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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 22, 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Terminal Disclaimer
The terminal disclaimer filed on December 17, 2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of any patent granted on Application Number 17/857,407 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Amendment
The amendment filed December 17, 2025 has been entered. Claims 1. 3-12, and 14-22 remain pending in the instant application. Applicant’s amendments to the Claims have overcome each and every 112(a) rejection previously set forth in the Non-Final Office Action mailed September 17, 2025.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 3-12, and 14-22 have been considered but are moot because the new ground of rejection, necessitated by Applicant’s amendment does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-8 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran et al. (U.S. Pub. No. 2023/0169220 A1, filed Nov. 29, 2021), hereinafter Ramanasankaran, in view of Dharmadhikari et al. (U.S. Pat. No. 10,198,766 B1), hereinafter Dharmadhikari.
Regarding Claim 1, Ramanasankaran teaches A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions (“The applications 110, the twin manager 108, the cloud platform 106, and the edge platform 102 can be implemented on one or more computing systems, e.g., on processors and/or memory devices. For example, the edge platform 102 includes processor(s) 118 and memories 120, the cloud platform 106 includes processor(s) 124 and memories 126, the applications 110 include processor(s) 164 and memories 166, and the twin manager 108 includes processor (s) 148 and memories 150.”) (e.g., paragraph [0078]).
that, when executed by the at least one processor, cause the computing platform to: receive historical property information (“In step 602, the twin manager 108 receives information from a physical device and stores the information, or a link to the information, in the graph 529,” wherein the information is property information. “The telemetry component 560 can store the received information in the graph 529 by relating a node storing the information to a node representing the physical device.” In other words,) (e.g., paragraph [0156]).
wherein the historical property information includes: a number of bedrooms, a number of bathrooms […] floorplans (“The graph projections generated by the graph projection manager 156 and stored in the graph projection database 162 can be a knowledge graph and is an integration point. For example, the graph projections can represent floor plans and systems associated with each floor.” The floorplans and systems associated with each floor may comprise bathrooms or bedrooms, wherein the knowledge graph may be used to represent these types of rooms in the same way as the claimed invention.) (e.g., paragraph [0097]).
weather impacts […] effects of daily use (“The AI model storage 568 can store models for making energy load predictions for a building, weather forecasting models for predicting a weather forecast, action/decision models to take certain actions responsive to certain conditions being met, an occupancy model for predicting occupancy of a space and/or a building, etc. [...] In some embodiments, the core algorithm 598 can generate the timeseries 566 as an inference for a data point, e.g., a prediction of values for the data point at future times. The timeseries 564 may be actual data for the data point. In this regard, the core algorithm 598 can learn and train by comparing the inferred data values against the true data values.” The true data values are historical property information, wherein the values are input into a corresponding model modeling, for instance, weather forecast. Weather forecast is interpreted as weather impacts, and action/decision models are interpreted as effects of daily use.) (e.g., paragraphs [0150] and [0157]).
train, using the historical property information, a digital twin property evaluation model, configured to model a physical property based on characteristics of the physical property using a computer simulation (“The core algorithm 598 can run the model 576, e.g., train the model 576 and/or use the model 576 to make inferences and/or predictions,” wherein the model is associated with an AI agent. “Referring now to FIG. 6, a process 600 for executing an artificial intelligence agent to infer and/or predict information is shown, according to an exemplary embodiment.”) (e.g., paragraphs [0152] and [0155]).
wherein: training the digital twin property evaluation model further configures the digital twin property evaluation model to output event processing information for the physical property (“In step 604, the twin manager 108 and/or the cloud platform 106 receives an indication to execute an artificial intelligence agent of an entity represented in the graph 529, the AI agent being associated with a model [...] Responsive to receiving the indication, in step 606, the AI agent 570 causes a client instance 592 to run the model 576 based on the information received in step 602.”) (e.g., paragraphs [0157] and [0159]).
and training the digital twin property evaluation model comprises generating a knowledge graph (“In step 608, the AI agent 570 stores the inferred and/or predicted information in the graph 529 (or stores the inferred and/or predicted information in a separate data structure with a link to the graph 529).”) (e.g., paragraph [0160]).
