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 .
Notice to Applicant
This communication is in response to the amendment filed 7/27/26. Claims 1, 11, 21, and 24 have been amended. Claims 7, 17, and 27 are cancelled. Claims 1-6, 8-16, 18-26, and 28-30 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-6, 8-16, 18-26, and 28-30 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The newly added recitation of "continuously monitoring a diction of a medical specialist using a virtual assistant during performance of clinical activities without requiring the medical specialist to actively invoke the virtual assistant…compares the personalized feature vector against a Universal Background Model associated with the medical specialist to authenticate an identity of the medical specialist" within claims 1, 11, and 21 appears to constitute new matter.
In particular, Applicant does not point to, nor was the Examiner able to find support for this newly added language within the specification as originally filed. As such, Applicant is respectfully requested to clarify the above issues and to specifically point out support for the newly added limitations in the originally filed specification and claims.
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Objections
Claims 4, 5, 8, 14, 15, 18, 24, 25, and 28 are objected to because of the following informalities: change “the task” to “the at least one task”. Appropriate correction is required.
Claims 1, 11, and 21 are objected to because of the following informalities: change “upon both: (i) identification… “ to “upon both: (i) the identification….“ Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 3-6, 8, 9, 11, 13-16, 18, 19, 21, 23-26, 28, and 29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallopyn et al. (US 2014/0249830 A1) in view of Trehan (US 2021/0334473 A1), in view of Kim et al. (US 2016/0093304 A1), in view of Devaraj et al. (US 2018/0182389 A1), and further in view of Wright et al. (US 2018/0165723 A1).
(A) Referring to claim 1, Gallopyn discloses A computer-implemented method, executed on a computing device, comprising (para. 25 & 26 of Gallopyn):
monitoring speech of a medical specialist using a virtual assistant (para. 38-40 & 43 of Gallopyn; virtual medical assistant application 110 may receive input (e.g., free-form instruction from a medical professional) in the form of speech, and ASR component 130 may be utilized to identify the content of the speech (e.g., recognize the constituent words in the speech input). In particular, a medical professional may speak free-form instruction to the host device (e.g., mobile device 110) and/or an interface component capable of accessing, directly or indirectly, the host device.);
wherein effectuating the at least one task on the medical management system includes:
parsing the at least one task into a plurality of subtasks (para. 99 and 89-92 of Gallopyn; a medical professional may have provided the virtual medical assistant the instruction: "Show me John Doe's chart." In response to the instruction, the virtual medical assistant may have initiated processes to obtain John Doe's patient history or other relevant records from, for example, an EHR system accessible by the virtual medical assistant, and presented at least a portion of John Doe's "chart" to the medical professional. In response or subsequent to receiving patient information for John Doe, the medical professional may speak the free-form instruction "Please refill prescription." The virtual medical assistant may process the instruction and identify that the medical professional would like to order a medication, but also ascertain that there is information missing that is needed to perform the requested medical task. The virtual medical assistant may infer one or more of these missing parameters from previous instruction or information obtained from performing tasks responsive to previous instructions. For example, the virtual medical assistant may recognize that the medical professional is likely referring to John Doe due to the previous instruction to "Show me John Doe's chart." In addition, the virtual medical assistant may understand that the medical professional is ordering a refill on a prescription and that the prescription information may be present in the patient history obtained in response to the instruction to obtain John Doe's chart, or otherwise obtainable from an EHR system storing records for John Doe); and commandeering a local user interface, normally used by the medical specialist, of the medical management system to effectuate the plurality of subtasks on the medical management system (see Fig. 2, para. 65, 14, 17, 18, 31, 1, and 79-82 of Gallopyn; the virtual medical assistant may be configured to access order fulfillment system 190 to facilitate the dispatch of orders received by the virtual medical assistant from the medical professional. To facilitate electronic ordering, the virtual medical assistant may be configured to utilize any industry standard electronic interfaces to connect with and communicate with order fulfillment system 190, or may use any specialized or proprietary interfaces or formats needed to utilize one or more order fulfillment systems. Virtual medical assistant application 110 is configured to provide a graphical user interface (GUI) on display 105 of mobile device 101 that presents to the medical professional the mechanisms by which the medical professional may provide input to the virtual medical assistant. The virtual medical assistant may interact with the medical professional in real-time to assist the medical professional in performing medical tasks and/or in performing medical tasks on behalf of the medical professional.), wherein commandeering the local user interface of the medical management system includes causing the local user interface to visually display, to the medical specialist, the virtual assistant navigating the local user interface to effectuate each of the plurality of subtasks on the medical management system (see Figures 