Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
2. Applicant’s arguments with respect to claims 1-8, 10-17, 19 and 20 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. Specifically, newly added limitations are taught by newly cited Beilby (US 8,630,961).
Claim Rejections - 35 USC § 103
3. 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.
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.
4. Claims 1-8,10-17 and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over D’Agostino (US 2020/0099633) in view of Beilby (US 2012/0041903).
Regarding Claim 1:
D’Agostino discloses a system (D’Agostino: Fig. 1 ‘system 100’), comprising:
a memory (D’Agostino: p[0043]);
and at least one computing device (D’Agostino: e.g. Fig. 2 ‘client device 202’ [0051-0052]) in communication with the memory (D’Agostino: e.g. Fig. 2 ‘conversational manager 206’ [0052]), wherein the at least one computing device is configured to:
receive a first conversational input of a plurality of sequential conversational inputs via at least one user input device (D’Agostino: e.g. Fig. 2 ‘user 201 interacts with a client device 202 to provide a conversational or user input 204 to the conversational manager 206’ [0052] and Fig. 5 flowchart Step 502), wherein the first conversational input is received via a particular uniform resource locator (URL) address (D’Agostino: p[0024] and p[0051] The conversational manager receives inputs from client devices through a conversational interface. p[0054] and p[0078] describes inputs linked to contextual sources such as a chat bot interface or systems, which extends to URL-based contexts);
determine a context based on the particular URL address, the first conversational input (D’Agostino: p[0054-0056] discloses that the NLP system analyzes inputs for context determination and determines a context based on user input)
and determine at least one intent based on the context, the first conversational input (D’Agostino: p[0054-0056] discloses receiving inputs for context determination then responds to the user with this context and input)
generate a response to the first conversational input based on the context and the at least one intent (D’Agostino: p[0054-0056] discloses that the NLP system analyzes inputs for context determination, the determined context is done based on user input to align with the proper chatbot then responds to the user with this context
subsequent to receiving the first conversational input, receive a second conversational input of the plurality of sequential conversational inputs (D’Agostino: p[0024], p[0059], p[0085] discloses the conversational managers ability to manage multi-turn interactions, including receiving subsequent inputs)
process the second conversational input via the NLP algorithm to generate at least one updated intent based on the first conversational input, the second conversational input, and the particular URL address (D’Agostino: p[0059] and p[0085-0086] disclose iterative context determination and intent refinement for handling sequential conversational inputs);
and generate a second response to the second conversational input based on the at least one updated intent and the context (D’Agostino: p[0059] and p[0085-0086] disclose iterative context determination and intent refinement for handling sequential conversational inputs).
D’Agostino does not explicitly disclose:
associate the first conversational input with a first container from a plurality of containers based on the particular URL address;
update a user interface displayed on a mobile device accessing the particular URL, wherein the user interface is updated with branding associated with the first container;
process the first conversational input via at least one natural language processing (NLP) algorithm and a knowledge base comprising one or more hierarchical tiers associated with the first container to:
perform a scan of the one or more hierarchical tiers of the knowledge base based on the first conversational input;
wherein generating the response comprises:
generating a response variable based on the context and the at least one intent;
retrieving a decision tree from the knowledge base associated with the , wherein the decision tree comprises a first response track and a second response track, each of which comprise a plurality of ranked responses;
determining that a top-ranked response of the first track does not satisfy the response variable;
determining that a top-ranked response of the second track does satisfy the response variable;
updating the response variable with the top-ranked response in the second track, wherein the top-ranked response comprises a dynamic content variable; and updating the dynamic content variable with a value retrieved from the knowledge base associated with the first container;
…via the particular URL address associated with the first container, wherein the second conversational input is associated with the first container;
However, Beilby discloses:
associate the first conversational input with a first container from a plurality of containers based on the particular URL address (Beilby: ¶[0080], ¶[0114], ¶[0219] teaches multiple distinct chatbot instances that are deployed to specific webpages/URLs, such that conversational inputs received via a webpage are associated with the corresponding chatbot instance);
update a user interface displayed on a mobile device accessing the particular URL, wherein the user interface is updated with branding associated with the first container (Beilby: ¶[0117], ¶[0194] and ¶[0259] teaches updating and rendering a chat user interface displayed on a client device (including mobile devices) accessing the webpage where the chatbot is deployed; ¶[0152], ¶[0195] discloses branding the chatbot interface on a per deployment per chatbot basis, corresponding to branding associated with the container);
