CTNF 18/481,132 CTNF 94573 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claimed computer-readable storage media, the scope of the recited “computer-readable storage medium” encompasses transitory media such as signals or carrier waves, where, as here the Specification does not limit the computer-readable storage media to non-transitory forms. See Ex parte Mewherter, 107 USPQ2d 1857, 1862. Applicants are advised to amend the claim by prefacing the term “ computer-readable storage medium” in claim 17 with “ non-transitory ”. This would render Claim 17 statutory under 35 U.S.C. 101 based on the latest guidance available to the examiner. Regarding Dependent Claims 18-20, fail to cure the deficiency of independent Claim 17, and therefore are also rejected under 35 USC § 101 as being directed to non-statutory subject matter for the same reason addressed above. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims’ subject matter eligibility will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”). With respect to claim 1. Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—claim 1 recites an apparatus, which is a machine. Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the limitations identified below each, under its broadest reasonable interpretation, covers mental processes abstract idea grouping (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)), see MPEP 2106.04(a)(2), subsection III and the 2019 PEG, but for the recitation of generic computer components: “identify a goal of the user from the conversation, identify a different user that is associated with the identified goal of the conversation, generating a call script comprising a description of content therein to be discussed with a different user based on execution of a generative artificial intelligence (GenAI) model on the identified goal”: (Mental processes- concept of observation and evaluation of identifying information and matching data based on description). Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—the judicial exception is not integrated into a practical application. “ receive a conversation of a user ;” involves the mere gathering of data, which is insignificant extra-solution activity. See MPEP § 2106.05(g). “a memory; and a processor coupled to the memory, the processor”, “execution of a generative artificial intelligence (GenAI) model” and “integrate the call script into a digital calendar of the different user within the memory”: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—there are no additional limitations beyond the mental processes identified above. The limitation treated above, are directed to the well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP § 2106.05(d)(II); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). It also includes limitations that Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The additional element is insignificant application, which is similar to examples of activities that the courts have found to be insignificant extra-solution activity, in accordance with MPEP 2106.05(g), Insignificant Extra-Solution Activity. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Claim 2. Step 1 : A apparatus, as above. Step 2A Prong 1 : same abstract idea from claim 1. Step 2A Prong 2, Step 2B : This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept. The claim recites that “record audio spoken during one or more of a call, a meeting, and a teleconference, and convert the recorded audio into text based on a speech-to-text converter”, Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Claim 3. Step 1 : A apparatus, as above. Step 2A Prong 1 : The claim recites that “identify a topic of interest based on execution of a machine learning model on the conversation .”: This limitation merely specifies mental processes- concept of observation and evaluation of identifying a topic using a generic computer. Step 2A Prong 2, Step 2B : This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept. The claim recites that “identify a topic of interest based on execution of a machine learning model on the conversation ”, Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Claim 4. Step 1 : A apparatus, as above. Step 2A Prong 1 : The claim recites that “generate conversation text about the topic of interest based on execution of the GenAI model on a corpus of pages corresponding to the topic of interest, and display the conversation text on a display via a user interface of a user device of the user.”: This limitation merely specifies mental processes- concept of observation and evaluation of making a dialog of a topic using a generic computer. Step 2A Prong 2, Step 2B : This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept. The claim recites that “generate conversation text about the topic of interest based on execution of the GenAI model on a corpus of pages corresponding to the topic of interest, and display the conversation text on a display via a user interface of a user device of the user ”, Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Claim 5. Step 1 : A apparatus, as above. Step 2A Prong 1 : The claim recites that “identify the different user based on a comparison of the identified goal to keywords included in a conversation log of a previous conversation with the different user.”: This limitation merely