DETAILED ACTION
This action is responsive to communications filed on September 25, 2024. This action is made Non-Final.
Claims 2-31 are pending in the case.
Claims 2, 12, and 22 are independent claims.
Claims 2-31 are rejected.
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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 2-31 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Shanmugam et al., US Patent Application Publication no. US 2019/0311036 (“Shanmugam”).
Claim 2:
Shanmugam discloses a computer-implemented method for assisted bot creation, the method comprising:
generating a display of a graphic using interface for bot customization (see Fig. 2-4; Fig. 10 A-O; para. 0024 - AI Manager/Conversation Construction and Management platform 102, configured with a chatbot system user interface 104; para. 0034 – graphical arrangement of a user interface form 200 for adding a user intent, and receiving alternative end user expressions for an intent, in one implementation of a process for chatbot conversation structuring and management; para. 0035 - which indicates the aspect being created or modified is a training set of end user expressions for the instant intent; para. 0082 - present a real time updated transcript of a chat session. the NPE finding one or more matches to a customer text input, the live agent display 1001 presenting to the live agent with the one or more NPE matches, and bot response associated with each; para. 0090 - NPE finds two intents that match Customer Input 1.);
receiving information regarding conversational content associated with customizing a bot, wherein the information is received in the graphical user interface (see Fig. 5 and 7-9; para. 0022 – a step-in chatbot assistant for live agents. The step-in chatbot assistant can bring machine learning to help improve agents' accuracy in responding to customer queries; para. 0034 - graphical arrangement of a user interface form 200 for adding a user intent, and receiving alternative end user expressions for an intent, in one implementation of a process for chatbot conversation structuring and management; para. 0035 - which indicates the aspect being created or modified is a training set of end user expressions for the instant intent; para. 0046 - continuous fallout detection and monitoring, and other negative bot outcome monitoring, and recording, and automatically apply chatbot training utilizing that monitoring and its recordings. Technical features include an inherent and automatic improvement. analyses of the fallout record, including machine learning processes. For example, implementations can provide fallout clustering, categorization of clusters into domains and intents, and incorporation back to an existing intent or dialogue tree, enabling a continuous learning process within the chatbot; para. 0047 - Bot Matching Improvement logic 602 can include the above-described clustering of fallouts and automatic training of the NLP engine's algorithm for providing the chatbot conversation; para. 0050 – collecting records of fallouts, wrong matches and other negative user feedback, for subsequent fallout clustering and AI manager updating, in one or more systems and methods for chatbot training Improvement; para. 0058 - provide updating of the AI Manager 102, based on end user feedback, such as provided by the wrong match reports, or non-helpful match reports, or both; para. 0061 - import bot performance data, for example, any one of, or any combination of, end user feedback (e.g., wrong match reports, non-helpful match reports), rephrase reports, and fallout reports.);
analyzing a plurality of portions within the conversational content using an artificially intelligent (AI) assist widget, wherein the AI assist widget analyzes the portions within the conversational content based on historical conversations and related metrics (see Fig. 5-9; para. 0022 – a step-in chatbot assistant for live agents. The step-in chatbot assistant can bring machine learning to help improve agents' accuracy in responding to customer queries; para. 0028 - AI Manager 102 can be configured to provide for construction of conversation controllers as small blocks of re-usable business logic, of various types, that can be readily integrated into conversation trees, and that can provide a range of easy-to-use options for run-time flow control; para. 0034 - graphical arrangement of a user interface form 200 for adding a user intent, and receiving alternative end user expressions for an intent, in one implementation of a process for chatbot conversation structuring and management; para. 0035 - which indicates the aspect being created or modified is a training set of end user expressions for the instant intent; para. 0046 - continuous fallout detection and monitoring, and other negative bot outcome monitoring, and recording, and automatically apply chatbot training utilizing that monitoring and its recordings. Technical features include an inherent and automatic improvement. metrics such as NLP engine performance, e.g., reduction of wrong matches; non-helpful responses; and other incidences that can cause end users to have bad experiences. analyses of the fallout record, including machine learning processes. For example, implementations can provide fallout clustering, categorization of clusters into domains and intents, and incorporation back