DETAILED ACTION
Notice to Applicant
This communication is in response to amendment and remarks dated 6/22/26. Independent claims 1, 13, and 18 have been amended. Claims 1-20 are pending.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-12 and 18-20 are drawn to patient intake and appointment booking system, which is within the four statutory categories (i.e. machine). Claims 13-17 are drawn to a computer program for patient intake and appointment booking, which is within the four statutory categories (i.e. article of manufacture).
Representative independent claim 1 includes limitations that recite at least one abstract idea. Specifically, independent claim 1 recites:
A system for reducing hallucinations in software that incorporates artificial intelligence ("AI"), the system comprising:
an interactive voice response ("IVR") system configured to:
receive a voice input from a caller; and
compute a first intent of the caller from the voice input;
an artificial intelligence ("AI") model that is configured to receive the voice input and compute a second intent of the caller from the voice input; and
a middleware system that is configured to programmatically:
formulate a medical service schedulable in an electronic health record (“EHR”) system based on the first intent and the second intent;
construct a scheduling request processible by the EHR system; and
transmit the scheduling request to the EHR system to schedule the medical service before the caller terminates the phone call.
These recited underlined limitations fall within the "Certain Methods of Organizing Human Activities" grouping of abstract ideas as it relates to certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II).
The limitations of receiving a voice input; computing an intent from the voice input; computing a second intent based on a model; formulating a response based on the first and second input; constructing a scheduling request; and transmitting the scheduling request as drafted and detailed above, are steps that, under its broadest reasonable interpretation, recites steps for organizing human interactions. The claimed invention is directed to providing appointment scheduling and patient/customer intake. These activities that can be and historically have been performed through interactions between people. For example, a caller may explain the reason for seeking an appointment, a scheduling representative may interpret the request, determine the appropriate appointment or service, formulate a scheduling request, and reserve the appointment. Other than reciting an IVR system; AI model; EHR system and a middleware system language, nothing in the claim element precludes the steps from practically being performed between people or by a person. If a claim limitation, under its broadest reasonable interpretation, covers interactions between people or managing personal behavior or relationships then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
A system for reducing hallucinations in software that incorporates artificial intelligence ("AI"), the system comprising:
an interactive voice response ("IVR") system configured to:
receive a voice input from a caller; and
compute a first intent of the caller from the voice input;
an artificial intelligence ("AI") model that is configured to receive the voice input and compute a second intent of the caller from the voice input; and
a middleware system that is configured to programmatically:
formulate a medical service schedulable in an electronic health record (“EHR”) system based on the first intent and the second intent;
construct a scheduling request processible by the EHR system; and
transmit the scheduling request to the EHR system to schedule the medical service before the caller terminates the phone call.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application. The additional elements (i.e. the limitations not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f).
the recitations performing the functions by the IVR system and middleware system amounts to merely invoking a computer as a tool to perform the abstract idea.
the recitation of computing intent by an AI Model recites only the idea of a solution or outcome (i.e. claim fails to recite details of how a solution to a problem is accomplished).
in order to transform a judicial exception into a patent-eligible application, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’".
Examiner submits that these limitations amount to merely using software to tailor information and provide it to the user on a generic computer. Applicant does not provide adequate evidence or technical reasoning on how the process improves the efficiency of the computer and is beyond conventional use of components, as opposed to the efficiency of the process, or of any other technological aspect of the computer.
generally link the abstract idea to a particular technological environment or field of use, see MPEP 2106.05(h)– computer systems, Software, and the EHR system are recited generally, merely limit the abstract idea the environment of a computer.
The claim only generally recites an EHR system and does not recite a particular EHR data structure, message format, API protocol data mapping, interoperability protocol or technological procedure to convert the EHR processable scheduling request. Instead the claim only generally likes the abstract idea to an electronic health record.
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Independent claim 1 does not include additional elements that are sufficient to amount to “significantly more” than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally linking the abstract idea to a particular technological environment or field of use and the same analysis applies with regards to whether they amount to “significantly more.” Therefore, the additional elements do not add significantly more to the at least one abstract idea.
Independent claims 13 and 18 are directed to certain methods of organizing human activity for similar reasons as claim 1. Furthermore, for similar reasons as representative independent claim 1, analogous independent claims 13 and 18 do not recite additional elements that integrate the judicial exception into a practical application nor add significantly more.
