DETAILED ACTION
Status of the Application
This Final Office Action is in response to Application Serial 18/652,320. In response to Examiner’s action mail dated January 05,2026. Applicant submitted arguments and amendments, mail dated February 25,2026. Claims 1-20 are pending.
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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
Applicant did not submit an information disclosure statement (IDS) for consideration.
Response to Amendments
Claims 1-20 are pending in this application. Claim(s) 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 15, 17, 19, 20 are amended.
Regarding the 35 U.S.C. 101 rejection, the amendments are not persuasive. The claims 1-20 are rejected under 35 U.S.C. 101, see below.
Regarding the prior art rejection, the amendments to the claims are not persuasive. The claims 1-20 are rejected under 35 U.S.C. 103, see below.
Response to Arguments
Applicant’s arguments filed on February 25, 2026, have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below.
Rejections under 101
On pages 14- 18 of the Applicant’s 35 U.S.C. 101 arguments, the Applicant traverses the currently amended claims are not directed to an abstract idea under Alice 2A. Moreover, the currently amended claims recite additional elements amounting to significantly more than any asserted abstract idea under Alice Step 2B.
A. The currently amended claims are patent-eligible because the currently amended claims do not recite an abstract idea under Alice Step 2A Prong One. As amended the claims recite specific requirements governing system operations within a calendar application environment and do not recite an enumerated abstract idea under MPEP 2106.04(a). Applicant respectfully submits that the amended claims are directed to statutory subject matter and are eligible under 101.
Examiner respectfully disagrees with Applicant’s arguments regarding Step 2A Prong One. Examiner submits the claims recite generating and displaying a calendar with interrelated events. Calendaring is managing personal behavior, and thus, recites certain methods or organizing human activity. Therefore, the claims recite calendaring and displaying a calendar with interrelated events, are directed to a judicial exception.
B. The currently amended claims are patent-eligible because the currently amended claims do not recite an abstract idea under Alice Step 2A Prong Two. Under the Alice Step 2A Prong Two of the patent eligibility analysis, the currently amended independent claims integrate any alleged abstract idea into a practical application.
The currently amended claims address a computer-centric problem with a computer- centric solution. Analogous to DDR, the Specification describes technical shortcomings of conventional systems that arise in the context of structuring and rendering calendar events within an integrated calendar interface. For example, the Specification explains that existing systems generate calendar events that are "allocated for individual time commitments" and "provid[e] no indication of event relatedness or continuity." Specification [0002]. The Specification further explains that "[s]uch isolated calendar event generat[ion] prevents existing systems from adapting calendar events based on interdependencies. Accordingly, the currently amended claims integrate any alleged abstract idea into a practical application under Alice Step 2A, Prong Two. For this reason, the currently amended claims are not directed to a judicial exception, and the Applicant respectfully requests the § 101 rejection of the currently amended claims be withdrawn.
Examiner respectfully disagrees with Applicant’s arguments. Applicant’s arguments recite a judicial exception and use a large language model to input information and the large language model provides an output. As recited the large language model is “apply it”. See MPEP 2106.05(f). Examiner Applicant to instant specification [028], [055].
Regarding DDR, the Applicant’s claims are not analogous because the claims are not rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks. Here, the claims are using a large language model to generate a calendar of allocated time commitments. As supported by the specification, Applicant is encouraged recite the large language model integration into the judicial exception so that the claims amount to a practical application .
The currently amended claims integrate any alleged abstract idea into a specific improvement to computer technology. Applicant traverses the currently amended claims are directed to a technological improvement akin to the improvement found eligible in Core Wireless. The claims in Core Wireless were eligible because they “increased the efficiency with which users could navigate through various views and windows.” Applicant points to specification [0027] –[ 0028] to discuss the system adapts the “calendar structure to achieve the target objective through a series of interrelated calendar events.” Applicant submits the currently amended claims recites a specific improvement to the operation of a calendar application and integration with conversational input mechanisms. Under Core wireless, such an improvement to user interface functionality integrates any alleged abstract idea into a practical application. Applicant respectfully request the 35 U.S.C. 101 rejection of the currently amended claims be withdrawn.
Examiner respectfully disagrees with Applicant’s arguments. Applicant’s arguments recite a judicial exception and use a large language model to input information and the large language model provides an output. As recited the large language model is “apply it”. See MPEP 2106.05(f). Examiner Applicant to instant specification [028], [055]. In Core Wireless the claims were "directed to a particular manner of summarizing and presenting information in electronic devices." In Core Wireless, the claims were directed toward specific implementations.
Here, the claims are using a large language model to generate a series of interrelated calendar events … and generating a calendar event corresponding to the event input of that is presented in an interface … displayed in a calendar interface. Applicant is encouraged to further identify the steps, as supported by the specification, that integrate into the judicial exception into a practical application. Additionally, Applicant can identify the technical improvement that is rooted in technology.
C. The currently amended claims are patent-eligible because they recite an inventive concept under Alice Step 2B. Applicant traverses, the currently amended claims recite an ordered combination of specific graphical user interface elements, large language model-based event generation, and use of an integrated catalyst calendar interface, which together amount to significantly more than any alleged abstract idea. As such, even if individual elements recited by the currently amended independent claims were known, their ordered combination in a non- conventional arrangement supplies the requisite inventive concept under Alice Step 2B. See BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016). Accordingly, Applicant respectfully requests withdrawal of the 35 U.S.C. § 101 rejection.
