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 .
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 1-2, 6-7, 9-12, 16-17, 19-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite elements which, under their broadest reasonable interpretation, are directed to mental processes. This judicial exception is not integrated into a practical application as explained below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as explained below.
Regarding Claim 1, the claim recites a method for managing an execution plan for an artificial intelligence (AI)- based assistance device, the method comprising:
receiving a first input, via a microphone, indicating a voice command from a user;
determining a first context associated with the AI-based assistance device and a user intent, based on the first input;
generating a first execution plan, based on the first context, wherein the first execution plan indicates one or more first tasks to be executed in response to the first context, and one or more second execution plans, based on the first context, wherein the one or more second execution plans indicate one or more second tasks to be executed in response to the first context;
generating a first timeline connecting the first execution plan and the one or more second execution plans;
detecting a change from the first context based on the first timeline and at least one of a second context of the AI-based assistance device or a second context of the user;
generating an updated execution plan based on the change; and
generating a second timeline by modifying the first timeline based on the updated execution plan.
Claim Interpretation: Under the broadest reasonable interpretation, the terms of the claim are
presumed to have their plain meaning consistent with the specification as it would be interpreted by
one of ordinary skill in the art. See MPEP 2111.
Recites receiving a verbal command. A human can receive a verbal command
Recites determining a first context and user intent. A human can determine the context and intent from a command
Recites creating first and second execution plans. A human can create plans of action based on the context they got from a command
Recites associating the execution plans with a timeline. A human can create a timeline and schedule their action plans.
Recites detecting a change in the first context and a second context. A human can detect a change that would affect the contexts
Recites generating a new execution plan based on the change. A human can adapt and alter their plan of action based on change in the context/environment
Recites modifying the first timeline based on the new execution plan. A human can change their schedule based on a new plan of action.
Additional elements recited are: microphone and AI-based assistance device
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory
category. See MPEP 2106.03. The claim is directed to a method, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a
judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception
when the judicial exception is “set forth” or “described” in the claim.
As discussed above, the broadest reasonable interpretation of limitations (c)-(g) that those
elements fall within the mental process groupings of abstract ideas because they cover concepts
performed in the human mind, including observation, evaluation, judgment, and opinion . See MPEP
2106.04(a)(2), subsection III.
Limitation a is directed to a mental step because a human can receive a verbal command. Limitation b is directed to a mental step because a human can determine the context and intent from a command. Limitation c is directed to a mental step because a human can generate plans of actions based on the command given. Limitation d is directed to a mental step because a human can schedule their plans. Limitation e is directed to a mental step because a human can detect a change in contexts/environments. Limitation f is directed to a mental step because a human can alter their plans of action based on a perceived change. Limitation g is directed to a mental step because a human can modify their initial schedule based on a change in plan. Hence, these steps can be performed by a human, using “observation, evaluation, judgment, [and] opinion,” because they involve making determinations and identifications, which are mental tasks humans routinely do, and thus can practically be performed in the human mind, In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022). Therefore, these limitations are considered together as an abstract idea for further analysis. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole
integrates the recited judicial exception into a practical application of the exception or whether the
claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether
there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating
those additional elements individually and in combination to determine whether the claim as a whole
integrates the exception into a practical application. See MPEP 2106.04(d). The additional elements recited were microphone and AI-based assistance device.
These additional elements provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amount to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
At Step 2A, the additional elements of microphone and AI-based assistance device were found to represent nothing more than mere instructions to apply the judicial exception on a computer using generic computer components. Mere instructions to “apply” the abstract ideas, cannot provide an inventive concept. See MPEP 2106.05(f). The analysis under Step 2A, Prong Two is carried through to Step 2B. The recited additional elements are well understood, routine and conventional. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
As such Claim 1 is patent illegible.
The analysis above is applicable to Claims 11 and 20.
Regarding Claim 2, a human can generate a text format of the verbal input and separate the text into domains. The ASR and NLU models are additional elements which represent nothing more than mere instructions to apply the judicial exception on a computer using generic computer components.
The analysis for Claim 12 is analogous to that of Claim 2.
Regarding Claim 6, a human can divide their plans of action based on time and place and then recombine them temporarily before dynamically scheduling them. The aggregator cache is an additional element which represents nothing more than mere instructions to apply the judicial exception on a computer using generic computer components.