process the first conversational input via at least one natural language processing (NLP) algorithm and a knowledge base comprising one or more hierarchical tiers associated with the first container (Beilby: ¶[0151], ¶[0219], teaches a knowledge base specific to each chatbot instance, such that conversational input is processed using knowledge associated with that container) to:
perform a scan of the one or more hierarchical tiers of the knowledge base based on the first conversational input (Beilby: ¶[0024], ¶[0143] and ¶[0242] discloses scanning hierarchical tiers of knowledge base (folders and sub-folders of nodes) by searching those tiers using the received conversational input);
wherein generating the response comprises:
generating a response variable based on the context and the at least one intent (Beilby: ¶[0085], ¶[0182] discloses generating and using variables/criteria derived from conversational state (content and intent) to control response selection, corresponding to a response variable);
retrieving a decision tree from the knowledge base associated with the, wherein the decision tree comprises a first response track and a second response track, each of which comprise a plurality of ranked responses (Beilby: ¶[0080], ¶[0148] discloses conversation data structure is a decision tree comprising multiple conversation paths, each path corresponding to a response track retrievable from the knowledge base; ¶[0140] discloses ranking candidate responses and selecting a top ranked node, corresponding to response tracks comprising plural ranked responses);
determining that a top-ranked response of the first track does not satisfy the response variable (Beilby: ¶[0085] discloses that a selected (top-ranked) response fails to satisfy a criterion, corresponding to a top-ranked response that does not satisfy the response variable);
determining that a top-ranked response of the second track does satisfy the response variable (Beilby: ¶[0080], ¶[0240] teaches selecting and processing an alternative response path whose node does satisfy the criteria corresponding to a top-ranked response of a second track satisfying the response variable);
updating the response variable with the top-ranked response in the second track, wherein the top-ranked response comprises a dynamic content variable (Beilby: ¶[0014] teaches updating a variable based on processing the selected response node, corresponding to updating the response variable with the selected top ranked response);
and updating the dynamic content variable with a value retrieved from the knowledge base associated with the first container (Beilby: ¶[0219] discloses populating response variables with values retrieved from the chatbot’s stored knowledge, corresponding to updating the dynamic content variable with a value from the knowledge);
…via the particular URL address associated with the first container, wherein the second conversational input is associated with the first container (Beilby: ¶[0080] teaches receiving subsequent conversational inputs during an ongoing conversation via the same webpage-deployed chatbot instance, such that the second conversational input remains associated with the same container).
D’Agostino and Beilby are combinable because they are from the same field of endeavor, chatbots and computer agents designed to have conversations with humans. D’Agostino discloses a robust dialogue system and Beilby expressly teaches dynamically selecting between alternative conversation paths when an initially selected response does not satisfy a criterion in a way that is functionally a decision tree based conversational control technique. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose a decision-tree conversational control process. The suggestion/motivation for doing so is expressly disclosed in Beilby in ¶[0085]-[0086]: “determining whether the selected node and/or the received input message satisfies one or more predetermined criteria; [0086] and if the determination is not in the affirmative, automatically selecting a second node from a list of nodes;” in other words it is useful to improve dialogue robustness by selecting an alternative response path when an initial response is unsuitable, yielding predictable results.
Regarding Claim 2:
The combination of D’Agostino and Beilby further disclose the system of claim 1, wherein the context is a first context and the at least one computing device is further configured to:
identify a contextual change in a subset of the plurality of sequential conversational inputs (D’Agostino: p[0056-0058] discloses analyzing sequential inputs to detect changes in conversational context, with mechanisms to track ongoing conversations and detect when context shifts);
and initiate a change from the first context to a second context based on the contextual change (D’Agostino: p[0069-0072] and p[0056] discloses that the conversational manager routes subsequent inputs to different chat bots or contexts when a change in intent or subject matter is identified seamlessly transitioning between contexts).
Regarding Claim 3:
The combination of D’Agostino and Beilby further disclose the system of claim 2, wherein the at least one computing device is further configured to iteratively process a plurality of second sequential conversational inputs, via the natural language processing (NLP) algorithm and based on the second context, to generate a plurality of second responses individually corresponding to a respective one of the plurality of second sequential conversational inputs (D’Agostino: p[0071-0072] discloses tracking conversation history and iteratively refining context for subsequent inputs to generate contextually relevant responses).
Regarding Claim 4:
The combination of D’Agostino and Beilby further disclose the system of claim 3, wherein the at least one computing device is further configured to iteratively process the plurality of sequential conversational inputs based on the second context prior to generating the plurality of second responses (D’Agostino: p[0070-0072] refines inputs iteratively within the new context before generating responses ensuring continuity, it can also switch to a different device/conversational manager).
