DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The Amendment filed on November 24, 2025 has been entered. The examiner acknowledges the amendments to claims 1, 4, 9, 15-20, the cancellation of claim 5, and the addition of claim 21.
Previous rejections under 35 U.S.C § 101: Applicant’s amendments to claims 15-20 have rendered those claims to be statutory subject matter, thus earlier rejection under 35 U.S.C § 101 for these claims is withdrawn.
Rejections under 35 U.S.C. § 101: Applicant argues that amended claims provide technological solutions to technological problems. Examiner disagrees. Specifying the machine learning model as a neural network to generate a mathematical model to analyze content provides an example of applying software on a generic processor. Although it may improve data processing as an input to the processor, the technological improvement is not evident. The addition of detail on the OCR classifier providing output as an input for the speech recognition classifier appears to improve inputs without a change to the underlying technology.
Additionally, using the MLMs to calculate priority scores associated with meetings to provide the importance of the meeting may have utility, but it is not clear how this solves a technical problem associated with determining a meeting to attend. As argued, the invention generates ‘meeting-specific suggestions,’ and in reality, they are only suggestions with no control or action to be taken to achieve an outcome based on the knowledge gleaned. It is also argued that a reduction in network traffic may be gained, but these appear to be inconsequential levels of reduction.
An additional argument amplifies the classifiers representing a closed loop that allows further training and output by the MLM to produce a priority score for a meeting. The Examiner questions the idea of a closed loop for further training in instances where the meeting planned is the initial meeting for a topic or subject. It is not clear how this would be trained. The specification, [0056], cites “factors” that might be considered, including number of invitees, roles/positions, previous attendance, late arrival, the use of reminders, and who joined after a reminder was sent. It is not clear how these factors, seemingly not related to any specific meeting topic, would contribute to a meeting priority, except for roles/positions- if the CEO calls a meeting, the priority for attendance becomes quickly apparent. Additional factors listed include administrator override (not defined), meeting title and description, and meeting importance which appears to be synonymous with the desired output of the invention- the priority score. The argument of closed loop training is not compelling, as any specific theme to build a knowledge base upon is not apparent. If there is something deeper in the underlying analytical plan than what has already been provided, it needs to be disclosed to continue prosecution. In its current state, it is not apparent that a practical application exists and as a result, the rejections under 35 U.S.C. § 101 will not be withdrawn.
Rejections under 35 U.S.C. § 103: Applicant argues that Lu does not teach the OCR functions of claim 1. Examiner disagrees noting that OCR uses various image processing and pattern recognition algorithms and Lu clearly states the use of image recognition algorithms. Applicant additionally argues that prior art does not teach that “the OCR classifier automatically outputs the printed text to serve as an input for the speech recognition classifier.” Examiner first notes that the claim of “automatic” input of OCR output to the speech recognition classifier does not appear to be supported in the specification. In addition, the OCR inputs appear to be one among a number of additional or alternative classifiers to analyze different content types received, [0058]. This does not appear to be any different than Lu’s teaching the capture of virtual session data via speech recognition, NLP, objects or text in images using OCR or object recognition, voice tone in spoken audio and emotion, all as input to a determination of a priority score for a meeting. Lacking additional disclosure of the invention’s prioritization process, the Examiner disagrees that claim 1 is not obvious in view of Bhattacharya and Lu, and as a result, rejections for independent claim 1, and corresponding claims 9 and 15 will not be withdrawn.
Additional arguments for the dependent claims 2-4, 6, 10-12, 14 and 16-18, and 7 and 19, 8 and 20 based on different prior art and based on their dependency from claims 1, 9, and 15, are not compelling in view of the continued rejection of the independent claims. Rejections to these claims based on 35 U.S.C. § 103 will not be withdrawn.
Claim Rejections – 35 U.S.C. § 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.
Claims 1-4, 6-21, are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. The claims, 1-4, 6-21, are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-4, 6-21, are directed to a process (method), machine (system), and product/article of manufacture, which are statutory categories of invention.
Step 2A
Claims 1-4, 6-21, are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of organizing and prioritizing meetings for people and sending and receiving responses to the invitations.
