DETAILED ACTION
This Final Office Action is in response to Applicant's amendments and arguments filed on August 14, 2025. Applicant has amended claims 1 and 9 and added claims 17-21. Currently, claims 1-21 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
The 35 U.S.C. 101 rejections of claims 1-16 are maintained in light of applicant’s amendments to claims 1 and 9.
Response to Arguments
Applicant remarks submitted on 8/14/25 have been considered and are not persuasive. Applicant argues on p. 8 of the remarks that the 101 rejection is improper. Examiner disagrees. Applicant argues on p. 9 of the remarks that the claims are improvements to networked computer systems and because the claims recite an unconventional combination of elements that is significantly more than the abstract idea. Applicant argues on p. 10-11 of the remarks that the claims recite specific network components that improve upon computer functionality. Examiner disagrees and notes that such elements are merely computer tools for implementing the abstract idea or linking the abstract to specific computing environments as opposed to improving the computer itself. Applicant argues on p. 12 that the claims enable disparate computer systems to work together. Examiner notes using disparate systems is not an improvement to computing technologies but rather application of computing technology to implement a solution where the solution is an abstract idea. Applicant further argues on p. 14 of the remarks that the claims amount to significantly more than the abstract idea. Examiner disagrees and notes the ordered combination of elements has been considered and is not considered unconventional because data is received, analysis and results are generated and then output which is a conventional order of elements in data modeling. Therefore, the 101 rejections are maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 21 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The acronym BI is not properly defined in the claim and thus indefinite.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-21 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (a platform and a method). Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 9 and 17 recite the abstract idea of receiving user input data and third party data wherein the user input data comprises a set of requirements by the user, and the third party data comprises climate data, remote sensing data, floodplain data and storm drainage systems data and extracting the third party data and using a flood hydrograph data model manager to run a continuous simulation using physical hydrology and hydraulic model with updated periodically parameters that represent either past or future meteorological conditions and continuously update with simulation results and integrate the extracted third party data with the physical hydrology and hydraulic model and analyze the user input data and the third party data and provide a predictive simulation output as an impact on the storm drainage system and a mitigation action based on the user input data and third party data and using visualization-actions module configured to provide a visualization wherein the visualization actions module comprises a flow control algorithm configured to analyze the predictive simulation output and identify storm drainage system assets that might be subject to overload beyond capacity limit or failure and a flood assessment algorithm configured to analyze the predictive simulation output and identify potentially flood area. The claims are directed to a type of data analysis to determine a potentially flooded area. Under prong 1 of Step 2A, these claims are considered abstract because the claims are certain methods of organizing human activity including mitigating risk and mental processes including an observation, evaluation, judgment or opinion. The claims are considered organizing human activity such as mitigating risk because the claims show a visualization to the user (a type of human activity) for the purpose of identify assets that may fail and potentially flood area which can be considered a type of risk mitigation. The claims are also a type of mental process including evaluation or judgment because the claims are analyzing data to make a judgement about assets that may overload and identification of potentially flooded areas. Under prong 2 of Step 2A, the judicial exception is not integrated into a practical application because the claims (the judicial exception and any additional elements individually or in combination such as a mobile computing device, data in real time, a knowledge extractor module configured to extract the data using a set of RESTful web services and a database and an artificial neural network wherein the artificial neural network comprises neurons and is configured to learn and store information through a training process and integrate the extracted third party data with the physical hydrology and a visualization on the mobile computing device) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception. These limitations at best are merely implementing an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as a mobile computing device, data in real time, a knowledge extractor module configured to extract the data using a set of RESTful web services and a database and an artificial neural network wherein the artificial neural network comprises neurons and is configured to learn and store information through a training process and integrate the extracted third party data with the physical hydrology and a visualization on the mobile computing device (as evidenced by para [0024], [0027], [0034] and Figs 2-4 of applicant’s own specification) are well understood, routine and conventional in the field. Dependent claims 2-8, 10-16, 18 also do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements either individually or in combination are merely an extension of the abstract idea itself by further showing wherein the third party data further comprises flow monitoring data and wherein the third party data further comprises vulnerable asset data and wherein the user input data further comprises vulnerable asset data and wherein the visualization comprises a summary of possible upcoming storm event and identification of the storm event’s return period and wherein the visualization comprises prediction of bottle necks for at least one of a stormwater and a wastewater conveyance system and wherein the visualization comprises identification of one or more potentially flooded streets and wherein the visualization comprises identification of an asset’s criticality level under at least one of a projected storm event and an historical storm event and wherein the user input data comprises a set of requirements by the user. Dependent claims 19-21 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination such as wherein the third party data comprises climate data, remote sensing data, floodplain data, and storm drainage systems and wherein the knowledge extractor module is configured to extract the third party data using a set of RESTFul web services and wherein the visualization is provided through a Power BI dashboard (as evidenced by para [0024], [0027], [0034] and Figs 2-4 of applicant’s own specification) are well understood, routine and conventional in the field.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 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.
