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 .
Introduction
This office action is response to communications filed 12/29/2025. Claims 1-20 are pending and likewise have been examined.
Terminal Disclaimer
The terminal disclaimer filed on 12/29/2025 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Patent No# 11314944 and 11727220 has been reviewed and is accepted. The terminal disclaimer has been recorded.
Response to Amendment
Amendment filed 12/29/2025 has been fully considered by Examiner. The Double Patenting rejections of Claims 1-20 have been withdrawn due to the Terminal Disclaimer discussed above.
Response to Arguments
Applicant’s arguments, see Remarks, Pg 8, filed 12/29/2025(which cites arguments discussed in Examiner Interview summary filed 12/23/2025), with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Schlesinger et al. (US 20180090135 A1), and further in view of Harris et al. (US 20180226066 A1).
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schlesinger et al. (US 20180090135 A1), and further in view of Harris et al. (US 20180226066 A1).
Regarding Claim 1:
Schlesinger teaches a method implemented using one or more processors, comprising(Para [0130], Ln 15-20, computing functionality 1902 may perform any of the functions described above when the hardware processor device(s)):
receiving, at one or more input components of a computing device operated by a user, one or more instances of free form natural language input from the user during a first human-to-computer dialog between the user and an automated assistant(Para [0038], Ln 1-10, conversational interface component 104 provides a user interface presentation for receiving a message from the user. The user supplies the message (also referred to as an utterance herein) using any input device. The backend of the messaging application then transfers the message to a selected BOT and receives the BOT's response to the message);
generating a first dialog context that is based on the one or more instances of free-form natural language input(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog);
storing one or more first parameters of the first human-to-computer dialog between the user and the automated assistant in association with the first dialog context(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog);
receiving, at one or more of the input components of the computing device, one or more further instances of free form natural language input from the user during a second human-to- computer dialog between the user and the automated assistant(Para [0052], Ln 1-6, FIG. 2 shows an example in which a user creates two bookmarks in the course of interacting with a BOT, at two respective junctures of the conversational flow. Para [0038], Ln 1-10, conversational interface component 104 provides a user interface presentation for receiving a message from the user. The user supplies the message (also referred to as an utterance herein) using any input device. The backend of the messaging application then transfers the message to a selected BOT and receives the BOT's response to the message);
generating a second dialog context that is based on the one or more instances of free-form natural language input(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog);
storing one or more second parameters of the second human-to-computer dialog between the user and the automated assistant in association with the second dialog context(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog);
and subsequent to the second human-to-computer dialog between the user and the automated assistant, and based on the stored one or more first parameters associated with the first dialog context and the stored one or more second parameters associated with the second dialog context, causing a display of the same computing device or a different computing device to render output based on the stored parameters, wherein the output conveys an enumerated list of selectable links corresponding to available past dialog contexts that includes the first and second dialog contexts(Para [0044], Ln 1-6, conversation resumption component 116 provides a backend service that allows the user to invoke a particular bookmark in the data store(s) 114. In response to invoking the bookmark, the conversation resumption component 116 resumes a previous dialog at a particular juncture in the dialog designated by the bookmark. Also see Fig 5. Para [0062], Ln 1-9, present a popup presentation 414 that displays the full content of the first bookmark when the user hovers over or taps on the visual representation 410. Para [0065], Ln 1-5, user may select a bookmark in the panel 504 in any manner, e.g., by clicking on a visual representation of the bookmark with a mouse device).
Schlesinger does not teach wherein the second dialog context is semantically distinct from the first dialog context.
In the same field of dialogue state tracking, Harris teaches wherein the second dialog context is semantically distinct from the first dialog context(Para [0025], Ln 1-15, In frame tracking, a conversational agent may simultaneously track multiple semantic frames (queries or sets of items matching a query) throughout the dialogue. For example, two frames may be constructed and recalled while comparing two products—each containing the properties of a specific item. Frame tracking may be an extension of a state tracking task. In state tracking, information summarizing a dialogue history may be compressed into one semantic frame. In contrast, several frames may be kept in memory during frame tracking, such that each frame may correspond to a particular context, e.g., one or more vacation packages in this example. Para [0054], Ln 1-8, though frames are created for each offer or suggestion made by the wizard, the active frame may only be changed by the user. If the user asks for more information about a specific offer or suggestion, the active frame is changed to the frame introduced with that offer or suggestion. This change of frame is indicated by a “switch_frame” act.).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Schlesinger with the dialogue tracking system of Harris, as it improves user convenience(Para [0005], Ln 7-18).
