Detailed Action
This Office Action is in response to the remarks entered on 01/06/2026. Amended claims 1, 19, and 20 have been entered. Claims 1, 3-11, and 14-20 are currently 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 .
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.
Claims 1, 3-6, 10-11, 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vaananen (US 20200334642 A1, hereinafter ‘Vaananen’) in view of Lindner (US 20190311300 A1, hereinafter ‘Lindner’) and further in view of MCBRIDE et al., (US 20200302358 A1, hereinafter ‘MCBRIDE’).
Regarding claim 1, Vaananen teaches:
A method for predicting meeting availability for a user, comprising: ([Vaananen, 0133] In phase 406, the cloud based intelligent secretary application performs calculation and determines the suggested meeting means, place, and time based on many parameters including user profiles, salary, past behavior in meetings or past behaviors in scheduling meetings.)
accessing: a calendar associated with a user of a communication platform, comprising a schedule of one or more scheduled future meetings on the communication platform, information associated with a plurality of past requested meetings associated with the user of the communication platform, and a user behavioral profile associated with the user, comprising a plurality of user behaviors associated with the plurality of past requested meetings; ([Vaananen, 0066; 0085; 0153] All data relating to activities of the members including routine activities, travel to office, lunch time, home, sport time are accessed by the system (a plurality of user behaviors). [Vaananen, 0131] In phase 402, the cloud based intelligent secretary application receives the information from cloud accounts of the user, the calendar applications (calendar associated with the user), the email applications, mobile device locations, and/or the reminder applications (information associated with a plurality of past requested meetings, as reminder applications reminds the user about approaching meeting schedule), by gaining access to the information.)
receiving a meeting request for the user; ([Vaananen, 0132] In phase 404, the intelligent secretary application receives a meeting request from a member of the said group.)
determining an earliest available time slot for the requested meeting; ([Vaananen, 0133] In phase 406, the cloud based intelligent secretary application performs calculation and determines the suggested meeting means, place, and time based on many parameters including user profiles, salary, past behavior in meetings or past behaviors in scheduling meetings. [Vaananen, 0174] The cloud based intelligent secretary application schedules the meeting at earliest available slot.)
determining that at least one scheduled future meeting of the one or more scheduled future meetings are scheduled earlier than the earliest available time slot for the requested meeting; ([Vaananen, 0065] The application triggers scheduling operation by accessing all events scheduled in a particular month, dates and timings of such events through a calendar application of the member (determining the scheduled meetings that are scheduled earlier than the available time slot). [Vaananen, 0119 and 0169] The application determines whether the ‘occupied’ time slots (scheduled future meetings scheduled earlier than the available time) that is the earliest and available for all people in the group, and figure out whether the occupied time slot is actually available or not by comparing weights of events. The application considers the scheduled meeting and provide weight to the occupied schedule slot as well as empty slots. [Vaananen, 0161 and 0166] discloses determining whether the earliest option 1: 1:30PM Monday is available before option 2. The paragraph also discloses enabling Sally to reschedule Joe’s early scheduled activity if Joe is not available for the option 2. [Vaananen, 0174] The application determines to schedule a meeting at earliest available slot)
for each scheduled future meeting of the one or more scheduled future meetings which are earlier than the earliest available meeting time slot, deploying an artificial intelligence (AI) model to: ([Vaananen, 0140] The cloud based intelligent secretary application manages the meetings based on the weights of the parameters. The cloud based intelligent secretary application varies the weights of the parameters over a period of time by learning from user preferences, user behaviors, etc.)
analyze the user behavioral profile with respect to the one or more similar past meeting requests; and ([Vaananen, 0135-0136] User profiles are weighted based on past behaviors or past behavior in scheduling meetings. [Vaananen, 0137] If a user behaves terribly within a group, the profile weight of the user is decreased for meetings in other groups as well. The meeting scheduled for the user in the first group and the meeting scheduled for the user in other groups are interpreted as ‘similar meeting’ as both are group meetings)
based on the analysis of the user behavioral profile, generate an availability prediction score for the scheduled future meeting, the availability prediction score indicating a likeliness of the user being available for the requested meeting during a time slot for the scheduled future meeting; and ([Vaananen, 0133] The cloud based intelligent secretary application performs weighted average calculation based on various parameters. Different weights (availability prediction score) are given to different user profiles based on profile attributes and past behavior in meetings or past behavior in scheduling meetings. [Vaananen, 0136] The weights are assigned based on past user behavior. For example, a user who generally accepts the meeting invitation will be given higher weight as compared to a user who declines or request for meeting rescheduling. [Vaananen, 0140] The cloud based intelligent secretary application varies the weights of the parameters over a period of time by learning from user preferences, user behaviors, etc.)
