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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 24 March 2026 has been entered.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 3-6, 8, 10-12, 14, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zou et al. (US Pub. No. 2024/0020556, effective filing date of Jan. 2021 (all citations may be found in Chinese Patent Application No. 202110099097.6), hereinafter “Zou”) in view of Pandit et al. (US Pub. No. 2019/0250893, published Aug. 2019, hereinafter “Pandit”).
Regarding claim 1, Zou teaches a computer-implemented method for providing a user interface to media devices that maximizes an objective function, comprising:
receiving sampling data from one or more media devices over a network (Zou, [0043] – “In step 101, a parameter optimization request initiated by a target user is detected and a parameter sampling algorithm matching the target user is determined.”, [0044] – “The information processing method according to some embodiments of the present disclosure can be applied to a service end. The service end is relative to the user end, and the service end and the user end correspond to a Client-Server (C/S) architecture… The user end can be configured on a user device. The user end can be, for example, a mobile phone, a tablet computer, a computer, a virtual reality device, an augmented reality device, a wearable device, or the like.”, and in [0046] – “The parameter optimization request can be generated by the target user based on the parameter to be optimized. Taking content recommendation in the field of e-commerce as an example, when a target user browses an e-commerce website, the website can push to the target user the content that is of interest to that target user. However, since there are various types of content and different types of content have different contributions to the click-through rate, the ratios of recommended content for various types can be used as parameters to be optimized, to perform parameter optimization.” – teaches receiving sampling data (optimization request initiated by user and parameter sampling algorithm is initiated) from one or more media devices over a network (service end is relative to user end in a C/S architecture, thus teaching a network, and user devices may be a phone, computer, VR device, AR device, etc., thus user devices are media devices));
in response to receiving the sampling data, generating, by at least one computer processor, a plurality of hyperparameter configurations for a machine learning model for providing a user interface to a media device based on the sampling data, wherein the plurality of hyperparameter configurations is associated with a learning algorithm (Zou, [0039] – “The main process of black box optimization is to use a black box algorithm to constantly generate new samples, and use the simulation function that simulates the actual calculation effect of users, i.e., the objective function, to simulate the use effect of the generated new samples to obtain the simulation results for the samples. By constantly generating new samples and constantly performing effect simulation of the newly generated samples, the sample with the highest use effect is selected as the global objective solution from many samples obtained.”, [0048] – “ In some optional embodiments, the parameter sampling algorithm can generate a plurality of samples at the same time for the parameter to be optimized, to evaluate the plurality of samples at the same time and improve the efficiency of parameter optimization. In order to identify parameter optimization requests of different users, the parameter optimization request may also include a trial identifier that is set for this parameter optimization trial of the target user, to distinguish the optimization processes for different parameters.”, [0054] – “The objective function can be determined by the optimization objective of the parameter to be optimized. For example, in the content recommendation scenario, the parameter to be optimized can be the parameter corresponding to the recommended content, and the test sample is the recommended content that is found according to the parameter to be optimized and shown to the browsing user. The higher the probability that the user clicks on the recommended content, the more effective the content recommendation is. Therefore, the prediction model for the click-through rate can be used as the objective function to predict the click-through rate of the recommended content by the browsing user, and the click-through rate obtained through the prediction is the simulation result for the test sample.”, and in [0056] – “Optionally, the operation of outputting the simulation result for the test sample for the target user may include: sending the simulation result for the test sample to the user end of the target user for the user end to display the output result for the test sample.” – teaches generating, in response to receiving the sampling data (user inputs parameters optimization request, parameter sampling initiates), by at least one computer processor (as in Zou at [0286]), a plurality of hyperparameter configurations (continuously samples parameters and performs effect simulations of parameters to identify the best parameters for optimization, includes parameter optimization trials for different users, thus teaching a plurality of hyperparameter configurations) for a machine learning model (in content recommendation example, prediction model for click-through rate can be used as objective function to predict click-through rate of recommended content, thus optimizing parameters for a machine learning model. Further examples for generating plurality of hyperparameter configurations for a machine learning model are taught in Zou at [0174] and [186]) for providing a user interface to a media device based on the sampling data (parameter to be optimized corresponds to recommended content, and the test sample is the recommended content that is shown to browsing user. Additionally, outputting the simulation result for the test sample includes sending result to user end to be displayed), wherein the plurality of hyperparameter configurations are associated with a learning algorithm (uses black-box algorithm and optimization to determine parameter trials, or configurations, for prediction model that learns content recommendations));
