DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/11/2026 has been entered.
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(s) (IDS) submitted on 02/11/2026 is/are being considered by the examiner.
Response to Amendment
The Amendment filed 05/11/2026 has been entered. Claims 3 and 7 have been cancelled. Therefore, claims 1-2, 4-6, and 8-11 remain pending in the application.
Response to Arguments
Applicant’s arguments, filed on 05/11/2026, have been fully considered but are not persuasive.
With respect to the 35 U.S.C. 103 rejection, on pages 6-9, of claims 1-2, 5-6, and 9 under Movshovitz-Attias et al. (US Patent Application Publication No. 2022/0292261), hereinafter referred to as Movshovitz-Attias, in view of Jung et al. (US Patent Application Publication No. 2014/0019885), hereinafter referred to as Jung, and of claims 4, 8, 10, and 11 under Movshovitz-Attias, in view of Jung, and further in view of Kyu-tae et al. (KR102199423B1), the Applicant asserts that Movshovitz-Attias does not disclose “transmit each word of a first text to the classification server in real-time in response to a trigger signal, the trigger signal being generated upon a user input of a space bar on the first interface while entering the first text”, “receive first emotion information from the classification server in response to each transmission of each word of the first text, the first emotion information including a plurality of emotions of a virtual audience corresponding to the first text and respective generation probabilities of the plurality of emotions”, and “control the display to display the plurality of emotions in an order of descending generation probabilities on the second interface, wherein some of the emotions with higher generation probabilities are displayed as emoticons and probabilities, and the remaining emotions are displayed as text and probabilities”. The Applicant further asserts that neither Jung nor Kyu-tae cures the deficiencies of Movshovitz-Attias.
The Examiner respectfully disagrees. Applicant’s argument concerning Jung is moot, as the new ground of rejection no longer relies on this reference. Further, concerning Movshovitz-Attias’ failure to disclose “transmit each word of a first text to the classification server in real-time in response to a trigger signal, the trigger signal being generated upon a user input of a space bar on the first interface while entering the first text”, Movshovitz-Attias teaches transmit each word of a first text to the classification server through paragraph [0106]: "In one example, computing device 902 may include one or more server computing devices having a plurality of computing devices, e.g., a load balanced server farm or cloud computing system, that exchange information with different nodes of a network for the purpose of receiving, processing and transmitting the data to and from other computing devices. For instance, computing device 902 may include one or more server computing devices that are capable of communicating with any of the computing devices 912-922 via the network 912. Information from emotion-related classifications performed by the server may be shared with one or more of the client computing devices, such as by suggesting an emoji, sticker or GIF to a user." This paragraph of Movshovitz-Attias shows that the information from multiple computing devices (i.e. each word of a text) is transmitted to the server for classification purposes and then communicated back with the computing devices. While Movshovitz-Attias fails to disclose “in real-time in response to a trigger signal, the trigger signal being generated upon a user input of a space bar on the first interface while entering the first text”, Grieves et al. (US Patent Application Publication No. 2015/0100537), hereinafter referred to as Grieves, discloses this through paragraph [0033]: “Accordingly, the language model 128 may monitor and collect data regarding text and/or emoji entries made by a user of a device. The monitoring and data collection may occur across the device in different interaction scenarios that may involve different applications, people (e.g., contacts or targets), text input mechanisms, and other contextual factors for the interaction,” Grieves para [0031] and “The prediction candidates may be provided as selectable elements (e.g., keys, button, hit areas) that when selected cause input of corresponding text.” These paragraphs show that any keys, including a space bar, can be utilized to begin input to the prediction unit, thereby functioning as a trigger signal. Concerning Movshovitz-Attias’ failure to disclose “receive first emotion information from the classification server in response to each transmission of each word of the first text, the first emotion information including a plurality of emotions of a virtual audience corresponding to the first text and respective generation probabilities of the plurality of emotions”, Movshovitz-Attias Fig. 2E shows possible emotional response information from individuals/audiences with their respective probabilities, while Movshovitz-Attias paragraph [0068] states: “The output is a set of prediction scores corresponding to each of the emotions of interest (e.g., joy, amusement, gratitude, surprise, disapproval, sadness, anger, and