Claim 1 discloses a method, comprising: A method, the method comprising:
receiving, from a user a request to generate a meeting between additional users wherein the user is included in, or excluded from, the additional users; (following rules or instructions, observation, evaluation, judgment, opinion, managing personal behavior),
retrieving, meeting data indicating previously conducted meetings between users and factors associated with the previously conducted meetings; (following rules or instructions, observation, evaluation, judgment, opinion, managing personal behavior),
inputting, as training data, the meeting data into a model to output priority scores indicating an importance of the previously conducted meetings and to generate a trained model and to analyze at least one of speech or audio included in one or more meeting requests to identify the factors; (following rules or instructions, observation, evaluation, judgment, opinion),
identify one or more topics associated with the previously conducted meetings; and analyze images representing printed text to determine the printed text, (following rules or instructions, observation, evaluation, judgment, opinion, managing personal behavior),
inputting the request to generate the meeting (following rules or instructions, observation, evaluation, judgment, opinion),
receiving, a determined importance of the meeting; (following rules or instructions, observation, evaluation, judgment, opinion),
determining, based at least in part on one or more calendars associated with the additional users, an availability of the additional users; (following rules or instructions, observation, evaluation, judgment, opinion, mitigating risk)
sending, to the additional users, an invitation to the meeting, wherein the invitation includes the priority score; (following rules or instructions, observation, evaluation, judgment, opinion, managing personal behavior) and
receiving at least one response to the invitation from at least one additional user of the additional users, (following rules or instructions, observation, evaluation, judgment, opinion).
Additional limitations further characterize the method including elements of the meeting priority score: urgency, priority, duration, reoccurrence of the meeting or position or role of an additional user, (following rules or instructions, observation, evaluation, judgement, opinion, managing personal behavior - claim 2), providing for updating the priority score, (following rules or instructions, observation, evaluation, judgement, opinion - claim 3), identifying a conflict for a user and sending a notification of the conflict, determining based on availability, that the number of users is below a minimum threshold and determining a second meeting time, which is sent to the organizer as a suggested new meeting time, (following rules or procedures, observation, evaluation, judgement, opinion, mitigating risk, managing personal behavior - claim 4), determining that a user has a conflict with the meeting and sending them a notification of the conflict (following rules or procedures, observation, evaluation, judgement, opinion, mitigating risk, managing personal behavior – claim 6), sending reminders to users that have not yet joined that the meeting has started (following rules or procedures, observation, evaluation, judgement, opinion, mitigating risk, managing personal behavior - claim 7), and enabling response that accept, decline, or propose a new time for the meeting, (following rules or procedures, observation, evaluation, judgement, opinion, mitigating risk, managing personal behavior – claim 8), determining based on the priority score and at least one response to the invitation, a conflict with respect to the meeting and cancelling the meeting, (following rules or procedures, observation, evaluation, judgement, opinion, mitigating risk, managing personal behavior, claim 21).
Claims 9-20 recite similar abstract ideas as those identified with respect to claims 1-4, 6-8, 21.
Thus, the concepts set forth in claims 1-4, 6-21, recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-4, 6-21, recite additional limitations which are hardware or software elements such as, one or more computing devices of a group-based communication platform, one or more servers associated with the group-based communication platform, a machine-learning model, the machine-learning model is an artificial neural network that is implemented by one or more server devices and generates a mathematical model using the training data, one or more classifiers trained to analyze different content types associated with the meeting data, a text classifier, an optical character recognition (OCR) classifier trained to automatically output the printed text to serve as input for the speech recognition classifier; one or more non-transitory computer-readable media, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-4, 6-21, are directed to abstract ideas.
Step 2B
Claims 1-4, 6-21, do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-4, 6-21, that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §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 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.
Claims 1-3, 6, 9-11, and 14-18, are rejected under 35 U.S.C. § 103 as being taught by
Bhattacharya (US 2020/0293999 A1), hereafter Bhattacharya, “Artificial Intelligence for Calendar Event Conflict Resolution,” in view of Lu, (US 11558438 B1), hereafter Lu, “Status Prediction for Meetings and Participants.”