identifying one or more predicted available times for the user to attend the requested meeting based on availability prediction scores for the one or more scheduled future meetings, wherein the AI model is trained using user behavioral data associated with a set of users on the communication platform with respect to a set of past requested meetings and information [Vaananen, 0140] The cloud based intelligent secretary application manages the meetings based on the weights of the parameters. The cloud based intelligent secretary application varies the weights of the parameters over a period of time by learning from user preferences, user behaviors, etc. [Vaananen, 0141] In phase 408, the application generates a suggestion and provides an option to members of the group to accept or decline the suggested schedule)
automatically creating a user interface (UI) component at a predicted available time selected from the one or more predicted available times, in a calendar UI associated with the user for scheduling the requested meeting with the user; ([Vaananen, 0150] The AI module 516 performs calculation and determines the suggested meeting means, place and time. [Vaananen, 0151 and 0152; Fig. 6] The suggestion module 518 of the cloud provides a meeting schedule suggestion including the meeting means, place and time, based on the calculation. The suggestion is provided as an option which is interpreted as an UI component. The figure 6 clearly shows suggestion UI that shows date, place, time and means 640 included in the calendar UI of 610)
fine-tuning the Al model using the predicted available time ([Vaananen, 0075] discloses updating the self-learning files and/or databases for future considerations/iterations when a user declined the suggested meeting invitation suggested using the suggestion module 518 for specific time and date)
Vaananen does not specifically disclose:
identify one or more similar past meeting requests based on the scheduled future meeting exceeding a similarity threshold with respect to one or more past requested meetings; wherein the AI model is trained using user behavioral data associated with a set of users on the communication platform with respect to a set of past requested meetings and information whether predicted available times that were selected for the set of past requested meetings were times in which users actually attended the set of past requested meetings; fine-tuning the Al model using the user behavior data with respect to whether the user attends the requested meeting via reinforcement learning.
Lindner teaches:
identify one or more similar past records (meeting requests) based on the scheduled future meeting exceeding a similarity threshold with respect to one or more past requested meetings. ([Lindner, 0097] At step 842, if the similarity score for at least two records meets or exceeds the similarity score threshold, the similar records (i.e., records that met or exceeded the similarity score threshold) are linked, or combined into a group. Identifying past records is taught by [Vaananen, 0131])
Before the effective filing date of the invention to a person of ordinary skill in the art, it would have been obvious, having the teachings of Vaananen and Lindner to use the method of identifying similar past records (meeting requests) based on the scheduled future meeting exceeding a similarity threshold of Lindner to implement the meeting availability prediction method of Vaananen. The suggestion and/or motivation for doing so is to improve the computation speed of the meeting availability prediction machine learning model by setting numerical thresholds, as setting thresholds makes the machine learning model easier to distinguish relevant data and non-relevant data.
Vaananen in view of Linder does not specifically disclose:
wherein the AI model is trained using user behavioral data associated with a set of users on the communication platform with respect to a set of past requested meetings and information whether predicted available times that were selected for the set of past requested meetings were times in which users actually attended the set of past requested meetings, wherein whether the user actually attended the set of past requested meetings at the predicted available times indicates importance levels of the set of past requested meetings, and wherein the past requested meetings are related to past meetings requests from other users;
fine-tuning the Al model using the user behavior data with respect to whether the user attends the requested meeting via reinforcement learning.