confusion).” This shows that the classification server shown in Movshovitz-Attias outputs emotion information in response to a text, that emotion information including multiple emotions and their respective probabilities of occurring. Concerning Movshovitz-Attias’ failure in disclosing “control the display to display the plurality of emotions in an order of descending generation probabilities on the second interface, wherein some of the emotions with higher generation probabilities are displayed as emoticons and probabilities, and the remaining emotions are displayed as text and probabilities”, Movshovitz-Attias Fig. 2E shows emotional response information displayed with both emoticons and their respective probabilities in a descending order of probability, and Movshovitz-Attias paragraph [0068] states: “The output is a set of prediction scores corresponding to each of the emotions of interest (e.g., joy, amusement, gratitude, surprise, disapproval, sadness, anger, and confusion).” It would be obvious to eventually only include text with its respective probability, as doing so saves display space and allows for easier understanding of the interface from the user. Grieves also discloses this “on the second interface” per Fig. 3 reference character 304, which shows a first interface for displaying text, and reference character 308 shows a second interface for displaying emotion information. Hence, Applicant’s argument is not persuasive.
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.
Claim(s) 1-2, 5-6, and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Movshovitz-Attias et al. (US Patent Application Publication No. 2022/0292261), hereinafter referred to as Movshovitz-Attias, in view of Grieves et al. (US Patent Application Publication No. 2015/0100537), hereinafter referred to as Grieves.
Regarding claim 1, Movshovitz-Attias discloses an electronic device for providing real-time emotional feedback to a user’s writing, the electronic device comprising: a display (Movshovitz-Attias Fig. 9B shows multiple devices: reference characters 912, 914, 916, 918, 920, and 922) configured to display a interface for displaying text information ("Client devices may include one or more of a desktop computer 912, a laptop or tablet PC 914, in-home devices that may be fixed (such as a temperature/thermostat unit 916) or moveable units (such as smart display 918). Other client devices may include a personal communication device such as a mobile phone or PDA 920, or a wearable device 922 such as a smart watch, head-mounted display, clothing wearable, etc," para [0100] Movshovitz-Attias) and a interface for displaying emotion information ("Client devices may include one or more of a desktop computer 912, a laptop or tablet PC 914, in-home devices that may be fixed (such as a temperature/thermostat unit 916) or moveable units (such as smart display 918). Other client devices may include a personal communication device such as a mobile phone or PDA 920, or a wearable device 922 such as a smart watch, head-mounted display, clothing wearable, etc," para [0100] Movshovitz-Attias);
and a processor configured to: transmit each word of a first text to a classification server ("In one example, computing device 902 may include one or more server computing devices having a plurality of computing devices, e.g., a load balanced server farm or cloud computing system, that exchange information with different nodes of a network for the purpose of receiving, processing and transmitting the data to and from other computing devices,... Information from emotion-related classifications performed by the server may be shared with one or more of the client computing devices, such as by suggesting an emoji, sticker or GIF to a user," Movshovitz-Attias para [0106] AND "Given a conversation, training examples may be constructed by searching for messages that were replied to by an emoji/sticker or other graphical element (either as the only reply, or as the beginning of the reply). Training examples include (i) an input message (the conversation context that preceded the emoji/sticker or other graphical element) and (ii) a target emotion (where the emoji/sticker or other graphical element acts as a representative of the expressed emotion", Movshovitz-Attias para [0026]),
receive first emotion information from the classification server in response to each transmission of each word of the first text, the first emotion information including a plurality of emotions of a virtual audience corresponding to the first text and respective generation probabilities of the plurality of emotions (Movshovitz-Attias Fig. 2E shows emotional response information with their respective probabilities AND “The output is a set of prediction scores corresponding to each of the emotions of interest (e.g., joy, amusement, gratitude, surprise, disapproval, sadness, anger, and confusion)”, Movshovitz-Attias para [0068]),