Regarding Claim 1, A method, implemented at least in part by one or more
computing devices of a group-based communication platform, Bhattacharya teaches, (systems, methods and devices for prioritizing calendar events with artificial intelligence are presented, [Abstract]), the method comprising:
receiving, from a user associated with the group-based communication platform, a request to generate a meeting between additional users of the group-based communication platform, (Receive Event Scheduling Request (502) [FIG. 5], and the systems, methods, and devices described herein provide technical advantages for scheduling calendar events, [0019]), wherein the user is included in, or excluded from, the additional users; (Bhattacharya teaches (the meeting organizer may be one of attendees, [0031]),
retrieving, from one or more servers associated with the group-based communication
platform, meeting data indicating previously conducted meetings between users of the group-based communication platform (In some examples, network and processing sub-environment 114 may host an electronic calendar service and/or a digital assistant service associated with an electronic calendar service. These services maybe hosted by one or more server computing devices, such as server computing device 118, [0020]; The electronic calendar service may store past events, pending events, and future events for users of the service, and information about those events (duration of event, location of event, invitees, attendees, whether an event was canceled, whether an event was moved, etc.), [0039], The electronic calendar service may extract these features from information included in an event invite/request, an electronic message containing an event invite/request, and/or one or more user accounts associated with the electronic calendar service, [0023], and factors associated with the previously conducted meetings; (the parameters associated with the calendar event itself that are utilized to generate an event priority score may include one or more of: a time when an event was booked; an agenda associated with an event; a duration associated with an event; a location where an event is to be held; a type of attendance requested for an event, a time of day that an event is to be held; a day of the week when an event is to be held; and/or an event history of an event, [0023]. The user store may include information associated with electronic calendar users, including: a seniority of a user within an organization; a tenure of a user within an organization; a salary range of a user within an organization; performance review data for a user within an organization; a geographic office location of a user; and/or an organizational title of a user. One or more of these attributes may be utilized by the electronic calendar service in generating an event priority score for an event, Bhattacharya, [0024]).
inputting, as training data, the meeting data into a machine-learning model to output
priority scores indicating an importance of the previously conducted meetings and to generate a trained machine-learning model, (FIG. 4 is a diagram 400 illustrating the training of a feature selection machine learning model based on human interaction, which causes event priority scores for a plurality of events to be modified. The machine learning model applied in generating the event priority scores may be trained with user feedback. For example, when a user accepts/agrees that an existing event should be replaced with a new event based on the new event having a calculated event priority score that is higher than the existing event, an unsupervised clustering model may be applied to cluster events with similar features to one or both of those events for making improved suggestions in the future, [0017]. The machine learning model may be trained to generate a score reflecting the relative importance of a calendar event, [0025]), wherein the machine-learning model is an artificial neural network that is implemented by one or more server devices, that generates a mathematical model using the training data, and that includes one or more classifiers trained to analyze different content types associated with the meeting data, wherein the one or more classifiers include:
a speech recognition classifier trained to analyze at least one of speech or audio
included in one or more meeting requests to identify the factors;
a text classifier trained to identify one or more topics associated with the previously
conducted meetings; and
an optical character recognition (OCR) classifier trained to analyze images
representing printed text to determine the printed text, wherein OCR classifier automatically outputs the printed text to serve as input for the speech recognition classifier; Bhattacharya does not teach, Lu teaches, (a supervised machine learning classification model may be trained to predict the emotion or state of mind of individual participants, [7: 54-56], obtaining a plurality of virtual meeting information from a server, [1:32-33]. One or more of the following machine learning algorithms may be used: logistic regression, naive Bayes, support vector machines, deep neural networks, random forest, decision tree, gradient-boosted tree, multilayer perceptron, and one-vs-rest, [7:56-60], to determine a meeting topic and participant emotion, any of several techniques may be used to analyze the virtual meeting session data that has been captured. For instance, image recognition algorithms may be used with extracted images from the captured video of the meeting session to determine specific body language or other known visual cues such as facial expressions or eye movements to determine a participant's level of interest at any given time during the meeting session. In the same way, any text conversation that may be captured in the course of the virtual meeting session may be analyzed for content that may indicate a participant's emotion or interest in the current meeting topic, Lu, [8:6-18], and using techniques such as optical character recognition (OCR) or object recognition algorithms, the current image on the screens of the currently connected participants may be detected and analyzed for relevant content, [8:19-22], and Automatic speech recognition (ASR) techniques in conjunction with speech-to-text (STT)algorithms may also be used to analyze the captured audio from the virtual meeting session to determine meeting progress, [8: 38-41]).