Regarding Claim 2:
The combination of Schlesinger and Harris teaches the method of claim 1, and Schlesinger teaches wherein the one or more first parameters and the one or more second parameters are stored in computer memory that is local to the computing device operated by the user(Para [0047], Ln 1-5, designates all of the above-described features that entail the processing of bookmarks as “bookmark processing functionality,” or BPF. See Fig 9, Local computing device. Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores).
Regarding Claim 3:
The combination of Schlesinger and Harris teaches the method of claim 1, and Schlesinger teaches wherein the one or more first parameters and the one or more second parameters are retrieved from computer memory that is local to the computing device operated by the user(Para [0047], Ln 1-5, designates all of the above-described features that entail the processing of bookmarks as “bookmark processing functionality,” or BPF. See Fig 9, Local computing device. Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores).
Regarding Claim 4:
The combination of Schlesinger and Harris teaches the method of claim 2, and Schlesinger teaches wherein storing the one or more first parameters comprises creating a configuration file for the one or more first parameters and storing the configuration file in the computer memory(Para [0043], Ln 1-13, Still other implementations can formulate a bookmark in other ways, such as a file. Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0130], Ln 1-10, storage).
Regarding Claim 5:
The combination of Schlesinger and Harris teaches the method of claim 2, and Schlesinger teaches wherein storing the one or more second parameters comprises creating a configuration file for the one or more second parameters and storing the configuration file in the computer memory(Para [0043], Ln 1-13, Still other implementations can formulate a bookmark in other ways, such as a file. Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0130], Ln 1-10, storage).
Regarding Claim 6:
The combination of Schlesinger and Harris teaches the method of claim 1, and Schlesinger teaches wherein the one or more second parameters comprise one or more intents of the user in the second human-to-computer dialog and/or one or more slot values associated with the one or more intents of the user in the second human-to-computer dialog(Para [0087], Ln 1-6, intent determination component 1104, and at least one slot value determination component. Para [0113], Ln 1-10, bookmark 1602 can include the same type of metadata values described above with respect to FIG. 15. The bookmark 1602 can also store the information used to represent the bookmark in the user interface presentations. In addition, the bookmark 1602 can include a BOT-specific serialized state associated with a particular juncture in the dialog designated by the bookmark. Para [0114], Ln 1-15, serialized state can also include any information that expresses conclusions reached by the BOT in the course of the dialog……BOT concludes that the user is performing a particular task that involves specifying a set of slot values, and that the user has successfully supplied a subset of these slot values, but not other slot values. The BOT-derived conclusions can identify the slot values that the user has supplied. Also See Para [0089], Ln 1-9, and Para [0090], Ln 1-9, For examples of slot values and intent, and how they are associated with each other).
Regarding Claim 7:
The combination of Schlesinger and Harris teaches the method of claim 1, and Schlesinger teaches wherein the one or more first parameters comprise one or more intents of the user in the first human-to-computer dialog and/or one or more slot values associated with the one or more intents of the user in the first human-to- computer dialog(Para [0087], Ln 1-6, intent determination component 1104, and at least one slot value determination component. Para [0113], Ln 1-10, bookmark 1602 can include the same type of metadata values described above with respect to FIG. 15. The bookmark 1602 can also store the information used to represent the bookmark in the user interface presentations. In addition, the bookmark 1602 can include a BOT-specific serialized state associated with a particular juncture in the dialog designated by the bookmark. Para [0114], Ln 1-15, serialized state can also include any information that expresses conclusions reached by the BOT in the course of the dialog……BOT concludes that the user is performing a particular task that involves specifying a set of slot values, and that the user has successfully supplied a subset of these slot values, but not other slot values. The BOT-derived conclusions can identify the slot values that the user has supplied. Also See Para [0089], Ln 1-9, and Para [0090], Ln 1-9, For examples of slot values and intent, and how they are associated with each other).