specifies mental processes- concept of observation and evaluation based on comparison. Step 2A Prong 2, Step 2B : This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept. Claim 6. Step 1 : A apparatus, as above. Step 2A Prong 1 : The claim recites that “generate text to be discussed during an upcoming call and images to be displayed on a display during the upcoming call based on execution of the GenAI model on the identified goal ”: This limitation merely specifies mental processes- concept of observation and evaluation of making a dialog of a topic using a generic computer. Step 2A Prong 2, Step 2B : This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept. The claim recites that “generate text to be discussed during an upcoming call and images to be displayed on a display during the upcoming call based on execution of the GenAI model on the identified goal ” , Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Claim 7. Step 1 : A apparatus, as above. Step 2A Prong 1 : same abstract idea from claim 1. Step 2A Prong 2, Step 2B : This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept. The claim recites that “train the GenAI model to generate the call script based on execution of the GenAI model a corpus of historical call scripts between the user and a plurality of users”, Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Claim 8. Step 1 : A apparatus, as above. Step 2A Prong 1 : same abstract idea from claim 1. Step 2A Prong 2, Step 2B : This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept. The claim recites that “simultaneously integrate the call script into a digital calendar of the user”, Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Claims 9-16 Step 1 : The claims recite a method; therefore, they fall into the statutory category of machines. Step 2A Prong 1 : The claims recite the same mental processes as claims 1-8, respectively. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. Claims 9-16 analysis mirrors that of claims 1-8, respectively. Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The analysis, mirrors that of claims 1-8, respectively. Claims 17-20 Step 1 : The claims recite a computer-readable storage medium; see non-statutory subject matter rejection above. Step 2A Prong 1 : The claims recite the same mental processes as claims 1-4, respectively. Step 2A Prong 2 : This judicial exception is not integrated into a practical application. Claims 17-20 analysis mirrors that of claims 1-4, respectively. Step 2B : The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The analysis, mirrors that of claims 1-4, respectively. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liensberger et al. (US 20170286853 A1) in view of Zharikova et al. (“DeepPavlov Dream: Platform for Building Generative AI Assistants”, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 3: System Demonstrations, pages 599–607 July 10-12, 2023) . Regarding claim 1. Liensberger teaches an apparatus comprising: a memory; and a processor coupled to the memory, the processor ( see ¶ 22, “techniques may reduce an amount of time and/or resources (e.g., processor, memory, network bandwidth) that are consumed to manage the schedule of the user” ) configured to: receive a conversation of a user ( see ¶ 30, “Servers 106A-106N are configured to execute computer programs that provide information to users in response to receiving requests from the users.”, also see ¶ 51, “FIG. 2, the method of flo wchart 200 begins at step 202. In step 202, communication(s) from a first user are analyzed (e.g., programmatically analyzed or processed) to identify a first communication from the first user that indicates that the first user has an intent to have a first meeting between at least the first user and second user(s ). Examples of a communication include but are not limited to a telephone call, an in-person conversation, a conversation via a software application (e.g., Skype®, developed and distributed originally by Skype Technologies S.A.R.L. and subsequently by Microsoft Corporation; Whatsapp® Messenger, developed and distributed by WhatsApp Inc.; and Facebook® Messenger, developed and distributed by Facebook, Inc.), a voice mail, an email, a text message, a short message service (SMS) message, and a social update.”, also see ¶ 57, “At step 206, the digital personal assistant is caused to automatically propose and/or automatically schedule a time to have the first meeting between at least the first user and the second user(s) based at least in part on the first communication and the second communication(s) indicating that the first user and the second user(s) have the intent to have the first meeting.” ), identify a goal of the user from the conversation ( see ¶ 35, “intent-based scheduling logic 110 causes (e.g., programmatically configures) a digital personal assistant to automatically propose and/or automatically schedule a time to have the first meeting between at least the first user and the second user(s) based at least in part on the first communication and the second communication(s) indicating that the first user and the second user(s) have the intent to have the first meeting.”, also see ¶ 52, “Analysis logic 308 may use any of a variety of techniques to determine that the first communication indicates that the first user has the intent