MCBRIDE teaches:
wherein the AI model is trained using user behavioral data associated with a set of users on the communication platform with respect to a set of past requested meetings and information whether actually attended the set of past requested meetings, wherein whether the user actually attended the set of past requested meetings ; ([0011] The appointment schedule 110 is the available time that were selected (by the patient) for the past requested meetings, and the database includes information about who scheduled the appointment, the previous appointments and whether the patients showed up. The no-show may indicate the importance of the appointment, because the symptoms and reasons (i.e., importance level) for scheduling the appointment are potential indicators 130 of no-show probability. [0014] The database is the communication platform as the no-show probability is communicated to the user, and [0016] outputs mitigating response 160 depending on the calculated probability, such as sending a reminder, a request for confirmation, or scheduling additional appointment. ‘Scheduling additional appointment’ may be performed automatically or manually by the user (e.g., doctor) based on specific factors. The existing appointment (made by patients) are related to additional appointments (past meetings requested from other users, such as the doctor) as the existing appointments are created based on the existing appoitment. [0017-0018] discloses training the AI model 280 utilizing reinforcement learning. The no-show probability calculated using the current algorithm is compared against whether the user actually attended the appointment to re-train the algorithm 140)
fine-tuning the Al model using whether the user attends the requested meeting via reinforcement learning. ([0018] discloses training the AI 280 utilizing reinforcement learning. The no-show probability calculated using the current algorithm is compared against whether the user actually attended the appointment to re-train the algorithm 140)
Before the effective filing date of the invention to a person of ordinary skill in the art, it would have been obvious, having the teachings of Vaananen and MCBRIDE to use the method of training the AI model using information about whether the users actually attended the set of past requested meetings of MCBRIDE to implement the meeting availability prediction method of Vaananen. The suggestion and/or motivation for doing so is to improve the accuracy of the meeting availability prediction machine learning model by using more diverse types of input data about meeting attendance of users.
Regarding claim 3, Vaananen teaches further comprising: receiving notification that the user declined the one or more predicted available times for the requested meeting; and sending, to the user, a new proposed time and an option to accept or decline the new proposed time. ([Vaananen, 0141] In phase 408, the cloud based intelligent secretary application provides an option to members of the group to accept or decline the suggested meeting schedule. Once the meeting suggestion is declined (receiving notification that the user declined the meeting schedule), the cloud based intelligent secretary application initiates a calculation for another meeting suggestion)
Regarding claim 4, Vaananen teaches wherein at least a subset of the plurality of user behaviors associated with the plurality of past requested meetings relates to accepting or declining the plurality of past requested meetings. ([Vaananen, 0136] The weights are assigned based on past user behavior. For example, a user who generally accepts the meeting invitation will be given higher weight as compared to a user who declines or request for meeting rescheduling)
Regarding claim 5, Vaananen in view of Lindner and further in view of MCBRIDE teaches:
wherein at least a subset of the user behaviors associated with the plurality of past requested meetings relates to attending or not attending the plurality of past requested meetings that have been accepted. ([0011] The datasets which include demographics of the patients, the patients’ engagement with the office, previous appointments and whether the patient showed up, the cost of the appointments, diagnoses of the patients … are the subset of user behaviors associated with whether the patient will show up or no-show. The appointment is interpreted as ‘accepted meetings’ because the patient has to first schedule and accept the appointment time and date)
Regarding claim 6, Vaananen in view of Lindner and further in view of MCBRIDE teaches:
wherein at least a subset of the user behaviors associated with the plurality of past requested meetings relates to the user participating or not participating in the plurality of past requested meetings that were attended by the user. ([0011] The datasets which include demographics of the patients, the patients’ engagement with the office, previous appointments and whether the patient showed up, the cost of the appointments, diagnoses of the patients … are the subset of user behaviors associated with whether the patient will show up or no-show. The appointment is interpreted as ‘accepted meetings’ because the patient has to first schedule and accept the appointment time and date)
Regarding claim 10, Vaananen teaches wherein at least a subset of the user behaviors associated with the plurality of past requested meetings relates to likelihood the user will attend a meeting with one or more specific additional users, and wherein the one or more specific additional users are users within a particular hierarchy or subhierarchy of an organization. ([Vaananen, 0135] The people within corporate, business and/or public service may be given different weights based on their professional ranks, importance, and profile time requirements. The weight is interpreted as the likelihood the user will attend a meeting. A CEO of an organization would be given higher weight as compared to an executive in the same organization)