inputting the request to generate the meeting into the trained machine-learning model; receiving, from the trained machine-learning model, a priority score associated with the meeting, wherein the priority score represents a determined importance of the meeting; Bhattacharya teaches, (a statistical machine learning model, such as a feature selection model, may be applied to a plurality of factors associated with each of the events. Those factors may include event parameters (e.g., duration of event, time and day of event, location of event, etc.). Those factors may also include one or more attributes of potential attendees of the events (e.g., seniority of attendees, organizational title of attendees, office location of attendees, etc.). An event priority score can then be generated for each of the events, and those events can be ranked according to their relative scores, [0004], In some examples, the machine learning model applied in generating the event priority scores may be trained with user feedback, [0017]).
determining, based at least in part on one or more calendars associated with the additional users, an availability of the additional users; (users may utilize an electronic calendar service to schedule and keep track of their events/meetings. When users of the electronic calendar service schedule new events they may utilize a digital calendar assistant associated with the service that uses a natural language processing engine. The digital calendar assistant may use the natural language processing engine to identify event/meeting intents associated with the scheduling of new events and automate the scheduling process. However, when one or more potential invitees of an event have conflicting events already on their calendars, the service would typically have had to either pick a time that falls outside of the identified meeting intent or move one of the conflicting meetings to a different time from which it was already scheduled, [0016]),
sending, to the additional users, an invitation to the meeting, wherein the invitation includes the priority score; (the new calendar event is presented if the event priority score for the new calendar event is higher than the event priority score for the conflicting calendar event. [0042] and FIG. 5., and receive feedback from the invitee of the meeting indicating that the ranking of the calendar events is incorrect; and modify an event priority score associated with at least one existing calendar event [Claim 13]); and
receiving at least one response to the invitation from at least one additional user of the additional users, (the system [ ] receives feedback from the organizer of the meeting, Bhattacharya, [Claim 12]).
Bhattacharya and Lu are both considered to be analogous to the claimed invention because they are both in the field of meeting event management and optimization. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the meeting prioritization using AI of Bhattacharya with the automated meeting content analysis techniques of Lu to indicate participants’ emotion or interest in the current meeting topic, Lu, [8:6-18], or indicate that the meeting is moving towards an ending of a specific topic or the meeting as a whole, [8:52-54]).
Regarding Claim 2, The method of claim 1, wherein the priority score is based at least in part on at least one of:
an urgency associated with the meeting;
a priority associated with the meeting;
a duration of the meeting;
a reoccurrence of the meeting;
or a position or role of an additional user of the additional users.; (Bhattacharya teaches one or more of the event parameters 210 and the attendee attributes 212 may be provided to a machine learning model. In this example, those features are provided to feature selection model 218,which is utilized to generate an event priority score 220 for each of the attendees (i.e., Attendee A, Attendee B, Attendee C). A feature selection model may be applied to a plurality of factors associated with each of the events. Those factors may include event parameters (e.g., duration of event, time and day of event, location of event, etc.). Those factors may also include one or more attributes of potential attendees of the events (e.g., seniority of attendees, organizational title of attendees, office location of attendees, etc.). An event priority score can then be generated for each of the events, and those events can be ranked according to their relative scores, [0004] and FIG. 2).
Regarding Claim 3, the method of claim 1, wherein the priority score is a first priority score, the method further comprising:
receiving, from the user, an indication to update the first priority score;
receiving, from the user, a second priority score associated with the meeting; and
based at least in part on receiving the indication to update the first priority score, updating
the first priority score to the second priority score, (Bhattacharya teaches processors are responsive to the instructions [ ] to receive feedback from the invitee of the meeting indicating that the ranking of the calendar events is incorrect; and modify an event priority score associated with at least one existing calendar event on the invitee's electronic calendar, [Claim 13]).