Examiner respectfully disagrees with Applicant’s Step 2B arguments. In Bascom, the court explained that the inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces. The court also explained that the inventive concept can be found in the ordered combination of the claim elements.
Examiner does not argue Bascom. Examiner considers the claims as a whole, and similar to Step 2A prong two, Applicant is encouraged to recite the large language model integration into the judicial exception.
The claims remain rejected under 35 U.S.C. 101, see below.
Rejections under 103
On pages 21-25 of the Applicant’s prior art arguments, traverses the combination of Setteboun and Vuskovich fails to describe, teach, or suggest each limitation recited by the currently amended independent claims 1, 8, and 15. Applicant submits, the pending amended claims 1, 8, and 15 are allowable. Claims 2-7, 9-14, and 16-20 depend from currently amended independent claims 1, 8, and 15 and are allowable over Setteboun, whether considered singly or in combination with the other cited references. Applicant therefore, requests that the 103 rejection of independent claims 1, 8, and 15 and corresponding dependent claims be withdrawn.
Examiner submits, the claim amendments necessitate grounds for a new amendment.
Claim Rejections - 35 USC § 101
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.
Claim(s) 1-7 are process.
Claim(s) 8- 14 are machine.
Claim(s) 15-20 are manufacture.
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 claim 1 recites, “…receiving, … an event input indicating event parameters for a calendar event; in response to receiving the event input, generating the calendar event utilizing … to process the event parameters; and providing, … an event element comprising rich calendar content presented within an integrated view … wherein the rich calendar content reflects the calendar event generated: generating, for display…; receiving, … an event input indicating event parameters for a calendar event; generating, … and based on a target objective, a series of interrelated calendar events including at least an ordering, priority, or duration relationship among the series of interrelated calendar events relative to an existing calendar event...”. Claim 8 recites, “… generate …; receive, … an event input indicating event parameters for a calendar event; generate, …, a series of interrelated calendar events including at least an ordering, priority, or duration relationship among the series of interrelated calendar events relative to an existing calendar event of a calendar application; generate, based on the series of interrelated calendar events, a calendar event corresponding to the event input; cause, in response to receiving the event input, … to generate the calendar event based on the event parameters; and provide, for display …, an event element comprising rich calendar content presented within an integrated view of a calendar application, wherein the rich calendar content reflects the calendar event generated … and reflects an ordering, priority, or duration relationship of the calendar event relative to the existing calendar event of the calendar application….” Claim 15 recites, “ … generate, for display …; receive, … . an event input comprising text describing event parameters for a calendar event; generating, … and based on a target objective, a series of interrelated calendar events including at least an ordering, priority, or duration relationship among the series of interrelated calendar events relative to an existing calendar event …; generating, based on the series of interrelated calendar events, a calendar event corresponding to the event input; in response to receiving the event input, generate the calendar event utilizing … to process the event parameters; and provide, for display …, an event element comprising rich calendar content presented …, wherein the rich calendar content reflects the calendar event generated … and reflects an ordering, priority, or duration relationship of the calendar event relative to the existing calendar event ….” Claims 1-20 in view of the claim limitations, are an abstract idea of scheduling, and thus, the claims are managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), and thus, the claims recite concepts that are grouped into the abstract grouping of certain methods of organizing human activity. The claims are directed to an judicial exception under the first prong of Step 2A.
This judicial exception are not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of, “A computer-implemented method comprising”, “on a client device, a catalyst calendar interface comprising a chat window and an integrated calendar window”; “via the chat window of the catalyst calendar interface,” “using a large language model”, “of a calendar application”, “utilizing a large language model”, “of the catalyst calendar interface,” “an integrated view of a calendar application,” in claim 1; “A system comprising: at least one processor; and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to”, “ for display on a client device, a catalyst calendar interface comprising a chat window and an integrated calendar window,” “via the chat window of the catalyst calendar interface”, on claim 8; “A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to”, “on a client device, a catalyst calendar interface comprising a chat window and an integrated calendar window”, “via the chat window of the catalyst calendar interface”, “using a large language model”, “a calendar application” in claim 15; however, when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05 (f).
The additional elements included in the dependent claim that are not within the independent claims.
Claim 3 , 4, 11, 17, 18: a selectable option;
Claim 12: a selectable element;
Claim 14: an interactive element;
The additional element “a selectable option”, “a selectable element”, and “an interactive element” are generally linking the use of the judicial exception to a particular technological environment or field of use- See MPEP 2106.05(h).
Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting transformation or reduction of a particular article to a different state or thing.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
At step 2B, it is MPEP 2106.05 (d) – Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Dependent claims 2-7 further narrow the abstract idea of independent claim 1. Dependent claims 9-14 further narrow the abstract idea of independent claim 8. Dependent claims 16-20 further narrow the abstract idea of independent claim 15. The claims 1-20 are not patent eligible.
Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-7, 9-14, & 16-20 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Setteboun (US 2020/0,274,726 A1) in view of Rao (2024, Integrating Digital Calendars with Large Language Models for Stress Management Interventions) and Ost (US 2023/0,046,890 A1).
Regarding Claim 1, (Currently Amended)
A computer-implemented method comprising: generating, for display on a client device, a catalyst calendar interface comprising a chat window and an integrated calendar window;
Setteboun [0184] teaches Figure 10 illustrates a GUI presenting a New Meeting Page. Setteboun [0149] teaches at 264 a summary of scheduled meetings and/or events for the predefined groups , are presented within an interactive calendar in the GUI. Setteboun [013] teaches create meeting and/or event feature for creating new meetings and/or events for the one group via the GUI, a reminder of meeting and/or event feature for creating custom reminders for upcoming scheduled meetings and/or events for transmission to client terminals of the plurality of user credentials of the one group, a view group calendar feature for presenting within the GUI a calendar including upcoming scheduled meetings and/or events, a plurality of folders each storing previously posted content and/or chats and/or content for viewing within the GUI, a group log storing a list of activities of the one group for viewing the GUI. Setteboun [013], [019], [065].
receiving, via the chat window of the catalyst calendar interface, an event input indicating event parameters for a calendar event;
See above - Setteboun [013], [019], [065] – chat and calendar interface
Setteboun [0122] –[0123] teaches timeslots, location, and for an invitation automatically computed by a classifier. Setteboun [0122] –[0124];
Setteboun [Fig. 2C] teaches a group chat, presenting group chat content features, and present upcoming meetings and/or events., Setteboun [Figure 2C and the associated text], [0146]-[0155].
Applicant’s specification [042] discloses for example, event parameters can include contextual information for determining the time, purpose, and/or duration to create a series of calendar events. For instance, event parameters can specify the optimal timing for an event based on historical attendance patterns, the rationale behind scheduling certain events in relation to overarching goals, or the duration for new events following a trend analysis within the knowledge graph. Setteboun [066] teaches the invitation includes a single selected time slot and a single selected location. The single time slot and single location may be selected, for example, by a user manually entering the time slot and location, and/or by code that automatically analyzes past attendance patterns of the user credentials and/or code that automatically analyzes availability of the user credentials (e.g., based on other scheduled events and/or meetings such as in a calendar).
Although highly suggested, Setteboun does not explicitly teach:
interrelated calendar events including at least an ordering, priority, or duration relationship among the series of interrelated calendar events relative to an existing calendar event of a calendar application; generating, based on the series of interrelated calendar events, a calendar event corresponding to the event input; and providing, for display within the integrated calendar window of the catalyst calendar interface, an event element comprising rich calendar content presented within an integrated view of a calendar application, wherein the rich calendar content reflects the calendar event generated by the large language model and reflects an ordering, priority, or duration relationship for the calendar event relative to the existing calendar event of the calendar application.
Rao teaches:
generating, using a large language model and based on a target objective, a series of interrelated calendar events including at least an ordering, priority, or duration relationship among the series of interrelated calendar events relative to an existing calendar event of a calendar application;
Rao utilizes text and time-based digital calendar data, large language model (LLM) may possess the power to compose text messages tailored to the user’s daily schedules (priority) by seeding relevant information from calendars within the messages and sending time-sensitive reminders. Rao [introduction page 2].; Rao ensure[s] the relevance of scheduled messages, the system checks for updates to users’ calendars every thirty minutes, adjusting scheduled times or message content if necessary. Rao [system design page 3].;
Rao teaches a time sensitive reminder based on calendar data, using a large language model, and thus, Rao teaches a target objective defined a high-order goal … that the disclosed systems break down into individual executable processes using a context engine.
generating, based on the series of interrelated calendar events, a calendar event corresponding to the event input; … reflects the calendar event generated by the large language model and reflects an ordering, priority, or duration relationship for the calendar event relative to the existing calendar event of the calendar application.
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In Rao, the server selects events from a user’s calendar, generates associated messages using OpenAI’s GPT-4 model … , and schedules them, all within study parameters (see Section 4). To ensure the relevance of scheduled messages, the system checks for updates to users’ calendars every thirty minutes, adjusting scheduled times or message content if necessary. Rao [system design page 3].;
In the LLM condition, thirty minutes before a selected event on their calendar, participants will receive a prompt asking about their emotional and mental state. A 15-minute response window will be provided; if they reply, their response, as well as the summary and description of the calendar event, will be given to the LLM to inform the subsequent personalized message. If no response is received within the window, a follow-up message will be sent based solely on the user’s calendar information., Rao [study design page 4].
Setteboun teaches graphical user interfaces designed to schedule meetings. Rao discloses integrating text messaging systems with digital calendars presenting a unique opportunity to leverage information for personalized stress management interventions. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with leveraging dynamic contexts from users’ digital calendar data, interactions between users and Open AI’s GPT-4 large language model (LLM) to create time-sensitive, context-aware text messaging, as taught by Rao, to consider for a helpful, time-sensitive intervention and important design considerations for LLM-supported tools for stress reduction., Rao [abstract].