to an existing intent or dialogue tree, enabling a continuous learning process within the chatbot; para. 0047 - Bot Matching Improvement logic 602 can include the above-described clustering of fallouts and automatic training of the NLP engine's algorithm for providing the chatbot conversation; para. 0050 – collecting records of fallouts, wrong matches and other negative user feedback, for subsequent fallout clustering and AI manager updating, in one or more systems and methods for chatbot training Improvement; para. 0058 - provide updating of the AI Manager 102, based on end user feedback, such as provided by the wrong match reports, or non-helpful match reports, or both; para. 0061 - import bot performance data, for example, any one of, or any combination of, end user feedback (e.g., wrong match reports, non-helpful match reports), rephrase reports, and fallout reports.);
generating a recommendation for an identified point within the conversational content based on the analysis by the AI assist widget, wherein the recommendation is for an automated action to be performed by the customized bot at the identified point within the conversational content (see Fig. 5-9, 10L-O; para. 0028 - AI Manager 102 can be configured to provide for construction of conversation controllers as small blocks of re-usable business logic, of various types, that can be readily integrated into conversation trees, and that can provide a range of easy-to-use options for run-time flow control; para. 0031 - type of conversation controller 118 can provide a business logic functionality, for example, a capability to make a sale, or to upgrade or downgrade a service plan or a subscription. conversation controller 118 of the business logic functionality type can be configured to perform the actual submission, and thereby complete the upgrade process; para. 0075 – AI Suggested Response," to the live chat server 909. At 916 the live chat server 909 can forward to the live agent console 912-as shown by box 917-the box 915 "AI Suggested Response; para. 0100 - "Suggested Response" in field 1022.); and
generating the customized bot based on the recommendation, wherein the customized bot performs the automated action during a new conversation at a point corresponding to the identified point (see Fig. 5-9, 10L-O; para. 0028 - AI Manager 102 can be configured to provide for construction of conversation controllers as small blocks of re-usable business logic, of various types, that can be readily integrated into conversation trees, and that can provide a range of easy-to-use options for run-time flow control; para. 0031 - type of conversation controller 118 can provide a business logic functionality, for example, a capability to make a sale, or to upgrade or downgrade a service plan or a subscription. conversation controller 118 of the business logic functionality type can be configured to perform the actual submission, and thereby complete the upgrade process; para. 0044 - include training updates, which can be fed to existing dialogs for improvement in recognizing additional expressions of the same intent, e.g., additional ways and variations of ways that customers ask a question; para. 0046 - continuous fallout detection and monitoring, and other negative bot outcome monitoring, and recording, and automatically apply chatbot training utilizing that monitoring and its recordings. Technical features include an inherent and automatic improvement. metrics such as NLP engine performance, e.g., reduction of wrong matches; non-helpful responses; and other incidences that can cause end users to have bad experiences. analyses of the fallout record, including machine learning processes. For example, implementations can provide fallout clustering, categorization of clusters into domains and intents, and incorporation back to an existing intent or dialogue tree, enabling a continuous learning process within the chatbot; para. 0047 - Bot Matching Improvement logic 602 can include the above-described clustering of fallouts and automatic training of the NLP engine's algorithm for providing the chatbot conversation; para. 0058 - provide updating of the AI Manager 102, based on end user feedback, such as provided by the wrong match reports, or non-helpful match reports, or both; para. 0061 - import bot performance data, for example, any one of, or any combination of, end user feedback (e.g., wrong match reports, non-helpful match reports), rephrase reports, and fallout reports.); para. 0059 - Features and benefits can include, but are not limited to, improved speed and accuracy of the bat's pattern matching, enlargement of the bat's recognition vocabulary and, in an aspect, addition of new intents, thereby reducing fallout, request for re-phrasing, and other flow interruptions and, accordingly, providing smoother chatbot conversation flow; para. 0068 – application automated machine learning algorithms, providing and automatic continuous improvement in bot performance; para. 0075 – AI Suggested Response," to the live chat server 909. At 916 the live chat server 909 can forward to the live agent console 912-as shown by box 917-the box 915 "AI Suggested Response; para. 0100 - "Suggested Response" in field 1022.).
Claim(s) 12 and 22:
Claim(s) 12 and 22 correspond to Claim 2, and thus, Shanmugam discloses the limitations of claim(s) 12 and 22 as well.