determining, using a hyperparameter tuning method, a hyperparameter configuration from the plurality of hyperparameter configurations that causes a training of the machine learning model using the learning algorithm to maximize an objective function based on one or more of computational efficiency, computer memory utilization, or power efficiency of the one or more media devices (Zou, [0037] – “The test sample may be used for determining a simulation result by way of calculation of an objective function, to output the simulation result for the test sample for the target user. Through interactions with the target user, a cloudification service for parameter optimization is implemented to achieve fast parameter optimization in the cloud, thereby improving the efficiency of parameter optimization. In addition, by acquiring the parameter optimization request and sample acquisition request from the user, the service demand of the user is acquired, and by providing effective response and feedback for the service demand of the user, the black box service is provided to the user effectively, so that the extended application of the black box service is realized, thus improving the utilization efficiency of the black box algorithm.”, [0039] – “The main process of black box optimization is to use a black box algorithm to constantly generate new samples, and use the simulation function that simulates the actual calculation effect of users, i.e., the objective function, to simulate the use effect of the generated new samples to obtain the simulation results for the samples. By constantly generating new samples and constantly performing effect simulation of the newly generated samples, the sample with the highest use effect is selected as the global objective solution from many samples obtained.” and in [0052] – “The test sample can be a parameter value that is set for the parameter to be optimized. Specifically, it can be a candidate solution generated by the parameter sampling algorithm for the parameter to be optimized, and when this candidate solution satisfies the optimization condition, it constitutes the final optimization result of the parameter to be optimized.” – teaches determining, using a hyperparameter tuning method (black-box optimization), a hyperparameter configuration from the plurality of hyperparameter configurations that causes training of the machine learning model using the learning algorithm to maximize an objective function (uses black-box algorithm to constantly generates new samples, or configurations, and uses the simulation function to simulate effects of the samples, sample with highest use effect is selected as global objective solution. When candidate solution satisfies optimization condition, it constitutes final optimization result, thus determining a hyperparameter configuration that causes training of the machine learning model to maximize an objective function) based on one or more of computational efficiency, computer memory utilization, or power efficiency (simulation result determined by calculation of objective function with test sample and through interaction with target user, response and feedback are received to define objectives based on one or more of efficiency of parameter optimization, thus computational efficiency, and/or utilization, or memory utilization, efficiency of the black box algorithm));
training the machine learning model according to the hyperparameter configuration using the learning algorithm to maximize the objective function for the user interface (Zou, [0184] – “Second, for the social field, content recommendation for social users and material recommendation for students are also common in the social field. Recommendations in the social field are usually that social users browse social applications, and the applications display social content of interest to the users in the display interface. Generally, recommendations in the social field are usually that options such as historical browsing behavior, areas of interest, and user information of users form feature parameters, and combinations of different options can form parameters to be processed. When test samples corresponding to the parameters to be processed are determined, the recommendation of social content can be made according to the test samples.” and in [0185] – “The technical solution of embodiments of the present disclosure can be configured in the cloud server, and the parameter optimization request can be initiated by the operation and maintenance personnel, who can use the user information, social type, and so on, as the parameters to be processed, and constantly generate test samples using the parameter sampling algorithm. Simulation results for test parameters are obtained by simulating the use effect of the test samples, to select target samples from a plurality of test samples. Further, the feature parameters used for social content recommendation are set according to the target samples, to achieve accurate recommendations.”– teaches training the machine learning model according to the hyperparameter configuration using the learning algorithm (simulates test samples to determine optimal parameter configuration and uses optimal configuration to generate content recommendation, thus training the machine learning model according to the hyperparameter configuration. Zou also teaches in [0186] training the machine learning model according to the hyperparameter configuration in stock indices example) to maximize the objective function (when optimization condition is satisfied, system provides final optimization result of parameters as in Zou at [0052]) for the user interface (recommendation of social content that is displayed to users in the display interface is based on the test samples, or configurations, to achieve accurate recommendations));
Zou fails to explicitly teach modifying, using the trained machine learning model, how the user interface is displayed to maximize the objective function; and providing the modified user interface to the one or more media devices.
