transforming, by a data gateway, the one or more prompts into a normalized API request; (Col 3, lines 43-57, “To run a task using resources managed by the cloud service, information about the task must be provided to a virtual machine on the cloud service capable of handling the task. This information is called “task parameters” herein. Task parameters may be received in a variety of manners and may be received as “task input” prior to being organized, formatted, encoded, transformed [transforming], et cetera, into the parametric arrangement used by cloud computing data processing systems and methods disclosed herein and the virtual machines of disparate cloud services used in conjunction therewith. Task parameters can include common parameters and proprietary parameters (i.e., wherein normalized api request). Common parameters include those which are used for two or more distinct cloud services, or which are used by a cloud computing data processing system or method disclosed herein.”)
wherein the vendor-specific response is translated to a normalized API response; and (Col 8, lines 45-, “Configuration component 166 can be used to configure task requests and other interaction between cloud computing data processing system 152 and the cloud services it leverages or manages including dedicated solutions 182 and shared solutions 188. Each solution may define its own techniques for communications or require particular information, or particularly formatted information, for use. Configuration component 166 ensures that information sent to each solution accords with its requirements, and that traffic from solutions to cloud computing data processing system 152 is understood. In embodiments, configuration component 166 can be used for encoding, transformation, translation [translated], et cetera, of task information or other information to standardize such information in accordance with the arrangement of task repository 160 or other components of cloud computing data processing system”)
Ganteaume and Asefi are both related to the same field of endeavor (i.e., natural language processing). In view of the teachings of Ganteaume it would have been obvious for a person of ordinary skill in the art to apply the teachings of Ganteaume to Asefi before the effective filing date of the claimed invention in order to improve API framework for communicating with multiple third-party vendors (Ganteaume, Abstract, “Systems and methods allow users to leverage multiple disparate cloud solutions, offered by disparate service providers, in a unified and cohesive manner. A system includes a task repository configured to store a plurality of task parameters, wherein the task parameters cause one or more tasks to run on cloud services when provided to the cloud services including a dedicated solution and a shared solution, wherein the task parameters include common parameters and proprietary parameters, wherein the common parameters are common to two or more disparate cloud services, and wherein the proprietary parameters are unique to one of the cloud services. The system also includes an interface configured to receive task input including the plurality of task parameters.”)
Reza teaches:
generating one or more prompts based on the NLP task request; ([0034], “the dynamic prompting can be highly beneficial to develop a pre-trained model by appending the prompts to each set of input with an opinion and aspect. This will provide a better in-context learning and capture the opinion context information, which can lead to effective semantic information modelling and can benefit tasks like sentence sentiment, aspect sentiment, and question answering. Moreover, the dynamic prompting can be highly beneficial to improve confidence for downstream tasks.”)
for generating a vendor-specific response by the one or more neural network based NLP models, and ([0043], “The resource manager 225 and pre-training processing system 230 configure one or more models (e.g., a machine learning language model) for performing a task using one or more pre-training technologies. The one or more pre-training technologies may include without limitation: Word2vec, which is a shallow neural network that produces static word embeddings, BERT, which is a Transformer-encoder-based autoencoder language model for creating word embeddings and language modeling using fine-tuning-based and encoder-based methods, ELMo, which is a Long short-term memory (LSTM) based model for creating word embeddings and language modeling using encoder-based methods, GPT (i.e., wherein one or more neural network based NLP models), which is a transformer-decoder-based autoregressive language model for creating word embeddings and language modeling using fine-tuning-based methods, XLNet, which is an autoregressive and denoise autoencoder based model for language modeling, or any combination thereof.”)
Reza and Asefi are both related to the same field of endeavor (i.e., natural language processing). In view of the teachings of Reza it would have been obvious for a person of ordinary skill in the art to apply the teachings of Reza to Asefi before the effective filing date of the claimed invention in order to improve API framework for communicating with multiple third-party vendors by generating prompts for requests (Reza, Abstract, “Techniques for dynamically developing a contextual set of prompts based on relevant aspects extracted from s set of training data. One technique includes obtaining training data comprising text examples and associated labels, extracting aspects from the training data, generating prompting templates based on the training data and the extracted aspects, concatenating each of the text examples with the respective generated prompting template to create prompting functions, training a machine learning language model on the prompting functions to predict a solution for a task, where the training is formulated as a masked language modeling problem with blanks of the prompting templates being set as text labels and expected output for the task being set as specified solution labels, and the training learns or updates model parameters of the machine learning language model for performing the task. The machine learning language model is provided with the learned or updated model parameters.”)
Regarding claim 2 and analogous claim 9, 16:
Asefi, as modified by Ganteaume and Reza, teaches the method of claim 1.
