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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/30/2025 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/17/2025 The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Claims 1, 4-5, 7-8, 11-12, 14-15, 18-19, 21-22, 24, and 26 are amended. Claims 2, 9, and 16 are cancelled. Claims 28-30 are newly added. As such, claims 1, 4-5, 7-8, 11-12, 14-15, 18-19, and 21-30 are presented for examination.
Response to Arguments
Rejection under 35 U.S.C. 103
Applicant’s arguments 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.
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, 4, 7-8, 11, 14-15, 18, 21, and 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Testuggine et al. (US 10877784 B1; hereinafter referred to as Testuggine) in view of Soni (US 20180152407 A1) and Zeng et al. (US 20180053119 A1; hereinafter referred to as Zeng).
Regarding claim 1, Testuggine teaches: a computer-implemented method, comprising: receiving in real-time a set of messages between a member and a representative as the set of messages are being exchanged ([col 2, lines 26-30] A virtual assistant uses information extracted from a set of related messages exchanged between a group of users to assist those users in organizing an event. The messages may include text, audio, images, video, and/or other types of content), wherein the set of messages are received through a communications session between the member and the representative ([col 1, lines 29-31] virtual assistant interfaces with a messenger application or other messaging service through which the users communicate);
processing the set of messages through a task creation machine learning algorithm to identify a task performable on behalf of the member and one or more parameters associated with the task ([col 2, lines 60-67] additional messages between the group may indicate that three people are definitely planning on attending, they want to eat pizza, and none of them want to travel more than five miles from their current locations. Combined with information available from other sources (e.g., opening times, customer reviews, etc.) and preferences of the three users (e.g., one of them has previously liked the social network page of a local pizza restaurant), this may enable the model to generate a manageable list of places and times for the group to meet for dinner), wherein the task creation machine learning algorithm is trained using historical data corresponding to previously identified tasks and previous messages associated with the previously identified tasks… ([col 14, lines 51-55] Additionally or alternatively, the model can initially be trained using existing data about events (e.g., retrieved from content store 210) and the training updated based on feedback received for events organized using the virtual assistant 230);
processing the one or more parameters ([col 6, lines 56-60] The virtual assistant 230 analyzes messages to identify an intent to organize an event and corresponding requirements for the event (e.g., limitations on who, what, where, and when). The virtual assistant 230 applies a machine-learned model to generate recommendations for the event) and a member profile associated with the member ([col 18, lines 65-67] the virtual assistant 230 supplements the initial parameters based on additional data (e.g., from the user profile store 205, content store 210, and/or edges store 225) indicating suggested parameters for the event in which the users are likely to find value) through a task information machine learning algorithm to dynamically generate one or more prompts for additional information required for the identified task… ([col 1, lines 31-50] As the users converse about the event with each other, the virtual assistant develops an understanding of the user's preferences for the event. Based on its understanding of the user's preferences, the virtual assistant generates recommendations for the event... users can interact with the recommendations (e.g., by providing feedback));
updating the interface to incorporate the additional information ([col 11, lines 15-17, 26-32] context refinement module 330 refines the initial event parameters using messages related to the initial message (related messages)… the context refinement module may retrieve additional messages related to the additional message, such as messages between the same group of users as the current thread but in a different thread, messages between at least two of the users in the current thread in a different thread that involved organizing an event (e.g., that is also associated with an event data object), or the like) into one or more data fields corresponding to the identified task… ([col 15, lines 61-66] The context refinement module 320 then updates the parameters in the fields of the event data object based on any additional messages obtained and the recommendation module 330 generates a new ranked list of recommendations and redetermines whether any potential recommendations should be surfaced);
and performing the identified task ([col 18, lines 25-30] The steps of FIG. 5 are illustrated from the perspective of the virtual assistant 230 performing the method 500. However, some or all of the steps may be performed by other entities and/or components. In addition, some embodiments may perform the steps in parallel, perform the steps in different orders, or perform different steps), wherein the identified task is performed according to the one or more parameters associated with the identified task and the additional information ([col 3, lines 35-38] This may enable the virtual assistant 230 to quickly schedule and plan events that meet the requirements and preferences of participants without prompting the user to provide a large amount of information or direction).