Ost teaches:
and providing, for display within the integrated calendar window of the catalyst calendar interface, an event element comprising rich calendar content presented within an integrated view of a calendar application, wherein the rich calendar content …
Ost Figure 5 – item 524, Figure 6 item 624 teach schedule a calendar event. Ost [026] discloses the content that is displayed to client device 102 may be transmitted from web client application server 114 to client device 102, and subsequently processed by application 110 for display through a graphical user interface (GUI) of client device 102.
Setteboun teaches graphical user interfaces designed to schedule meetings. Ost discloses a calendar event scheduling system utilizing an artificial intelligence assistant. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with intelligently scheduling a slate of calendar event in real-time, as taught by Ost, to improve conventional approaches to scheduling software by utilizing natural language process and natural language understanding techniques., Ost [018].
Regarding Claim 2, (Currently Amended)
The computer-implemented method of claim 1, further comprising receiving, via the chat window of the catalyst calendar interface, the event input via a text input field for interacting with the large language model through natural language text prompts.
See Claim 1 – Setteboun [0184], [Figure 10] and Rao disclosing the server selects events from a user’s calendar, generates associated messages using OpenAI’s GPT-4 model [29], and schedules them, all within study parameters. Rao [system design p.3]
Setteboun teaches graphical user interfaces designed to schedule meetings. Rao discloses integrating text messaging systems with digital calendars presenting a unique opportunity to leverage information for personalized stress management interventions. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with leveraging dynamic contexts from users’ digital calendar data, interactions between users and Open AI’s GPT-4 large language model (LLM) to create time-sensitive, context-aware text messaging, as taught by Rao, to consider for a helpful, time-sensitive intervention and important design considerations for LLM-supported tools for stress reduction., Rao [abstract].
Regarding Claim 3, (Original)
The computer-implemented method of claim 1, further comprising: generating, for display via the chat window of the catalyst calendar interface, a selectable option comprising a recommendation to execute an executable task associated with the calendar event;
See claim 1 - Setteboun [Figure 11 and the associated text]- Ranking meeting times using drag and drop.
receiving, via the chat window of the catalyst calendar interface, a selection of the selectable option; and based on the selection, executing the executable task by communicating with …
See claim 1 - Setteboun [Figure 10 and the associated text], [Figure 11 and the associated text].
Although highly suggested, Setteboun does not explicitly teach:
one or more external models.
Rao teaches:
one or more external models.
Rao discloses in Figure 1, after users provide their preferred phone number to receive text messages and access to their Google Calendar, they are directed to their dashboard, where they can view past messages received from our system. The server selects events from a user’s calendar, generates associated messages using OpenAI’s GPT-4 model.
Setteboun teaches graphical user interfaces designed to schedule meetings. Ost discloses a calendar event scheduling system utilizing an artificial intelligence assistant. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with leveraging dynamic contexts from users’ digital calendar data, interactions between users and Open AI’s GPT-4 large language model (LLM) to create time-sensitive, context-aware text messaging, as taught by Rao, to consider for a helpful, time-sensitive intervention and important design considerations for LLM-supported tools for stress reduction., Rao [abstract].
Ost further teaches:
Ost teaches a message may refer to messages through various communication channels. For example, a message may refer to an email, a text message (e.g., SMS, iMessage, WhatsApp, Facebook Messenger, etc.), a voice message, and the like.
Setteboun teaches graphical user interfaces designed to schedule meetings. Ost discloses a calendar event scheduling system utilizing an artificial intelligence assistant. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with calendaring using external application (e.g., SMS, iMessage, WhatsApp, Facebook Messenger, etc,) as taught by Ost, to improve conventional approaches to scheduling software by utilizing natural language process and natural language understanding techniques., Ost [018].
Regarding Claim 4, (Currently Amended)
The computer-implemented method of claim 1, further comprising: generating, for display via the chat window of the catalyst calendar interface, an indication of a-the target objective associated with the series of calendar events calendar event and a selectable option associated with modifying the calendar event based on the target objective;
Setteboun [0193] teaches The GUI is designed for selection of multiple (e.g., up to 5 or more or fewer) optional time slots for the meeting. The options may be assigned relative ranking scores indicative of priority. Upon clicking the “choose” button near the time ranges section, a calendar is presented for selection of the most suitable time slots for meetings. Clicking a specific time slot box at any specific time in the calendar may add it as an optional time slot for the meeting The prioritization of each optional time slot may be done by arranging the options in a list (e.g., by dragging and dropping the options) where relative time slots along the list denote the relative priority of the option. Setteboun [0193]-[0194], [Figure 11 and the associated text].
Applicant’s [006] discloses where the target objective defines a high-order goal or objective. Setteboun teaches rank, therefore, Setteboun teaches a target objective (e.g. ranking meeting times).
receiving, via the chat window of the catalyst calendar interface, a selection of the selectable option;
Setteboun [0193] teaches clicking a specific time slot box at any specific time in the calendar may add it as an optional time slot for the meeting. The current user may choose a whole day meeting. The prioritization of each optional time slot may be done by arranging the options in a list (e.g., by dragging and dropping the options) where relative time slots along the list denote the relative priority of the option. Setteboun [0194] teaches region 1102 of the GUI is designed or presenting the selected time slots and/or providing a mechanism for selecting ranking scores for prioritizing the selected time slots.
generating, … a modified calendar event from the calendar event based on the selection of the selectable option;
Setteboun [0193]-[0194] teaches modifying a calendar by dragging and dropping. Setteboun [0123] teaches time slots, and relative ranking scores may be automatically computed by a classifier (e.g., neural network) based on an analysis of the user’s calendar, and a future meeting dataset of a current user organizing the meeting.
and providing the modified calendar event for display within the integrated calendar window of the catalyst calendar interface, the modified calendar event.