Regarding Claim 5:
The combination of D’Agostino and Beilby further discloses the system of claim 1, wherein: the at least one computing device is configured to receive the first conversational input via the at least one computing device by the first container (D’Agostino: p[0068-0069] discloses routing input to context-specific chat bots (containers) for their respective topics i.e., “routes to chat bot 3 because chat bot 3’s expertise is in social media”);
the context is a first context of the first container (Beilby: ¶[0080], ¶[0114], ¶[0219] teaches multiple distinct chatbot instances that are deployed to specific webpages/URLs, such that conversational inputs received via a webpage are associated with the corresponding chatbot instance);
the at least one computing device is further configured to: prior to generating the at least one updated intent, determine to change to a second container based on at least one of:
the second conversational input, a profile associated with a particular user account, and the first context of the first container (D’Agostino: p[0058] user conversational and account data can be used to seamlessly switch to a new chat bot);
and relay the second conversational input to a second container (D’Agostino: p[0058] discloses switching chat bots’).
D’Agostino and Beilby are combinable because they are from the same field of endeavor, chatbots and computer agents designed to have conversations with humans. D’Agostino discloses a robust dialogue system and Beilby expressly teaches dynamically selecting between alternative conversation paths when an initially selected response does not satisfy a criterion in a way that is functionally a decision tree based conversational control technique. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose a decision-tree conversational control process. The suggestion/motivation for doing so is expressly disclosed in Beilby in ¶[0085]-[0086]: “determining whether the selected node and/or the received input message satisfies one or more predetermined criteria; [0086] and if the determination is not in the affirmative, automatically selecting a second node from a list of nodes;” in other words it is useful to improve dialogue robustness by selecting an alternative response path when an initial response is unsuitable, yielding predictable results.
Regarding Claim 6:
The combination of D’Agostino and Beilby further discloses the system of claim 5, wherein the system comprises: a first application that, when executed by the at least one computing device (D’Agostino p[0024-0025] discloses the first application operates as an initial processing agent, handling the receipt and preliminary processing of conversational inputs, also see Fig. 2 204 through 210), causes the first application to:
receive the first conversational input the at least one user input device (D’Agostino p[0051-0052] the first application takes input from a user via devices like keyboards microphones or touchscreens);
process the first conversational input via the at least one natural language processing (NLP) algorithm to:
determine the first context of the first container based on the particular URL address and the first conversational input (D’Agostino: p[0051-0052] and p[0069] identifies the context by associating the input with a link/route and categorizing it in the appropriate ‘container’. It is noted this is done over a network as seen in Fig. 1 and therefore must include some form of URL or internet/intranet directory system);
and determine the at least one intent based on the first context of the first container and the first conversational input (D’Agostino: p[0071-0077] the intent is derived from analyzing the input within the context established by the container);
and generate the response to the first conversational input based on the context and the at least one intent (D’Agostino: p[0059] );
receive, by the first container, the second conversational input (D’Agostino: p[0082] continues tracking user interaction and processes additional input with received second sets of input);
prior to generating the at least one updated intent, determine whether to change to the second container based on at least one of the second conversational input, the profile associated with the particular user account, and the first context of the first container (D’Agostino: p[0069] system decides whether to switch to a new container based on updated information);
and relay the second conversational input to the second container (D’Agostino: p[0084] when a context change is detected the input is forwarded to the relevant container);
and a second application that, when executed by the at least one computing device, causes the second application to (D’Agostino: at least Fig. 3 314 discloses a plurality of applications with different instances of chatbots all with their own individual interface):
receive, by the second container, the second conversational input relayed from the first container (D’Agostino: p[0084] the inputs are relayed to the second chatbot, the second container begins processing the relayed input);
determine a second context of the second container based on the second conversational input (D’Agostino: p[0084] when a context change is detected the input is forwarded to the relevant container);
process the second conversational input via the NLP algorithm to generate at least one updated intent based on the first conversational input, the second conversational input, and the particular URL address (D’Agostino: p[0084] the system leverages historical and current inputs to update its understanding);
and process at least one second conversational input, based on the at least one updated intent and the second context in the second container, to generate at least one second response (D’Agostino: p[0059] second container generates a response tailored to the updated intent and context {Claim interpretation: wherein D’Agostino’s response tailored to the updated intent and content reads on the claimed ‘at least one second response’}).