Testuggine does not explicitly, but Soni teaches: generating an interface specific to the identified task ([0041] UX component 120 may communicate with one or more other components to provide an interface for viewing and/or accessing (or opening) one or more delegated tasks. In aspects, UX component 120 may provide a "delegated tasks pane" within a messaging interface), wherein the interface is generated in response to the task being identified ([0047] an interface for a delegated task may be generated based on a data structure corresponding to the delegated task. That is, parameter types corresponding to fields stored in a data structure may be provided in an interface for receiving parameter values as input from the message sender. For instance, the task delegator interface may provide text boxes, drop-down menus, etc., for receiving one or more parameter values for a delegated task. In some cases, some parameter types may be automatically populated with parameter values in the task delegator interface based on their automatic identification by the task delegation manager);
facilitating a task-specific communications session between the member and the representative through the interface ([0064] each of the one or more delegated tasks may be selectable so as to display one or more parameters associated with the delegated task (e.g., recipient(s), deadline, assignment date, follow-up schedule, follow-up history, progress update history, and the like). In some aspects, the one or more parameters may be editable. For instance, parameters such as deadline, follow-up schedule, notification type, etc., may be edited by the sender/delegator. Additionally, one or more controls may be provided within the delegated tasks pane for sending reminders, updating progress... Information about the task can be communicated with reminders and progress updates.). wherein the task-specific communications session and the communications session are distinct ([0050] Statuses of delegated tasks may be provided via a "delegated tasks pane" within a messaging interface). and wherein the task-specific communications session reduces messages exchanged through the communications session while ensuring new messages exchanged through the task-specific communications session are relevant to the identified task… ([0050] one or more controls may be provided within the delegated tasks pane for sending reminders, updating progress, marking a delegated task complete, changing a type of visual representation for displaying task status (e.g., from a bar to a pie chart, etc.), and the like. Sending short reminders and status updates in the tasks pane only includes task-relevant information and reduces messages sent in the messaging interface.);
updating the interface to present the one or more prompts through the task-specific communications session ([0040] the progress of the delegated tasks may be continuously updated and displayed in a delegated tasks pane or window, as described above. The delegated tasks pane may provide visual or textual notifications associated with one or more of the delegated tasks).
Testuggine and Soni are considered analogous in the field of natural language processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Testuggine to combine the teachings of Soni because doing so would provide users with a specific interface to efficiently communicate about the details and update progress of a particular task, leading to better task management based on communications (Soni [0060] In some aspects, the progress of the delegated tasks may be continuously updated and displayed in a delegated tasks pane or window, as described above. The delegated tasks pane may provide visual or textual notifications associated with one or more of the delegated tasks).
The combination of Testuggine and Soni does not explicitly, but Zeng teaches: processing new messages exchanged between the member and the representative ([0096] The dynamic dialog state analyzer 210 can keep track of the dialog state of the conversation with the user and the user's intent based on continuously received user input. The dialog state and user intent are also continuously updated based on the new input from the user) through the task-specific communications ([0057] When an input is received from a chat user at 150, the input from the chat user is analyzed, at 152, to estimate the intent of the chat user. It is then determined, at 154 based on the estimated intent, whether the chat user should be directed to a human or virtual agent. If the chat user is directed to a human agent, the process proceeds to 166 where the dialog with the chat user is conducted with a human agent. The dialog with the human agent may continue until a service is delivered, at 164, to the chat user. Dialog with a new human or virtual agent can be considered a task-specific communication session.) session to obtain the additional information ([0101] FIG. 4B is a flowchart of an exemplary process for a dynamic dialog state analyzer in a service virtual agent, e.g. the dynamic dialog state analyzer 210 in FIG. 4, according to an embodiment of the present teaching. A user input is received first at 420, and is parsed, at 430, based on language models/dictionary. Customized FAQ, customized task information, and general knowledge are obtained at 440), wherein the new messages are processed using natural language processing (NLP) to obtain the additional information… ([0047] The present teaching can enable online dialogue systems to generate high quality responses by effectively leveraging and learning from different types of information via different technologies, including artificial intelligent (AI), natural language processing (NLP)).
Testuggine, Soni, and Zeng are considered analogous in the field of natural language processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Testuggine and Soni to combine the teachings of Zeng because doing so would improve the quality of task creation and representative responses by using specific communications to obtain additional contextual information about a user’s needs (Zeng [0048] generating higher quality and better utterance/response messages for the conversation and interaction. Moreover, the disclosed system can provide more effective products/services recommendations in the conversation by using not only user transaction history and user demographic information that are normally used in traditional recommendation engines, but also additional contextual information about the user needs).