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.
Claims 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (US 2021/0149929 A1) (hereinafter Shen).
Claim 17:
Shen, as shown, discloses the following limitations of claim 17:
An actionable stormwater services platform (Fig 1, showing a platform for flood trigger system) comprising: an interface, wherein the interface is interactive between a user and the platform (see para [0006], "The system includes a plurality of computer processing modules: a flood trigger system, a SAR data query system, and a RAPID kernel algorithm system, running in real time, to identify the potential flood zones, query SAR data, and finally compute the inundation maps, respectively. As disclosed herein, the RAPID kernel algorithm is extended to a fully automated flood mapping system that requires no human interference from the initial flood events discovery to the final flood map production." and see para [0040], "As such, it should be noted that a plurality of hardware- and software-based devices, as well as a plurality of different structural components, may be utilized to implement the invention. For example, “mobile device,” “computing device,” and “server” as described in the specification may include one or more electronic processors, one or more memory modules including non-transitory computer-readable medium, one or more input/output interfaces, and various connections (for example, a system bus) connecting the components." and see para [0063]-[0065], showing communication interface for the platform);
a components layer, wherein the components layer comprises a knowledge extractor module (see para [0122], "Water coverage-related features are extracted as input variables, and the water result from the compensation step (described above) is used as a “prediction result” to train the LBC." and see para [0145], "By combining statistical classification, morphological processing, multi-threshold compensation, and machine learning-based correction, RAPID extracts at high spatial resolution HO m) inundated areas that have been flooded from existing water bodies and isolated lowlands and reduces over- and under-detection and speckle noise without applying any filtering techniques, which cause severe problems using existing algorithms. By combining the strength of state-of-art technologies, such as radar polarimetry and machine learning, with information from multi-source remote-sensing datasets and products at high resolution (>30 m), including LCC, water probability, terrain data, and river bathymetry, RAPID achieved full automation and accuracy, as validated by selected flood events in Hubei, China, and Texas, United States, caused by Typhoon Nepartak (2016) and Hurricane Harvey (2017), respectively."), a data-model manager module (see para [0010], "The RAPID system can serve as the kernel algorithm to derive flood inundation products from satellites—both existing and to be launched—equipped with high-resolution SAR sensors, including Envisat, Radarsat, NISAR, Advanced Land Observation Satellite (ALOS)-1/2, Sentinel-1, and TerraSAR-X." and see para [0065]-[0067], showing RAPID system use of neural networks and hydrography data sets where the flood trigger system includes simulated tidal water level), and a visualization-actions module (see para [0017], "the invention provides a system to generate a flood inundation map. The system comprises a flood trigger system configured to identify a flood occurring zone having one or more bodies of water, a SAR data query system to identify relevant satellite images for the flood occurring zones, and a kernel algorithm system.");
wherein the knowledge extractor module is configured to receive user input data and extract third party data in real time, and wherein the knowledge extractor is configured to inform the data-model manager module with the user input data and the third party data (see para [0006], "The system includes a plurality of computer processing modules: a flood trigger system, a SAR data query system, and a RAPID kernel algorithm system, running in real time, to identify the potential flood zones, query SAR data, and finally compute the inundation maps, respectively. As disclosed herein, the RAPID kernel algorithm is extended to a fully automated flood mapping system that requires no human interference from the initial flood events discovery to the final flood map production." and see para [0122], "Water coverage-related features are extracted as input variables, and the water result from the compensation step (described above) is used as a “prediction result” to train the LBC." and see para [0145], "By combining statistical classification, morphological processing, multi-threshold compensation, and machine learning-based correction, RAPID extracts at high spatial resolution HO m) inundated areas that have been flooded from existing water bodies and isolated lowlands and reduces over- and under-detection and speckle noise without applying any filtering techniques, which cause severe problems using existing algorithms. By combining the strength of state-of-art technologies, such as radar polarimetry and machine learning, with information from multi-source remote-sensing datasets and products at high resolution (>30 m), including LCC, water probability, terrain data, and river bathymetry, RAPID achieved full automation and accuracy, as validated by selected flood events in Hubei, China, and Texas, United States, caused by Typhoon Nepartak (2016) and Hurricane Harvey (2017), respectively." );