wherein each node of the knowledge graph comprises a machine learning model and each edge of the knowledge graph represents relationships between features corresponding to each machine learning model (“The nodes 202-240 represent different types of entities, devices, locations, points, persons, policies, and software services (e.g., API services). The edges 250-272 represent relationships between the nodes 202-240, e.g., dependent calls, API calls, inferred relationships, and schema relationships ( e.g., BRICK relationships) [...] In step 604, the twin manager 108 and/or the cloud platform 106 receives an indication to execute an artificial intelligence agent of an entity represented in the graph 529, the AI agent being associated with a model.” ) (e.g., paragraphs [0113] and [0157]).
receive, from a client device, an event processing request identifying a first physical property (“In step 604, the twin manager 108 and/or the cloud platform 106 receives an indication to execute an artificial intelligence agent of an entity represented in the graph 529 […] In some embodiments, the indication is a triggering event that triggers the agent and is received from the building subsystems 122 and/or another agent.”) (e.g., paragraph [0157]).
generate, using the digital twin property evaluation model, a computer simulation of the first physical property (“Digital twins can be digital replicas of physical entities that enable an in-depth analysis of data of the physical entities and provide the potential to monitor systems to mitigate risks, manage issues, and utilize simulations to test future solutions [...] Responsive to receiving the indication, in step 606, the AI agent 570 causes a client instance 592 to run the model 576 based on the information received in step 602.”) (e.g., paragraphs [0098] and [0159]).
execute, over a period of time for simulation, the computer simulation of the first physical property to output event processing information for the first physical property (“In some embodiments, the core algorithm 598 can generate the timeseries 566 as an inference for a data point, e.g., a prediction of values for the data point at future times [...] In some embodiments, the AI agent 570 executes the model 576 based on the inferred and/or predicted information.”) (e.g., paragraphs [0154] and [0161]).
and send, to the client device, the event processing information and one or more commands directing the client device to display the event processing information (“The client 2802 can ingest the values of the retrieved information into the graphical building model 2804 which can be displayed on the user device 176.”) (e.g., paragraph [0261]).
wherein sending the one or more commands directing the client device to display the event processing information causes the client device to display the event processing information (“In some embodiments, when a particular visual component is being displayed on the user device 176 for the virtual model 2804, e.g., a building, the corresponding information for the building can be displayed in the interface, e.g., inferences, predictions, and/or operational data.”) (e.g., paragraph [0261]).
However, Ramanasankaran does not appear to specifically teach wherein the historical property information includes: […] location details […] a zip code, maintenance costs, and dates of repairs.
On the other hand, Dharmadhikari, which relates to improving loan quality and risk management, does teach wherein historical property information includes […] location details […] a zip code (“FIG. 2 illustrates exemplary loan data 121.” Figure 2 discloses a zip code, which is also interpreted as providing location details, as loan information associated with a property.”) (e.g., figure 2; column 6, lines 39-40).
maintenance costs, and dates of repairs (“Updated financial disclosure data 124 may also reflect various costs that a borrower incurs during a given year […] Other examples of costs include […] home repair costs (for example due to a disaster such as a flood, hurricane, fire, or earthquake.” Data regarding home repair costs during a given year are interpreted as including dates of repairs.) (e.g., column 12, lines 22-23 and 27-29).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine Ramanasankaran with Dharmadhikari. The claimed invention is considered to be combining prior art elements according to known methods to yield predictable results, see MPEP § 2143(I)(A). Ramanasankaran teaches a knowledge graph comprising a plurality of models to evaluate a digital twin. However, Ramanasankaran does not appear to specifically teach wherein the historical property information includes location details, a zip code, maintenance costs, and dates of repairs. On the other hand, Dharmadhikari does provide a model for selecting loan payment terms using location details, a zip code, maintenance costs, and dates of repairs. Furthermore, Ramanasankaran discloses that “Digital twins can be digital replicas of physical entities that enable an in-depth analysis of data of the physical entities and provide the potential to monitor systems to mitigate risks” (e.g., Ramanasankaran; paragraph [0098]). One of ordinary skill in the art could have merely added a node or multiple nodes to the knowledge graph of Ramanasankaran in order to also model the loan information disclosed in Dharmadhikari; in combination, the features used for loan risk management in Dharmadhikari and the knowledge graph of Ramanasankaran merely perform the same functions as they do separately, and one of ordinary skill in the art would have recognized the results of the combination as predictable. Therefore, it would have been obvious to a person of ordinary skill in the art to combine Ramanasankaran with Dharmadhikari in order to provide more accurate modelling of a building.