Regarding claim 4, the combination of Testuggine, Soni, and Zeng teaches: the computer-implemented method of claim 1. Testuggine further teaches: generating one or more proposal options for completion of the identified task ([col 2, lines 52-59] As the thread develops, with more messages being added by users in the group, the virtual assistant refines the possible values for the fields. As the possible values are refined, the potential recommendations are also updated. At some point, the output from the model includes a subset of manageable size (e.g., approximately five recommendations) that can be meaningfully distinguished from other potential recommendations), wherein the one or more proposal options are generated based on the identified task and the member profile ([col 18, lines 65-67] the virtual assistant 230 supplements the initial parameters based on additional data (e.g., from the user profile store 205, content store 210, and/or edges store 225));
updating the interface to present the one or more proposal options ([col 16, lines 30-35] a set of recommendations (e.g., the five highest ranked recommendations) are displayed to users (e.g., in a dedicated part of the user interface within a messenger application) as soon as (or shortly after) the intent determination module 320 determines a message corresponds to intent to organize an event);
and performing the identified task according to a proposal option selected through the interface and from the one or more proposal options ([col 3, lines 7-10] If the group selects one of the options (e.g., by majority or unanimous vote), the virtual assistant creates an event (e.g., by adding it to the users' calendar data) based on the selected option).
Regarding claim 7, the combination of Testuggine, Soni, and Zeng teaches: the computer-implemented method of claim 1. Zeng further teaches: processing a newly exchanged message ([0096] The dynamic dialog state analyzer 210 can keep track of the dialog state of the conversation with the user and the user's intent based on continuously received user input. The dialog state and user intent are also continuously updated based on the new input from the user) through the task-specific communications session ([0057] When an input is received from a chat user at 150, the input from the chat user is analyzed, at 152, to estimate the intent of the chat user. It is then determined, at 154 based on the estimated intent, whether the chat user should be directed to a human or virtual agent. If the chat user is directed to a human agent, the process proceeds to 166 where the dialog with the chat user is conducted with a human agent. The dialog with the human agent may continue until a service is delivered, at 164, to the chat user. Dialog with a new human or virtual agent can be considered a task-specific communication session.) to detect a request for new information required for the identified task… ([0103] Upon the updated context, the context-based action manager 230 may, based on the received dialog data (which may be forwarded by the current task context updater 510 or directly received (not shown)), determine the next action to be performed based on the deep learning models 225 and/or the information related to the specific customers on the specific tasks stored in 139. In such a determination, the current context may also be considered. The next action may be to (1) respond to an inquiry from the user by invoking machine utterance generator 240 based on information gathered based on the current context).
Testuggine further teaches: updating the interface and the identified task ([col 15, lines 61-66] The context refinement module 320 then updates the parameters in the fields of the event data object based on any additional messages obtained and the recommendation module 330 generates a new ranked list of recommendations and redetermines whether any potential recommendations should be surfaced) to incorporate and present the new information ([col 16, lines 4-7] The event selection module 350 provides one or more recommendations that the recommendation module 340 determines should be surface to be presented to the users at client devices 110);
and updating the task creation machine learning algorithm ([col 14, lines 51-61] Additionally or alternatively, the model can initially be trained using existing data about events (e.g., retrieved from content store 210) and the training updated based on feedback received for events organized using the virtual assistant 230. For example, an event may be scored based on explicit feedback (e.g., ratings or “likes” of the event provided by the participants) and/or implicit feedback (e.g., how many people actually attend the event). Thus, positive and negative examples of events may be identified) and the member profile based on the request ([col 22, lines 52-62] a recommendation receiving the most positive feedback (e.g., the most user votes) may be selected and implemented. The virtual assistant 230 sends 796 event data (e.g., the values for who, what, where, and when of the selected recommendation) to the first client device 110A which adds 798 the event to the first user's calendar. The event data is also sent 797 to the second client device 110B to be added 799 to the second user's calendar. Additionally or alternatively, the event may be added 798, 799 to the users' calendars by updating the users' user profiles in the user profile store 205).
Regarding claim 8, Testuggine teaches: a system, comprising: one or more processors; and memory storing thereon instructions that, as a result of being executed by the one or more processors ([col 17, lines 58-62] example computer 400 includes at least one processor 402 coupled to a chipset 404. The chipset 404 includes a memory controller hub 420 and an input/output (I/O) controller hub 422. A memory 406 and a graphics adapter 412 are coupled to the memory controller hub). The rest of the claim recites similar limitations as claim 1 and therefore is rejected similarly.
Regarding claim 11 and 18, they recite similar limitations as claim 4 and therefore are rejected similarly.
Regarding claims 14 and 21, they recite similar limitations as claim 7 and therefore are rejected similarly.
Regarding claim 15, Testuggine teaches: a non-transitory, computer-readable storage medium storing thereon executable instructions that, as a result of being executed by a computer system, cause the computer system to… ([col 18, lines 1-6] In the embodiment shown in FIG. 4, the storage device 408 is a non-transitory computer-readable storage medium such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 406 holds instructions and data used by the processor 402). The rest of the claim recites similar limitations as claim 1 and therefore is rejected similarly.