Regarding Claim 6, The method of claim 1, further comprising:
determining that an additional user of the additional users has a conflict with the meeting; (Bhattacharya teaches the service may provide a selectable option to users to replace events when a conflicting event with a higher event priority score is received, [0018]), and
sending, based at least in part on determining that the additional user has the conflict with the meeting and to the additional user, a notification of the conflict, (the electronic calendar service provides the potential attendee with an option to replace conflicting event 112 with the new event associated with electronic message 106. This is illustrated by electronic message 124 displayed on computing device 122 in event conflict resolution sub-environment 120. Specifically, electronic message 124, from [DIGITAL ASSISTANT] to the potential attendee (the organizer) states: “You have lunch meetings scheduled already on those days. Your Friday lunch meeting appears to be less important. Can I move it to next week?” [0026]).
Claims 9-11, and 14-18 are similarly rejected for reasons corresponding to claims 1-4 and 6. In these claims, the addition of one or more processors; and one or more non-transitory media, does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art, (Bhattacharya teaches a system for prioritizing calendar events with artificial intelligence, comprising: a memory for storing executable program code; and one or more processors, functionally coupled to the memory [Claim 11]).
Claims 4, 12-13, are rejected under 35 U.S.C. § 103 as being taught by Bhattacharya (US
20200293999 A1), hereafter Bhattacharya, “Artificial Intelligence for Calendar Event Conflict Resolution,” in view of Lu, (US 11558438 B1), hereafter Lu, “Status Prediction for Meetings and Participants,” in further view of Ban, (US 20230147297 A1), hereafter Ban, “Coordination Between Overlapping Web Conferences.”
Regarding Claim 4, The method of claim 1, wherein the request to generate the
meeting is associated with a first time, further comprising:
determining that an additional user of the additional users has a conflict with the meeting; Bhattacharya teaches, (the electronic calendar service [ ] illustrated by first conflicting event 100 and second conflicting event 112, the potential attendee does not have any available time to meet during the period of time specified in electronic message 106. [0021], and has compared each of the scores and made a determination that second conflicting event 112 has a lower event priority score to the potential attendee (in this example the meeting organizer), and therefore the electronic calendar service provides the potential attendee with an option to replace conflicting event 112 with the new event associated with electronic message 106. This is illustrated by electronic message 124 displayed on computing device 122, [0026 and FIG.2.];
sending, based at least in part on determining that the additional user has the conflict with the meeting and to the additional user, a notification of the conflict, (an option to replace conflicting event 112 with the new event associated with electronic message 106 [ ] is illustrated by electronic message 124 displayed on computing device 122, [0026 and FIG.2.]),
determining that a number of the additional users is below a threshold number of users; Bhattacharya does not teach, (Ban teaches an overall meeting priority level may be reduced based on a number of meeting attendees or a percentage of overall meeting attendees with a current meeting schedule conflict exceeding a threshold amount. This reduction may be indicated via the meeting priority level displayed to the meeting host and/or the meeting attendees, [0056]),
Bhattacharya and Ban are considered to be analogous to the claimed invention because they are in the field of developing systems for efficiently scheduling and coordinating meetings for organizations. It would have been obvious to one of ordinary skill in the art before the effective filing date to add Ban’s meeting acceptance notification to the systems, methods, and devices of Bhattacharya, to allow the meeting coordination program to process the acceptance feedback [Ban 0047],
determining, based at least in part on the one or more calendars, a second meeting time; Bhattacharya teaches, (the electronic calendar service may automatically reschedule existing events that have an event priority score that is lower than new conflicting events with higher event priority scores, [0018]), and
sending, to the user, the second meeting time as a suggested new meeting time for the meeting, the service may present the user with multiple options, including timeslots that do not have any events currently booked in them, [0026]).
Claims 12-13 are rejected for reasons corresponding to those provided for Claim 4. In this claim, the addition of one or more processors; and one or more non-transitory media, does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art, (Bhattacharya in view of Ban teach a system for prioritizing calendar events with artificial intelligence, accounting for a threshold number of users, rescheduling meetings and comprising: a memory for storing executable program code; and one or more processors, functionally coupled to the memory [Claim 11]).