wherein the data-model manager module is configured to integrate the user input data and third party data with a machine learning module comprised of an artificial neural network (see para [0017], "In one embodiment, the invention provides a system to generate a flood inundation map. The system comprises a flood trigger system configured to identify a flood occurring zone having one or more bodies of water, a SAR data query system to identify relevant satellite images for the flood occurring zones, and a kernel algorithm system. The kernel algorithm system includes an electronic processor configured to receive the data from the flood trigger system, receive the satellite images from the SAR data query system, generate a binary classification of water and non-water at pixel level of the satellite images, morphologically process the satellite images to reduce over-detection of the bodies of water and to reduce under-detection of the bodies of water, apply a multi-threshold compensation to reduce speckle noise in the bodies of water, apply machine learning-based correction for speckle, and generate a flood inundation map." and see para [0062], " To address these issues, a fully automated, radar-produced inundation diary (RAPID) system to detect open flood extent was developed. Operating in NRT, RAPID fully integrates radar polarimetry, SAR statistics, morphology, and machine-learning methods to address the identified issues in detecting open flood water. No individual operator attention is needed, although RAPID does not detect flooding under vegetation due to difficulties outlined above. As discussed below, the four automated processing steps are described and show the advantage of synergies of multisource ancillary data, including high-resolution topography, high-resolution water occurrence, land cover classification (LCC), and river width, hydrography, and water type databases." and see para [0067], " With reference to FIG. 3, the RAPID kernel algorithm system 106 can apply learning (artificial intelligence) to mimic cognitive functions, including but not limited to learning and problem solving. Machine learning generally refers to the ability of a computer program to learn without being explicitly programmed. In some embodiments, a computer program (sometimes referred to as a learning engine) is configured to construct a model (for example, one or more algorithms) based on example inputs. Supervised learning involves presenting a computer program with example inputs and their desired (actual) outputs. The computer program is configured to learn a general rule (a model) that maps the inputs to the outputs. The computer program may be configured to perform machine learning using various types of methods and mechanisms. For example, the computer program may perform machine learning using decision tree learning, association rule learning, artificial neural networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, and genetic algorithms. Using all of these approaches, a computer program may ingest, parse, and understand data and progressively refine models for data analytics. Once trained, the computer system may be referred to as an intelligent system, an artificial intelligence (AI) system, a cognitive system, or the like. The RAPID kernel algorithm system 106 may be “trained” using various machine learning techniques. The classified result from the previous steps is used to train the classifier. It is an advantage of RAPID because a user does not have to do manually labeling. Each image learns from itself.");
wherein the artificial neural network comprises neurons and is configured to analyze the user input data and the third party data and provide a predictive simulation output, whereby it learns and stores information through a training process (see para [0067], "With reference to FIG. 3, the RAPID kernel algorithm system 106 can apply learning (artificial intelligence) to mimic cognitive functions, including but not limited to learning and problem solving. Machine learning generally refers to the ability of a computer program to learn without being explicitly programmed. In some embodiments, a computer program (sometimes referred to as a learning engine) is configured to construct a model (for example, one or more algorithms) based on example inputs. Supervised learning involves presenting a computer program with example inputs and their desired (actual) outputs. The computer program is configured to learn a general rule (a model) that maps the inputs to the outputs. The computer program may be configured to perform machine learning using various types of methods and mechanisms. For example, the computer program may perform machine learning using decision tree learning, association rule learning, artificial neural networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, and genetic algorithms. Using all of these approaches, a computer program may ingest, parse, and understand data and progressively refine models for data analytics. Once trained, the computer system may be referred to as an intelligent system, an artificial intelligence (AI) system, a cognitive system, or the like. The RAPID kernel algorithm system 106 may be “trained” using various machine learning techniques. The classified result from the previous steps is used to train the classifier. It is an advantage of RAPID because a user does not have to do manually labeling. Each image learns from itself." Where it would be obvious to one of ordinary skill in the art that the data from the RAPID kernel algorithm system can be considered the neurons as that is the data that is trained and used as nodes in a machine learning technique);