and control the display to display the plurality of emotions in an order of descending generation probabilities, wherein some of the emotions with higher generation probabilities are displayed as emoticons and probabilities, and the remaining emotions are displayed as text and probabilities (Movshovitz-Attias Fig. 2E shows emotional response information displayed with both emoticons and their respective probabilities in a descending order of probability AND “The output is a set of prediction scores corresponding to each of the emotions of interest (e.g., joy, amusement, gratitude, surprise, disapproval, sadness, anger, and confusion)”, Movshovitz-Attias para [0068], it would be an obvious inclusion to portray some of the emotions with only text and their respective probability, as this would save display space and allow for easier understanding of the user),
wherein the classification server infers the first emotion information corresponding to the first text by providing the first text to a model, the model being a trained model for identifying emotions of the virtual audience in response to an input text (“According to aspects of the technology, a machine learning approach is employed in order to generate effective predictions for the emotion of a user based on messages they send in a conversation (direct emotion prediction), for predicting the emotional response of a user (induced emotion prediction), and predicting appropriate graphical indicia (e.g., emoji, stickers or GIFs). Fully supervised and few-shot models are trained on data sets that may be particularly applicable to specific communication approaches (e.g., chats, texts, online commenting platforms, videoconferences, support apps, etc.)”, Movshovitz-Attias para [0097]).
However, Movshovitz-Attias fails to disclose a first interface for displaying text information and a second interface for displaying emotion information; in real-time in response to a trigger signal, the trigger signal being generated upon of a space bar while entering the first text, and on the second interface. Grieves teaches a system and method to use emojis for text predictions.
Grieves teaches a first interface for displaying text information and a second interface for displaying emotion information (Grieves Fig. 3 reference character 304 shows a first interface for displaying text, and reference character 308 shows a second interface for displaying emotion information);
in real-time in response to a trigger signal, the trigger signal being generated upon of a space bar while entering the first text (“Accordingly, the language model 128 may monitor and collect data regarding text and/or emoji entries made by a user of a device. The monitoring and data collection may occur across the device in different interaction scenarios that may involve different applications, people (e.g., contacts or targets), text input mechanisms, and other contextual factors for the interaction,” Grieves para [0031] and “The prediction candidates may be provided as selectable elements (e.g., keys, button, hit areas) that when selected cause input of corresponding text,” Grieves para [0033], this shows that any keys, including a space bar, can be utilized to begin input to the prediction unit),
on the second interface (Grieves Fig. 3 reference character 304 shows a first interface for displaying text, and reference character 308 shows a second interface for displaying emotion information).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Movshovitz-Attias’ method of emotion classification in text by including Grieves’ teaching of utilizing multiple different interfaces for information and for using a key press to transmit information to the classification server. This inclusion would facilitate the automation of the emotion classification, working simultaneously with the input of the text. It would eliminate the need for manual transmittal once the entire piece of text is written, and it allows for more real-time classification of the emotion that would be induced by said text.
Regarding claim 2, Movshovitz-Attias, in view of Grieves, discloses all the limitations of claim 1. However, Movshovitz-Attias does not disclose wherein the processor is further configured to transmit changed text to the classification server whenever receiving the user input for modifying the first text.
Grieves discloses wherein the processor is further configured to transmit changed text to the classification server whenever receiving the user input for modifying the first text (“The prediction candidates may be provided as selectable elements (e.g., keys, button, hit areas) that when selected cause input of corresponding text. The user may interact with the selectable elements to select one of the displayed candidates by way of touch input from a user's hand 206, or otherwise. In addition or alternatively, prediction candidates derived by a text prediction engine 122 may be used for auto-correction of input text, to expand underlying hit areas for one or more keys of the keyboard 202, or otherwise used to facilitate character entry and editing,” Grieves para [0033]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Movshovitz-Attias’ method of emotion classification in text by including Grieves’ teaching of using a key press to transmit information to the classification server. This inclusion would facilitate the automation of the emotion classification, working simultaneously with the input of the text. It would eliminate the need for manual transmittal once the entire piece of text is written, and it allows for more real-time classification of the emotion that would be induced by said text.