Asefi does not explicitly teach:
wherein the normalized API request comprises a parameter specifying a specific vendor and/or a specific neural network based NLP model
Ganteaume further teaches:
wherein the normalized API request comprises a parameter specifying a specific vendor (Col 3, lines 43, “To run a task using resources managed by the cloud service, information about the task must be provided to a virtual machine on the cloud service capable of handling the task. This information is called “task parameters” herein. Task parameters may be received in a variety of manners, and may be received as “task input” prior to being organized, formatted, encoded, transformed, et cetera, into the parametric arrangement used by cloud computing data processing systems and methods disclosed herein and the virtual machines of disparate cloud services used in conjunction therewith. Task parameters can include common parameters and proprietary parameters”)
Reza further teaches:
and/or a specific neural network based NLP model ([0043], “The resource manager 225 and pre-training processing system 230 configure one or more models (e.g., a machine learning language model) for performing a task using one or more pre-training technologies. The one or more pre-training technologies may include without limitation: Word2vec, which is a shallow neural network that produces static word embeddings, BERT, which is a Transformer-encoder-based autoencoder language model for creating word embeddings and language modeling using fine-tuning-based and encoder-based methods, ELMo, which is a Long short-term memory (LSTM) based model for creating word embeddings and language modeling using encoder-based methods, GPT (i.e., wherein one or more neural network based NLP models), which is a transformer-decoder-based autoregressive language model for creating word embeddings and language modeling using fine-tuning-based methods, XLNet, which is an autoregressive and denoise autoencoder based model for language modeling, or any combination thereof.”)
The motivation is the same motivation as for claim 1.
Regarding claim 3 and analogous claim 10, 17:
Asefi, as modified by Ganteaume and Reza, teaches the method of claim 1.
Asefi further teaches:
wherein the data gateway takes a form of a web application (Col 3, lines 12, “The user 105 may be interacting with the interactive channel provider 115. For example, the user may be chatting with a chatbot via a chat user interface presented in webpage [web application] provided by the interactive channel provider 115. The interactive channel provider 115 may provide chatbot services from the AI NLP platform service 120. The AI orchestrator 130 may send and receive chatbot messages via the AI NLP platform service 120 that originate and terminate with the interactive channel provider 115. The conversation gateway 135 may obtain a first dialogue configuration ruleset for a first intent for a first entity.”)
The motivation is the same motivation as for claim 1.
Regarding claim 4 and analogous claim 11, 18:
Asefi, as modified by Ganteaume and Reza, teaches the method of claim 1.
Asefi, as modified by Reza, does not explicitly teach:
incorporating the one or more prompts into a payload of the normalized API request
Ganteaume further teaches:
incorporating the one or more prompts into a payload of the normalized API request (Col 3, lines, 60, “Accordingly, cloud computing data processing systems and methods disclosed herein can encode, format, translate, convert, or otherwise transform input or information related to a task to a standard field for consistent storage of task parameters and to facilitate tasks which are input in one manner to be available for running on cloud services which accept tasks in a different manner.”)
The motivation is the same motivation as for claim 1.
Regarding claim 5 and analogous claim 12, 19:
Asefi, as modified by Ganteaume and Reza, teaches the method of claim 1.
Asefi further teaches:
retrieving, from a database of context features, one or more context vectors based on the NLP task request; and (Col 4, lines 4, “The conversation gateway 135 may obtain a first dialogue configuration ruleset for a first intent for a first entity. Entities as used herein refers to the object of an intended action. For example, entities may include customer accounts, computing systems, organizations, databases, products, services, etc. For example, the user 105 may request a checking account balance and the entity may be account types. The configuration ruleset may include a variety of intents (e.g., the most likely requested action to be performed, etc.) associated with a variety of entities. The configuration rulesets may designate actions (e.g., responses, prompts, workflows, etc.) to be performed when an entity-intent combination is recognized in a chatbot session. The configuration ruleset may be stored in the database [from a database]155. The conversation gateway 135 may receive a first user input including a first text string from a chatbot session. For example, text of the request for the checking account balance may be received”)
Asefi, as modified by Ganteaume, does not explicitly teach:
wherein the one or more prompts are generated by: searching a database of user-specified prompt templates based on the NLP task;
populating one or more user-specified prompt templates with the retrieved one or more context vectors
Reza further teaches:
wherein the one or more prompts are generated by: searching a database of user-specified prompt templates based on the NLP task; ([0027]-[0028]“Even for tasks where prompt-based methods are known to be effective, a model's performance will depend on both the templates/vectors being used and the answer being considered. How to simultaneously search or learn [searching] for the best combination of template/vector [templates] and answer remains a challenge. To overcome these challenges and others, the techniques described herein are directed to improving prompt-based learning by dynamically developing a contextual set of prompts based on relevant aspects extracted. More specifically, an auto prompting framework is provided for training language models using prompt-based learning based on aspect information, which would make the model training more contextual based and would eventually help the models to be even more accurate in recalling the factual knowledge learned by the pre-trained and/or fine-tuned models on a given subject area.”)