D’Agostino and Beilby are combinable because they are from the same field of endeavor, chatbots and computer agents designed to have conversations with humans. D’Agostino discloses a robust dialogue system and Beilby expressly teaches dynamically selecting between alternative conversation paths when an initially selected response does not satisfy a criterion in a way that is functionally a decision tree based conversational control technique. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose a decision-tree conversational control process. The suggestion/motivation for doing so is expressly disclosed in Beilby in ¶[0085]-[0086]: “determining whether the selected node and/or the received input message satisfies one or more predetermined criteria; [0086] and if the determination is not in the affirmative, automatically selecting a second node from a list of nodes;” in other words it is useful to improve dialogue robustness by selecting an alternative response path when an initial response is unsuitable, yielding predictable results
Regarding Claim 7:
The combination of D’Agostino and Beilby further discloses the system of claim 6, wherein the first application is executed by a first computing device of the at least one computing device and the second application is executed by a second computing device of the at least one computing device (D’Agostino: p[0029] and p[0058] discloses a distributed architecture where applications/chat bots can run on separate computing devices).
Regarding Claim 8:
Claim 8 has been analyzed with regard to claims 1 (see rejection above) and
is rejected for the same reasons of obviousness as used above.
Regarding Claim 10:
The combination of D’Agostino and Beilby further discloses the method of claim 9, wherein processing the second conversational input via the at least one NLP algorithm comprises scanning through the one or more hierarchical tiers of the knowledge base to determine the at least one updated intent (D’Agostino: p[0059] and p[0085-0086] disclose iterative context determination and intent refinement for handling sequential conversational inputs, this combines reasonably with the hierarchical structure disclosed in Beilby ¶[0085]).
D’Agostino and Beilby are combinable because they are from the same field of endeavor, chatbots and computer agents designed to have conversations with humans. D’Agostino discloses a robust dialogue system and Beilby expressly teaches dynamically selecting between alternative conversation paths when an initially selected response does not satisfy a criterion in a way that is functionally a decision tree based conversational control technique. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose a decision-tree conversational control process. The suggestion/motivation for doing so is expressly disclosed in Beilby in ¶[0085]-[0086]: “determining whether the selected node and/or the received input message satisfies one or more predetermined criteria; [0086] and if the determination is not in the affirmative, automatically selecting a second node from a list of nodes;” in other words it is useful to improve dialogue robustness by selecting an alternative response path when an initial response is unsuitable, yielding predictable results
Regarding Claim 11:
The combination of D’Agostino and Beilby further discloses the method of claim 10, wherein the plurality of tiers of knowledge bases (D’Agostino: p[0069] the conversational manager organizes requests into conversational contexts and routes them to specific chat bots that act as containers for their respective topics) are assigned to a hierarchy based on an as-signed information scope (D’Agostino: p[0071] the intent deciphering module determines intent by analyzing contextual data, which can include hierarchical relationships between topics or contexts).
Regarding Claim 12:
The combination of D’Agostino and Beilby further discloses the method of claim 8, wherein generating the response to the first conversational input comprises processing a response tree algorithm based on the context and the at least one intent (D’Agostino: p[0068] discloses the system applies structured decision-making algorithms to select the most appropriate response and p[0093] discloses that the system accesses structured repositories of information to refine responses based on contextual importance and priority; Claim Interpretation {wherein the decision making algorithm is analogous to a tree algorithm as it chooses from branches of hierarchical output}).
Regarding Claim 13:
The combination of D’Agostino and Beilby further discloses the method of claim 12, wherein generating the second response comprises processing the response tree algorithm based on the context and the at least one updated intent (D’Agostino: p[0059] responses are generated based on the context and intent identified through natural language processing of first and second communications).
Regarding Claim 14:
The combination of D’Agostino and Beilby further discloses the method of claim 12, wherein generating the response to the first conversational input comprises:
formatting the response comprising the top-ranked response and the value based on a channel type corresponding to a current user session and the dynamic content variable (D’Agostino: p[0043] the domain intelligence may include contextual repositories, the chat bot library and the user profile, all of which is used to determine the type of session/chat bot that should be utilized; Beilby: ¶[0085] and ¶[0134] discloses that a selected (top-ranked) response fails to satisfy a criterion, corresponding to a top-ranked response that does not satisfy the response variable, this can be used to present a specific response an or update a variable).
Regarding Claim 15:
The combination of D’Agostino and Beilby further discloses the method of claim 14, wherein the step of processing the first conversational input via the at least one natural language processing (NLP) algorithm to determine the at least one intent is further based on a plurality of past conversational inputs corresponding to the current user session (D’Agostino: p[0058] the conversational system tracks and stores historical inputs within an ongoing session to refine the determination of context and intent, it is also used to provide continuity and refine response generation across multi-step interactions).