to have the first meeting. For example, analysis logic 308 may use natural language processing to infer the intent from the first communication.”, also see ¶ 57, “At step 206, the digital personal assistant is caused to automatically propose and/or automatically schedule a time to have the first meeting between at least the first user and the second user(s) based at least in part on the first communication and the second communication(s) indicating that the first user and the second user(s) have the intent to have the first meeting . In an example implementation, causation logic 310 causes digital personal assistant 306 to automatically propose and/or automatically schedule a time to have the first meeting between at least the first user and the second user(s) based at least in part on the first communication and the second communication(s) indicating that the first user and the second user(s) have the intent to have the first meeting. ”, i.e. intent to schedule a meeting using keywords ), identify a different user that is associated with the identified goal of the conversation ( see ¶ 35, “intent-based scheduling logic 110 causes (e.g., programmatically configures) a digital personal assistant to automatically propose and/or automatically schedule a time to have the first meeting between at least the first user and the second user(s) based at least in part on the first communication and the second communication(s) indicating that the first user and the second user(s) have the intent to have the first meeting.”, also see ¶ 36, “intent-based scheduling logic 110 identifies interactions among users (e.g., users of any one or more of user devices 102A-102M)”, also see ¶ 38, “intent-based scheduling logic 110 infers an intent to have a meeting between the users. In the aforementioned illustration of this approach, intent-based scheduling logic 110 may infer that at least one of the first, second, third, and fourth users has an intent to have a meeting among the first, second, third, and fourth users. For instance, the intent may be inferred from statement(s) made during the in-person conversation and/or the telephone conference call and/or a different conversation by one or more of the first, second, third, and fourth users.” ), generating a call script comprising a description of content therein to be discussed with a different user based on execution of a generative artificial intelligence (GenAI) model on the identified goal ( see ¶ 57, “At step 206, the digital personal assistant is caused to automatically propose and/or automatically schedule a time to have the first meeting between at least the first user and the second user(s) based at least in part on the first communication and the second communication(s) indicating that the first user and the second user(s) have the intent to have the first meeting. In an example implementation, causation logic 310 causes digital personal assistant 306 to automatically propose and/or automatically schedule a time to have the first meeting between at least the first user and the second user(s) based at least in part on the first communication and the second communication(s) indicating that the first user and the second user(s) have the intent to have the first meeting . For example, causation logic 310 may cause digital personal assistant 306 to automatically propose and/or automatically schedule the time to have the first meeting in response to receipt of the intent indicator 326 . In accordance with this example, causation logic 310 may cause digital personal assistant 306 to automatically propose and/or automatically schedule the time based at least in part on the intent indicator 326 specifying that the first user and the second user(s) have the intent to have the first meeting.”, also see ¶¶ 33-35 and 23 ), and integrate the call script into a digital calendar of the different user within the memory ( see ¶ 59, “causing the digital personal assistant to automatically propose and/or automatically schedule the time to have the first meeting at step 206 includes causing the digital personal assistant to automatically schedule the time to have the first meeting. In accordance with this embodiment, the digital personal assistant is caused to automatically configure a visual representation of calendar(s) of the respective second user(s) to indicate that attendance of the second user(s) at the meeting is tentative. In an aspect of this embodiment, another digital personal assistant may be caused to automatically configure a visual representation of a calendar of the first user to indicate that attendance of the first user at the meeting is tentative.” ). Liensberger do not specifically teach generating call script based on execution of a generative artificial intelligence (GenAI) model. Zharikova teaches generating call script based on execution of a generative artificial intelligence (GenAI) model ( see page 602, “Skills in the Dream Platform define response generators. There are different types of algorithms for response generation, e.g., template-based, retrieval, and generative models. The skills that plan the dialog more than one step ahead are called scripted skills. These skills are able to get the dialog to develop in depth, which is already a generally accepted expectation of users from conversational