Regarding claim 28, the combination of Testuggine, Soni, and Zeng teaches: the computer-implemented method of claim 1. Zeng further teaches: wherein: the task creation machine learning algorithm further generates one or more recommendations for questions ([0046] bot design involves primarily human activities, relying on human service representatives to design information needs associated with their customers, including what questions to be asked to gather what types of information… In order to effectively reduce the human labor and cost of developing/designing those service agents which offer and maintain real-time online user service dialogue systems, the present teaching discloses methods for designing and developing intelligent virtual agents, which can automatically generate and recommend response/reply messages for assisting human representatives) presentable to the member to obtain the additional information ([0048] based on machine learning and AI technique, the disclosed system can learn how to strategically ask user questions… The disclosed system can use the knowledge base and historical conversations for recommending high quality response messages for future conversation), wherein the one or more recommendations are generated based on a set of member preferences ([0074] in addition to the user intent estimated during the conversation, the recommendation engine 250 may also further individualize the recommendation by accessing the user's profile from the user database 132. A user profile can contain preferences.);
and the computer-implemented method further comprises updating a representative interface associated with the representative to provide the one or more recommendations ([0135] That is, it is an interface used by a human agent who is assisted by a virtual agent. The interface include different dialog boxes in which each side (chat user and the bot-assisted agent) can each enter their sentences (820, 830, and 840). This agent-side interface also includes various types of information and different actionable sub-interfaces. For example, it includes some historical information related to the current ongoing conversation, shown to list “previous tickets/talks” (850). It also provides agent-selectable actions (860) which may be presented, once clicked, as a drop-down list, editable tags (870)… The agent is assisted by a bot. For example, when the chat user asked “What is your return policy?” (in 840), the bot that is assisting the human agent provides a list of possible responses corresponding to a list of possible utterances tagged as “Assisted by Rulai.”).
Testuggine, Soni, and Zeng are considered analogous in the field of natural language processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Testuggine, Soni, and Zeng to further combine the teachings of Zeng because doing so would allow for a representative to use a specific representative interface to effectively respond to different users based on a user’s preferences, improving efficiency in task creation and representative responses (Zeng [0137] In this interface, different bot suggested responses may be presented to the agent. The bot-assisted agent can activate “Send” of a desired response and send the corresponding response utterance to the chat user. Such suggested responses may be used by the agents to carry on a conversation. When assisted by bot suggested responses, the agents according to the present teaching can handle multiple customer requests simultaneously via this interface at ease).
Regarding claims 29 and 30, they recite similar limitations as claim 28 and therefore are rejected similarly.
Claims 5, 12, 19, 22, 24, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Testuggine in view of Soni and Zeng, as applied to claims 1, 4, 7-8, 11, 14-15, 18, 21, and 28-30 above, and further in view of Roller et al. (US 20190140995 A1; hereinafter referred to as Roller).
Regarding claim 5, the combination of Testuggine, Soni, and Zeng teaches: the computer-implemented method of claim 1. The combination of Testuggine, Soni, and Zeng does not explicitly, but Roller teaches: selecting a task template, wherein the task template is selected based on the one or more parameters ([0043] The template generator 340 may automatically generate one or more templates based on instructions received from the action identifier 335. For example, if the action identifier 335 suggests a reply action, the template generator 340 may use an email template, the binary classifications, the extracted metadata, or some combination of these to generate an email to send in response to the communication message);
updating the task template according to the one or more parameters ([0045] Based on the user feedback, the Signals Platform 310 may modify the NLP process, the action recommendation process, or any other part of the Signals Platform 310 functionality to more accurately classify messages, extract metadata, and suggest actions. Additionally or alternatively, the database server or user device 305 may update the template generation process based on the feedback);
and updating the interface to present the identified task ([0044] For the user to select one or more actions to perform, the user device 305 include an action indicator 345, which may display the suggested actions in a user interface of the user device 305), wherein the identified task is presented according to the updated task template ([0031] According to the instructions, the user device 205 may automatically populate one or more fields of a template using information related to the extracted metadata or a binary classification. This generated communication template may be prepared for sending, by the user device 205, to another user device, or may be displayed at the user device) and the additional information ([0015] When a new communication message is sent to a user device, the new communication message may additionally be received by the database server for processing (e.g., using the streaming data pipeline)).