Regarding Claim 8:
Schlesinger teaches a system comprising one or more processors and memory storing instructions that, in response to execution of the instructions, cause the one or more processors to(Para [0130], Ln 1-25, computing functionality 1902 may perform any of the functions described above when the hardware processor device(s)):
receive, at one or more input components of a computing device operated by a user, one or more instances of free form natural language input from the user during a first human-to- computer dialog between the user and an automated assistant(Para [0038], Ln 1-10, conversational interface component 104 provides a user interface presentation for receiving a message from the user. The user supplies the message (also referred to as an utterance herein) using any input device. The backend of the messaging application then transfers the message to a selected BOT and receives the BOT's response to the message);
generate a first dialog context that is based on the one ore more instances of free-form natural language input(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog);
store one or more first parameters of the first human-to-computer dialog between the user and the automated assistant in association with the first dialog context(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog);
receive, at one or more of the input components of the computing device, one or more further instances of free form natural language input from the user during a second human-to- computer dialog between the user and the automated assistant(Para [0052], Ln 1-6, FIG. 2 shows an example in which a user creates two bookmarks in the course of interacting with a BOT, at two respective junctures of the conversational flow. Para [0038], Ln 1-10, conversational interface component 104 provides a user interface presentation for receiving a message from the user. The user supplies the message (also referred to as an utterance herein) using any input device. The backend of the messaging application then transfers the message to a selected BOT and receives the BOT's response to the message);
generate a second dialog context that is based on the one or more instances of free-form natural language input(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog).
store one or more second parameters of the second human-to-computer dialog between the user and the automated assistant in association with the second dialog context(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog);
and subsequent to the second human-to-computer dialog between the user and the automated assistant, and based on the stored one or more first parameters associated with the first dialog context and the stored one or more second parameters associated with the second dialog context, cause a display of the same computing device or a different computing device to render output based on the stored parameters, wherein the output conveys an enumerated list of selectable links corresponding to available past dialog contexts that includes the first and second dialog contexts(Para [0044], Ln 1-6, conversation resumption component 116 provides a backend service that allows the user to invoke a particular bookmark in the data store(s) 114. In response to invoking the bookmark, the conversation resumption component 116 resumes a previous dialog at a particular juncture in the dialog designated by the bookmark. Also see Fig 5. Para [0062], Ln 1-9, present a popup presentation 414 that displays the full content of the first bookmark when the user hovers over or taps on the visual representation 410. Para [0065], Ln 1-5, user may select a bookmark in the panel 504 in any manner, e.g., by clicking on a visual representation of the bookmark with a mouse device).
Schlesinger does not teach wherein the second dialog context is semantically distinct from the first dialog context.
In the same field of dialogue state tracking, Harris teaches wherein the second dialog context is semantically distinct from the first dialog context(Para [0025], Ln 1-15, In frame tracking, a conversational agent may simultaneously track multiple semantic frames (queries or sets of items matching a query) throughout the dialogue. For example, two frames may be constructed and recalled while comparing two products—each containing the properties of a specific item. Frame tracking may be an extension of a state tracking task. In state tracking, information summarizing a dialogue history may be compressed into one semantic frame. In contrast, several frames may be kept in memory during frame tracking, such that each frame may correspond to a particular context, e.g., one or more vacation packages in this example. Para [0054], Ln 1-8, though frames are created for each offer or suggestion made by the wizard, the active frame may only be changed by the user. If the user asks for more information about a specific offer or suggestion, the active frame is changed to the frame introduced with that offer or suggestion. This change of frame is indicated by a “switch_frame” act.).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Schlesinger with the dialogue tracking system of Harris, as it improves user convenience(Para [0005], Ln 7-18).
Regarding Claim 9:
Claim 9 contains similar limitations as Claim 2 and is therefore rejected for the same reasons.
Regarding Claim 10:
Claim 10 contains similar limitations as Claim 3 and is therefore rejected for the same reasons.
Regarding Claim 11:
Claim 11 contains similar limitations as Claim 4 and is therefore rejected for the same reasons.
Regarding Claim 12:
Claim 12 contains similar limitations as Claim 5 and is therefore rejected for the same reasons.
Regarding Claim 13:
Claim 13 contains similar limitations as Claim 6 and is therefore rejected for the same reasons.
Regarding Claim 14:
Claim 14 contains similar limitations as Claim 7 and is therefore rejected for the same reasons.