systems. The scripts may utilize either slot filling (Baymurzina et al., 2021a) in template-based responses or controllable generation via LLMs.”, also see page 603, “Developers may create a prompt 602 based generative distribution featuring their own prompts by copy-pasting several lines of code and configuration descriptions and selecting the generative services of interest as a parameter (more detailed instructions are provided in our tutorials and documentation). One of the main features of DeepPavlov Dream is a multi-skill support which allows a dialog system to contain and switch between several different prompts during a dialog session. More details in Section 7”, also page 603, section 7, “Each Generative Skill is controlled by one user-specified prompt, utilizes selected Generative Service and is built using Dialog Flow Framework to provide an opportunity for the developer to control the skill in a script-based manner. To build an assistant, which comes in a form of a custom Generative AI Assistant Distribution, several Generative Skills can be combined with help of Prompt Selector picking the most relevant prompts among presented and Response Selector managing the dialog. Prompt selection could be performed in different ways: simple ranking of prompt-context pairs, ranking of pairs of context and prompts goals extracted from prompts using LLMs, predicting with LLMs based on prompts descriptions.” ). Both Liensberger and Zharikova pertain to the problem of automatic dialog assistance, thus being analogous. It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Liensberger and Zharikova to teach the above limitations. The motivation for doing so would be “In DeepPavlov Dream, multi skill Generative AI Assistant consists of NLP components that extract features from user utterances, conversational skills that generate or retrieve a response, skill and response selectors that facilitate choice of relevant skills and the best response, as well as a conversational orchestrator that enables creation of multi-skill Generative AI Assistants scalable up to industrial grade AI assistants. The platform allows to integrate large language models into dialog pipeline, customize with prompt engineering, handle multiple prompts during the same dialog session and create simple multimodal assistants.” (see Zharikova Abstract). Regarding claim 2. Liensberger and Zharikova teaches the apparatus of claim 1, Liensberger further teaches wherein the processor is configured to record audio spoken during one or more of a call, a meeting, and a teleconference, and convert the recorded audio into text based on a speech-to-text converter ( see ¶ 86, “The interaction information 822 may include an audio representation of one or more of the interactions, a video representation of one or more of the interactions, and/or a textual representation of one or more of the transactions. The interaction information 822 may include a transcription of one or more of the interactions, keywords extracted from one or more of the interactions, etc.”, also see ¶ 30-31, 51-53, 71, and 102 ). Zharikova teaches convert the recorded audio into text based on a speech-to-text converter (see ¶ 603, “While the audio input can be converted to text almost without losing sense”). The motivation utilized in the combination of claim 1, super, applies equally as well to claim 2. Regarding claim 3. Liensberger and Zharikova teaches the apparatus of claim 1, Liensberger further teaches wherein the processor is configured to identify a topic of interest based on execution of a machine learning model on the conversation ( see ¶ 35, “intent-based scheduling logic 110 causes (e.g., programmatically configures) a digital personal assistant to automatically propose and/or automatically schedule a time to have the first meeting between at least the first user and the second user(s) based at least in part on the first communication and the second communication(s) indicating that the first user and the second user(s) have the intent to have the first meeting.”, also see ¶ 52, “Analysis logic 308 may use any of a variety of techniques to determine that the first communication indicates that the first user has the intent to have the first meeting. For example, analysis logic 308 may use natural language processing to infer the intent from the first communication.”, also see ¶ 57, “At step 206, the digital personal assistant is caused to automatically propose and/or automatically schedule a time to have the first meeting between at least the first user and the second user(s) based at least in part on the first communication and the second communication(s) indicating that the first user and the second user(s) have the intent to have the first meeting . In an example implementation, causation logic 310 causes digital personal assistant 306 to automatically propose and/or automatically schedule a time to have the first meeting between at least the first user and the second user(s) based at least in part on the first communication and the second communication(s) indicating that the first user and the second user(s) have the intent to have the first meeting. ”, i.e. intent to schedule a meeting using keywords ). Zharikova teaches machine learning model on the conversation ( see page 603, “Developers may create a prompt 602 