Claim 3:
Shanmugam further discloses wherein the conversational content includes a transcript that includes a plurality of statements classified into different statement types, and wherein analyzing the plurality of points within the conversational content includes classifying a statement in accordance with the statement types (see para. 0027 - of intents and, associated with each intent, a response, and customer expressions for the intent. One example of an intent can be "Need Hotel," for which a response can be a request to the customer for hotel booking parameters, e.g., location, date(s), price range, brand preference, and so forth. Customer expressions for intent "Need Hotel" can include, for example, "I need to stay tonight in Chicago," and "I need accommodations for four, tonight, in San Luis Obispo."; para. 0028 - conversation controllers as small blocks of re-usable business logic, of various types, that can be readily integrated into conversation trees, and that can provide a range of easy-to-use options for run-time flow control; para. 0038 - include blocks for entering a conditional response to an end user intent; para. each matched intent found also includes its associated responses, in template form; para. 0040 - assignment at 504 of an NLP engine. Assignment at 504 can be based on the type or intended context of the conversation entered; para. 0048 - entered text can be the end user search illustrated in FIG. 6. The AI connector platform 110, in response, can connect to selected one of a the NLP engines; para. 0073 - intent(s) returned in 905 may be a second step in the same conversation tree, or can be a first step of another conversation tree that matched the follow up questions. each matched intent found also includes its associated responses, in template form; para. 0082 - assuming an intent of "Needs Taxi," the associated bot response can be "Please tell me the number of passengers, and the pick-up address, etc.").
Claim(s) 13 and 23:
Claim(s) 13 and 23 correspond to Claim 3, and thus, Shanmugam discloses the limitations of claim(s) 13 and 23 as well.
Claim 4:
Shanmugam further discloses identifying the identified point based on statement classification associated with the identified point within the conversational content (see Fig. 10 A-O; para. 0029 - conversation controller 118, referenced herein for convenience as a "conversation flow controller," can control flow based on a condition, with control including run-time retrieval of information to determine whether the condition is met, and control of the aspects of the flow that can depend on that determination; para. 0032 - personalize the NPE response to a customer question or other customer intent; para. 0038 - start chatbot. conversation is for a moving service, the entry in such a start box could be: <"can you move a household content from [CITY, STATE] to [CITY, STATE]?">. The graphical template can also include a start chatbot prompt box, such as <:' 1 see that you may be looking for a moving service. If you are, tell me and perhaps I can help''>. include blocks for entering a conditional response to an end user intent. <If user indicates [YES], "please tell us when you would like to move">.; para. 0040 - assignment at 504 of an NLP engine. Assignment at 504 can be based on the type or intended context of the conversation entered; para. 0048 - entered text can be the end user search illustrated in FIG. 6. The AI connector platform 110, in response, can connect to selected one of a the NLP engines; para. 0073 - intent(s) returned in 905 may be a second step in the same conversation tree, or can be a first step of another conversation tree that matched the follow up questions. each matched intent found also includes its associated responses, in template form; para. 0082 - present a real time updated transcript of a chat session. the NPE finding one or more matches to a customer text input, the live agent display 1001 presenting to the live agent with the one or more NPE matches, and bot response associated with each; para. 0090 - NPE finds two intents that match Customer Input 1; para. 0093 - operations where the Autopilot is ON, NPE match results, or only NPE match results that meet threshold conditions, can be automatically communicated to the customer's UE device.).
Claim(s) 14 and 24:
Claim(s) 14 and 24 correspond to Claim 4, and thus, Shanmugam discloses the limitations of claim(s) 14 and 24 as well.