Furthermore, for similar reasons as representative independent claim 1, analogous independent claims 13 and 18 do not recite additional elements that integrate the judicial exception into a practical application nor add significantly more. Furthermore the recitation of integration with the EHR system only generally links the abstract idea to a technological environment.
The following dependent claims further the define the abstract idea or are also directed to an abstract idea itself:
Dependent claims 8, 12, 14, 16 and 17 further define the at least one abstract idea (and thus fail to make the abstract idea any less abstract).
In relation to claims 3, 6, 7, 10, 11, and 19 these claims specify limitations considered under certain methods of organizing human activity, under its broadest reasonable interpretation, covers interactions between people or managing personal behavior or relationships
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 2, 4, 5, 9, 15, and 20: These claims recite among other things, applying artificial intelligence broadly, which thus amount to mere instructions to apply an exception by invoking the computer as a tool OR reciting the idea of a solution (i.e. claim fails to recite details of how a solution to a problem is accomplished) or outcome (see MPEP § 2106.05(f)).
The dependent claims further do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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.
Claims 1–12 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava (US 9,105,268) in view of Fusillo (US 10,430,727) and Kotiyar (US 2022/0392622).
As per claim 1, Srivastava teaches a system for reducing hallucinations in software that incorporates artificial intelligence ("AI"), the system comprising:
an interactive voice response ("IVR") system configured to:
receive a voice input from a caller (Srivastava; Col. 5, lines 58-60 customer responds… natural language response); and
compute a first intent of the caller from the voice input (Srivastava; Col. 5, lines 14-21 computes probability score of each intent);
Srivastava does not expressly teach an artificial intelligence ("AI") model that is configured to receive the voice input and compute a second intent of the caller from the voice input; and a middleware system that is configured to, programmatically: formulate a medical service schedulable in an electronic health record ("EHR") system based on the first intent and the second intent; construct a scheduling request processible by the EHR system; and transmit the scheduling request to the EHR system to schedule the medical service before the caller terminates the phone call.
Srivastava does not explicitly teach (1) computing two separate “intents” using two distinct models, or (2) a middleware system that formulates a response based simultaneously on two intent values. Fusillo teaches modifying or tuning machine-learning model behavior and training characteristics (Fusillo; Col. 6, lines 1-12; configured… based on training examples… one or more input values), which would have made it obvious to use multiple model components or multiple intent determinations to stabilize AI performance and reduce errors. Combining the teachings of Srivastava and Fusillo would have yielded predictable results, given that both references concern natural-language interpretation and machine-learning model behavior. Therefore, it would have been obvious to one of ordinary skill to incorporate a second intent calculation and an intermediate middleware layer to coordinate model outputs, in order to improve stability and accuracy of the system.
Srivastava further does not expressly teach integrating with an EHR system for exchanging healthcare and appointment information. However, this is old and well known in the art as evidenced by Kotiyar. Kotiyar para. [0034] further teaches a medical contact-center environment integrated with EHR/EMR systems for exchanging healthcare and appointment information and facilitating medical appointment scheduling during a telephone interaction. It would have been obvious to further modify the combined system of Srivastava and Fusillo to employ the EHR-integrated scheduling functionality of Kotiyar so that, once the caller's intent and requested medical service are determined from the caller's natural-language input, the system could electronically communicate the corresponding scheduling information to the EHR-integrated scheduling system and complete the requested medical appointment during the same telephone call.
As per claim 2, Srivastava in view of Fusillo teaches the system of claim 1 wherein based on a confidence measure associated with the first intent, the middleware system is configured to throttle a level of regularization applied to the AI model.
Srivastava teaches using confidence measures and thresholds (Col. 5, lines 17-22-- compares the score… to a predefined threshold). Fusillo teaches adjusting model configuration parameters, including model tuning and regularization based on the distribution of input examples (Col. 21, lines 32-54). It would have been obvious to throttle regularization based on the confidence of the first intent in order to improve stability and accuracy of the system.
As per claim 3, Srivastava in view of Fusillo teaches the system of claim 1, wherein the middleware system is configured to pass the response to the IVR system and the IVR system is configured to generate audio output based on the medical service (Srivastava; Fig. 5 and Col. 7, lines 25-26).
AS per claim 4, Srivastava in view of Fusillo teaches the system of claim 2 wherein the middleware system is configured to increase the level of regularization based on a threshold confidence measure associated with the first intent.