As to claim 5, method claim 5 and system claim 1 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly, claim 5 is similarly rejected under the same rationale as applied above with respect to the method claim.
As to claim 6, method claim 6 and system claim 2 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly, claim 6 is similarly rejected under the same rationale as applied above with respect to the method claim.
Regarding claim 9, Movshovitz-Attias discloses a system for providing real-time emotional feedback to a user’s writing, the system comprising: (“For induced emotion, the system focuses on predicting the emotional response of users, based on a conversation they participate in,” Movshovitz-Attias para [0029]):
an electronic device comprising a first processor and a display, wherein the display is configured to display a interface for displaying text information and a interface for displaying emotion information (“The computing devices may include all of the components normally used in connection with a computing device such as the processor and memory described above as well as a user interface subsystem for receiving input from a user and presenting information to the user (e.g., text and/or graphical indicia). The user interface subsystem may include one or more user inputs (e.g., a mouse, keyboard, touch screen and/or microphone) and one or more display devices (e.g., a monitor having a screen or any other electrical device that is operable to display information (e.g., text and graphical indicia),” Movshovitz-Attias para [0104]); and a classification server comprising a second processor and a model, wherein the model is trained to identify emotions of a virtual audience in response to an input text (“According to aspects of the technology, a machine learning approach is employed in order to generate effective predictions for the emotion of a user based on messages they send in a conversation (direct emotion prediction), for predicting the emotional response of a user (induced emotion prediction), and predicting appropriate graphical indicia (e.g., emoji, stickers or GIFs). Fully supervised and few-shot models are trained on data sets that may be particularly applicable to specific communication approaches (e.g., chats, texts, online commenting platforms, videoconferences, support apps, etc.)”, Movshovitz-Attias para [0097]);
wherein the first processor is configured to: transmit each word of a first text to the classification server ("In one example, computing device 902 may include one or more server computing devices having a plurality of computing devices, e.g., a load balanced server farm or cloud computing system, that exchange information with different nodes of a network for the purpose of receiving, processing and transmitting the data to and from other computing devices,... Information from emotion-related classifications performed by the server may be shared with one or more of the client computing devices, such as by suggesting an emoji, sticker or GIF to a user," Movshovitz-Attias para [0106] AND "Given a conversation, training examples may be constructed by searching for messages that were replied to by an emoji/sticker or other graphical element (either as the only reply, or as the beginning of the reply). Training examples include (i) an input message (the conversation context that preceded the emoji/sticker or other graphical element) and (ii) a target emotion (where the emoji/sticker or other graphical element acts as a representative of the expressed emotion", Movshovitz-Attias para [0029]),
receive first emotion information from the classification server in response to each transmission of each word of the first text, the first emotion information including a plurality of emotions of a virtual audience corresponding to the first text and respective generation probabilities of the plurality of emotions (Movshovitz-Attias Fig. 2E shows emotional response information with their respective probabilities AND “The output is a set of prediction scores corresponding to each of the emotions of interest (e.g., joy, amusement, gratitude, surprise, disapproval, sadness, anger, and confusion)”, Movshovitz-Attias para [0068]),
and control the display to display the plurality of emotions in an order of descending generation probabilities, wherein some of the emotions with higher generation probabilities are displayed as emoticons and probabilities, and the remaining emotions are displayed as text and probabilities (Movshovitz-Attias Fig. 2E shows emotional response information displayed with both emoticons and their respective probabilities in a descending order of probability AND “The output is a set of prediction scores corresponding to each of the emotions of interest (e.g., joy, amusement, gratitude, surprise, disapproval, sadness, anger, and confusion)”, Movshovitz-Attias para [0068], it would be an obvious inclusion to portray some of the emotions with only text and their respective probability, as this would save display space and allow for easier understanding of the user),