Regarding Claim 16:
The combination of D’Agostino and Beilby further discloses the method of claim 8, wherein the response is a top-ranked entry of a main response track and the method further comprises:
determining that the top-ranked entry of the main response track is unavailable (D’Agostino: p[0058] discloses generating and prioritizing responses based on content and ranking mechanisms);
determining that a top-ranked entry of a fallback response track satisfies the at least one intent (D’Agostino: p[0058] discloses the conditions where specific chatbots may not be able to handle a request, prompting fallback mechanisms by transitioning to another chat bot between response tracks);
and identifying the top-ranked entry of the fallback response track as the response (D’Agostino: p[0049] and p[0058] explicitly describes finalizing the response by selecting the most appropriate fallback option when the primary is unavailable and producing the response).
Regarding Claim 17:
Claim 17 has been analyzed with regard to claims 1 (see rejection above) and
is rejected for the same reasons of obviousness as used above.
Regarding Claim 19:
D’Agostino and Beilby further disclose the non-transitory, computer-readable medium (D’Agostino: e.g. ‘“software” includes computer-readable instructions’ [0033]) of claim 18, wherein the program, when executed by the at least one computing device, further causes the at least one computing device to:
receive first criteria associated with a client (D’Agostino: p[0055] and p[0065] discloses the chat bot decision engine receiving user-specific conversational inputs and credentials to determine authentication and guide subsequent actions. These inputs act as the “first criteria” associated with a client);
obtain a plurality of knowledge volumes based on the first criteria (D’Agostino: p[0064] discloses accessing a domain intelligence repository containing contextual information and intent data associated with the multiple chatbots these knowledge volumes are retrieved based on user-specific criteria, such as a conversational context or authentication data), wherein each of the plurality of knowledge volumes comprises a respective plurality of contexts and a respective plurality of intents (D’Agostino: p[0064] discloses accessing a domain intelligence repository containing contextual information (user provides, transaction types) and intent information (financial inquiries and social media tasks));
and generate the one or more hierarchical tiers a plurality of knowledge tiers based on the plurality of knowledge volumes (D’Agostino: p[0065] discloses that chat bot engine organizing retrieved knowledge into actionable tiers or hierarchies).
Regarding Claim 20:
Claim 20 has been analyzed with regard to claims 11 (see rejection above) and
is rejected for the same reasons of obviousness as used above.
Regarding Claim 21:
The combination of D’Agostino and Beilby further discloses the system of claim 1, wherein generating the response variable comprises concatenating the context and the at least on intent (Beilby: ¶[0085], ¶[0182] generating the profile variable involves combining information about the behavior and interests and intentions of the correspondents, the variable is initially generated based on context and intent prediction, and can further be incremented based on later similar intent that is produced).
D’Agostino and Beilby are combinable because they are from the same field of endeavor, chatbots and computer agents designed to have conversations with humans. D’Agostino discloses a robust dialogue system and Beilby expressly teaches dynamically selecting between alternative conversation paths when an initially selected response does not satisfy a criterion in a way that is functionally a decision tree based conversational control technique. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose a decision-tree conversational control process. The suggestion/motivation for doing so is expressly disclosed in Beilby in ¶[0085]-[0086]: “determining whether the selected node and/or the received input message satisfies one or more predetermined criteria; [0086] and if the determination is not in the affirmative, automatically selecting a second node from a list of nodes;” in other words it is useful to improve dialogue robustness by selecting an alternative response path when an initial response is unsuitable, yielding predictable results
Regarding Claim 22:
The combination of D’Agostino and Beilby further discloses the system of claim 1, wherein the at least one computing device is further configured to: instruct the mobile device to display the user interface in response to the mobile device accessing the particular URL (Beilby: ¶[0181] discloses that a browser view is shown to the device output).
D’Agostino and Beilby are combinable because they are from the same field of endeavor, chatbots and computer agents designed to have conversations with humans. D’Agostino discloses a robust dialogue system and Beilby expressly teaches dynamically selecting between alternative conversation paths when an initially selected response does not satisfy a criterion in a way that is functionally a decision tree based conversational control technique. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to disclose a decision-tree conversational control process. The suggestion/motivation for doing so is expressly disclosed in Beilby in ¶[0276]“For instance, the chatbot may be associated with an avatar in a virtual world and used to engage in conversations with real people or other avatars in the virtual world, or they may be present in a personal companion robot, or a machine or mobile device.” In other words, it is useful to communicate with users via mobile devices as it is a common and highly accessible method of delivery.
Conclusion
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.
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/IAN SCOTT MCLEAN/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654