Claims 7 and 19 are rejected under 35 U.S.C. § 103 as being taught by Bhattacharya (US
2020/0293999 A1), hereafter Bhattacharya, “Artificial Intelligence for Calendar Event Conflict Resolution,” in view of Lu, (US 11558438 B1), hereafter Lu, “Status Prediction for Meetings and Participants,” in further view of Ban, (US 20230147297 A1), hereafter Ban, “Coordination Between Overlapping Web Conferences,” in further view of hereafter Chhabra, (US 20200112450 A1), hereafter Chhabra, “System and Method for Automatically Connecting to a Conference”, in further view of Libin, (US20140358613A1), hereafter Libin, “Content Associations and Sharing for Scheduled Events.”
Regarding Claim 7, The method of claim 1, Bhattacharya does not teach acceptance responses, however Ban teaches, wherein the at least one response is an acceptance of the
invitation by a first additional user of the additional users, (this application programming interface connection may allow the virtual meeting coordination program to receive, from the digital meeting software program and/or from the calendaring program, a notification of meeting acceptance or meeting registration for a meeting owner or participant, Ban – [0029]),
Bhattacharya and Ban are considered to be analogous to the claimed invention because they are in the field of developing systems for efficiently scheduling and coordinating meetings for organizations. It would have been obvious to one of ordinary skill in the art before the effective filing date to add Ban’s meeting acceptance notification to the systems, methods, and devices of Bhattacharya, to allow the meeting coordination program to process the acceptance feedback [Ban 0047],
Bhattacharya does not teach; however, Chhabra teaches the method further comprising:
receiving, from the user, an indication of a duration of time after a start time associated
with the meeting (these notifications or indicators can also occur once the scheduled meeting has begun, for example, via a reminder email to the user that has failed to connect to the meeting on time, [Chhabra 0050]).
determining that a current time is subsequent to the start time; (these notifications or indicators can also occur once the scheduled meeting has begun, [Chhabra 0050]),
determining, based at least in part on the current time being subsequent to the start time
that the meeting has started; (these notifications or indicators can also occur once the scheduled meeting has begun, for example, via a reminder email to the user that has failed to connect to the meeting on time, [Chhabra 0050]).
determining that the duration of time since the meeting has started has elapsed (these notifications or indicators can also occur once the scheduled meeting has begun, for example, via a reminder email to the user that has failed to connect to the meeting on time, [Chhabra 0050]).
And sending, to the first additional user, a reminder to join the meeting, (these notifications or indicators can also occur once the scheduled meeting has begun, for example, via a reminder email to the user that has failed to connect to the meeting on time, [Chhabra 0050]).
Bhattacharya and Chhabra are considered to be analogous to the claimed invention because they are in the field of developing systems for efficiently scheduling and coordinating meetings for organizations. It would have been obvious to one of ordinary skill in the art before the effective filing date to add the meeting start time and reminders of Chhabra to the systems, methods, and devices of Bhattacharya to
to ensure notifications with details associated with a specific event are received [Chhabra 0050].
Bhattacharya does not teach; however, Libin teaches, determining, based at least in part on the current time being subsequent that a second additional user of the additional users has joined the meeting, (computer software, according to claim 20, wherein verifying occurrence of an event further includes confirming that at least an event organizer is at the scheduled location at the scheduled time and there at least one other one of the participants, [Libin Claims 22 and 25]).
Bhattacharya and Libin are considered to be analogous to the claimed invention because they are in the field of developing systems for efficiently scheduling and coordinating meetings for organizations. It would have been obvious to one of ordinary skill in the art before the effective filing date to add the minimum attendance criteria of Libin to the systems, methods, and devices for prioritizing calendar events with artificial intelligence of Bhattacharya to enable making a meeting validation determination based on the minimum number of users present at the meeting location or using remote tools, [Libin – Claims 22 and 25].
Claim 19 is rejected for reasons corresponding to those provided in claim 7. This claim adds a computer-readable media comprising instructions that, when executed by one or more processors cause the processors to perform operations, (Ban teaches a computer-readable storage devices that contain software programs comprising executable instructions that, when executed by one or more processors, execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. [Ban 0119]). The addition of the computer-readable media does not change the rationale for the rejections.
