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 .
Drawings
The drawings were submitted on 04/07/2025. These drawings are reviewed and accepted by the examiner.
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) 1-7 and 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Konam et al. (US 20230334263 A1) in view of Goldberg et al. (US 20250156153 A1)
Regarding claims 1, 10, and 16, Konam teaches:
“obtaining, while a virtual meeting is being conducted, a live transcript of the virtual meeting, the live transcript comprising current content discussed by a plurality of participants of the virtual meeting” (par. 0027, virtual environment; ‘Additionally, although the environment 100 is shown as a room in which both parties 110 are co-located, in various embodiments, the environment 100 may be a virtual environment or two distant spaces that at linked via teleconference software, a telephone call, or other situation where the parties 110 are not co-located, but are linked technologically to hold the conversation 120.’; par. 0040, ongoing conversation, transcript of recording)
“determining, based on the live transcript, whether the current content indicates a request of a participant of the plurality of participants for an operation to be performed with respect to the virtual meeting” (par. 0054; ‘Additionally or alternatively, the extractor 232 or augmenter 236 can include or use an action-item creator 300 (discussed in greater detail in regard to FIG. 3) that identifies terms from the transcript related to a planned follow up action to the conversation and fills in any details omitted from or left ambiguous in the conversation with supplemental data (e.g., the phone number to call the other party back at) for the conversation.’);
“responsive to determining that the current content indicates the request of the participant for the operation, identifying one or more of a context or a sentiment associated with the request” (par. 0055; ‘Stated differently, the most-semantically-relevant segment is the portion of the conversation that has the greatest effect on how the analysis system 230 interprets the meaning and importance of the key point within the conversation.’; See also par. 0063) ; and
“[[generating, based on the request and the one or more of the context or the sentiment, a prompt for an artificial intelligence (AI) model]], wherein the AI model is trained to perform the operation with respect to the virtual meeting” (par. 0061, action-item; ‘In various embodiments, the action-item creator 300 is provided as an MLM and the associated modules of computer executable code to identify various action items to follow up on based on a conversation and the information included or omitted therefrom.’; par. 0181, artificial intelligence; ‘As used herein, the machine learning models 926 may include various algorithms used to provide “artificial intelligence” to the computing device 900, which may include Artificial Neural Networks, decision trees, support vector machines, genetic algorithms, Bayesian networks, or the like.’ See also par. 0022).
However, Konam does not expressly teach generating a prompt for an AI model, as in:
“generating, based on the request and the one or more of the context or the sentiment, a prompt for an artificial intelligence (AI) model, wherein the AI model is trained to perform the operation with respect to the virtual meeting.”
In a similar field of endeavor (video conference transcription, see par. 0014), Goldberg teaches:
“generating, based on the request and the one or more of the context or the sentiment, a prompt for an artificial intelligence (AI) model, wherein the AI model is trained to perform the operation with respect to the virtual meeting” (par. 0027; ‘In various embodiments, enhancements module 205 utilizes model evaluation framework 209 and trained machine learning models 211 to automatically generate new project tasks using generative artificial intelligence (AI) prompts and a trained large language model. For example, a trained large language model of trained machine learning models 211 can be provided with a transcript of a feature and project contextual data along with a generative AI prompt to automatically generate a task specification for the associated feature.’).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Konam’s machine learning models by incorporating Goldberg’s generative AI prompt in order to generate, based on a request and context or sentiment, a prompt for the artificial intelligence (AI) model. The combination provides a method in which a large language model can be used to automatically determine and generate the desired scheduled tasks. (Goldberg: par. 0012)
Regarding claims 2 (dep. on claim 1), 11 (dep. on claim 10), and 17 (dep. on claim 16), the combination of Konam in view Goldberg further teaches:
at least one of: preparing meeting minutes associated with the virtual meeting, preparing a meeting summary associated with the virtual meeting, generating tasks out of action items corresponding to one or more discussion points of the live transcript, storing meeting notes associated with the virtual meeting for later reference, presenting an electronic document via a user interface (UI) of a client device of the participant, or generating a response to a question of the participant” (Konam: par. 0067, queries; par. 0068, action-item identifier; Goldberg: par. 0012, generated desired scheduled tasks).
Regarding claims 3 (dep. on claim 1), 12 (dep. on claim 10), and 18 (dep. on claim 16), the combination of Konam in view Goldberg further teaches:
“detecting, during the virtual meeting, an audio signal representing one or more verbal statements of a respective participant of the plurality of participants” (Konam: par. 0082, in-progress transcript);
“providing the audio signal as an input to a transcription engine” (Konam: par. 0082, in-progress transcript);
“obtaining one or outputs of the transcription engine, the one or more outputs comprising a textual version of the one or more verbal statements of the respective participant” (Konam: par. 0082, in-progress transcript); and
“updating the live transcript of the virtual meeting to include the textual version of the one or more verbal statements” (Konam: par. 0082, in-progress transcript).