based generative distribution featuring their own prompts by copy-pasting several lines of code and configuration descriptions and selecting the generative services of interest as a parameter (more detailed instructions are provided in our tutorials and documentation). One of the main features of DeepPavlov Dream is a multi-skill support which allows a dialog system to contain and switch between several different prompts during a dialog session. More details in Section 7”, also page 603, section 7, “Each Generative Skill is controlled by one user-specified prompt, utilizes selected Generative Service and is built using Dialog Flow Framework to provide an opportunity for the developer to control the skill in a script-based manner. To build an assistant, which comes in a form of a custom Generative AI Assistant Distribution, several Generative Skills can be combined with help of Prompt Selector picking the most relevant prompts among presented and Response Selector managing the dialog. Prompt selection could be performed in different ways: simple ranking of prompt-context pairs, ranking of pairs of context and prompts goals extracted from prompts using LLMs, predicting with LLMs based on prompts descriptions.”). The motivation utilized in the combination of claim 1, super, applies equally as well to claim 3. Regarding claim 4. Liensberger and Zharikova teaches the apparatus of claim 3, Liensberger further teaches wherein the processor is configured to generate conversation text about the topic of interest based on execution of the GenAI model on a corpus of pages corresponding to the topic of interest, and display the conversation text on a display via a user interface of a user device of the user ( see ¶ 51, “The communication(s) 320 may be received (e.g., detected or sensed) via any suitable interface, including but not limited to a sensor (e.g., a microphone) or a digital interface (e.g., a digital receiver). Analysis logic 308 may include one or more such interfaces.”, also ¶ 245, “A display device 1444 (e.g., a monitor) is also connected to bus 1406 via an interface, such as a video adapter 1446. In addition to display device 1444, computer 1400 may include other peripheral output devices (not shown) such as speakers and printers” ). Zharikova teaches wherein the processor is configured to generate conversation text about the topic of interest based on execution of the GenAI model on a corpus of pages corresponding to the topic of interest ( see page 602, “Skills in the Dream Platform define response generators. There are different types of algorithms for response generation, e.g., template-based, retrieval, and generative models. The skills that plan the dialog more than one step ahead are called scripted skills. These skills are able to get the dialog to develop in depth, which is already a generally accepted expectation of users from conversational systems. The scripts may utilize either slot filling (Baymurzina et al., 2021a) in template-based responses or controllable generation via LLMs.”, also see page 603, “Developers may create a prompt 602 based generative distribution featuring their own prompts by copy-pasting several lines of code and configuration descriptions and selecting the generative services of interest as a parameter (more detailed instructions are provided in our tutorials and documentation). One of the main features of DeepPavlov Dream is a multi-skill support which allows a dialog system to contain and switch between several different prompts during a dialog session. More details in Section 7”, also page 603, section 7, “Each Generative Skill is controlled by one user-specified prompt, utilizes selected Generative Service and is built using Dialog Flow Framework to provide an opportunity for the developer to control the skill in a script-based manner. To build an assistant, which comes in a form of a custom Generative AI Assistant Distribution, several Generative Skills can be combined with help of Prompt Selector picking the most relevant prompts among presented and Response Selector managing the dialog. Prompt selection could be performed in different ways: simple ranking of prompt-context pairs, ranking of pairs of context and prompts goals extracted from prompts using LLMs, predicting with LLMs based on prompts descriptions.” ) The motivation utilized in the combination of claim 1, super, applies equally as well to claim 4. Regarding claim 5. Liensberger and Zharikova teaches the apparatus of claim 1, Liensberger further teaches wherein the processor is configured to identify the different user based on a comparison of the identified goal to keywords included in a conversation log of a previous conversation with the different user ( see ¶ 52, “Analysis logic 308 may compare each of the likelihoods to a likelihood threshold. A likelihood that is greater than or equal to the likelihood threshold may indicate that the first user has the intent with which the likelihood corresponds. A likelihood that is less than the likelihood threshold may indicate that the first user does not have the intent with which the likelihood corresponds. Accordingly, analysis logic 308 may determine that the first communication indicates that the first user has the intent to have the first meeting based at least in part on natural language