The analysis for Claim 16 is analogous to that of Claim 6.
Regarding Claim 7, a human can schedule their plans of actions according to the priority of the tasks, what tasks depend on another and the semantic meaning of the tasks. The neural network of the dynamic execution plan generator is an additional element which represents nothing more than mere instructions to apply the judicial exception on a computer using generic computer components.
The analysis for Claim 17 is analogous to that of Claim 7.
Regarding Claim 9, a human can correlate their schedule with the new context to detect a change from the original context. Additionally, a human can detect the change of a plurality of variables including if the equipment is turned off and an action plan. The context monitoring service and state monitoring service are additional elements which represent nothing more than mere instructions to apply the judicial exception on a computer using generic computer components.
The analysis for Claim 19 is analogous to that of Claim 9.
Regarding Claim 10, a human can modify their schedule by extending a plan or replacing a plan with another.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 10-12, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Presant(US PGPub 20210117214) in view of Mitra(US PGPub 20220343155).
Regarding Claim 1, Presant teaches a method for managing an execution plan for an artificial intelligence (AI)- based assistance device, the method comprising:
receiving a first input, via a microphone, indicating a voice command from a user (the user request may be based on speech [0101]);
determining a first context associated with the AI-based assistance device (The multimodal signals may comprise one or more of date, time, location, visual signal, sound signal, entity update, or user context[0100], world events[0102]) and a user intent (determine the intents and slots associated with the user request[0101]), based on the first input (The context extractor 303 may extract
contextual information associated with the user request (user input) [0076]);
generating a first execution plan(task), based on the first context(dialog state tracker 337 may track the events related to a user and suggest candidate tasks based on one or more of the intents, the slots, the multimodal events, the world events, or the social events[0102], assistant system 140 may create a proactive task either in an online fashion (e.g., created immediately given a user's explicit request)[0105]), wherein the first execution plan indicates one or more first tasks to be executed in response to the first context(Given suggested candidate tasks, the action selector 341 may decide on the actions(mapped to tasks) to take in order to fulfill the tasks.[0102]), and one or more second execution plans(candidate tasks based on one or more of the intents, the slots, the multimodal events, the world events, or the social events[0102]), based on the first context, wherein the one or more second execution plans indicate one or more second tasks to be executed in response to the first context(Given suggested candidate tasks, the action selector 341 may decide on the actions(mapped to tasks) to take in order to fulfill the tasks.[0102]);
generating a first timeline connecting the first execution plan and the one or more second execution plans (smart scheduler 525 may be used for scheduling a task(mapped to execution plan) if it has not been scheduled yet and enforcing rate limiting, priority setting and any other personalization requirements before generating payloads for delivery. The smart scheduler 525 may determine a delivery schedule of the proactive content [0104]);
detecting a change from the first context and at least one of a second context of the AI-based assistance device or a second context of the user (The completion of a task(Interpretation: context of the assistance device has changed with the completion of a task), a change in the user context(Interpretation: change from the first context/second context), or a relevant multimodal signal, may trigger a proactive policy.[0098]);
generating an updated execution plan based on the change (Based on the proactive policy, the
assistant system may determine what kind of proactive content to execute. The assistant system 140
may take in prior interactions with the user or prior knowledge about the user to determine what
proactive content(proactive task) is suitable. The assistant system 140 may generate chains of
proactive content based on user feedback to each turn of dialog or in a multimodal context in which
the assistant system 140 may continue proactively providing content in response to the user
interacting with the proactive content [0098] (Interpretation: change triggers a proactive policy which
determines what the proactive task will be which is an updated execution plan));
Presant does not teach detecting a change from the first context based on the first timeline nor generating a second timeline by modifying the first timeline based on the updated execution plan.
However, Mitra teaches generating a first timeline(generate a schedule for a set of tasks(execution plan)[0005, Fig 8A) connecting the first execution plan and the one or more second execution plans; and based on a change(monitor progress of the schedule of tasks and determine disruptions, changes, or modifications (e.g., new tasks, missed tasks, or changed tasks)[0005]), generating a second timeline by modifying the first timeline based on the updated execution plan(systems utilize the reinforcement learning model to automatically re-arrange tasks, reassign tasks to other users, and intelligently generate a modified schedule[0005]).