1 & 4A-4H and para. 31-35, 39, 108-111 of Gallopyn; FIG. 4A illustrates an exemplary introductory presentation, for example, that may be presented when the medical professional activates the virtual medical assistant (e.g., by launching the virtual medical assistant application on the mobile device). In this example, the virtual medical assistant application displays dialog (and may also audibly render the dialog) indicating that virtual medical assistant is ready to assist and provides guidance to the medical professional by inviting the medical professional to tap the microphone icon and tell the virtual medical assistant what the medical professional would like done. Initially, a medical professional may be unfamiliar with functionality provided by the virtual medical assistant and the virtual medical assistant application may be present a menu of exemplary tasks that the virtual medical assistant can assist the medical professional with or perform on behalf of the medical professional. To this end, the medical professional may say "What can you do for me?" and in response the virtual medical assistant application may present a number of example tasks the virtual medical assistant can perform by providing representative free-form instruction suitable to instruct the virtual medical assistant to perform the associated task, or begin the process of performing such a task.).
Gallopyn does not disclose expressly disclose that the speech includes diction; continuously monitoring a diction of a medical specialist using a virtual assistant during performance of clinical activities without requiring the medical specialist to actively invoke the virtual assistant; wherein continuously monitoring the diction includes: processing at least a portion of the diction to identify one or more task-indicative trigger words and one or more task-indicative conversational structures, wherein the one or more task- indicative trigger words and the one or more task-indicative conversational structures are generated using an artificial intelligence (AI) process that processes initial seed data associated with task-indicative trigger words and task-indicative conversational structures specific to medical management tasks, wherein identifying the one or more task-indicative trigger words and the one or more task- indicative conversational structures identifies at least one task to be performed within a medical management system; and concurrently processing the diction through voiceprint authentication that performs feature extraction on one or more speech samples from the diction to generate a personalized feature vector representing vocal characteristics of the medical specialist, and compares the personalized feature vector against a Universal Background Model associated with the medical specialist to authenticate an identity of the medical specialist; and upon both: (i) identification of at least one task to be performed within the medical management system and (ii) successful authentication of the identity of the medical specialist by the voiceprint authentication, effectuating the at least one task on the medical management system.
Trehan discloses various attributes associated with user speech such as diction (see paragraph 22 of Trehan).
Kim discloses continuously monitoring speech of a specialist using a virtual assistant during performance of activities without requiring the specialist to actively invoke the virtual assistant; wherein continuously monitoring the speech includes: processing at least a portion of the speech to identify one or more task-indicative trigger words and one or more task-indicative conversational structures, task-indicative trigger words and task-indicative conversational structures specific to management tasks, wherein identifying the one or more task-indicative trigger words and the one or more task- indicative conversational structures identifies at least one task to be performed within a management system (para. 35, 37, 17, 20, & 43 of Kim; process 300 can be performed by a system similar or identical to system 100 having a user device similar or identical to user device 102 configured to implement a virtual assistant capable of continuously (or intermittently over an extended period of time) monitoring an audio input for a receipt of a trigger phrase that initiates activation of the virtual assistant. For example, a user device implementing the virtual assistant can continuously or intermittently monitor sounds, speech, and the like detected by a microphone of the user device without performing an action, such as performing a task flow, generating an output response in an audible (e.g., speech) and/or visual form, or the like, in response to the monitored sounds and speech. The trigger phrase can include any desired set of one or more predetermined words, such as “Hey Siri.” The trigger phrase can be used to activate the virtual assistant and signal to the virtual assistant that a user input, such as a request, command, or the like, will be subsequently provided. For example, a user may utter the trigger phrase “Hey Siri,” followed by the command “Call Mom,” to activate the virtual assistant and request that the virtual assistant initiate a phone call to the phone number associated with “Mom” in the user's contact list. Typically, the user request seeks either an informational answer or performance of a task by the virtual assistant. A satisfactory response to the user request can include provision of the requested informational answer, performance of the requested task, or a combination of the two. For example, a user can ask the virtual assistant a question, such as “Where am I right now?” Based on the user's current location, the virtual assistant can answer, “You are in Central Park.” The user can also request the performance of a task, for example, “Please remind me to call Mom at 4 p.m. today.” In response, the virtual assistant can acknowledge the request and then create an appropriate reminder item in the user's electronic schedule.).