However, analogous to the field of the claimed invention, Pandit teaches:
an objective function based on one or more of computational efficiency, computer memory utilization, or power efficiency of the one or more media devices (Pandit, [0055] – “At 808, an optimal amelioration action may be selected and/or prioritized based on at least one optimization objective function and the generated amelioration action. An optimization objective function may be an objective that advances a company's goal and/or guidelines.” and in [0056] – “The prioritization of action may be based on the steps or actions, which need to be performed during a step by step execution in order to prevent any damage to the working code. The prioritization may be based on space, memory usage, whether the code is needed or not. For instance, an optimization action that takes up less space and memory usage may be prioritized before an optimization action that takes up more space and memory usage.” – teaches maximizing an objective function (optimal amelioration action prioritized based on at least one optimization objective function) based on one or more of a computational efficiency, computer memory utilization, or power efficiency of the one or more media devices (prioritization may be based on space, memory usage));
training the machine learning model according to the hyperparameter configuration using the learning algorithm to maximize the objective function for the user interface (Pandit, [0030] – “The number of layers and activation nodes, and other hyperparameters are configurable and may be updated based on feedback” and in [0031] – “After the determined action or actions have been performed, a user feedback is gathered in order to establish whether a predicted action is suitable or not. The learning process also takes into consideration the user's profile (e.g., seniority, number of years in industry, past accuracy, and/or others) and the user's historical actions are also weighted accordingly. Based on the user feedback after a number of initial iterations, a filtered vector space of state and actions are used to re-train the model in order to re-learn an improved classification.” – teaches training the machine learning model according to the hyperparameter configuration using the learning algorithm (re-trains the model after receiving feedback, wherein the configurable hyperparameters are be updated, thus training the model according to the hyperparameter configuration). Pandit further teaches in [0039] – “The training part includes understanding the preferences of the user, e.g., the sources and rankings (e.g., including a search engine ranked trusted links in order of usage and citations of the code), which are frequently used by the user. Feedback mechanism helps in building the confidence level of those respective sources in order to take the ameliorative action. In this example, the neural network algorithm determines an appropriate action which can be taken.” and in [0055] – “At 808, an optimal amelioration action may be selected and/or prioritized based on at least one optimization objective function and the generated amelioration action. An optimization objective function may be an objective that advances a company's goal and/or guidelines. For instance, as discussed with reference to 810 below, once the risk code analysis is performed, the code may be adjusted or modified to comply with a company's guidelines.” – teaches training the machine learning model to maximize the objective function for a user interface (trains model to determine appropriate action, action is selected based on optimization objective function for modifying code of application to comply with company’s goal or guidelines, thus maximizing the objective function for a user interface));
modifying, using the trained machine learning model, how the user interface is displayed to maximize the objective function (Pandit, [0041] – “In some embodiments, an action may include changing characteristics of a graphical user interface (GUI) or a user interfere (UI). A GUI (and/or UI) controller may be provided for filtering, delaying, changing of the characteristics and nature of the UI interfaces, devices, and/or the like” and in [0055] – “At 808, an optimal amelioration action may be selected and/or prioritized based on at least one optimization objective function and the generated amelioration action. An optimization objective function may be an objective that advances a company's goal and/or guidelines. For instance, as discussed with reference to 810 below, once the risk code analysis is performed, the code may be adjusted or modified to comply with a company's guidelines. A piece of code may be rectified based on historical patterns of the user or monitored sources which was learnt by the neural networks algorithm.” – teaches updating, using the trained machine learning model (as in Pandit at [0035]), how the user interface is displayed to maximize the objective function (action selected based on optimization objective function, action includes changing characteristics of a GUI or UI, thus modifying how the user interface is displayed to maximize the objective function)); and