It would have been obvious to a person having ordinary skill in the art before the effective filing
date of the claimed invention to combine the system of Presant with the schedule of Mitra because it
would efficiently arrange and prioritize tasks(Mitra 0005).
Regarding Claim 2, Presant teaches prior to the determining the first context, the method comprises:
generating a text format corresponding to the first input by converting the first input into text based on an automated speech recognition (ASR) model(In particular embodiments, the audio data may be received at a remote automatic speech recognition (ASR) module 208. The ASR module 208 may allow a user to dictate and have speech transcribed as written text[0050]); and
segregating the text format into a plurality of domains by classifying the text format based on a natural level understanding (NLU)(while applicant defines NLU model as natural level understanding, the definition in paragraph 0053 of the specification is consistent with the Natural Language Understanding model of Presant) model(the output of the ASR module 208 may be sent to a remote natural-language understanding (NLU) module 210[0052], the NLU module 210 may identify one or more of a domain, an intent, or a slot from the user input[0053]).
Regarding Claim 10, Presant teaches the updated execution plan indicates an extension(The assistant system may generate chains of proactive content(chains of proactive content maps to extension) based on user feedback to each turn of dialog [0098]) of the one or more second execution plans or a replacement of the one or more second execution plans.
Claim 11 recites similar limitations to Claim 1 and is rejected under the same rationale.
Claim 20 recites similar limitations to Claim 1 and is rejected under the same rationale.
Claim(s) 3, 9, 13, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Presant(US PGPub 20210117214) in view of Mitra(US PGPub 20220343155) as applied to claim 1 above, and further in view of Cui(LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution).
Regarding Claim 3, Presant teaches generating word embeddings(a word-embeddings model may be used to map an n-gram to a vector representation in the vector space 1300[0129]) corresponding to the first input based on a conversion of a segregated text format corresponding to the first input into the word embeddings; identifying a plurality of pieces of data(intent) corresponding to the first input, based on a vector search of the word embeddings in a predefined vector database(The intent classifier may then calculate probabilities of the user request being associated with different predefined intents based on a vector comparison between the vector representing the user request and the vectors representing different predefined intents.[0077]), wherein the plurality of pieces of data indicates a plurality of services associated with the first input; generating a predetermined number of prioritized pieces of data, corresponding to the first input(third-party agents may be designated for a particular domain. As an example and not by way of limitation, the domain may comprise weather, transportation, music, shopping, social, videos, photos, events, locations, work, etc. In particular embodiments, the assistant system 140 may use a plurality of agents collaboratively to respond to a user input[0064]), from the plurality of pieces of data by re-ranking the plurality of pieces of data based on the first input, wherein the predetermined number of prioritized pieces of data indicate one or more pieces of data having highest ranks compared with remaining pieces of data from among the plurality of pieces of data(The dialog intent resolution 336 may further rank dialog intents based on signals from the NLU module[0085]); providing, from a plurality of external services provider(third-party systems 170 or other entities, or to allow users to interact with these entities through an application programming interfaces (API)[0041]), information corresponding to a plurality of external services upon requesting the plurality of external services(assistant system 140 may interact with the … third-party system 170 when retrieving information or requesting services for the user[0037]) based on the predetermined number of prioritized pieces of data, and the first input, wherein the information corresponding to the plurality of external services indicates a plurality of operations associated with the first input(intent comes from first input); determining the first context(The multimodal signals may comprise one or more of date, time, location, visual signal, sound signal, entity update, or user context[0100], world events[0102]), based on the information corresponding to the plurality of external services and a plurality of predetermined pieces of custom data(combination of user input, location awareness, and the ability to access information from a variety of online sources(Interpretation: external services and API) (such as weather conditions, traffic congestion, news, stock prices, user schedules(Interpretation: custom data), retail prices, etc.)[0003]).
Presant does not teach merging the information with multi- device environment (MDE) data.
However Cui teaches merging multi- device environment (MDE) data to create context(The context repository contains the contextual information…1)Environment Information: A list of available AI modules and IoT devices with their function descriptions, as well as the installation locations of IoT devices.[ Functionalities and composition of the coordinator])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having the teaching of Presant and Mitra to further include the multi-device data because it would enable the system to be aware of the available AI modules, IoT devices and their capabilities(Cui).