Testuggine, Soni, Zeng, and Roller are considered analogous in the field of natural language processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Testuggine, Soni, and Zeng to combine the teachings of Roller because doing so would allow for a user/representative to effectively respond to many messages in an efficient and configurable manner using task templates based on analyzed parameters (Roller [0004] reading or listening to each of these messages in order to draft a response—even a stock response with very few modifications-may be very inefficient and result in delayed responses to the communication messages. A user may desire an effective way to manage and respond to this large quantity of messages in a time -sensitive yet configurable manner).
Regarding claims 12 and 19, they recites similar limitations as claim 5 and therefore are rejected similarly.
Regarding claim 22, the combination of Testuggine, Soni, and Zeng teaches: the computer-implemented method of claim 1. Zeng further teaches: and dynamically updating the representative interface to present the one or more parameters… ([0135] This agent-side interface also includes various types of information and different actionable sub-interfaces. For example, it includes some historical information related to the current ongoing conversation, shown to list “previous tickets/talks” (850). It also provides agent-selectable actions (860) which may be presented, once clicked, as a drop-down list, editable tags (870). The bot-assisted agent may also add topic tags about the current chat. The agent is assisted by a bot).
The combination of Testuggine, Soni, and Zeng does not explicitly, but Roller teaches: updating a representative interface associated with the representative ([0044] For the user to select one or more actions to perform, the user device 305 include an action indicator 345, which may display the suggested actions in a user interface of the user device 305. A user may select one or more of the displayed actions, and the user device 305 may perform the selected action (e.g., using the generated communication templates)) to present the identified task ([0041] Some examples of actions that the action identifier 335 may select include sending availability, sending a calendar invite, replying to an message (e.g., using a template), creating a task based on information within the communication message);
detecting selection of the identified task through the representative interface… ([0050] if the user selects (e.g., clicks on) one of these actions, the user interface 420 may display the corresponding generated template, and may allow the user to modify the template if desired. Additionally, the user may select to provide feedback based on the identified insights, the suggested actions, or the generated templates. If the user selects to perform one of the actions, the user device may send a communication template corresponding to that action);
and a task template associated with the identified task ([0043] The template generator 340 at the user device 305 may create a calendar invite based on the indicated actions and metadata (e.g., suggested dates or times in the text of the communication message 315) determined by the Signals Platform 310, and may send this calendar invite in response to the communication message 315 if selected by the user).
Regarding claims 24 and 26, they recites similar limitations as claim 22 and therefore are rejected similarly.
Claims 23, 25, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Testuggine in view of Soni and Zeng, as applied to claims 1, 4, 7-8, 11, 14-15, 18, 21, and 28-30 above, and further in view of Ghotbi et al. (US 20170004396 A1; hereinafter referred to as Ghotbi).
Regarding claim 23, the combination of Testuggine, Soni, and Zeng teaches: the computer-implemented method of claim 1. The combination of Testuggine, Soni, and Zeng does not explicitly, but Ghotbi teaches: detecting that the identified task has been completed ([0090] an explicit feedback interface 400 is shown, in accordance with the aspect of the technology described herein. The feedback interface 400 asks the user why the task reminder was dismissed. Four selectable options are provided in FIG. 4. The first option 404 is that the task has already been completed);
updating the interface to present a new prompt for feedback associated with the identified task, wherein the new prompt is presented through the task-specific communications session ([0004] an interface to solicit feedback is surfaced when a user dismisses the reminder. The specific feedback can specify whether the task was correctly identified and whether the task was surfaced in the correct context. The specific feedback can also help identify the types of tasks the user actually wants help tracking);
and updating the task creation machine learning algorithm and the task information machine learning algorithm based on the feedback ([0059] the feedback interface follows a schema that can be incorporated into the model for training purposes. In one aspect, the feedback is used to label communications that were originally analyzed to generate the task reminder. Processing the task reminder with the additional annotated data can help train the model to take the desired action in the future).
Testuggine, Soni, Zeng, and Ghotbi are considered analogous in the field of natural language processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Testuggine, Soni, and Zeng to combine the teachings of Ghotbi because doing so would allow for user feedback to be used to improve task identification and performance in providing relevant response to users (Ghotbi [0022] The identification of tasks and presentation of reminders can facilitate multiple complex scenarios. The feedback can help determine which scenarios are of interest to the user and improve the performance of useful scenarios).
Regarding claims 25 and 27, they recite similar limitations as claim 23 and therefore are rejected similarly.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nathan Tengbumroong whose telephone number is (703)756-1725. The examiner can normally be reached Monday - Friday, 11:30 am - 8:00 pm EST.
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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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/NATHAN TENGBUMROONG/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654