processing of the first communication revealing that the likelihood that the first user has the intent to have the first meeting is greater than or equal to the likelihood threshold.”, also see ¶ 109, “Causation logic 810 may compare the importance of each user in the subset (e.g., as specified by the importance information 828) to the threshold importance to determine which of the users in the subset have an importance that is greater than or equal to the threshold importance and which of the users in the subset have an importance that is less than the threshold importance.”, also see ¶¶ 34-38, “intent-based scheduling logic 110 identifies tools that are used to facilitate the interactions. In the aforementioned illustration of this approach, intent-based scheduling logic 110 may identify the in-person engagement and the telephone network as the tools that are used to facilitate the in-person conversation and the telephone conference call, respectively.” ). Regarding claim 6. Liensberger and Zharikova teaches the apparatus of claim 1, Liensberger further teaches wherein the processor is configured to generate text to be discussed during an upcoming call and images to be displayed on a display during the upcoming call based on execution of the GenAI model on the identified goal ( see page 602, “Skills in the Dream Platform define response generators. There are different types of algorithms for response generation, e.g., template-based, retrieval, and generative models. The skills that plan the dialog more than one step ahead are called scripted skills. These skills are able to get the dialog to develop in depth, which is already a generally accepted expectation of users from conversational systems. The scripts may utilize either slot filling (Baymurzina et al., 2021a) in template-based responses or controllable generation via LLMs.”, also see page 603, “Developers may create a prompt 602 based generative distribution featuring their own prompts by copy-pasting several lines of code and configuration descriptions and selecting the generative services of interest as a parameter (more detailed instructions are provided in our tutorials and documentation). One of the main features of DeepPavlov Dream is a multi-skill support which allows a dialog system to contain and switch between several different prompts during a dialog session. More details in Section 7”, also page 603, section 7, “Each Generative Skill is controlled by one user-specified prompt, utilizes selected Generative Service and is built using Dialog Flow Framework to provide an opportunity for the developer to control the skill in a script-based manner. To build an assistant, which comes in a form of a custom Generative AI Assistant Distribution, several Generative Skills can be combined with help of Prompt Selector picking the most relevant prompts among presented and Response Selector managing the dialog. Prompt selection could be performed in different ways: simple ranking of prompt- context pairs, ranking of pairs of context and prompts goals extracted from prompts using LLMs, predicting with LLMs based on prompts descriptions.”, also see section 8, “Users’ expectations from chatbots are rapidly increasing, so multimodality, which is operating with images, audio and video, is becoming an important direction in dialog systems’ development. While the audio input can be converted to text almost without losing sense (except of intonations and emotions), received images may bring a key information to a dialog.” ) The motivation utilized in the combination of claim 1, super, applies equally as well to claim 6. Regarding claim 7. Liensberger and Zharikova teaches the apparatus of claim 1, Liensberger further teaches wherein the processor is configured to train the GenAI model to generate the call script based on execution of the GenAI model a corpus of historical call scripts between the user and a plurality of users ( see page 602, “Skills in the Dream Platform define response generators. There are different types of algorithms for response generation, e.g., template-based, retrieval, and generative models. The skills that plan the dialog more than one step ahead are called scripted skills. These skills are able to get the dialog to develop in depth, which is already a generally accepted expectation of users from conversational systems. The scripts may utilize either slot filling (Baymurzina et al., 2021a) in template-based responses or controllable generation via LLMs.”, also see page 603, “Developers may create a prompt 602 based generative distribution featuring their own prompts by copy-pasting several lines of code and configuration descriptions and selecting the generative services of interest as a parameter (more detailed instructions are provided in our tutorials and documentation). One of the main features of DeepPavlov Dream is a multi-skill support which allows a dialog system to contain and switch between several different prompts during a dialog session. More details in Section 7”, also page 603, section 7, “Each Generative Skill is controlled by one user-specified prompt, utilizes selected Generative Service and is built using Dialog Flow Framework to provide an opportunity for the developer to control the skill in a script-based manner. To build an assistant, which comes in a form