Claims 8 and 20 are rejected under 35 U.S.C. § 103 as being taught by Bhattacharya (US
2020/0293999 A1), hereafter Bhattacharya, “Artificial Intelligence for Calendar Event Conflict Resolution,” in view of Lu, (US 11558438 B1), hereafter Lu, “Status Prediction for Meetings and Participants,” in further view of Chhabra, (US 20200112450 A1), hereafter Chhabra, “System and Method for Automatically Connecting to a Conference.”
Regarding Claim 8, The method of claim 1, wherein the at least one response includes at least one of:
a first indication to accept the invitation;
a second indication to decline the invitation; or
a third indication of a proposed new time to associate with the meeting.
Bhattacharya does not teach a method for user response to accept or decline, rather she teaches coordination between systems to deconflict and prioritize calendar events before meetings commence. Chhabra teaches methods and systems for connecting to conferences, as well as accepting or declining the meeting. (The first device to display a first user interface means including the time of the first conference event, a first selectable option indicating accepting or declining the meeting invite, [0006]).
Bhattacharya and Chhabra are both considered to be analogous to the claimed invention because they are in the field of developing systems for efficiently scheduling and coordinating meetings for organizations. It would have been obvious to one of ordinary skill in the art before the effective filing date to add to the meeting prioritization capabilities of Bhattacharya the accept or decline features of Chhabra to easily and efficiently identify their conference session preferences in conjunction with their day-to-day calendar, Chhabra: [0003].
Claim 20 is rejected for reasons corresponding to those provided in claims 8. This claim adds a computer-readable media comprising instructions that, when executed by one or more processors cause the processors (Bhattacharya teaches a computer-readable storage device comprising executable instructions that, when executed by one or more processors, prioritizing calendar events with artificial intelligence, the computer-readable storage device including instructions executable by the one or more processors, [Claim 16]), to perform operations, (receiving a request to schedule a new calendar event [ ]; identifying a conflicting calendar event [ ]; comparing an event priority score [ ] for the conflicting calendar event, [ ] and presenting a selectable option to replace the conflicting calendar event with the new calendar event [Claim 16]. The addition of the computer-readable media does not change the rationale for the rejections.
Claim 21 is rejected under 35 U.S.C. § 103 as being taught by Bhattacharya, (US
2020/0293999 A1), hereafter Bhattacharya, “Artificial Intelligence for Calendar Event Conflict Resolution,” in view of Lu, (US 11558438 B1), hereafter Lu, “Status Prediction for Meetings and Participants,” in further view of Curbow, (US 20040088362 A1), hereafter Curbow, “System and Method for Automatically Manipulating Electronic Calendar Invitations.”
Regarding claim 21, The method of claim 1, further comprising:
determining, based at least in part on the priority score and receiving the at least one
response to the invitation, a conflict with respect to the meeting; and automatically canceling the meeting on behalf of the additional users. Bhattacharya does not teach, Curbow teaches, (A method for processing electronic calendar invitations comprising: a) accessing an electronic calendar invitation received over a computer network and associated with a calendar application; b) automatically identifying a predetermined rule that is applicable to said electronic calendar invitation, wherein said predetermined rule is part of a rules database comprising a plurality of rule definitions; c) automatically performing an action based on said predetermined rule; and d) repeating steps a)-c) for a plurality of electronic calendar
invitations, [claim 15], if there is a double booking, priority can be given to particular invitations, based on attributes previously mentioned, and the system will give notice to the sender of the previously accepted invitation that the invitation is now declined, [0012], and [ ] automatically accepting said electronic calendar invitation and automatically cancelling attendance of any previously accepted meetings that conflict with said electronic calendar invitation, [claim 18]).
Bhattacharya and Curbow are both considered to be analogous to the claimed invention because they are in the field of developing systems for efficiently scheduling and coordinating meetings for organizations. It would have been obvious to one of ordinary skill in the art before the effective filing date to add to the meeting prioritization capabilities of Bhattacharya to the rule-based cancellation features of Curbow to avoid manual responses that are burdensome and time consuming for users to respond to all invitations received, and as a result, many times, a user will not respond to an invitation causing uncertainty of attendance [0008].
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624