Claim 5:
Shanmugam further discloses wherein generating the recommendation for the automated action is based on an automation template associated with a bot type (see para. 0029 - exemplary type of conversation controller 118, referenced herein for convenience as a "conversation flow controller," can control flow based on a condition; para. 0030 - type of conversation controller 118 can provide a run-time look up and injection of data into the dialogue's response back to the end user; para. 0031 - type of conversation controller 118 can provide a business logic functionality, for example, a capability to make a sale, or to upgrade or downgrade a service plan or a subscription. conversation controller 118 of the business logic functionality type can be configured to perform the actual submission, and thereby complete the upgrade process; para. 0032 - conversation controllers 118 can interface with a personalization module 120, having functionality by which the AI connector platform 110 can personalize the NPE response to a customer question or other customer intent; para. 0038 - conversation is for a moving service, the entry in such a start box could be: <"can you move a household content from [CITY, STATE] to [CITY, STATE]?">. The graphical template can also include a start chatbot prompt box, such as <:' 1 see that you may be looking for a moving service. If you are, tell me and perhaps I can help''>. include blocks for entering a conditional response to an end user intent; para. 0040 - assignment at 504 of an NLP engine. Assignment at 504 can be based on the type or intended context of the conversation entered; para. 0042 - to parameterize template responses; para. 0048 - entered text can be the end user search illustrated in FIG. 6. The AI connector platform 110, in response, can connect to selected one of a the NLP engines; para. 0073 - stored intents for a match to the customer question. intents can also particular steps in a chatbot conversation tree. each matched intent found also includes its associated responses, in template form. Each matched intent found at 905 can have its associated response, in template form; para. 0082 - the NPE finding one or more matches to a customer text input, the live agent display 1001 presenting to the live agent with the one or more NPE matches, and bot response associated with each; para. 0090 - NPE finds two intents that match Customer Input 1.).
Claim(s) 15 and 25:
Claim(s) 15 and 25 correspond to Claim 5, and thus, Shanmugam discloses the limitations of claim(s) 15 and 25 as well.
Claim 6:
Shamugam further discloses wherein generating the customized bot includes filling in a slot of an automation template with a corresponding parameter, and wherein the customized bot performs the automated action further based on the corresponding parameter (see para. 0029 - exemplary type of conversation controller 118, referenced herein for convenience as a "conversation flow controller," can control flow based on a condition; para. 0030 - type of conversation controller 118 can provide a run-time look up and injection of data into the dialogue's response back to the end user; para. 0031 - type of conversation controller 118 can provide a business logic functionality, for example, a capability to make a sale, or to upgrade or downgrade a service plan or a subscription. conversation controller 118 of the business logic functionality type can be configured to perform the actual submission, and thereby complete the upgrade process; para. 0032 - conversation controllers 118 can interface with a personalization module 120, having functionality by which the AI connector platform 110 can personalize the NPE response to a customer question or other customer intent; para. 0038 - conversation is for a moving service, the entry in such a start box could be: <"can you move a household content from [CITY, STATE] to [CITY, STATE]?">. The graphical template can also include a start chatbot prompt box, such as <:' 1 see that you may be looking for a moving service. If you are, tell me and perhaps I can help''>. include blocks for entering a conditional response to an end user intent; para. 0040 - assignment at 504 of an NLP engine. Assignment at 504 can be based on the type or intended context of the conversation entered; para. 0042 - to parameterize template responses; para. 0048 - entered text can be the end user search illustrated in FIG. 6. The AI connector platform 110, in response, can connect to selected one of a the NLP engines; para. 0073 - stored intents for a match to the customer question. intents can also particular steps in a chatbot conversation tree. each matched intent found also includes its associated responses, in template form. Each matched intent found at 905 can have its associated response, in template form; para. 0082 - the NPE finding one or more matches to a customer text input, the live agent display 1001 presenting to the live agent with the one or more NPE matches, and bot response associated with each; para. 0090 - NPE finds two intents that match Customer Input 1.).
Claim(s) 16 and 26:
Claim(s) 16 and 26 correspond to Claim 6, and thus, Shanmugam discloses the limitations of claim(s) 16 and 26 as well.
Claim 7:
Shanmugam further discloses wherein analyzing the plurality of points within the conversational content includes assigning a score to a statement at the one or more of the points (see para. 0026 - NPE response can include the matching intent, and a measure of the confidence of match found by the NPE; para. 0071 - the NPEs can be configured to include a confidence value with the NLP response to the NL search data; para. 0082 - NPE finding one or more matches to a customer text input, the live agent display 1001 presenting to the live agent with the one or more NPE matches, and bot response associated with each. NPE confidence in the match. The confidence measure can be expressed, for example, as a percentage; para. 0090 - matched intent field 1013 displaying the matching intent, a confidence measure field 1014 that displays a confidence measure in the match, and a matching response field 1015 that displays the response associated with the matching intent identified; para. 0091 - value in confidence measure field 1014 of the upper NPE match can be a displayable numerical value, for example, a percentage value, that identifies the NPE confidence in the matching intent; para. 0098 - NPE having found a matching intent "IT-2" and due, for example, to a high confidence value of the match, the Autopilot has automatically switched ON, and auto-sent a matching response as "Auto-Reply Response; para. 0099 - the NPE finds a matching intent and due, for example, to a high confidence match, the system remains in AutoPilot and auto sends the matching response.).