Claim 13 recites similar limitations to Claim 3 and is rejected under the same rationale.
Regarding Claim 9, Presant in view of Mitra teaches detecting the change comprises: detecting the change from the first context, based on correlating the first timeline with at least one of the second context or the second context of the user(The completion of a task(Interpretation: context of the assistance device has changed with the completion of a task), a change in the user context(Interpretation: change from the first context/second context), or a relevant multimodal signal, may trigger a proactive policy.[0098]), via a context monitoring service(UCE 315 may update the user context[0113]);
Mitra teaches detecting a change of a plurality of variables associated with the first execution plan and the one or more second execution plans, based on correlating the first timeline with at least one of the second context or the second context of the user(monitor progress of the schedule of tasks and determine disruptions, changes, or modifications (e.g., new tasks, missed tasks, or changed tasks)[0005]), wherein the plurality of variables comprises a plan provided in the first execution plan and at least one of the one or more second execution plans(disruptions, changes, or modifications (e.g., new tasks(mapped to execution plan), missed tasks, or changed tasks)[0005]).
Neither Presant nor Mitra teach detecting a change via a state monitoring service or an execution plan validator, wherein the plurality of variables comprises an initial state of at least one of user equipment, a type of at least one of the user equipment.
However, Cui teaches detecting a change via a state monitoring service or an execution plan validator(After receiving a user instruction, the system strives for efficiency by first searching for validated and feasible historical to regenerate the script based on the updated context. If the script continues to fail after multiple retries (more than three attempts), the system collects error information and reports it to the user. (Interpretation: system is validating the script(execution plan))), wherein the plurality of variables comprises an initial state of at least one of user equipment(executor monitors hardware status), a type of at least one of the user equipment(A list of available AI modules and IoT devices with their function descriptions, as well as the installation locations of IoT devices. This enables the LLM to be aware of the available AI modules, IoT devices, and their capabilities.).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having the teaching of Presant and Mitra to further include the multi-device data because it would enable the system to be aware of the available AI modules, IoT devices and their capabilities(Cui).
Claim 19 recites similar limitations to Claim 9 and is rejected under the same rationale.
Claim(s) 4, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Presant(US PGPub 20210117214) in view of Mitra(US PGPub 20220343155) as applied to claim 1 above, and further in view of Shabat(US PGPub 20240203404).
Regarding Claim 4, Presant teaches merging the first context(multimodal inputs and world events) and an outcome of a query(assistant system 140 may interact with the … third-party system(API query) 170 when retrieving information or requesting services for the user[0037]) corresponding to the first input(updated multimodal signals[0113]); extracting activity information corresponding to a plurality of future activities to be performed, wherein the activity information(domain) corresponds to the first input and a plurality of categories of predefined activities(the NLU module 210 may perform domain classification/selection on user request based on the features resulted from the featurization to classify the user request into predefined domains[0077]); and generating the first execution plan and the one or more second execution plans(candidate tasks based on one or more of the intents, the slots, the multimodal events, the world events, or the social events[0102]) based on the activity information and the plurality of categories(In particular embodiments, each of the first-party agents or third-party agents may be designated for a particular domain. As an example and not by way of limitation, the domain may comprise weather, transportation, music, shopping, social, videos, photos, events, locations, work, etc.[0064], the assistant recommender 530 may communicate with different agents 350 if the proactive suggestion(task) requires to be executed by agents 350[0102]).
Neither Presant nor Mitra teaches extracting activity information corresponding to a plurality of future activities to be performed, based on at least one of: a fine-tuning technique, an adapter technique or a rag technique.
However, Shabat teaches extracting activity information(for a given spoken utterance (or a transcription thereof), a high-level domain can be identified[0017], various domains, such as making a reservation at a restaurant, scheduling an appointment at a hair salon[0018]), based on at least one of: a fine-tuning technique(fine-tuned LLM 120), an adapter technique or a rag technique.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention with the teaching of Presant and Mitra to use the fine-tuned model of Shabat because it would improve the performance of the model(Shabat 0019)
Claim 14 recites similar limitations to Claim 4 and is rejected under the same rationale.