Regarding Claim 3, Ramanasankaran in view of Dharmadhikari teaches The computing platform of claim 1, wherein the machine learning models comprise one or more of: a climate change model, a credit history model, or a stock market model (“The AI model storage 568 can store models for making energy load predictions for a building, weather forecasting models for predicting a weather forecast, action/decision models to take certain actions responsive to certain conditions being met, an occupancy model for predicting occupancy of a space and/or a building, etc.”) (e.g., paragraph [0150]).
Regarding Claim 4, Ramanasankaran in view of Dharmadhikari teaches The computing platform of claim 1, wherein the machine learning models are characterized by different time scales (Figure 42 discloses a time scale of a day, and Figure 43 discloses a different time scale of multiple days. These time scales are applied to the digital twin, comprising the machine learning models.) (e.g., figures 42 and 43).
Regarding Claim 5, Ramanasankaran in view of Dharmadhikari teaches The computing platform of claim 1, wherein the machine learning models output information for each of a plurality of features (“The AI model storage 568 can store models for making energy load predictions for a building, weather forecasting models for predicting a weather forecast, action/decision models to take certain actions responsive to certain conditions being met, an occupancy model for predicting occupancy of a space and/or a building, etc.”) (e.g., paragraph [0150]).
wherein feature engineering is used to provide a model output by a single machine learning model (“The core algorithm 598 can run the model 576, e.g., train the model 576 and/or use the model 576 to make inferences and/or predictions […] In some embodiments, the core algorithm 598 can read and/or analyze the nodes and relationships of the graph 529 to make decisions.”) (e.g., paragraphs [0152] and [0153]).
and wherein the feature engineering causes at least one of the plurality of features to not be analyzed by the single machine learning model (“The model 576 can be loaded into the AI agent 570 from a set of AI models stored in the AI model storage 568. The AI model storage 568 can store models for making energy load predictions for a building, weather forecasting models for predicting a weather forecast, action/decision models to take certain actions responsive to certain conditions being met, an occupancy model for predicting occupancy of a space and/or a building, etc.” Each model is interpreted as analyzing only a single type of prediction (i.e., weather, occupancy), such that the weather prediction model does not analyze an energy prediction.) (e.g., paragraph [0150]).
Regarding Claim 6, Ramanasankaran in view of Dharmadhikari teaches The computing platform of claim 1, wherein the relationships between the features indicate how each feature affects other features (“The enrichment manager 138 can identify relationships between the thermostat and spaces, e.g., a zone that the thermostat is located in.” The thermostat is interpreted as affecting the temperature of a zone containing the thermostat.) (e.g., paragraph [0099]).
Regarding Claim 7, Ramanasankaran in view of Dharmadhikari teaches The computing platform of claim 6, wherein the digital twin property evaluation model automatically learns the relationships over time (“In some embodiments, the result of the inferences can be the timeseries 566. In some embodiments, the timeseries 564 is an input into the model 576 that predicts the timeseries 566.” This automatic feedback of the output timeseries to the input of the model is interpreted as the model automatically learning the relationship over time.) (e.g., paragraph [0153]).
Regarding Claim 8, Ramanasankaran in view of Dharmadhikari teaches The computing platform of claim 6, wherein the relationships are manually defined (“The twin manager 108 can be configured to manage and maintain a digital twin,” wherein the configuration may be performed manually by a user. “The twin manager 108 can include [...] an entity, relationship, and event database 160.”) (e.g., paragraph [0094]).
Regarding Claims 12 and 14-19, the claims recite substantially similar limitations to Claims 1-8, and the claims are rejected under 35 U.S.C 103 for the same reasons.
Regarding Claim 20, Ramanasankaran teaches One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to (“The applications 110, the twin manager 108, the cloud platform 106, and the edge platform 102 can be implemented on one or more computing systems, e.g., on processors and/or memory devices. For example, the edge platform 102 includes processor(s) 118 and memories 120, the cloud platform 106 includes processor(s) 124 and memories 126, the applications 110 include processor(s) 164 and memories 166, and the twin manager 108 includes processor (s) 148 and memories 150.”) (e.g., paragraph [0078]).
The remaining limitations of Claim 20 are substantially similar to Claim 1, and the claim is rejected under 35 U.S.C 103 for the same reasons.