See Above. - Setteboun [0193]-[0194], [Figure 11 and associated text].
Although highly suggested, Setteboun, does not explicitly teach:
… via the large language model, …
Rao teaches:
of a-the target objective associated with the series of calendar events … via the large language model,
Rao discloses if they reply, their response, as well as the summary and description of the calendar event, will be given to the LLM to inform the subsequent personalized message. If no response is received within the window, a follow-up message will be sent based solely on the user’s calendar information., Rao [study design page 4].
Rao discloses in Figure 1, after users provide their preferred phone number to receive text messages and access to their Google Calendar, they are directed to their dashboard, where they can view past messages received from our system. The server selects events from a user’s calendar, generates associated messages using OpenAI’s GPT-4 model.
Setteboun teaches graphical user interfaces designed to schedule meetings. Rao discloses integrating text messaging systems with digital calendars presenting a unique opportunity to leverage information for personalized stress management interventions. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with leveraging dynamic contexts from users’ digital calendar data, interactions between users and Open AI’s GPT-4 large language model (LLM) to create time-sensitive, context-aware text messaging, as taught by Rao, to consider for a helpful, time-sensitive intervention and important design considerations for LLM-supported tools for stress reduction., Rao [abstract].
Ost further teaches:
of a-the target objective associated with the series of calendar events …
Ost [034] teaches “round robin” of calendar events. For example, the user scheduling the calendar event may specify, with the calendar event request, that the calendar event should include one person from a first group (e.g., sales) and one person from a second group (e.g., marketing) to attend. Scheduling coordinator 120 may generate a round robin group for the first group (e.g., sales) that may include more than one individual. Scheduling coordinator 120 may generate a second round robin group for the second group (e.g., marketing) that may include more than one individual. Scheduling coordinator 120 may analyze all user calendars and combine them into one temporary calendar to identify available time slots. Scheduling coordinator 120 may provide the available time slots to the candidate. When a candidate selects a time, including by use of natural language, scheduling coordinator 120 may, in turn, identify those attendees that have this time available. Scheduling coordinator 120 may then select an attendee or attendees to join, based on different factors (e.g., the attendee with the least amount of currently scheduled calendar events) in real-time. In this manner, scheduling coordinator 120 may combine logic of soonest available with load balancing functionality.
Setteboun teaches graphical user interfaces designed to schedule meetings. Ost discloses a calendar event scheduling system utilizing an artificial intelligence assistant. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with calendaring using external application (e.g., SMS, iMessage, WhatsApp, Facebook Messenger, etc,) as taught by Ost, to improve conventional approaches to scheduling software by utilizing natural language process and natural language understanding techniques., Ost [018].
Regarding Claim 5, (Currently Amended)
The computer-implemented method of claim 1, wherein determining the series of interrelated calendar events further comprises establishing at least one dependency relationship that specifies an execution order among the series of interrelated calendar events such that the calendar event is scheduled to occur before an additional calendar event. … a series of calendar events comprising the calendar event and whose completion accomplishes a target objective.
Setteboun [078] –[079] disclose the ranking and prioritizing time slots for a meeting. Setteboun [Figure 10] illustrates a calendar. Setteboun [0188] discloses prioritizing invited credentials or group categories and location.
Although highly suggested, Setteboun does not teach:
… generating, via the large language model and in response to the event input …
Rao teaches:
… via the large language model and in response to the event input …
See above. Rao Figure 1 and the associated text.
Setteboun teaches graphical user interfaces designed to schedule meetings. Rao discloses integrating text messaging systems with digital calendars presenting a unique opportunity to leverage information for personalized stress management interventions. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with leveraging dynamic contexts from users’ digital calendar data, interactions between users and Open AI’s GPT-4 large language model (LLM) to create time-sensitive, context-aware text messaging, as taught by Rao, to consider for a helpful, time-sensitive intervention and important design considerations for LLM-supported tools for stress reduction., Rao [abstract].
Ost further teaches:
wherein determining the series of interrelated calendar events further comprises establishing at least one dependency relationship that specifies an execution order among the series of interrelated calendar events such that the calendar event is scheduled to occur before an additional calendar event. further comprising generating, … a series of calendar events comprising the calendar event and whose completion accomplishes a target objective.
Ost [041] teaches scheduling coordinator 120 may be configured to schedule the interview using one or more techniques: in consecutive order, in any consecutive order, or over multiple days. For in consecutive order, scheduling coordinator 120 may be configured if there is sufficient time available for the original order of interviews. For example, if the user specified that the candidate should meet with Person A, Person B, and Person C, scheduling coordinator 120 may try to schedule the multiple interviews in the order: Person A, Person B, and Person C. If there is not enough time for the original order of interviews, scheduling coordinator 120 may determine whether there is sufficient availability for any consecutive order involving Person A, Person B, and Person C. If there is not enough time for any consecutive order involving Person A, Person B, and Person C, scheduling coordinator 120 may determine that the interviews would need to be scheduled over multiple days. In such circumstances, intelligent assistant 116 may notify the users.