Regarding claim 11, Vaananen teaches wherein the one or more predicted available times are provided in one of: descending order of corresponding predicted availability scores; or chronological order wherein a rating can be assigned to each of the one or more predicted available times. ([Vaananen, 0199] The meeting suggestions may be ranked based on amount of disruption and costs, which corresponds to availability score)
Regarding claim 14, Vaananen teaches further comprising: providing, to the user, a decision digest comprising one or more decisions for scheduling requested meetings based on corresponding predicted available times. ([Vaananen, 0141] In phase 408, the cloud based intelligent secretary application provides an option, to members of the group who are provided with the suggestion about the meeting means, place and time, to accept or decline the suggestion. The suggestion about the meeting means, place and time is the decision digest)
Regarding claim 15, Vaananen teaches further comprising: determining that an option to inform a host of a previously scheduled meeting about a decision related to the previously scheduled meeting has been enabled; and providing a notification to the host about the decision related to the previously scheduled meeting. ([Vaananen, 0141] The host (who initiated the meeting) can be notified about the decision related to the scheduled meeting, as the cloud based intelligent secretary application drops the meeting suggestion and initiate a calculation for another meeting suggestion to suggest another time)
Regarding claim 16, Vaananen teaches wherein receiving the meeting request for the user comprises: receiving an input from an additional user of the communication platform within a scheduling interface, the input relating to scheduling the requested meeting within at least the calendar associated with the user. ([Vaananen, 0063] The cloud based intelligent secretary application receives a request to schedule a meeting with at least two members of the group. the cloud based intelligent secretary application reads an email communication addressed to the member from another member requesting for a meeting. The cloud based intelligent secretary application interprets the email seeking for the meeting, as a meeting request. [Vaananen, 0065] The application triggers scheduling operation by accessing all events scheduled in a particular month, dates and timings of such events through a calendar application of the member (the input relating to scheduling the requested meeting within the calendar).)
Regarding claim 17, The method of claim 1, wherein receiving the meeting request for the user comprises: receiving a message for the user related to the requested meeting; sending, to the user, the message and an option to accept or decline the requested meeting; and receiving, from the user, an acceptance of the requested meeting. ([Vaananen, 0063] The cloud based intelligent secretary application receives a request to schedule a meeting with at least two members of the group. the cloud based intelligent secretary application reads an email communication addressed to the member from another member requesting for a meeting. The cloud based intelligent secretary application interprets the email seeking for the meeting, as a meeting request (receiving message for the user). [Vaananen, 0141] In phase 408, the application generates a suggestion and provides an option to members of the group to accept or decline the suggested schedule)
Regarding claim 18, Vaananen in view of Lindner teaches wherein determining that the scheduled record (future meeting) meets or exceeds a similarity threshold with respect to the plurality of past requested meetings comprises determining that the scheduled record (future meeting) is a recurring record (meeting) that has been requested for the user at least once before. ([Lindner, 0097] At step 842, if the similarity score for at least two records meets or exceeds the similarity score threshold, the similar records (i.e., records that met or exceeded the similarity score threshold) are linked, or combined into a group. Identifying past records is taught by [Vaananen, 0131])
Regarding claim 19, Vaananen teaches a communication system comprising one or more processors configured to perform the operation of: ([Vaananen, 0145] The system comprises a processor, a GPU, and a memory. [Vaananen, 0146] The cloud based intelligent secretary application is stored on a non-transient memory medium)
Claim 19 is a system claim having similar limitation to the claim 1. Therefore, the claim is rejected under the same rationale as the claim 1 above.
Regarding claim 20, Vaananen teaches a non-transitory computer-readable medium containing instructions for predicting meeting availability for a user, comprising instructions for: ([Vaananen, 0146] The cloud based intelligent secretary application is stored on a non-transient memory medium)
Claim 20 is a non-transitory computer-readable medium claim having similar limitation to the claim 1. Therefore, the claim is rejected under the same rationale as the claim 1 above.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Vaananen in view of Lindner in view of MCBRIDE and further in view of Hansson (“AI Meeting Monitoring”, hereinafter ‘Hansson’).