populating one or more user-specified prompt templates with the retrieved one or more context vectors ([0061], “The dynamic prompting module 247 concatenates the generated prompting template with the respective text example to create a prompting function. For example, a prompting template and respective text example may be used as input for a concatenate function configured to join the two text strings into a single text string: the text example; the prompting template, such that the two text strings are now linked or associated with one another. The prompting functions may be stored in a storage device and/or communicated to the pre-training system 205 and/or the fine-tuning system 215 in order to further train a model on the prompting functions to predict a solution for the given task.”)
The motivation is the same motivation as for claim 1.
Regarding claim 6 and analogous claim 13, 20:
Asefi, as modified by Ganteaume and Reza, teaches the method of claim 1.
Asefi further teaches:
transmitting, via the data gateway to the external vendor server, (Col 3, lines 54, “The third-party NLP connector 145 may act as a conduit between the NLP engine 140 and a variety of third-party NLP services (not shown in FIG. 1). The third-party NLP services may provide NLP services to the NLP engine 140 that may be used in evaluating received text. The third-party NLP connector 145 may include a variety of API mechanisms that may be used to transmit data between the NLP engine 140 and the third-party NLP services.”)
Asefi, as modified by Ganteaume, does not explicitly teach:
a request to return a list of neural network based NLP models
Reza further teaches:
a request to return a list of neural network based NLP models ([0043], “The resource manager 225 and pre-training processing system 230 configure one or more models (e.g., a machine learning language model) for performing a task using one or more pre-training technologies. The one or more pre-training technologies may include without limitation: Word2vec, which is a shallow neural network that produces static word embeddings, BERT, which is a Transformer-encoder-based autoencoder language model for creating word embeddings and language modeling using fine-tuning-based and encoder-based methods, ELMo, which is a Long short-term memory (LSTM) based model for creating word embeddings and language modeling using encoder-based methods, GPT (i.e., wherein one or more neural network based NLP models), which is a transformer-decoder-based autoregressive language model for creating word embeddings and language modeling using fine-tuning-based methods, XLNet, which is an autoregressive and denoise autoencoder based model for language modeling, or any combination thereof.”)
The motivation is the same motivation as for claim 1.
Regarding claim 7 and analogous claim 14:
Asefi, as modified by Ganteaume and Reza, teaches the method of claim 1.
Asefi further teaches:
wherein the customized generative Al infrastructure comprises a user-interactive chatbot user interface application that interactively receives one or more user utterances, and (Col 6, lines 21-, “A conversation gateway component (e.g., the conversation gateway 135 as described in FIG. 1, etc.) may be included with the AI NLP processing platform that processes conversational dialogue between the NLP engines and an interactive channel provider [chatbot user interface]. The conversation gateway may include a variety of components including a load configuration and conversation component that loads configuration rulesets and manages the conversation between a user and the AI NLP processing platform”…Col 8, lines 16, “The dialogue designer may use an intent creation user interface of the AI NLP processing platform to generate a get balance intent. Utterances [user utterances] that may indicate the intent may be provided for the intent. The intent and the entity may then be linked as an entity-intent pair. The dialogue designer may configure actions to be performed for the entity-intent pair. The actions may include responses, operations, API calls, and the like.”)
presents one or more responses generated by a neural network based NLP model through a secure connection (Col 8, lines 17, “The dialogue designer may use an intent creation user interface of the AI NLP processing platform to generate a get balance intent. Utterances that may indicate the intent may be provided for the intent. The intent and the entity may then be linked as an entity-intent pair. The dialogue designer may configure actions to be performed for the entity-intent pair. The actions may include responses [presents one or more responses], operations, API calls, and the like”…Col 5-6, lines 65, “A bot console (e.g., the configuration console 150 as described in FIG. 1, etc.) may be included with the NLP engines that provides a variety of user interfaces for configuring and managing the NLP engines. The bot console may include a single sign-on component and user interface for configuring and managing authentication [secure connection] between the various components of the AL NLP processing platform.”)
The motivation is the same motivation as for claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Malviya et al., (US20240362937A1) teaches prompt generation, prompt processing, and natural language processing relevant to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMINA BENOURAIDA whose telephone number is (571)272-4340. The examiner can normally be reached Monday-Friday 8:30am-5pm ET..
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, Michael J. Huntley can be reached at (303) 297-4307. 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.
/AMINA MORENO BENOURAIDA/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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