Setteboun teaches graphical user interfaces designed to schedule meetings. Ost discloses a calendar event scheduling system utilizing an artificial intelligence assistant. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with calendaring using external application (e.g., SMS, iMessage, WhatsApp, Facebook Messenger, etc,) as taught by Ost, to improve conventional approaches to scheduling software by utilizing natural language process and natural language understanding techniques., Ost [018].
Regarding Claim 6, (Currently Amended)
The computer-implemented method of claim 1, further comprising providing, for display within the integrated calendar window of the catalyst calendar interface, an additional event element comprising additional rich calendar content presented within the integrated view of the calendar application, wherein the additional rich calendar content reflects an additional calendar event of the series of calendar events generated by the large language model.
See above. Setteboun Figure 10 and Rao Figure 1 and the associated text for LLM and GPT.
Setteboun [0262] teaches an automated recommendation may be created and presented to the user and/or automatically made, for example, to assign the certain user to the meeting of the group deemed more important, for example, members of the group are more likely to attend one meeting over another, and/or the certain user credential is flagged as needed with a higher score in one meeting over another. Each group has various functionalities such as group hubs where users may chat and exchange files, it is also possible to make recurring group meetings and create predefined group contacts to ease scheduling new meetings.; Setteboun [0123] - classifier.
Setteboun teaches graphical user interfaces designed to schedule meetings. Rao discloses integrating text messaging systems with digital calendars presenting a unique opportunity to leverage information for personalized stress management interventions. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with leveraging dynamic contexts from users’ digital calendar data, interactions between users and Open AI’s GPT-4 large language model (LLM) to create time-sensitive, context-aware text messaging, as taught by Rao, to consider for a helpful, time-sensitive intervention and important design considerations for LLM-supported tools for stress reduction., Rao [abstract].
Regarding Claim 7, (Currently Amended)
The computer-implemented method of claim 1, further comprising: determining, based on the event input, [[a]] the target objective corresponding to the calendar event; and wherein the event element further comprises a visual indication of a correlation between the series of calendar events calendar event and the target objective.
Setteboun [0220] teaches the GUI presents, within the priority scale, coding (e.g., color, other markings) indicative of likelihood of attendance of the meeting scheduled at the single time slot and the single location by each of the user credentials invited to attend. For example, Red denotes declined the invitation, yellow denotes inconclusive about the meeting, green denotes accepted the invitation, grey denotes yet to respond.
See above. Ost [041] series and order of events.
Setteboun teaches graphical user interfaces designed to schedule meetings. Ost discloses a calendar event scheduling system utilizing an artificial intelligence assistant. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with calendaring using external application (e.g., SMS, iMessage, WhatsApp, Facebook Messenger, etc,) as taught by Ost, to improve conventional approaches to scheduling software by utilizing natural language process and natural language understanding techniques., Ost [018].
Regarding Claim 8, (Currently Amended)
A system comprising: at least one processor; and a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to: generate, for display on a client device, a catalyst calendar interface comprising a chat window and an integrated calendar window; receive, via the chat window of the catalyst calendar interface, an event input indicating event parameters for a calendar event; generate, using a large language model and based on a target objective, a series of interrelated calendar events including at least an ordering, priority, or duration relationship among the series of interrelated calendar events relative to an existing calendar event of a calendar application; generate, based on the series of interrelated calendar events, a calendar event corresponding to the event input; cause, in response to receiving the event input, a large language model to generate the calendar event based on the event parameters; and provide, for display within the integrated calendar window of the catalyst calendar interface, an event element comprising rich calendar content presented within an integrated view of a calendar application, wherein the rich calendar content reflects the calendar event generated by the large language model and reflects an ordering, priority, or duration relationship of the calendar event relative to the existing calendar event of the calendar application.
See Claim 1. Setteboun [013], [019], [065] – chat and calendar interface
Setteboun [0122] –[0123] teaches timeslots, location, and for an invitation automatically computed by a classifier. Setteboun [0122] –[0124];
Setteboun [Fig. 2C] teaches a group chat, presenting group chat content features, and present upcoming meetings and/or events., Setteboun [Figure 2C and the associated text], [0146]-[0155].
Setteboun [066] teaches single time slot and single location may be and automatically analyzes availability of the user credentials (e.g., based on other scheduled events and/or meetings such as in a calendar).
Rao discloses LLM and Rao[system design page 3], [study design page 4], [Figure 1].
Setteboun teaches graphical user interfaces designed to schedule meetings. Rao discloses integrating text messaging systems with digital calendars presenting a unique opportunity to leverage information for personalized stress management interventions. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with leveraging dynamic contexts from users’ digital calendar data, interactions between users and Open AI’s GPT-4 large language model (LLM) to create time-sensitive, context-aware text messaging, as taught by Rao, to consider for a helpful, time-sensitive intervention and important design considerations for LLM-supported tools for stress reduction., Rao [abstract].