Srivastava teaches using a confidence score and a threshold to determine system behavior. Specifically, Srivastava discloses computing a probability score for each intent and comparing the score to a predefined threshold in order to decide whether to proceed with a “standard” IVR journey or an “optimized” journey (Srivastava Col. 5, lines 14–22). Fusillo teaches adjusting machine-learning model configuration based on how well-supported particular regions of the model are by the training data, including pruning or modifying portions of the model that are associated with low-support regions (Fusillo; Col. 12, lines 5-30). It would have been obvious to one of ordinary skill in the art to increase the level of regularization when the confidence measure falls below a threshold, as a predictable application of combining Srivastava’s threshold-based confidence logic with Fusillo’s teaching of modifying model behavior to handle low-confidence or low-support regions.
As per claim 5, Srivastava in view of Fusillo teaches the system of claim 2 wherein the middleware system decreases the level of regularization when the first intent is above a threshold confidence measure.
Srivastava’s use of confidence scores and thresholds to differentiate between a standard and optimized path (Srivastava Col. 5, lines 14–22) in view of Fusillo’s teaching of adjusting model configuration and behavior based on data support and performance (Fusillo; Col. 12, lines 5-30) would have made it obvious to one of ordinary skill in the art to decrease the level of regularization when the confidence measure is above a threshold. Decreasing regularization in high-confidence regions is a routine and predictable design choice in machine-learning systems to avoid unnecessary constraints when the model has strong support or high certainty.
As per claim 6, Srivastava in view of Fusillo teaches the system of claim 1 wherein the middleware system is configured to:
instruct the AI model to compute the second intent before the IVR system computes the first intent;
compute a confidence measure associated with the second intent; and
when the confidence measure associated with the second intent is below a threshold level, instruct the IVR system to compute the first intent.
Srivastava teaches fallback logic that changes system behavior depending on whether the model’s confidence is above or below a threshold (Srivastava Col. 5, lines 14–22). Fusillo teaches adjusting model behavior in response to data-driven support levels (Fusillo; Col. 12, lines 5-30). It would have been obvious to have the middleware instruct the AI model to compute the second intent first, compute confidence, and then fall back to IVR intent computation when confidence is below a threshold.
As per claim 7, Srivastava in view of Fusillo teaches the system of claim 1 wherein the middleware system is configured to:
instruct the IVR system to compute the first intent;
assess a confidence measure associated with the first intent; and
when the confidence measure associated with the first intent is below a threshold level, instruct the AI model to compute the second intent.
Srivastava teaches the reverse fallback logic—computing intent through the IVR/NLU pathway first and branching into an alternative path if the system determines the confidence is below a threshold (Srivastava Col. 5, lines 14–22). Fusillo again supplies the rationale for adjusting model interaction patterns based on support levels (Fusillo; Col. 12, lines 5-30). It would have been obvious to implement the recited sequence of operations.
As per claim 8, Srivastava in view of Fusillo teaches the system of claim 1 wherein the voice input is a natural language statement of the caller (Srivastava; Col. 5, lines 58-60).
As per claim 9, Srivastava in view of Fusillo teaches the system of claim 1 wherein the AI model is configured to recursively train itself using a plurality of first intents generated by the IVR system (Srivastava; Col. 5, lines 23-31 and 38-45).
As per claim 10, Srivastava in view of Fusillo teaches the system of claim 1 wherein the AI model is configured to generate a plurality of intents in response to a prompt; and the IVR system is configured to determine whether the voice input matches one of the plurality of intents (Srivastava; Col. 5, lines 14-21).
As per claim 11, Srivastava in view of Fusillo teaches the system of claim 1 wherein, the AI model is configured to activate the IVR system and the middleware system in response to detecting a pre-defined trigger event (Srivastava; Col. 5, lines 17-23 and lines 48-60 – system events or caller interaction events read on a “pre-defined trigger event”).
As per claim 12, Srivastava in view of Fusillo teaches the system of claim 11, wherein the pre-defined trigger event is an inclement weather event at a target location. The type of pre-defined trigger event being inclement weather is considered non-functional descriptive material and given little to no patentable weight. Descriptive material that cannot exhibit any functional interrelationship with the way in which computing processes are performed does not distinguish the claim over the prior art and given little to no patentable weight.
Claims 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rana (8,364,501) in view of Srivastava (US 9,105,268) and Kotiyar (US 2022/0392622).