Claim(s) 9, 11, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran in view of Dharmadhikari, further in view of Gil et al. (U.S. Pub. No. 2015/0269670 A1), hereinafter Gil.
Regarding Claim 9, Ramanasankaran in view of Dharmadhikari teaches The computing platform of claim 1. However, neither Ramanasankaran nor Dharmadhikari appear to teach wherein the event processing information indicates one or more of: loan information or a risk score indicating a level of risk associated with providing a loan for the first physical property.
On the other hand, Gil, which provides a loan risk model, does teach wherein the event processing information indicates one or more of: loan information or a risk score indicating a level of risk associated with providing a loan for the first physical property. (“At step 125 a predictive multi-output risk model is trained with the received plurality of loan account histories, the predictive multi-output risk model indicating a loan risk level associated with each of said received plurality of loan account histories and loan accounts according to a periodic basis up to the predetermined maximum look-head timeframe p.”) (e.g., paragraph [0033]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine the modified reference of Ramanasankaran in view of Dharmadhikari with Gil. The claimed invention is considered to be merely combining prior art elements according to known methods to yield predictable results, see MPEP § 2143(I)(A). Ramanasankaran teaches a knowledge graph comprising a plurality of models to evaluate a digital twin. However, Ramanasankaran does not appear to specifically teach wherein the processed information comprise loan information. On the other hand, Gil provides a model for loan risk assessment that processes loan information. Furthermore, Ramanasankaran discloses that “Digital twins can be digital replicas of physical entities that enable an in-depth analysis of data of the physical entities and provide the potential to monitor systems to mitigate risks” (e.g., Ramanasankaran; paragraph [0098]). One of ordinary skill in the art could have merely added a node to the knowledge graph of Ramanasankaran in order to also predict a loan risk; in combination, the loan risk model of Gil and the knowledge graph of Ramanasankaran merely perform the same functions as they do separately, and one of ordinary skill in the art would have recognized the results of the combination as predictable. Therefore, it would have been obvious to a person of ordinary skill in the art to combine the modified reference of Ramanasankaran in view of Dharmadhikari with Gil in order to additionally predict loan information.
Regarding Claim 11, Ramanasankaran in view of Dharmadhikari teaches The computing platform of claim 1. Ramanasankaran further teaches wherein the event processing information indicates a second physical property, with a predetermined number of matching features to the first physical property (“In some embodiments, the digital twins of the system 900 can be solution twins, e.g., the people counter twin 902, the HVAC digital twin 904, the facility manager twin 906, etc. The digital twin can be a solution twin because it represents a particular software solutions for the building.” A solution twin is interpreted as a second physical property with a number of matching features.) (e.g., paragraph [0176]).
However, Ramanasankaran does not appear to specifically teach wherein the second property also indicates loan information for both the first physical property and the second physical property, wherein the second physical property has a lower risk score than the first physical property, and a lower interest rate than the first physical property.
On the other hand, Gil, which provides a loan risk model, does teach loan information for both the first physical property and the second physical property (“At step 125 a predictive multi-output risk model is trained with the received plurality of loan account histories, the predictive multi-output risk model indicating a loan risk level associated with each of said received plurality of loan account histories and loan accounts according to a periodic basis up to the predetermined maximum look-head timeframe p.” This loan risk model may be included as a node in each knowledge graph corresponding to each digital twin of each physical property.) (e.g., paragraph [0033]).
wherein the second physical property has a lower risk score than the first physical property, and a lower interest rate than the first physical property (“The graphical user interface 200 allows the user to select two or more loan accounts or groups of loan accounts for comparison at two or more points in time [...] If a user clicks on 205, "High Risk in t1, and a Greater Risk in t2," then all loan accounts with a high risk at time t1 and with a higher risk at time t2 will be displayed or printed. If the user selects 210, "High Risk in t1, and a Lower Risk in t2," then all loan accounts with high risk at time t1 and with a lower risk at time t2 will be displayed or printed.” Knowledge graphs for a first and second property may comprise nodes corresponding to loan accounts with higher and lower risks.) (e.g., paragraph [0053]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine the modified reference of Ramanasankaran in view of Dharmadhikari with Gil for the same reasons as in Claim 9, above.
Regarding Claim 21, the claim recites substantially similar limitations to Claim 9, and the claim is rejected under 35 U.S.C 103 for the same reasons.