wherein the second processor is configured to: receive the first text ("In one example, computing device 902 may include one or more server computing devices having a plurality of computing devices, e.g., a load balanced server farm or cloud computing system, that exchange information with different nodes of a network for the purpose of receiving, processing and transmitting the data to and from other computing devices,... Information from emotion-related classifications performed by the server may be shared with one or more of the client computing devices, such as by suggesting an emoji, sticker or GIF to a user," Movshovitz-Attias para [0106]),
provide the first text to the model for inferring the first emotion information, and ("In one example, computing device 902 may include one or more server computing devices having a plurality of computing devices, e.g., a load balanced server farm or cloud computing system, that exchange information with different nodes of a network for the purpose of receiving, processing and transmitting the data to and from other computing devices,... Information from emotion-related classifications performed by the server may be shared with one or more of the client computing devices, such as by suggesting an emoji, sticker or GIF to a user," Movshovitz-Attias para [0106] AND "Given a conversation, training examples may be constructed by searching for messages that were replied to by an emoji/sticker or other graphical element (either as the only reply, or as the beginning of the reply). Training examples include (i) an input message (the conversation context that preceded the emoji/sticker or other graphical element) and (ii) a target emotion (where the emoji/sticker or other graphical element acts as a representative of the expressed emotion", Movshovitz-Attias para [0029]),
transmit the first emotion information to the electronic device (Information from emotion-related classifications performed by the server may be shared with one or more of the client computing devices, such as by suggesting an emoji, sticker or GIF to a user," Movshovitz-Attias para [0106]).
However, Movshovitz-Attias fails to disclose a first interface for displaying text information and a second interface for displaying emotion information; in real-time in response to a trigger signal, the trigger signal being generated upon of a space bar while entering the first text, and on the second interface. Grieves teaches a system and method to use emojis for text predictions.
Grieves teaches a first interface for displaying text information and a second interface for displaying emotion information (Grieves Fig. 3 reference character 304 shows a first interface for displaying text, and reference character 308 shows a second interface for displaying emotion information);
in real-time in response to a trigger signal, the trigger signal being generated upon of a space bar while entering the first text (“Accordingly, the language model 128 may monitor and collect data regarding text and/or emoji entries made by a user of a device. The monitoring and data collection may occur across the device in different interaction scenarios that may involve different applications, people (e.g., contacts or targets), text input mechanisms, and other contextual factors for the interaction,” Grieves para [0031] and “The prediction candidates may be provided as selectable elements (e.g., keys, button, hit areas) that when selected cause input of corresponding text,” Grieves para [0033], this shows that any keys, including a space bar, can be utilized to begin input to the prediction unit),
on the second interface (Grieves Fig. 3 reference character 304 shows a first interface for displaying text, and reference character 308 shows a second interface for displaying emotion information).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Movshovitz-Attias’ method of emotion classification in text by including Grieves’ teaching of using a key press to transmit information to the classification server. This inclusion would facilitate the automation of the emotion classification, working simultaneously with the input of the text. It would eliminate the need for manual transmittal once the entire piece of text is written, and it allows for more real-time classification of the emotion that would be induced by said text.
Claim(s) 4, 8, and 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Movshovitz-Attias, in view of Grieves, and further in view of Kyu-tae et al. (KR102199423B1), hereinafter referred to as Kyu-tae.
Regarding claim 4, Movshovitz-Attias, in view of Grieves, discloses all the limitations of claim 1. Movshovitz-Attias fails to disclose wherein the model is a bi-directional Long Short-Term Memory (LSTM) model.