As per claim 13, Rana teaches a computer program comprising instructions that when executed on a processor:
answers a telephone call from a caller (Rana Col. 1, lines 23-35 communicating with patients through a series of phone calls for scheduling purposes);
prompts the caller to describe a medical condition using natural language (Rana Col. 6, line 59 to Col. 7 querying the patient for the reason for an appointment); and
communicates the electronic request to the EHR system and reserves the medical appointment for the caller during the telephone call (Rana Col. 4, lines 1-5 EMR communication channel used in scheduling).
Rana does not expressly teach determining an appointment type based on the natural language received from the caller. However this is old and well known in the art as evidenced by Srivastava. IN particular Srivastava teaches an interactive voice response ("IVR") system configured to: receive a voice input from a caller (Srivastava; Col. 5, lines 58-60 customer responds… natural language response); prompting the caller using lifelike speech (Srivastava; Col. 2, lines 48-54) and compute a first intent of the caller from the voice input (Srivastava; Col. 5, lines 14-21 computes probability score of each intent). It would have been obvious to incorporate Srivastava's natural-language IVR technique into Rana so that Rana's reason for the medical appointment could be obtained and determined automatically from the patient's natural-language speech, particularly because Rana expressly contemplates receiving the reason as voice data.
Rana and Srivastava further do not expressly teach programmatically, using the appointment type to formulate an electronic request for a medical appointment that is processible by an application programming interface ("API") of an electronic health record ("EHR") system. However, this was old and well-known in the art as evidenced by Kotiyar. Kotiyar paras. [0007], [0008] teaches a medical contact center integrated with EHR/EMR systems, including bidirectional automated updates and interoperable system-to-system communications using bidirectional HL7 and SOA web services for real-time exchange of appointment and healthcare data. Kotiyar para. [0028] further teaches a “one call solution” and expressly teaches scheduling an appointment during the first contact or call. It would have been obvious to further modify Rana and Srivastava with Kotiyar's EHR-integrated scheduling architecture so that, after determining the appointment type from the caller's natural-language input, the corresponding electronic appointment request is communicated to the EHR-integrated scheduling system and the appointment is reserved during the same call. A person of ordinary skill would have been motivated to do so to provide the streamlined, real-time, one-call scheduling expressly contemplated by Kotiyar.
Further, in view of Kotiyar's express teaching of interoperable HL7/SOA web-service interfaces for bidirectional exchange of appointment information with integrated EHR/EMR systems, it would have been obvious to implement the disclosed electronic scheduling communication as a request processible by an API of the EHR system, as the predictable use of a known software-interface technique for accomplishing the disclosed system-to-system exchange of appointment data.
As per claim 14, Rana does not expressly teach the computer program of claim 13 further comprising instructions, that when executed by the processor, prompt the caller using lifelike speech. However this is old and well known in the art as evidenced by Srivastava. IN particular Srivastava teaches an interactive voice response ("IVR") system configured to: receive a voice input from a caller (Srivastava; Col. 5, lines 58-60 customer responds… natural language response); prompting the caller using lifelike speech (Srivastava; Col. 2, lines 48-54) and compute a first intent of the caller from the voice input (Srivastava; Col. 5, lines 14-21 computes probability score of each intent). It would have been obvious to integrate Srivastava’s IVR/NLU system into the telephonic EHR-connected service system of Rana to automate intent understanding and improve user experience.
As per claim 15 Rana in view of Srivastava teaches the computer program of claim 14 further comprising instructions, that when executed by the processor: generate the lifelike speech using an artificial intelligence ("AI") model that converts text into the lifelike speech; and prompt the caller using the lifelike speech (Srivastava; Col. 3, lines 32-41; Col. 2, lines 48-54). It would have been obvious to integrate Srivastava’s IVR/NLU system into the telephonic EHR-connected service system of Rana to automate intent understanding and improve user experience.
As per claim 16, Rana in view of Srivastava teaches interactive telephone prompts and caller responses (Rana Col. 6, line 59 to Col. 7 querying the patient for the reason for an appointment) and handling natural language input and adapting responses based on varied linguistic patterns (Srivastava; Col. 4, lines 41-46). However, Rana in view of Srivastava does not expressly teach the computer program of claim 13 further comprising instructions, that when executed by the processor: prompt the caller in a first language; and in response to detecting a voice input provided by the caller in a second language, interact with the caller in the second language. However, providing interaction in a second language is an obvious variant of Rana in view of Srivastava teachings of adapting responses based on varied linguistic patterns. It would have been obvious to one of ordinary skill in the art to modify Rana in view of Srivastava with motivation of supporting multiple languages in order to interact with multilingual callers
AS per claim 17, Rana in view of Srivastava teaches the computer program of claim 13 further comprising instructions, that when executed by the processor, present at least one available slot to the caller using a first communication channel and a second communication channel (Rana; Col. 8, lines 55-67).