Claim(s) 10 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramanasankaran in view of Dharmadhikari and Gil, further in view of Filchenkov et al. (Filchenkov, Andrey, Natalia Khanzhina, Arina Tsai, and Ivan Smetannikov. "Regularization of autoencoders for bank client profiling based on financial transactions." Risks 9, no. 3 (2021): 54.), hereinafter Filchenkov.
Regarding Claim 10, Ramanasankaran in view of Dharmadhikari and Gil teaches The computing platform of claim 9. Gil further teaches wherein the event processing information includes an explanation of the loan information or the risk score (“In an embodiment of the invention, at step 140 the output of the predictive multi-output risk model previously trained is displayed to the logged-in user or users. The output of the predictive multi-output risk model indicates the loan risk level associated with each of the plurality of loan accounts according to the periodic basis up to the adjusted look-ahead timeframe p.”) (e.g., paragraph [0043]).
Dharmadhikari further teaches wherein the features comprise maintenance costs (“Updated financial disclosure data 124 may also reflect various costs that a borrower incurs during a given year […] Other examples of costs include […] home repair costs (for example due to a disaster such as a flood, hurricane, fire, or earthquake.” Data regarding home repair costs during a given year are interpreted as including dates of repairs.) (e.g., column 12, lines 22-23 and 27-29).
Ramanasankaran further teaches a likelihood of deterioration (In some embodiments, the AI agent is an agent for a specific entity represented in the graph 529. For example, the agent could be a VAV maintenance agent configured to identify whether a VAV (e.g., a VAV represented by the nodes 512, 530, and/or 516) should have maintenance performed at a specific time.” Identifying whether a VAV requires maintenance is interpreted as comprising a determination of a likelihood of deterioration.) (e.g., paragraph [0158]).
and market trends (“Thus, investor 170 and/or rating agency 160 may compare the relative trends of the loans underlying the [mortgage-backed securities (MBS)] to the nation as a whole.”) (e.g., column 28, lines 61-63).
However, neither Ramanasankaran nor Dharmadhikari nor Gil teaches wherein the explanation indicates features of the first physical property that primarily contributed to the loan information or the risk score.
On the other hand, Filchenkov, which relates similarly to bank client profiling and loan risk assessment, does teach wherein the explanation indicates features of the first physical property that primarily contributed to the loan information or the risk score (Figure 1 illustrates “Feature importance plot obtained after applying of Random Forest classifier. The y axes labels are the Merchant Category Codes,” wherein the category codes may instead be the features disclosed in Ramanasankaran, Dharmadhikari, and Gil.) (e.g., page 4, figure 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Applicant's claimed invention to combine the modified reference of Ramanasankaran in view of Dharmadhikari and Gil with Filchenkov. The claimed invention is considered to be using a known technique to improve similar devices (methods, or products) in the same way, see MPEP § 2143(I)(C). Ramanasankaran teaches a knowledge graph comprising a plurality of models to evaluate a digital twin, wherein the loan risk model of Gil may be added to the graph to provide a loan risk level. However, the Ramanasankaran-Gil combination does not appear to specifically teach wherein the output loan risk level includes an explanation of the risk score, wherein the explanation indicates features that primarily contributed to the risk score. On the other hand, Filchenkov does teach a method including providing a feature importance plot that describes which features primarily contribute to a risk score. As the Ramanasankaran-Gil combination provides a loan risk prediction model, one of ordinary skill in the art could have generated a feature importance plot from the features in the Ramanasankaran-Gil combination according to the method of Filchenkov, and one of ordinary skill in the art would have recognized the result as predictably identifying the features that most contributed to the loan risk score. Therefore, it would have been obvious to a person of ordinary skill in the art to combine the modified reference of Ramanasankaran in view of Dharmadhikari and Gil with Filchenkov in order to identify the most salient features of a digital twin that contributed to a loan risk prediction.
Regarding Claim 22, the claim recites substantially similar limitations to Claim 10, and the claim is rejected under 35 U.S.C 103 for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
O’Dierno et al. (U.S. Pub. No. 2023/0152764 A1) teaches a digital twin of a building using a knowledge graph.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE HWA-KAI TSENG whose telephone number is (571)272-3731. The examiner can normally be reached M-F 9A-5P PST.
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, Rehana Perveen can be reached at (571) 272-3676. 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.
/K.H.T./ Examiner, Art Unit 2189
/REHANA PERVEEN/ Supervisory Patent Examiner, Art Unit 2189