Claim(s) 17 and 27:
Claim(s) 17 and 27 correspond to Claim 7, and thus, Shanmugam discloses the limitations of claim(s) 17 and 27 as well.
Claim 8:
Shanmugam further discloses wherein assigning the score is based on a qualified degree of success associated with the statement, and wherein the degree of success includes participant sentiment associated with the statement (see para. 0026 - NPE response can include the matching intent, and a measure of the confidence of match found by the NPE; para. 0071 - the NPEs can be configured to include a confidence value with the NLP response to the NL search data; para. 0082 - NPE finding one or more matches to a customer text input, the live agent display 1001 presenting to the live agent with the one or more NPE matches, and bot response associated with each. NPE confidence in the match. The confidence measure can be expressed, for example, as a percentage; para. 0090 - matched intent field 1013 displaying the matching intent, a confidence measure field 1014 that displays a confidence measure in the match, and a matching response field 1015 that displays the response associated with the matching intent identified; para. 0091 - value in confidence measure field 1014 of the upper NPE match can be a displayable numerical value, for example, a percentage value, that identifies the NPE confidence in the matching intent; para. 0098 - NPE having found a matching intent "IT-2" and due, for example, to a high confidence value of the match, the Autopilot has automatically switched ON, and auto-sent a matching response as "Auto-Reply Response; para. 0099 - the NPE finds a matching intent and due, for example, to a high confidence match, the system remains in AutoPilot and auto sends the matching response.).
Claim(s) 18 and 28:
Claim(s) 18 and 28 correspond to Claim 8, and thus, Shanmugam discloses the limitations of claim(s) 18 and 28 as well.
Claim 9:
Shanmugam further discloses analyzing the corresponding point within the new conversation in real-time, wherein the customized bot performs the automated action further based on the real-time analysis (see para. 0029 - exemplary type of conversation controller 118, referenced herein for convenience as a "conversation flow controller," can control flow based on a condition; para. 0030 - type of conversation controller 118 can provide a run-time look up and injection of data into the dialogue's response back to the end user; para. 0031 - type of conversation controller 118 can provide a business logic functionality, for example, a capability to make a sale, or to upgrade or downgrade a service plan or a subscription. conversation controller 118 of the business logic functionality type can be configured to perform the actual submission, and thereby complete the upgrade process; para. 0032 - conversation controllers 118 can interface with a personalization module 120, having functionality by which the AI connector platform 110 can personalize the NPE response to a customer question or other customer intent; para. 0038 - conversation is for a moving service, the entry in such a start box could be: <"can you move a household content from [CITY, STATE] to [CITY, STATE]?">. The graphical template can also include a start chatbot prompt box, such as <:' 1 see that you may be looking for a moving service. If you are, tell me and perhaps I can help''>. include blocks for entering a conditional response to an end user intent; para. 0040 - assignment at 504 of an NLP engine. Assignment at 504 can be based on the type or intended context of the conversation entered; para. 0042 - to parameterize template responses; para. 0048 - entered text can be the end user search illustrated in FIG. 6. The AI connector platform 110, in response, can connect to selected one of a the NLP engines; para. 0073 - stored intents for a match to the customer question. intents can also particular steps in a chatbot conversation tree. each matched intent found also includes its associated responses, in template form. Each matched intent found at 905 can have its associated response, in template form; para. 0081 - the live agent bot assistance tool 122 can also present on the live agent display 1001 a field, such as the bot-to-agent assistance field 1006, for real time presentation by the live agent bot assistance tool 122 of assistance to the live agent; para. 0082 - the NPE finding one or more matches to a customer text input, the live agent display 1001 presenting to the live agent with the one or more NPE matches, and bot response associated with each; para. 0090 - NPE finds two intents that match Customer Input 1.).