Ost Figure 5 – item 524, Figure 6 item 624 teach schedule a calendar event. Ost [026] discloses graphical user interface (GUI) of client device 102 and calendar.
Setteboun teaches graphical user interfaces designed to schedule meetings. Ost discloses a calendar event scheduling system utilizing an artificial intelligence assistant. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with intelligently scheduling a slate of calendar event in real-time, as taught by Ost, to improve conventional approaches to scheduling software by utilizing natural language process and natural language understanding techniques., Ost [018].
Regarding Claim 9, (Currently Amended)
The system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to provide, via the integrated calendar window, the event element comprising the calendar event scheduled for an event duration within an available time interval, wherein the available time interval is displayed in relation to a current time.
Setteboun [0184], [ Figure 10]-[Figure 11] illustrates a GUI and calendar with time slots.
Regarding Claim 10, (Currently Amended)
The system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to: determine, from one or more connectors integrating data from external computer applications, background event parameters defining scheduling preferences for generating the series of calendar events calendar event; and arrange, for display within the integrated calendar window of the catalyst calendar interface, the event element based on the event parameters and the background event parameters.
Setteboun [0184], [ Figure 10]-[Figure 11] illustrates a GUI and calendar with time slots. See above. Ost [041] series and order of events.
Setteboun teaches graphical user interfaces designed to schedule meetings. Ost discloses a calendar event scheduling system utilizing an artificial intelligence assistant. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with intelligently scheduling a slate of calendar event in real-time, as taught by Ost, to improve conventional approaches to scheduling software by utilizing natural language process and natural language understanding techniques., Ost [018].
Regarding Claim 11, (Currently Amended)
The system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to determine, utilizing the large language model, a collaborative time interval for scheduling the calendar event for a first user account and a second user account; And provide, via the chat window of the catalyst calendar interface, a selectable option to schedule the calendar event within the collaborative time interval; receive, via the chat window of the catalyst calendar interface, a selection of the selectable option; and based on the selection, provide, for display within the integrated calendar window of the catalyst calendar interface, the event element within the collaborative time interval.
See Setteboun [0184], [ Figure 10]-[Figure 12] illustrates a GUI and calendar with time slots and collaborative elements.; Setteboun [0193]- arranging a list, and therefore a selectable option.
Regarding Claim 12, (Original)
The system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to: provide, for display via the chat window of the catalyst calendar interface, a selectable element comprising a suggestion for updating the calendar application to include an additional calendar event during an available time interval; receive, via the chat window of the catalyst calendar interface, a selection of the selectable element; and provide, based on the selection of the selectable element and for display within the integrated calendar window of the catalyst calendar interface, additional rich calendar content comprising a representation of the additional calendar event during the available time interval.
See Setteboun [0184], [ Figure 10]-[Figure 12] illustrates a GUI and calendar with time slots and selectable elements.; Setteboun [0193]- arranging a list, and therefore a selectable elements.
Regarding Claim 13, (Original)
The system of claim 8, further comprising instructions that, when executed by the at least one processor, cause the system to: receive, via the chat window of the catalyst calendar interface, the event parameters comprising an indication of a priority for the calendar event; and provide, for display within the integrated calendar window of the catalyst calendar interface, an indication of the priority for the calendar event within the rich calendar content.
Setteboun [0193]-[0194] teaches arranging a list, and therefore a selectable elements.
Regarding Claim 14, (Original)
The system of claim 8, wherein the rich calendar content comprises a visual indication of a priority of the calendar event, a detailed description of the calendar event, an interactive element for the calendar event, or an integration with an external model associated with the calendar event.
Setteboun [0193]-[0194] and [Figure 11] - [Figure 12]] teaches arranging a list, and therefore a selectable elements.
Regarding Claim 15, (Currently Amended)
A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to: generate, for display on a client device, a catalyst calendar interface comprising a chat window and an integrated calendar window; receive, via the chat window of the catalyst calendar interface, an event input comprising text describing event parameters for a calendar event; generating, using a large language model and based on a target objective, a series of interrelated calendar events including at least an ordering, priority, or duration relationship among the series of interrelated calendar events relative to an existing calendar event of a calendar application; generating, based on the series of interrelated calendar events, a calendar event corresponding to the event input; in response to receiving the event input, generate the calendar event utilizing a large language model to process the event parameters; and provide, for display within the integrated calendar window of the catalyst calendar interface, an event element comprising rich calendar content presented within an integrated view of a calendar application, wherein the rich calendar content reflects the calendar event generated by the large language model and reflects an ordering, priority, or duration relationship of the calendar event relative to the existing calendar event of the calendar application.
See Claim 1. Setteboun [013], [019], [065] – chat and calendar interface
Setteboun [0122] –[0123] teaches timeslots, location, and for an invitation automatically computed by a classifier. Setteboun [0122] –[0124].
Setteboun [Fig. 2C] teaches a group chat, presenting group chat content features, and present upcoming meetings and/or events., Setteboun [Figure 2C and the associated text], [0146]-[0155].
Setteboun [066] teaches single time slot and single location may be and automatically analyzes availability of the user credentials (e.g., based on other scheduled events and/or meetings such as in a calendar).