Regarding claim 7, Vaananen in view Lindner and further in view of MCBRIDE teaches:
The claim 6.
Vaananen in view Lindner and further in view of MCBRIDE does not specifically disclose wherein participating comprises one or more of: contributing vocally to the meeting, broadcasting a video feed, screen sharing, document collaboration, and textual messaging within a meeting interface.
Hansson teaches:
wherein participating comprises one or more of: contributing vocally to the meeting, broadcasting a video feed, screen sharing, document collaboration, and textual messaging within a meeting interface. ([Hansson, page 38, para 5.2.2 Audio feed, line 2-11] The transcribe function detect words, and detect who said the words with the help of the video feed (contributing vocally to the meeting) disclosed in [Hansson, page 38, para 5.2.1 Video feed, line 1-6])
Before the effective filing date of the invention to a person of ordinary skill in the art, it would have been obvious, having the teachings of Vaananen, Lindner, MCBRIDE and Hansson to use the method of utilizing a subset of the user behaviors associated with past requested meetings relates to the user participating or not participating in the past requested meetings that were attended by the user of Hansson to implement the meeting availability prediction method of Vaananen. The suggestion and/or motivation for doing so is to improve the performance of the meeting availability prediction machine learning model by utilizing more diverse data about whether the user really attended or participated in the meeting.
Regarding claim 8, Vaananen in view of Lindner and further in view of MCBRIDE teaches the method of claim 1.
Vaananen in view of Lindner and further in view of MCBRIDE does not specifically disclose:
wherein at least a subset of the user behaviors associated with the plurality of past requested meetings relates to user engagement within the plurality of past requested meetings that were attended by the user.
Hansson teaches:
wherein at least a subset of the user behaviors associated with the plurality of past requested meetings relates to user engagement within the plurality of past requested meetings that were attended by the user. ([Hansson, page 38, para 5.2.1 Video feed, line 1-6] The video feed is used to gather data about the participants, such as the number of participants and who participated. A face detection model is going to identify the number of people present and another model identifies who that person was.)
Before the effective filing date of the invention to a person of ordinary skill in the art, it would have been obvious, having the teachings of Vaananen, Lindner, MCBRIDE and Hansson to use the method of utilizing a subset of the user behaviors associated with past requested meetings relates to the user participating or not participating in the past requested meetings that were attended by the user of Hansson to implement the meeting availability prediction method of Vaananen. The suggestion and/or motivation for doing so is to improve the performance of the meeting availability prediction machine learning model by utilizing more diverse data about whether the user really attended or participated in the meeting.
Regarding claim 9, Vaananen in view Lindner in view of MCBRIDE and further in view of Hansson teaches:
wherein user engagement comprises one or more of: visual engagement via eye tracking, whether a meeting is within an active window of a user environment, and percentage of the meeting attended by the user. ([Hansson, page 38, para 5.2.1 Video feed, line 1-6] The video feed is used to gather data about the participants, such as the number of participants and who participated. A face detection model is going to identify the number of people present and another model identifies who that person was (whether the meeting is within an active window of the user environment, and percentage of the meeting attended).)
Response to Arguments
Response to Arguments under 35 U.S.C. 103
Arguments: Applicant asserts that (a) the suggested event in Singh is a recommendation not a request, and it is not associated with a past meeting request from another user, as specified in amended claim 1, (b) the suggested event that the user actually selected and attended reflected how far a user is willing to travel, and (c) Singh does not even disclose or make obvious the information in amended claim 1. [Remarks, pages 9-10]
Response to Arguments: Applicant’s arguments, see [Remarks, pages 9-10], filed 01/06/2026, with respect to the rejection(s) of claim(s) 1, 3-11, and 14-20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of MCBRIDE et al., (US 20200302358 A1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kozierok, “A LEARNING INTERFACE AGENT FOR SCHEDULING MEETINGS” (This prior art is pertinent because it discloses training a reinforcement learning model based on whether a user accepted or declined the requested meeting schedules)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUN KWON whose telephone number is (571)272-2072. The examiner can normally be reached Monday – Friday 7:30AM – 4:30PM ET.
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/JUN KWON/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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