Devaraj discloses concurrently processing the speech through voiceprint authentication that performs feature extraction on one or more speech samples from the speech to generate a personalized feature vector representing vocal characteristics of the specialist, and compares the personalized feature vector against a Universal Background Model associated with the specialist to authenticate an identity of the specialist (para. 89, 33, 62, & 113 of Devaraj; User recognition feature extraction may include performing frame level feature extraction and/or utterance level feature extraction. The frame level feature extraction may determine which frame of a universal background model (“UBM”) the frame corresponds to. The UBM may be a Gaussian mixture model, a deep neural network, etc. The utterance level feature extraction may analyze aligned speech frames to derive feature vectors of fixed length (i.e., the user recognition feature/vector data). The feature extraction may continue until voice activity is no longer detected in the input audio data, at which point an endpoint of the speech may be identified and speech processing may end. Feature extraction may, in some embodiments, be performed on all the audio data received from the electronic device 100. Alternatively, feature extraction may only be performed on audio data including speech. Feature extraction and user recognition feature extraction may include determining values (i.e., features) representing qualities of the frequency domain framed audio data, along with quantitating those features into values (i.e., acoustic feature vectors or audio feature vectors). Feature extraction may determine automatic speech recognition feature/vector data, which may assist with speech recognition processing for ASR system 258, and user recognition feature extraction may determine user recognition feature/vector data, which may assist with speaker identification/user recognition for speaker identification system 270. Speaker identification system 270, for example, may generate a voice print of the first audio data, and then attempt to match the voice print to one or more voice prints stored within memory as being associated with the first device identifier's particular group account. For example, first shared electronic device 100a may have first device identifier 111.xxx, which may be associated with first group account identifier 111.yyy. Speaker identification system 270 may access user accounts system 268 and/or communications accounts system 228 to determine which, if any, voice prints are stored for the first group account associated with the first group account identifier, to determine whether or not those voice prints substantially match the voice print generated for the first audio data.); and upon both: (i) identification of at least one task to be performed within the management system and (ii) successful authentication of the identity of the specialist by the voiceprint authentication, effectuating the at least one task on the management system (para. 11, 12, 15, 33-35, and 62 of Devaraj; The first audio data may be analyzed to determine a voice print (e.g., frequency spectrum, cadence, accent, meter, etc.) of the utterance (e.g., utterance 4), which may then be compared with the various voice biometric data stored for the group account associated with the first device. Upon performing the speaker identification, process 150 may proceed to step 156, where a determination may be made as to whether or not the speaker of the utterance represented by the first audio data is recognized. For instance, at step 156, a determination is made as to whether or not a voice print of the utterance matches voice biometric data associated with a member (e.g., a user account) of the group account. At step 160, a recipient group account may be determined. The recipient group account may correspond to a group account associated with an intended recipient of the message included within the spoken utterance. For example, utterance 4 may indicate that a message, “Want to have dinner at my place?”, is to be sent to a contact “John.” In one embodiment, the first audio data representing the utterance may be provided to a speech-processing system, which may employ speech recognition and natural language understanding functionality to the first audio data to determine an intent of the utterance. For example, the speech-processing system may determine that utterance 4 includes a message, and may also determine an intended recipient of that message.).