Rao discloses LLM and Rao[system design page 3], [study design page 4], [Figure 1].
Setteboun teaches graphical user interfaces designed to schedule meetings. Rao discloses integrating text messaging systems with digital calendars presenting a unique opportunity to leverage information for personalized stress management interventions. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with leveraging dynamic contexts from users’ digital calendar data, interactions between users and Open AI’s GPT-4 large language model (LLM) to create time-sensitive, context-aware text messaging, as taught by Rao, to consider for a helpful, time-sensitive intervention and important design considerations for LLM-supported tools for stress reduction., Rao [abstract].
Ost Figure 5 – item 524, Figure 6 item 624 teach schedule a calendar event. Ost [026] discloses graphical user interface (GUI) of client device 102 and calendar.
Setteboun teaches graphical user interfaces designed to schedule meetings. Ost discloses a calendar event scheduling system utilizing an artificial intelligence assistant. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with intelligently scheduling a slate of calendar event in real-time, as taught by Ost, to improve conventional approaches to scheduling software by utilizing natural language process and natural language understanding techniques., Ost [018].
Regarding Claim 16, (Original)
The non-transitory computer readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to: provide, for display within the integrated calendar window, an indication of an available time interval within the calendar application; and provide, for display within the chat window, a selectable option for scheduling an additional calendar event during the available time interval.
Setteboun [0193]-[0194] and [Figure 11] - [Figure 12] teaches arranging a list, and therefore a selectable elements.
Setteboun [061] teaches the preferences for time slots and/or locations of user credentials located relatively higher in the priority scale are assigned greater weight than preferences for time slots and/or locations of user credentials located relatively lower in the priority scale.
Regarding Claim 17, (Currently Amended)
The non-transitory computer readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to: provide, for display via the chat window of the catalyst calendar interface, a selectable option to generate display an additional calendar event associated with the calendar event of the series of calendar events generated by the large language model; receive, via the chat window of the catalyst calendar interface, a selection of the selectable option to generate the additional calendar event; and in response to the selection of the selectable option, generate display the additional calendar event for display within the integrated calendar window of the catalyst calendar interface utilizing the large language model.
Setteboun [0193]-[0194] and [Figure 11] - [Figure 12] teaches arranging a list, and therefore a selectable elements.
Regarding Claim 18, (Original)
The non-transitory computer readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to: provide, for display within the integrated calendar window of the catalyst calendar interface, a notification of a priority level associated with a scheduled calendar event; and provide, via the chat window, a selectable option to generate the calendar event to replace the scheduled calendar event based on the priority level.
Setteboun [0193]-[0194] and [Figure 11] - [Figure 12] teaches arranging a list, and therefore a selectable elements.
Setteboun [0220] teaches the GUI presents, within the priority scale, coding (e.g., color, other markings).
Regarding Claim 19, (Currently Amended)
The non-transitory computer readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to provide, for display within the integrated calendar window of the catalyst calendar interface, an update to the rich calendar content visually for the event element to reflect an association between the calendar event and [[a]] the target objective.
Setteboun [0193]-[0194] and [Figure 11] - [Figure 12] teaches arranging a list, and therefore a selectable elements such as time slots.
Regarding Claim 20, (Currently Amended)
The non-transitory computer readable medium of claim 15, further comprising instructions that, when executed by the at least one processor, cause the at least one processor to: provide, for display on the client device, a set of recommended calendar events the series of calendar events generated by the large language model comprising an additional calendar event; receive, via the chat window of the catalyst calendar interface, a selection of the additional calendar event; and provide, for display within the integrated calendar window of the catalyst calendar interface, an additional event element comprising additional rich calendar content presented within the integrated view of the calendar application, wherein the additional rich calendar content reflects the additional calendar event generated by the large language model.
Similar to claim 15. See Setteboun [013], [019], [065], [Figure 2C], [0146]-[0155] – chat and calendar interface. Setteboun [0184], [ Figure 10]-[Figure 11] illustrates a GUI and calendar. Setteboun [0122]-[0124] teaches classifier.
Rao discloses LLM and Rao[system design page 3], [study design page 4], [Figure 1].
Setteboun teaches graphical user interfaces designed to schedule meetings. Rao discloses integrating text messaging systems with digital calendars presenting a unique opportunity to leverage information for personalized stress management interventions. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with leveraging dynamic contexts from users’ digital calendar data, interactions between users and Open AI’s GPT-4 large language model (LLM) to create time-sensitive, context-aware text messaging, as taught by Rao, to consider for a helpful, time-sensitive intervention and important design considerations for LLM-supported tools for stress reduction., Rao [abstract].
Ost Figure 5 – item 524, Figure 6 item 624 teach schedule a calendar event. Ost [026] discloses graphical user interface (GUI) of client device 102 and calendar.
Setteboun teaches graphical user interfaces designed to schedule meetings. Ost discloses a calendar event scheduling system utilizing an artificial intelligence assistant. It would have been obvious to combine before the effective filing date using a neural network to classify time slots, as taught by Setteboun, with intelligently scheduling a slate of calendar event in real-time, as taught by Ost, to improve conventional approaches to scheduling software by utilizing natural language process and natural language understanding techniques., Ost [018].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/THEA LABOGIN/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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