Kyu-tae teaches a method for the machine learning of psychological counseling data. Kyu-tae teaches wherein the model is a bi-directional Long Short-Term Memory (LSTM) model ("The automatic dialogue device (700) can be used by adding a module called Long Short-Term Memory models (LSTM) or Gated Recurrent Unit (GRU) to Recurrent Neural Networks (RNN) for machine learning…, Additionally, one-way machine learning can be performed bidirectionally," Kyu-tae para [0101]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Movshovitz-Attias’ method of emotion classification in text by including Kyu-tae’s method of a bi-directional Long Short-Term Memory (LSTM) model. This inclusion would make it more efficient of capturing emotional context from the text. Bi-directional LSTMs allow for processing of sequential data in both directions, which allows for the capture of both past and future context. This would improve the accuracy of the emotions and sentiments captured from the analysis of the text. This combination would have been obvious to one of ordinary skill in the art.
As to claim 8, method claim 8 and system claim 4 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly, claim 8 is similarly rejected under the same rationale as applied above with respect to the method claim.
Regarding claim 10, Movshovitz-Attias, in view of Grieves, discloses all the limitations of claim 9. Movshovitz-Attias fails to disclose wherein the model is a bi-directional Long Short-Term Memory (LSTM) model.
Kyu-tae teaches wherein the model is a bi-directional Long Short-Term Memory (LSTM) model ("The automatic dialogue device (700) can be used by adding a module called Long Short-Term Memory models (LSTM) or Gated Recurrent Unit (GRU) to Recurrent Neural Networks (RNN) for machine learning…, Additionally, one-way machine learning can be performed bidirectionally," Kyu-tae para [0101]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Movshovitz-Attias’ method of emotion classification in text by including Kyu-tae’s method of a bi-directional Long Short-Term Memory (LSTM) model. This inclusion would make it more efficient of capturing emotional context from the text. Bi-directional LSTMs allow for processing of sequential data in both directions, which allows for the capture of both past and future context. This would improve the accuracy of the emotions and sentiments captured from the analysis of the text. This combination would have been obvious to one of ordinary skill in the art.
Regarding claim 11, Movshovitz-Attias, in view of Grieves, and further in view of Kyu-tae, discloses all the limitations of claim 10. Movshovitz-Attias further discloses wherein the second processor is configured to: identify emotions of the virtual audience based on contributions of the words included in the text (“Here, the system finds examples which are based on emotion-bearing phrases. The goal is to identify emotion-bearing text based on the phrases found in phase 1, and learn to classify these messages,” Movshovitz-Attias para [0051]).
However, Movshovitz-Attias fails to disclose identify a relationship between words included in the text based on bi-directional LSTMs included in the LSTM model analyzing texts in different directions.
Kyu-tae teaches identify a relationship between words included in the text based on bi-directional LSTMs included in the LSTM model analyzing texts in different directions ("The automatic dialogue device (700) can be used by adding a module called Long Short-Term Memory models (LSTM) or Gated Recurrent Unit (GRU) to Recurrent Neural Networks (RNN) for machine learning…, Additionally, one-way machine learning can be performed bidirectionally," Kyu-tae para [0101]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Movshovitz-Attias’ method of emotion classification in text by including Kyu-tae’s method of a bi-directional Long Short-Term Memory (LSTM) model. This inclusion would make it more efficient of capturing emotional context from the text. Bi-directional LSTMs allow for processing of sequential data in both directions, which allows for the capture of both past and future context. This would improve the accuracy of the emotions and sentiments captured from the analysis of the text. This combination would have been obvious to one of ordinary skill in the art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US Patent Application Publication No. 2008/0096533
US Patent Application Publication No. 2022/0129621
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM MICHAEL WEAVER whose telephone number is (571)272-7062. The examiner can normally be reached Monday-Friday, 8AM-5PM EST.
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, Richemond Dorvil can be reached at (571) 272-7602. 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.
/ADAM MICHAEL WEAVER/ Examiner, Art Unit 2658
/RICHEMOND DORVIL/ Supervisory Patent Examiner, Art Unit 2658
Prosecution Insights