wherein the data-model manager is configured to inform the visualization-actions module with results of its analysis (see para [0067], "The computer program may be configured to perform machine learning using various types of methods and mechanisms. For example, the computer program may perform machine learning using decision tree learning, association rule learning, artificial neural networks, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, sparse dictionary learning, and genetic algorithms. Using all of these approaches, a computer program may ingest, parse, and understand data and progressively refine models for data analytics. Once trained, the computer system may be referred to as an intelligent system, an artificial intelligence (AI) system, a cognitive system, or the like. The RAPID kernel algorithm system 106 may be “trained” using various machine learning techniques. The classified result from the previous steps is used to train the classifier. It is an advantage of RAPID because a user does not have to do manually labeling. Each image learns from itself.");
wherein the visualization-actions module comprises a flood control algorithm and a flood assessment algorithm that are configured to analyze the simulation output (see para [0017], "the invention provides a system to generate a flood inundation map. The system comprises a flood trigger system configured to identify a flood occurring zone having one or more bodies of water, a SAR data query system to identify relevant satellite images for the flood occurring zones, and a kernel algorithm system.");
wherein the visualization-actions module further comprises a visualization component that is configured to provide a visualization to the user through the interface (Fig 5-17, where the various maps and graphs are visualizations of the analysis)
Claim 18:
Further, Shen discloses the following limitations:
wherein the user input data comprises a set of requirements by the user (see par a[0122], "user-defined thresholds are applied to the predicted water probability to correct the water mask. Unlike in usual machine-learning procedures, the pixels for training and correction in RAPID are in the same set, and neither cross-validation nor optimization is needed in the training.")
Claim 19:
Further, Shen discloses the following limitations:
wherein the third party data comprises climate data, remote sensing data, floodplain data, and storm drainage systems (see para [0007], "he flood trigger system identifies flood occurring zones and allows identifying what Sentinel-1 images should be processed. This saves computational resources and storage, and allows automation. The flood trigger system combines above-flood-stage information from about 4,400 U.S. Geological Survey monitoring stations and cumulative IMERG precipitation (G. Huffman et al. 2014) at daily scale. (IMERG is the Integrated Multi-satellitE Retrievals for GPM; GPM is the Global Precipitation Measurement operation of NASA.) The IMERG precipitation is particularly important for triggering outside the CONUS area where we lack in situ observations. (CONUS refers to the continental United States.)" and see para [0009], ". Besides SAR data, the system integrates multisource remote-sensing data products, including land cover classification, water occurrence, hydrographical, water type, and river width products. In comparison to expert handmade flood maps, the fully-automated RAPID system exhibited “overall,” “producer,” and “user” accuracies of 93%, 77%, and 75%, respectively. RAPID accommodates commonly encountered over- and under-detections caused by noise-like speckle, water-like radar response areas, strong scatterers, and isolated inundation areas—errors that are in common practice to ignore, mask out, or be filtered out by coarsening the effective resolution." and see para [0017], "The system comprises a flood trigger system configured to identify a flood occurring zone having one or more bodies of water, a SAR data query system to identify relevant satellite images for the flood occurring zones, and a kernel algorithm system. The kernel algorithm system includes an electronic processor configured to receive the data from the flood trigger system, receive the satellite images from the SAR data query system, generate a binary classification of water and non-water at pixel level of the satellite images, morphologically process the satellite images to reduce over-detection of the bodies of water and to reduce under-detection of the bodies of water, apply a multi-threshold compensation to reduce speckle noise in the bodies of water, apply machine learning-based correction for speckle, and generate a flood inundation map." and see para [0122], "user-defined thresholds are applied to the predicted water probability to correct the water mask. Unlike in usual machine-learning procedures, the pixels for training and correction in RAPID are in the same set, and neither cross-validation nor optimization is needed in the training." see para [0151], "The RAPID system is driven by Sentinel-1 SAR imagery provided by the European Space Agency (ESA), which are the only freely available satellite SAR data with global coverage. By applying an automatic processing chain, the method could be further applied to more sources of SAR data, such as the soon to be launched Surface Water and Ocean Topography (SWOT) and NASA-ISRO SAR (NISAR), which is expected to deliver the next generation of global high quality surface water data (Frasson et al. 2019a; NASA 2019). Ancillary data include water surface occurrence, land cover classification, hydrography, and river width, as detailed in the RAPID kernel algorithm (Shen et al. 2019b). The accuracy of the dataset is assessed by visual and quantitative comparison with National Oceanic and Atmospheric Administration (NOAA) event reports, the Federal Emergency Management Agency (FEMA) derived floodplain maps, and the water extent from the USGS Dynamic Surface Water Extent (DSWE) product. The final product includes flood extent in raster format and the associated event table. The proposed dataset can, therefore, facilitate various applications, including flood monitoring, inundation models calibration and verification (Afshari et al. 2018; Zeng et al. 2020), flood damage and risk assessment (Wing et al. 2017), and mitigation management (Wing et al. 2020).” and see para [0152])
Claim 20:
Further, Shen discloses the following limitations:
wherein the knowledge extractor module is configured to extract the third party data using a set of RESTFul web services (see para [0065], "The communications interface 18 allows the server 12 to communicate with devices external to the server 12. For example, as illustrated in FIG. 1, the server 12 may communicate with a SAR database(s) 24 including geolocation and backscattering images and/or a geographical database(s) 26 (e.g., including ancillary geographic and hydrography datasets). In particular, the communications interface 18 may include a port for receiving a wired connection to an external device (for example, a universal serial bus (USB) cable and the like), a transceiver for establishing a wireless connection to an external device (for example, over one or more communication networks 11, such as the Internet, a local area network (LAN), a wide area network (WAN), and the like), or a combination thereof. It should be understood that FIG. 2 illustrates one example of the system 10 and, in some embodiments, the server 12 may communicate with fewer or additional systems and components than illustrated in FIG. 2. For example, the server 12 may be configured to communicate with multiple SAR databases, multiple data sharing systems (of the same SAR database), multiple ancillary geographic and hydrography datasets, or a combination thereof. Also, the systems and components illustrated in FIG. 1 may be combined and distributed in various configurations. For example, in some embodiments, the flood trigger system 102 may include the radar polarimetry Stage IV, IMERG, USGS water watch, simulated stream flow NOAA/tidal water level, or simulated tidal water level, or a combination thereof. In some embodiments, the server 12 may also communicate with one or more user devices (terminals, tablet computers, laptop computers, desktop computers, smart wearables, smart televisions, and the like) that include similar components as the server 12. For example, in some embodiments, a user may interact with the server 12 via a user device to configure the system 10, such as by configuring or customizing the functionality of the server 12 as described herein. Although not illustrated in FIG. 2 or described herein, the SAR database 24, and the geographical database 26 may include similar components as the server 12." where it would be obvious to one of ordinary skill in the art that such a network using external and local networks and databases to manage the distributed data from the flood system can be considered RESTful web services given broadest reasonable interpretation)
Claim 21:
Further, Shen discloses the following limitations:
wherein the visualization is provided through a Power BI dashboard (see para [0045], "The SAR data query system 104 provides access to high resolution images of the Earth. Mapping techniques were developed that rely on synthetic aperture radar (SAR) on-board earth-orbiting platforms." where a tool that accesses to map and image data can be considered a dashboard)
Allowable Subject Matter
Claims 1-16 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 101, set forth in this Office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20190311280 A1
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 SUJAY KONERU whose telephone number is (571)270-3409. The examiner can normally be reached M-F, 8:30 AM to 5 pm.
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, Patricia Munson can be reached on 571- 270-5396. 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.
/SUJAY KONERU/
Primary Examiner, Art Unit 3624