Regarding Claim 15:
Schlesinger teaches a non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by a processor, cause the processor to(Para [0130], Ln 1-25, RAM……computing functionality 1902 may perform any of the functions described above when the hardware processor device(s)):
receive, at one or more input components of a computing device operated by a user, one or more instances of free form natural language input from the user during a first human-to- computer dialog between the user and an automated assistant(Para [0038], Ln 1-10, conversational interface component 104 provides a user interface presentation for receiving a message from the user. The user supplies the message (also referred to as an utterance herein) using any input device. The backend of the messaging application then transfers the message to a selected BOT and receives the BOT's response to the message);
generate a first dialog context that is based on the one or more instances of free-form natural language input(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog);
store one or more first parameters of the first human-to-computer dialog between the user and the automated assistant in association with the first dialog context(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog);
receive, at one or more of the input components of the computing device, one or more further instances of free form natural language input from the user during a second human-to- computer dialog between the user and the automated assistant(Para [0052], Ln 1-6, FIG. 2 shows an example in which a user creates two bookmarks in the course of interacting with a BOT, at two respective junctures of the conversational flow. Para [0038], Ln 1-10, conversational interface component 104 provides a user interface presentation for receiving a message from the user. The user supplies the message (also referred to as an utterance herein) using any input device. The backend of the messaging application then transfers the message to a selected BOT and receives the BOT's response to the message);
generate a second dialog context that is based on the one or more instances of free-form natural language input(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog).
store one or more second parameters of the second human-to-computer dialog between the user and the automated assistant in association with the second dialog context(Para [0041], Ln 1-6, bookmark storing component 112 provides a backend service that stores the user's bookmarks in one or more data stores. Para [0042], Ln 1-9, bookmark can be defined with reference to whatever message immediately precedes it or follows it (or both). The preceding or following message may correspond to a user message or a BOT message. In addition, or alternatively, the bookmark can be defined with reference to a particular time in the course of the dialog, measured from the start of the dialog);
and subsequent to the second human-to-computer dialog between the user and the automated assistant, and based on the stored one or more first parameters associated with the first dialog context and the stored one or more second parameters associated with the second dialog context, cause a display of the same computing device or a different computing device to render output based on the stored parameters, wherein the output conveys an enumerated list of selectable links corresponding to available past dialog contexts that includes the first and second dialog contexts(Para [0044], Ln 1-6, conversation resumption component 116 provides a backend service that allows the user to invoke a particular bookmark in the data store(s) 114. In response to invoking the bookmark, the conversation resumption component 116 resumes a previous dialog at a particular juncture in the dialog designated by the bookmark. Also see Fig 5. Para [0062], Ln 1-9, present a popup presentation 414 that displays the full content of the first bookmark when the user hovers over or taps on the visual representation 410. Para [0065], Ln 1-5, user may select a bookmark in the panel 504 in any manner, e.g., by clicking on a visual representation of the bookmark with a mouse device).
Schlesinger does not teach wherein the second dialog context is semantically distinct from the first dialog context.
In the same field of dialogue state tracking, Harris teaches wherein the second dialog context is semantically distinct from the first dialog context(Para [0025], Ln 1-15, In frame tracking, a conversational agent may simultaneously track multiple semantic frames (queries or sets of items matching a query) throughout the dialogue. For example, two frames may be constructed and recalled while comparing two products—each containing the properties of a specific item. Frame tracking may be an extension of a state tracking task. In state tracking, information summarizing a dialogue history may be compressed into one semantic frame. In contrast, several frames may be kept in memory during frame tracking, such that each frame may correspond to a particular context, e.g., one or more vacation packages in this example. Para [0054], Ln 1-8, though frames are created for each offer or suggestion made by the wizard, the active frame may only be changed by the user. If the user asks for more information about a specific offer or suggestion, the active frame is changed to the frame introduced with that offer or suggestion. This change of frame is indicated by a “switch_frame” act.).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Schlesinger with the dialogue tracking system of Harris, as it improves user convenience(Para [0005], Ln 7-18).
Regarding Claim 16:
Claim 16 contains similar limitations as Claim 2 and is therefore rejected for the same reasons.
Regarding Claim 17:
Claim 17 contains similar limitations as Claim 3 and is therefore rejected for the same reasons.
Regarding Claim 18:
Claim 18 contains similar limitations as Claim 4 and is therefore rejected for the same reasons.
Regarding Claim 19:
Claim 19 contains similar limitations as Claim 5 and is therefore rejected for the same reasons.
Regarding Claim 20:
Claim 20 contains similar limitations as Claim 6 and is therefore rejected for the same reasons.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER G MARLOW whose telephone number is (571)272-4536. The examiner can normally be reached Monday - Thursday 10:00 am - 8:00 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richmond Dorvil can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER G MARLOW/ Assistant Examiner, Art Unit 2658
/RICHEMOND DORVIL/ Supervisory Patent Examiner, Art Unit 2658