Claim(s) 5, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Presant(US PGPub 20210117214) in view of Mitra(US PGPub 20220343155) in view of Shabat(US PGPub 20240203404). as applied to claim 4 above, and further in view of Cui(LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution).
Regarding Claim 5, Presant in view of Mitra teaches segregating(To illustrate, the task scheduling system 102 utilizes data associated with groups of similar users (e.g., based on demographics, explicit user groupings) or for similar schedule types (e.g., workday routines, construction schedules)[Mitra 0100] (Interpretation: Mitra teaches grouping/segregating tasks based on a similarity)) the first execution plan and the one or more second execution plans into a plurality of pre-determined groups(wherein each of the one or more proactive suggestions comprises one or more of a suggested survey, a suggested query, a suggested task, a follow-up survey, a follow-up question, or a follow-up task,[Presant 0120]), wherein the plurality of pre-determined groups comprises a proactive multi domain task group(follow-up task), a personalized dynamic recommendation group(suggested query).
Prior references do not teach a multi-device assistance group.
However, Cui teaches a multi-device assistance group(Instead, it generates control scripts(mapped to execution plan) to … send control commands to IoT devices).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention having the teaching of Presant, Mitra and Shabat to further include the multi-device data because it would enable the system to be aware of the available AI modules, IoT devices and their capabilities(Cui).
Claim 15 recites similar limitations to Claim 5 and is rejected under the same rationale.
Claim(s) 6, 7, 16, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Presant(US PGPub 20210117214) in view of Mitra(US PGPub 20220343155) as applied to claim 1 above, and further in view of Sit(US PGPub 20160217400).
Regarding Claim 6, Presant in view of Mitra teaches the first execution plan(task), second execution plan(task) and a dynamic execution plan generation(task scheduling system 102[Mitra], smart scheduler 525[Presant]). Mitra also teaches segregating the plans based on a plurality of parameters comprising a location and a time(the task scheduling system annotates the task nodes with values such as times, locations, etc., for separating similar tasks by context.[0022]).
Neither Presant nor Mitra teach aggregating the plans into a plurality of pre-determined groups, via an aggregator cache;
However, Sit teaches aggregating the plans into a cache(When plan data … is retrieved from the database 4054 and provided to the server framework 4030, the data may be stored in the caches 4052[0077]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention with the teachings of Presant and Mitra to incorporate the plan cache of Sit because it plan data can be retrieved more quickly in response to a subsequent request for the data(0077).
Claim 16 recites similar limitations to Claim 6 and is rejected under the same rationale.
Regarding Claim 7, Mitra teaches processing the aggregated first execution plan and the one or more second execution plans, via a neural network of the dynamic execution plan generator(intelligently generate and modify schedules of task sequences utilizing a graph neural network[Abstract]), based on a plurality of predetermined factors comprising a priority policy(given a priority list and/or additional information associated with the tasks, the existing systems utilize a scheduling algorithm to generate a schedule[0003]), a parameter dependency tracker(The task scheduling system utilizes the graph neural network to generate edge weights between the user nodes and the task nodes by capturing dependencies between different nodes in the bipartite graph[0021].), and a text map(the task scheduling system utilizes the graph neural network to generate edge weights between user nodes and task nodes according to contextual information associated with tasks[0022](Interpretation: contextual information in Mitra is mapped to text map)); and
generating an interconnected first execution plan and the one or more second execution plans and the first timeline connecting the interconnected first execution plan and the one or more second execution plans(intelligently generate and modify schedules of task sequences utilizing a graph neural network[Abstract]).
Claim 17 recites similar limitations to Claim 7 and is rejected under the same rationale.
Allowable Subject Matter
Claim 8 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The combination of context monitoring service segment, a state monitoring service segment, an execution plan validator segment, and an execution scheduler segment was found to be novel and non-obvious.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure. Khemka(US PGPub 20230409615) teaches proactive actions based on a context.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARJUN R SWAMY whose telephone number is (571)272-9763. The examiner can normally be reached Mon-Fri 8-5.
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, Hai Phan can be reached at (571) 272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ARJUN SWAMY/Examiner, Art Unit 2654
/Richa Sonifrank/Primary Examiner, Art Unit 2654
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