Wright discloses wherein the one or more task-indicative trigger words and the one or more task-indicative conversational structures are generated using an artificial intelligence (AI) process that processes initial seed data associated with task- indicative trigger words and task-indicative conversational structures (para. 57 of Wright; A randomly selected subset of the Truth Set data (referred to as the Seed Set) is used to generate one or more statistical or neural network based Conversational Models. For example, a statistical model may be created in which Category Tags in the truth set are correlated with the appearance of certain keywords or phrases in the corresponding natural language data with a weight assigned to each keyword or phrase. For example, the system may see that 100% of all conversations that include the word “cancel” and 50% of conversations that include the word “dissatisfied” have been tagged with the topic tag “Cancel my subscription”, and accordingly weight the word “cancel” higher than the word “dissatisfied” when deciding if a conversation should be tagged with the “Cancel my subscription” tag. In another example, the system may see that a particular phrase such as “Where is my order” is in 50% of all the conversations that have been tagged with the topic “Order Status” while only occurring in 10% of all other conversations. The system may then choose to weight a conversation with the “Where is my order” phrase higher for the “Order Status” topic than other topics. The model may use any number of statistical or other “machine learning” techniques (such as neural networks) in order to apply itself to a broader data set. In one embodiment, the application of the model to a given item results in the generation of a set of scores, each score corresponding to a different topic. In some embodiments, the system may generate different Truth Sets for different Communication Platforms. Different Truth Sets may be used, for example, to accommodate the use of different patterns of natural language in different channels (e.g., conversations patterns found in email as compared to SMS messages).).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Gallopyn’s virtual medical assistant to include the aforementioned features of Trehan, Kim, Devaraj, and Wright. The motivation for doing so would have been to analyze various attributes associated with the user speech and proactively identify assistance requirements (para. 54 of Trehan), to infer the user's intent and operationalize the user's intent into tasks (para. 3 of Kim), to identify the speaker (abstract of Devaraj), and to learn by examining patterns (para. 22 of Wright).
(B) Referring to claim 3, Gallopyn discloses wherein monitoring the speech of the medical specialist using the virtual assistant includes monitoring the speech of an ordering specialist using the virtual assistant (para. 38 & 90 of Gallopyn).
Gallopyn does not expressly disclose that the speech includes diction.
Trehan discloses various attributes associated with user speech such as diction (see paragraph 22 of Trehan).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Gallopyn’s virtual medical assistant to include the aforementioned feature of Trehan. The motivation for doing so would have been to analyze various attributes associated with the user speech and proactively identify assistance requirements (para. 54 of Trehan).
Insofar as the claim recites “one or more of,” it is immaterial whether or not the other elements are disclosed.
(C) Referring to claim 4, Gallopyn discloses wherein processing at least a portion of the speech to identify the task to be performed within the medical management system further includes processing at least the portion of the speech using natural language processing (para. 36, 46, & 48 of Gallopyn).
Gallopyn does not expressly disclose that the speech includes diction.
Trehan discloses various attributes associated with user speech such as diction (see paragraph 22 of Trehan).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Gallopyn’s virtual medical assistant to include the aforementioned feature of Trehan. The motivation for doing so would have been to analyze various attributes associated with the user speech and proactively identify assistance requirements (para. 54 of Trehan).
(D) Referring to claim 5, Gallopyn does not expressly disclose wherein processing at least a portion of the diction to identify the task to be performed within the medical management system includes: processing at least a portion of the diction on a cloud-based computing resource to identify the task to be performed within the medical management system.
Trehan discloses wherein processing at least a portion of the diction to identify the task to be performed within the medical management system includes: processing at least a portion of the diction on a cloud-based computing resource to identify the task to be performed within the medical management system (para. 22, 23, 69, 43, 44, 74, and 54 of Trehan).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Trehan within Gallopyn. The motivation for doing so would have been to analyze various attributes associated with the user speech and proactively identify assistance requirements (para. 54 of Trehan).
(E) Referring to claim 6, Gallopyn discloses wherein the medical management system includes one or more of: a medical office management system; a medical office billing system; and a pharmacy management system (para. 64 & 65 of Gallopyn).
Insofar as the claim recites “one or more of,” it is immaterial whether or not all the elements are disclosed.
(F) Referring to claims 8, 18, and 28, Gallopyn discloses wherein effectuating the task on the medical management system includes: accessing the medical management system using an application program interface of the medical management system to effectuate the task on the medical management system (para. 107, 86, and 87 of Gallopyn).
(G) Referring to claim 9, Gallopyn discloses further comprising: interfacing the virtual assistant with the medical management system (para. 64 & 65 of Gallopyn).
(H) Claim 11 differs from Claim 1 by reciting “A computer program product residing on a non-transitory computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:” (para. 3 of Gallopyn) and Claim 21 differs from Claim 1 by reciting “A computing system including: a memory; and a processor configured to perform operations comprising:” (para. 125 & 126 of Gallopyn).