providing the modified user interface to the one or more media devices (Pandit, [0039] – “The training part includes understanding the preferences of the user, e.g., the sources and rankings (e.g., including a search engine ranked trusted links in order of usage and citations of the code), which are frequently used by the user. Feedback mechanism helps in building the confidence level of those respective sources in order to take the ameliorative action. In this example, the neural network algorithm determines an appropriate action which can be taken” and in [0041] – “In some embodiments, an action may include changing characteristics of a graphical user interface (GUI) or a user interfere (UI). A GUI (and/or UI) controller may be provided for filtering, delaying, changing of the characteristics and nature of the UI interfaces, devices, and/or the like” – teaches providing the modified user interface to the media devices (an action determined to be taken includes changing characteristics of a user interface of a device, thus providing updated user experience to the one or more media devices)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the user interface modification and output of Pandit to generation and determination of hyperparameter configurations and content recommendation of Zou. Doing so would provide systems that recommend extensions or replacements to user interfaces to achieve higher fidelity (Pandit, [0042]) and provide actions based on optimization of an objective function to modify user interfaces of a device (Pandit, [0020])
Claims 8 and 14 incorporate substantively all the limitations of Claim 1 in a system and non-transitory computer-readable medium, and thus are rejected on the same grounds as above.
Regarding claim 3, the combination of Zou and Pandit teach the computer implemented method of claim 1, wherein the hyperparameter tuning method comprises a grid search algorithm, a random search algorithm, a Bayesian optimization algorithm, a gradient-based optimization algorithm, an evolutionary optimization algorithm, a population-based training algorithm, or an early stopping-based algorithm (Zou, [0067] – “The black box algorithm component can be a core algorithm component Suggestion Service provided for developers, which can include, for example, one or more of random search/grid search/cmaes (maximum likelihood estimation)/pma (partitioning around medoid). The early stop component can stop parameter simulation having unsatisfactory process as early as possible by early stop techniques, to reduce resource consumption and accelerate the test progress.” – teaches wherein the hyperparameter tuning method comprises a grid search algorithm, a random search algorithm, or an early stopping-based algorithm (black-box algorithm includes one or more of random search, grid search, and/or an early stop component)).
Claims 10 and 16 are similar to claim 3, hence similarly rejected.
Regarding claim 4, the combination of Zou and Pandit teach the computer implemented method of claim 1, wherein the objective function is based on a business target (Pandit, [0055] – “At 808, an optimal amelioration action may be selected and/or prioritized based on at least one optimization objective function and the generated amelioration action. An optimization objective function may be an objective that advances a company's goal and/or guidelines.” – teaches optimizing an objective function, wherein the objective function may be an objective that advances a company’s goals or guidelines, and in [0055] – “Examples of an optimization objective function may include expectation-maximization algorithm, gradient descent, stochastic hill climbing, and/or others. In this context, an optimization objective function is used to optimize a selection of an amelioration action. For instance, an optimization objective function is used to find the optimal amelioration action that reduces or minimizes a specified risk” – teaches the objective function being a business target).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the objective function based on a business target of Pandit to the objective functions and hyperparameter optimization of Zou. Doing so would provide objective functions that optimize parameters to achieve company goals or guidelines and reduce specified risks (Pandit, [0055])
Claims 11 and 17 are similar to claim 4, hence similarly rejected.