Regarding claims 4 (dep. on claim 1), 13 (dep. on claim 10), and 19 (dep. on claim 16), the combination of Konam in view Goldberg further teaches:
“providing at least a portion of the live transcript as an input to an intent classifier model” (Konam: par. 0065, identifying whether intent of a segment is associated with action item); and
“obtaining one or more outputs of the intent classifier model, wherein the one or more outputs comprise an indication of whether the portion of the live transcript comprises a reference to one or more operations to be performed with respect to the virtual meeting” (Konam: par. 0065, identifying whether intent of a segment is associated with action item),
“wherein the determination of whether the current content indicates the request of the participant is made based on the obtained one or more outputs of the intent classifier model” (Konam: par. 0065, identifying whether intent of a segment is associated with action item).
Regarding claims 5 (dep. on claim 1), 14 (dep. on claim 10), and 20 (dep. on claim 16), the combination of Konam in view Goldberg further teaches:
“providing at least a portion of the live transcript as an input to a discussion context model” (Konam: par. 0043; ‘Accordingly, an attention model 224, is used to provide context of the various different candidate words among each other.’); and
“obtaining one or more outputs of the discussion context model, wherein the one or more outputs comprise an indication of at least one of a predicted context or a predicted sentiment of a discussion corresponding to the least the portion of the live transcript” (Konam: par. 0043, semantic intent),
“wherein the identified one or more of the context or the sentiment associated with the request comprises the at least one of the predicted context or the predicted sentiment” (Konam: par. 0043, ‘… identify a semantic intent of the utterance’).
Regarding claims 6 (dep. on claim 1) and 15 (dep. on claim 10), the combination of Konam in view Goldberg further teaches:
“updating a user interface (UI) of a client device associated with the participant to include a UI element corresponding to the operation with respect to the virtual meeting, wherein the live transcript of the virtual meeting comprises an indication of a detection of a user interaction with the UI element” (Konam: par. 0082; ‘In various embodiments, the action-item creator 300 may operate on a completed transcript 225 (e.g., after the conversation has concluded) or operate on an in-progress transcript 225 (e.g., while the conversations is ongoing). Accordingly, the action-item creator 300 may, via the UI API 320, generate additional action items while the conversation is ongoing to prompt the participants to discuss additional topics.’);
“wherein determining whether the current content indicates the request of the participant for the operation to be performed with respect to the virtual meeting is based on the indication of the detection of the user interaction with the UI element” (Konam: par. 0082; ‘For example, during an ongoing conversation, the action-item creator 300 may identify an action item to “call other party back” from a partial transcript 225, but receives a reply from a supplemental data source 370 that no phone number is known for the other party (or other request denial), and therefore creates a new human readable message 380 to present an action item to be addressed during the conversation of “ask for phone number”.’).
Regarding claim 7 (dep. on claim 6), the combination of Konam in view Goldberg further teaches:
“determining a plurality of operations pertaining to one or more of an additional context or an additional sentiment associated with prior content of the live transcript, wherein the plurality of operations comprise the operation” (Konam: par. 0082; ‘For example, during an ongoing conversation, the action-item creator 300 may identify an action item to “call other party back” from a partial transcript 225, but receives a reply from a supplemental data source 370 that no phone number is known for the other party (or other request denial), and therefore creates a new human readable message 380 to present an action item to be addressed during the conversation of “ask for phone number”.’); and
“updating the UI to include a plurality of UI elements each corresponding to a respective operation of the plurality of operations, the plurality of UI elements comprising the UI element corresponding to the operation” (Konam: par. 0084; ‘Additionally, the network interfaces 350 provides the human-readable messages 380 as UI elements (e.g., via the UI API 320) to the user systems 390 acting as an output device 240, and updates the UI API 320 as the user interacts with the UI elements.’).
Regarding claim 9 (dep. on claim 1), the combination of Konam in view Goldberg further teaches:
“identifying a pre-defined prompt template that corresponds to at least one of a meeting type associated with the virtual meeting or an operation type associated with the operation, wherein the prompt for the AI model is further generated based on the identified pre- defined prompt template” (Goldber: par. 0044; ‘For example, the provided portions of the project transcript can include a discussion by different team members describing a new feature or a found defect that should be added as a new task to the project. In various embodiments, the generated prompt is a generative artificial intelligence (AI) prompt for a trained large language model. Included in the prompt and/or the system prompt is the requirement specification for the new task. For example, the new task can require the creation of a new task (such as a new story, feature, defect, or another enhancement task), a title, a description, acceptance criteria, and an assignment group.’).
Allowable Subject Matter
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 8 (dep. on claim 1), the combination of Konam in view Goldberg further teaches:
“providing the request and the one or more of the context or the sentiment as an input to a prompt generator model” (Goldberg: par. 0033, context data).
However, the Examiner deems the prior art, whether taken alone or in combination, fails to teach, inter alia, “obtaining one or more outputs of the prompt generator model, the one or more outputs comprising one or more prompts and, for each of the one or more prompts, an indication of a level of confidence that a respective prompt corresponds to an optimized prompt for the request” and “determining that the prompt for the AI model is associated with a level of confidence that satisfies one or more confidence criteria” in combination with the other claimed features.
Conclusion
Other pertinent prior art are cited in the PTO-892 for the applicant's consideration.
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MARK . VILLENA
Examiner
Art Unit 2658
/MARK VILLENA/Examiner, Art Unit 2658
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