The remainder of claims 11 and 21 repeat the same limitations as claim 1, and are therefore rejected for the same reasons given above.
(I) Claims 13-16, 19, and 23-26, and 29 repeat the same limitations as claims 3-6 & 9, and are therefore rejected for the same reasons given above.
Claim(s) 2, 10, 12, 20, 22, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gallopyn et al. (US 2014/0249830 A1), in view of Trehan (US 2021/0334473 A1), in view of Kim et al. (US 2016/0093304 A1), in view of Devaraj et al. (US 2018/0182389 A1), in view of Wright et al. (US 2018/0165723 A1), and further in view of Jessen (US 2018/0285595 A1).
(A) Referring to claims 2, 12, and 22, Gallopyn, Trehan, Kim, Devaraj, and Wright do not expressly disclose wherein monitoring the diction of the medical specialist using a virtual assistant includes: monitoring the diction of the medical specialist using the virtual assistant to listen for an utterance of a wake-up word.
Jessen discloses wherein monitoring the diction of the medical specialist using a virtual assistant includes: monitoring the diction of the medical specialist using the virtual assistant to listen for an utterance of a wake-up word (para. 13-16, 26, & 58 of Jessen).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Jessen within Gallopyn, Trehan, Kim, Devaraj, and Wright. The motivation for doing so would have been for hands-free initiation of an interactive communication session (para. 95 & 26 of Jessen).
(B) Referring to claims 10, 20, and 30, Gallopyn discloses wherein interfacing the virtual assistant with the medical management system includes: enabling functionality on the virtual assistant to effectuate communication between the virtual assistant and the medical management system (Fig. 1 and para. 24-26 & 37 of Gallopyn).
Gallopyn, Trehan, Kim, Devaraj, and Wright do not expressly disclose that the communication is cloud-based. However, cloud-based communication is old and well-known, as evidenced by Jessen (see para. 24 & 113-115 of Jessen).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Jessen within Gallopyn, Trehan, Kim, Devaraj, and Wright. The motivation for doing so would have been so that functionality may implemented through use of a distributed system (para. 113 of Jessen).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 11, and 21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's additional arguments filed 7/27/26 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 7/27/26.
(1) Applicant argues that Gallopyn's execution model operates entirely through independent application interfaces and backend systems - not by commandeering the specialist's own local user interface that the specialist normally operates. The subject action's stated motivations for combining the references (analyzing speech attributes, improving voice recognition convenience, and learning from patterns) provide no basis whatsoever for arriving at this specific user interface commandeering architecture with real-time visual display of subtask navigation.
(A) Regarding the argument that Gallopyn does not teach “causing the local user interface to visually display, to the medical specialist, the virtual assistant navigating the local user interface to effectuate each of the plurality of subtasks,” it is unclear how the language of the claim differs from the applied prior art. See Figures 1 & 4A-4H and para. 31-35, 39, 108-111 of Gallopyn. For example, note that Gallopyn discloses: “FIG. 4A illustrates an exemplary introductory presentation, for example, that may be presented when the medical professional activates the virtual medical assistant (e.g., by launching the virtual medical assistant application on the mobile device). In this example, the virtual medical assistant application displays dialog (and may also audibly render the dialog) indicating that virtual medical assistant is ready to assist and provides guidance to the medical professional by inviting the medical professional to tap the microphone icon and tell the virtual medical assistant what the medical professional would like done. Initially, a medical professional may be unfamiliar with functionality provided by the virtual medical assistant and the virtual medical assistant application may be present a menu of exemplary tasks that the virtual medical assistant can assist the medical professional with or perform on behalf of the medical professional. To this end, the medical professional may say "What can you do for me?" and in response the virtual medical assistant application may present a number of example tasks the virtual medical assistant can perform by providing representative free-form instruction suitable to instruct the virtual medical assistant to perform the associated task, or begin the process of performing such a task.”
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the motivations to combine came directly from the references.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited but not applied prior art teaches speaker authentication (US 2007/0198257 A1); a dialogue system incorporating unique speech to text conversion method for meaningful dialogue response (US 2018/0308487 A1); and smart authentication and identification via voiceprints (US 10,074,089 B1).
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 LENA NAJARIAN whose telephone number is (571)272-7072. The examiner can normally be reached Monday - Friday 9:30 am-6 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached at (571)270-1813. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687