Regarding claim 5, the combination of Zou and Pandit teach the computer implemented method of claim 1, wherein the generating, the determining, the training, and the providing are repeated according to a schedule (Pandit, [0031] – “After the determined action or actions have been performed, a user feedback is gathered in order to establish whether a predicted action is suitable or not. The learning process also takes into consideration the user's profile (e.g., seniority, number of years in industry, past accuracy, and/or others) and the user's historical actions are also weighted accordingly. Based on the user feedback after a number of initial iterations, a filtered vector space of state and actions are used to re-train the model in order to re-learn an improved classification. This re-training is done on a recurring schedule for ongoing refinement of the learning model” – teaches the generating, determining, training and providing are repeated according to a schedule (generating, determining, training, and provision are done on a recurring schedule for ongoing refinement of the model)).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the repetition according to a schedule of Pandit to the hyperparameter configurations and optimizations of Zou. Doing so would enable updating configurable hyperparameters based on user preferences and feedback (Pandit, [0030]) and provide ongoing refinement of the learning model (Pandit, [0031]).
Claim 18 is similar to claim 5, hence similarly rejected.
Regarding claim 6, the combination of Zou and Pandit teach the computer implemented method of claim 1, wherein the providing the modified user interface to the one or more media devices comprises: transmitting the modified user interface to the one or more media devices (Pandit, [0020] – “multidimensional risk vectors in relation to current and future use of undesired code are received, amelioration actions are classified using custom machine learning algorithms or models, an optimal amelioration action is selected based on at least one optimization objective function, and the amelioration action is executed or performed to control the undesired code use in an application. An example of an amelioration action may include changing of the characteristic and nature of a user interface (UI) of an application or device” – teaches transmitting the modified user interface to the media device (action may include changing of characteristics of user interface of a device, thus transmitting a modified user interface to the devices)).
Claims 12 and 19 are similar to claim 6, hence similarly rejected.
Claim(s) 2, 7, 9, 13, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zou and Pandit as applied to claims 1, 8, and 14 above, and further in view of Basu et al. (US2020/0202170, published June 2020, hereinafter “Basu”).
Regarding claim 2, the combination of Zou and Pandit teach the computer implemented method of claim 1.
The combination of Zou and Pandit fails to explicitly teach wherein the learning algorithm comprises at least one of an Upper Confidence Bound (UCB) algorithm, a Thompson sampling algorithm, or a cross entropy method (CEM) algorithm.
However, analogous to the field of the claimed invention, Basu teaches:
wherein the learning algorithm comprises at least one of an Upper Confidence Bound (UCB) algorithm, a Thompson sampling algorithm, or a cross entropy method (CEM) algorithm (Basu, [0042] – “the training module 320 implements an automatic tuning system to reduce the computational cost of training the combination model by using Thompson sampling and expected improvement (EI) Bayesian optimization.” – teaches wherein the learning algorithm used to train the machine learning model comprises at least one of a Thompson sampling algorithm).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the learning algorithm, specifically a Thompson sampling algorithm, of Basu to the method of Zou and Pandit in order to train the machine learning model using a hyperparameter configuration associated with a learning algorithm. Doing so would enable the training module to find the optimal hyperparameters in much less time than other solutions for determining hyperparameters (Basu, [0043]).
Claims 9 and 15 are similar to claim 2, hence similarly rejected.
Regarding claim 7, the combination of Zou and Pandit teach the computer implemented method of claim 1.
The combination of Zou and Pandit fails to explicitly teach wherein the generating the plurality of hyperparameter configurations for the machine learning model comprises: generating the plurality of hyperparameter configurations for the machine learning model based on historical offline data.