Claim 18 repeats substantially similar limitations as claim 13 and the reasons for rejection are incorporated herein.
As per claim 19, Rana in view of Srivastava teaches the automated system of claim 18 wherein the middleware system is configured to present an available appointment slot to the caller based on a set of filtering rules (Rana; Col. 2, lines 43-46).
As per claim 20, Rana in view of Srivastava teaches the automated system of claim 19 wherein the filtering rules are dynamically set by an AI model based on a real-time availability of appointment slots in the EHR system. However this is an obvious variant of the Rana teachings. Rana, Col. 3, lines 7-10 teaches the scheduling system can also communicate with a database providing travel time delays between the geographic locations and present schedule options which accommodate the travel time delay. Substituting an AI model to automatically set or tune these filtering rules is a predictable and routine application of modern machine-learning techniques to an existing rule-based scheduling system. It would have been obvious to one of ordinary skill in the art to modify the Rana teachings to output an accurate and user-friendly appointment options.
Response to Arguments
Applicant's arguments filed 6/22/2026 have been fully considered but they are not persuasive. It is noted that the rejections under 35 USC 101 and 103 have been updated in light of Applicant’s amendments. Some of Applicant’s arguments with respect to the claims have been considered but are moot in light of the updated grounds of rejection.
Applicant argues on pgs. 8-9 of the Remarks that the claims are analogous to Example 42 because they allegedly solve an interoperability problem between natural human speech and structured EHR information. Applicant characterizes the claimed system as converting natural speech into structured electronic information usable by an EHR. The argument is not persuasive because the claims do not recite the particular technological solution on which Applicant relies.
Applicant's characterization repeatedly describes the invention as converting information between incompatible formats and generating information in the “native electronic format” required by an EHR. For example, Applicant argues that the system “bridges the technological divide between unstructured human speech and the structured electronic data required by an EHR system.” However, the claims do not recite a particular conversion between incompatible data formats, a standardized format, a particular EHR-native data structure, or a technological mechanism that accomplishes such conversion. Rather, claim 1 requires only that middleware “construct a scheduling request processible by the EHR system,” claim 13 requires an electronic request “processible by” an EHR API, and claim 18 requires an API call “processible by” an EHR system. The claims therefore recite the desired result of compatibility, rather than a specific technological solution for achieving interoperability.
On pg. 9 of the 6/22/26 Remarks, Applicant argues that patients communicate using natural speech whereas EHR systems require structured electronic data and that the claims solve this technological gap. This argument is not persuasive. Even accepting Applicant's characterization of the problem, eligibility is evaluated based on the claim as a whole, rather than an unclaimed technological implementation described in the Specification or attorney argument. The claims do not specify how natural-language speech is transformed into a structured EHR representation. Instead, they broadly require determining an intent, medical condition, appointment type, or appointment service and then generating a request that the EHR can process.
Applicant’s arguments on pgs. 10-12 regarding the previously applied prior art is moot in view of the newly applied Kotiyar reference that teaches a medical contact center integrated with EHR/EMR systems. The rejection of the newly amended claims under 35 USC 103(a) have been updated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vyas et al. (Sonali Vyas & Deepshikha Bhargava. Algorithms and
Software for Smart Health. In: Smart Health Systems. Springer, Singapore.
https://doi.org/10.1007/978-981-16-4201-2_4. Pgs. 27-47. 2021) the closest
nonpatent literature of record teaches IVR and using technology in telehealth
apps to schedule appointments.
Balwani (WO-2015035309-A1) the closest foreign prior art of record
teaches appointment scheduling and filtering by parameters.
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 LINH GIANG MICHELLE LE whose telephone number is (571)272-8207. The examiner can normally be reached Mon- Fri 8:30am - 5:30pm 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, JASON DUNHAM can be reached at 571-272-8109. 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.
LINH GIANG "MICHELLE" LE
PRIMARY EXAMINER
Art Unit 3686
/LINH GIANG LE/Primary Examiner, Art Unit 3686 9/4/2026