of a custom Generative AI Assistant Distribution, several Generative Skills can be combined with help of Prompt Selector picking the most relevant prompts among presented and Response Selector managing the dialog. Prompt selection could be performed in different ways: simple ranking of prompt-context pairs, ranking of pairs of context and prompts goals extracted from prompts using LLMs, predicting with LLMs based on prompts descriptions.”, also see section 8, “Users’ expectations from chatbots are rapidly increasing, so multimodality, which is operating with images, audio and video, is becoming an important direction in dialog systems’ development. While the audio input can be converted to text almost without losing sense (except of intonations and emotions), received images may bring a key information to a dialog.” ) The motivation utilized in the combination of claim 1, super, applies equally as well to claim 7. Regarding claim 8. Liensberger and Zharikova teaches the apparatus of claim 1, Liensberger further teaches wherein the processor is configured to simultaneously integrate the call script into a digital calendar of the user ( see ¶¶ 57-60, “causing the digital personal assistant to automatically propose and/or automatically schedule the time to have the first meeting at step 206 includes causing the digital personal assistant to automatically schedule the time to have the first meeting. In accordance with this embodiment, the digital personal assistant is caused to automatically configure a visual representation of calendar(s) of the respective second user(s) to indicate that attendance of the second user(s) at the meeting is tentative. In an aspect of this embodiment, another digital personal assistant may be caused to automatically configure a visual representation of a calendar of the first user to indicate that attendance of the first user at the meeting is tentative.” ). Claims 9-16 recites a method to perform the apparatus recited in claims 1-8. Therefore the rejection of claims 1-8 above applies equally here. Claims 17-20 recites a computer-readable storage medium to perform the apparatus recited in claims 1-4. Therefore the rejection of claims 1-4 above applies equally here. Related prior arts: Peterson et al. (US 20170344572 A1) teaches recommendations of documents to a user of a document corpus. Document features are extracted and assigned weights, and a profile is likewise created for users. Documents are scored with respect to a given user based at least in part on the document features and the user's profile. The document scores may be adjusted to reflect organizational goals, such as promoting recommendation of newer documents. Based on the scores, recommendations are determined for a given user by identifying the top scores for that user and presented to the user in one of a variety of manners, such as within a web-based user interface, or via email. Interactions of the users with recommendations may be monitored and the recommendations updated accordingly. Mishra et al. (US 20230046881 A1) teaches a probability score associated with a determination made by the machine learning model with respect to capturing multimedia content associated with the conference call. The device may output a notification associated with capturing the multimedia content based on the output from the machine learning model. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to IMAD M KASSIM whose telephone number is (571)272-2958. The examiner can normally be reached 10:30AM-5:30PM, M-F (E.S.T.). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J. Huntley can be reached at (303) 297 - 4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /IMAD KASSIM/Primary Examiner, Art Unit 2129 Application/Control Number: 18/481,132 Page 2 Art Unit: 2129 Application/Control Number: 18/481,132 Page 3 Art Unit: 2129 Application/Control Number: 18/481,132 Page 4 Art Unit: 2129 Application/Control Number: 18/481,132 Page 5 Art Unit: 2129 Application/Control Number: 18/481,132 Page 6 Art Unit: 2129 Application/Control Number: 18/481,132 Page 7 Art Unit: 2129 Application/Control Number: 18/481,132 Page 8 Art Unit: 2129 Application/Control Number: 18/481,132 Page 9 Art Unit: 2129 Application/Control Number: 18/481,132 Page 10 Art Unit: 2129 Application/Control Number: 18/481,132 Page 11 Art Unit: 2129 Application/Control Number: 18/481,132 Page 12 Art Unit: 2129 Application/Control Number: 18/481,132 Page 13 Art Unit: 2129 Application/Control Number: 18/481,132 Page 14 Art Unit: 2129 Application/Control Number: 18/481,132 Page 15 Art Unit: 2129 Application/Control Number: 18/481,132 Page 16 Art Unit: 2129 Application/Control Number: 18/481,132 Page 17 Art Unit: 2129 Application/Control Number: 18/481,132 Page 18 Art Unit: 2129 Application/Control Number: 18/481,132 Page 19 Art Unit: 2129 Application/Control Number: 18/481,132 Page 20 Art Unit: 2129 Application/Control Number: 18/481,132 Page 21 Art Unit: 2129 Application/Control Number: 18/481,132 Page 22 Art Unit: 2129 Application/Control Number: 18/481,132 Page 23 Art Unit: 2129 Application/Control Number: 18/481,132 Page 24 Art Unit: 2129 Application/Control Number: 18/481,132 Page 25 Art Unit: 2129 Application/Control Number: 18/481,132 Page 26 Art Unit: 2129
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