However, analogous to the field of the claimed invention, Basu teaches wherein the generating the plurality of hyperparameter configurations for the machine learning model comprises:
generating the plurality of hyperparameter configurations for the machine learning model based on historical offline data (Basu, [0027] – “As shown in FIG. 2, a data layer may include several databases, such as a database 218 for storing profile data, including both member profile data and profile data for various organizations (e.g., companies, schools, etc.). Consistent with some embodiments, when a person initially registers to become a member of the social networking service, the person will be prompted to provide some personal information, such as his or her name, age (e.g., birthdate), gender, interests, contact information, home town, address, the names of the member's spouse and/or family members, educational background (e.g., schools, majors, matriculation and/or graduation dates, etc.), employment history, skills, professional organizations, and so on. This information is stored, for example, in the database 218. Similarly, when a representative of an organization initially registers the organization with the social networking service, the representative may be prompted to provide certain information about the organization. This information may be stored, for example, in the database 218, or another database (not shown). In some example embodiments, the profile data may be processed (e.g., in the background or offline) to generate various derived profile data.” – teaches historical offline data, and in [0033] – “FIG. 3 is a block diagram illustrating the feed optimization system 216, in accordance with an example embodiment. In some embodiments, the feed optimization system 216 comprises any combination of one or more of a user interface module 310, a training module 320, and one or more databases 330. The user interface module 310, the training module 320, and the database(s) 330 can reside on a computer system, or other machine, having a memory and at least one processor (not shown). In some embodiments, the user interface module 310, the training module 320, and the database(s) 330 can be incorporated into the application server(s) 118 in FIG. 1. In some example embodiments, the database(s) 330 is incorporated into database(s) 126 in FIG. 1 and can include any combination of one or more of databases 218, 220, and 222” – teaches using the historical offline data in a feed optimizer comprising a training module that implements an automatic tuning system for hyperparameters).
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the using of offline historical data to generate a hyperparameter configuration of Basu to the method of Zou and Pandit in order to generate a set of hyperparameter configurations for the machine learning model based on historical offline data. Doing so would enhance profile data for members and organizations (Basu, [0027]) and generate evaluations for different parameter configurations using historical user behavior data (Basu, [0063]).
Claims 13 and 20 are similar to claim 7, hence similarly rejected.
Response to Arguments
Applicant’s arguments, see pp. 1-3 of Remarks, filed 24 March 2026, with respect to the rejection(s) of claim(s) 1, 3-6, 8, 10-12, 14, and 16-19 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made over Zou et al. (US Pub. No. 2024/0020556, effective filing date of Jan. 2021, hereinafter “Zou”) in view of Pandit. Zou teaches the amended limitations of claim 1 regarding “receiving sampling data…”, “in response to receiving sampling data, generating…”, “determining, using a hyperparameter tuning method, a hyperparameter configuration…”, and “training the machine learning model according to the hyperparameter configuration…”. Pandit teaches the amended limitations of claim 1 regarding “an objective function based on one or more of…”, “training the machine learning model according to the hyperparameter configuration…”, “modifying, using the trained machine learning model, how the user interface is displayed…”, and “providing the modified user interface…”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. (US Pub. No. 2020/0327264, published Oct. 2020) teaches a system for enhanced power system model calibration, where the model includes a plurality of parameters that are calibrated using Bayesian optimization. Teaches an interactive graphical user interface for defining or adjusting certain parameters and for receiving generated recommendations or results from the system.
Golovin et al. (US Pub. No. 2020/0167691, published May 2020) teaches methods for utilizing an optimization algorithm to generate suggested variants of machine-learning model based on prior evaluations of performance and adjustable parameter values. Teaches optimization of hyperparameters to include optimization of user interfaces of web services and physical systems.
Kristoffersen et al. (NPL: The Importance of Context When Recommending TV Content: Dataset and Algorithms, published June 2020) teaches methods for optimization of context-aware recommender systems for home entertainment. Teaches generating a plurality of hyperparameter configurations and determining a hyperparameter configuration that maximizes an objective function for recommending TV content to users.
Aharon et al. (NPL: Adaptive Online Hyper-Parameters Tuning for Ad Event-Prediction Models, published April 2017) teaches methods for an online hyperparameter tuning algorithm to determine hyperparameter configurations for best performance at a certain time interval. Teaches tuning hyperparameters to maximize an objective function regarding cost-per-thousand impressions, thus modifying a user interface to maximize a business objective.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LOUIS C NYE whose telephone number is 571-272-0636. The examiner can normally be reached Monday - Friday 9:00AM - 5:00PM.
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, MATT ELL can be reached at 571-270-3264. 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.
/LOUIS CHRISTOPHER NYE/Examiner, Art Unit 2141
/DANIEL T PELLETT/Primary Examiner, Art Unit 2121
Prosecution Insights