Claim(s) 19 and 29:
Claim(s) 19 and 29 correspond to Claim 9, and thus, Shanmugam discloses the limitations of claim(s) 19 and 29 as well.
Claim 10:
Shanmugam further discloses wherein the real-time analysis includes an insight based on conversational content within the new conversation, and further comprising making the insight available to a subsequent bot customization graphic user interface (see para. 0029 - exemplary type of conversation controller 118, referenced herein for convenience as a "conversation flow controller," can control flow based on a condition; para. 0030 - type of conversation controller 118 can provide a run-time look up and injection of data into the dialogue's response back to the end user; para. 0031 - type of conversation controller 118 can provide a business logic functionality, for example, a capability to make a sale, or to upgrade or downgrade a service plan or a subscription. conversation controller 118 of the business logic functionality type can be configured to perform the actual submission, and thereby complete the upgrade process; para. 0032 - conversation controllers 118 can interface with a personalization module 120, having functionality by which the AI connector platform 110 can personalize the NPE response to a customer question or other customer intent; para. 0035 - which indicates the aspect being created or modified is a training set of end user expressions for the instant intent; para. 0038 - conversation is for a moving service, the entry in such a start box could be: <"can you move a household content from [CITY, STATE] to [CITY, STATE]?">. The graphical template can also include a start chatbot prompt box, such as <:' 1 see that you may be looking for a moving service. If you are, tell me and perhaps I can help''>. include blocks for entering a conditional response to an end user intent; para. 0040 - assignment at 504 of an NLP engine. Assignment at 504 can be based on the type or intended context of the conversation entered; para. 0042 - to parameterize template responses; para. 0046 - Features can also include storing the fallout event, for example, as an entry in a fallout record or bucket, which can include the end user's originally worded question, and can include other incident-related facts, e.g., characteristics of the end user; para. 0048 - entered text can be the end user search illustrated in FIG. 6. The AI connector platform 110, in response, can connect to selected one of a the NLP engines; para. 0053 - entry can include, for example, the end user expression entered at 702; para. 0054 – store additional information describing, or providing a finer-grained classification for, the user's negative experience; para. 0073 - stored intents for a match to the customer question. intents can also particular steps in a chatbot conversation tree. each matched intent found also includes its associated responses, in template form. Each matched intent found at 905 can have its associated response, in template form; para. 0081 - the live agent bot assistance tool 122 can also present on the live agent display 1001 a field, such as the bot-to-agent assistance field 1006, for real time presentation by the live agent bot assistance tool 122 of assistance to the live agent; para. 0082 - the NPE finding one or more matches to a customer text input, the live agent display 1001 presenting to the live agent with the one or more NPE matches, and bot response associated with each; para. 0090 - NPE finds two intents that match Customer Input 1.).
Claim(s) 20 and 30:
Claim(s) 20 and 30 correspond to Claim 10, and thus, Shanmugam discloses the limitations of claim(s) 20 and 30 as well.
Claim 11:
Shanmugam further discloses wherein the recommendation further includes one or more modifier options for the identified point (see Fig. 5-9, 10L-O; para. 0028 - AI Manager 102 can be configured to provide for construction of conversation controllers as small blocks of re-usable business logic, of various types, that can be readily integrated into conversation trees, and that can provide a range of easy-to-use options for run-time flow control; para. 0031 - type of conversation controller 118 can provide a business logic functionality, for example, a capability to make a sale, or to upgrade or downgrade a service plan or a subscription. conversation controller 118 of the business logic functionality type can be configured to perform the actual submission, and thereby complete the upgrade process; para. 0047 - monitoring and reporting of fallout and rephrase suggestions; para. 0059 - suggested rephrases; para. 0076 - means to select from, or approve, matching intents from the displayed list.).
Claim(s) 21 and 31:
Claim(s) 21 and 31 correspond to Claim 11, and thus, Shanmugam discloses the limitations of claim(s) 21 and 31 as well.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T McIntosh whose telephone number is (571)270-7790. The examiner can normally be reached M-Th 8:00am-5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571-272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW T MCINTOSH/Primary Examiner, Art Unit 2144
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