DETAILED ACTION
This Final Office Action is in response Applicant communication filed on
2/19/2026. In Applicant’s amendment, claims 1, 5, 10, 15, and 18 were amended. Claims 21-29 are added. Claims 2-3, 7-9, 16-17, and 19-20 are cancelled.
Claims 1, 4-6, 10-15, 18, and 21-29 are currently pending and have been rejected as follows.
Response to Amendments
Rejections under 35 USC 101 are maintained. Applicant’s amendments necessitated new grounds of rejection under 35 USC 103.
Response to Arguments
Applicant’s 35 USC 101 rebuttal arguments and amendments have been fully considered but they are not persuasive to overcome the rejection.
Applicant argues on p. 13 that the claims include recitations of particular processing operations including performing natural language processing and creating and processing data structures utilizing machine learning models to automate the generation of meeting invitations. Such recitations of particular processing operations are clearly not simply managing personal behavior or relationships or interactions between people.Examiner respectfully disagrees. Under Step 2A, Prong 1, examiners should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas. If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One. The claim then requires further analysis in Step 2A Prong Two, to determine whether any additional elements in the claim integrate the abstract idea into a practical application. Claim 1 identifies meeting topics and potential invitees, evaluates historical attendee interactions, predicts whether a person will attend, determines whether to invite that person, and selects an invitation type. The limitations organize participation in recurring meetings and manage the interaction among organizers and invitees. Performing these activities using NLP and machine learning models does not preclude them from being directed to an abstract idea.
Applicant argues on p. 14 that the claims are not directed to an abstract idea for reasons similar to those set forth in Ex Parte Desjardins et al., as previously-presented independent claims 1, 15 and 18 clearly integrate any such abstract idea into practical applications that provide improvements in computer technology through training of machine learning models including neural networks and natural language processing models for providing predictive intelligence for meetings.Examiner respectfully disagrees. Under Step 2A, Prong 2, examiners should evaluate whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. Limitations the courts have found indicative that an additional element (or combination of elements) may have integrated the exception into a practical application include:
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
Here, the processor and memory are recited at a high level to perform their ordinary functions. Obtaining the NLP description, potential invitee information, historical interaction information, and feedback amounts to mere data gathering. Creating the third data structure is mere data organization for analysis. Generating the invitation is insignificant post-solution activity communicating the result of the abstract process. There is no recitation or claimed improvement to machine learning technology itself. The asserted benefits to the problems of over-inviting attendees, unpredictable attendance, lack of feedback on meeting relevance to schedule future meetings are improvements to an administrative/organizational process, not improvements to computer technology. Desjardin is not analogous to the present claims because Desjardin addressed the technical problem of catastrophic memory loss in training machine learning models.
Applicant's prior art arguments have been fully considered but they are moot in light of the newly applied portions of the prior art references.
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.
Claims 1-20 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (apparatus, non-transitory processor-readable storage medium, and a method). Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea.
Regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (‘2019 PEG”), Claims 1-14 are directed toward the statutory category of a machine (reciting an “apparatus”). Claims 15-17 are directed toward the statutory category of an article of manufacturer (reciting a “non-transitory processor-readable storage medium”). Claims 18-20 are directed toward the statutory category of a process (reciting a “method”).
Regarding Step 2A, prong 1 of the 2019 PEG, Claims 1, 15, and 18 are directed to an abstract idea by reciting to obtain a first data structure characterizing a natural language description of a given instance of a recurring meeting … to obtain a second data structure characterizing one or more potential invitees for the given instance of the recurring meeting; to create a third data structure characterizing the identified one or more topics of the given instance of the recurring meeting and a given one of the one or more potential invitees for the given instance of the recurring meeting; to train, utilizing information characterizing a level of interaction of one or more historical invitees for one or more historical meetings associated with the one or more topics, … to generate predictions as to a likelihood of the one or more potential invitees attending the given instance of the recurring meeting; … and to generate an invitation to the given instance of the recurring meeting for the given potential invitee based at least in part on the prediction of the likelihood of the given potential invitee attending the given instance of the recurring meeting, the generated invitation for the given instance of the recurring meeting utilizing an invitation type for the given potential invitee based at least in part on post-meeting feedback for one or more previous instances of the recurring meeting (Example Claim 1).
The claims are considered abstract because these steps recite certain methods of organizing human activity like managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and mathematical concepts. The claims recite steps to collect meeting information, processing the collected information to train a model, generate a prediction regarding the likelihood of a person attending a meeting, and sending out meeting invitations based on the prediction. It is understood that the claimed steps aim to overcome challenges associated with various types of meetings involving users unfamiliar with each other, their roles, expertise, etc. (Applicant’s Specification, p. 13-14). By this evidence, the claims recite a type of “managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea (e.g., a “natural language processing system with machine learning” for meeting management).
Regarding Step 2A, prong 2 of the 2019 PEG, the judicial exception is not integrated into a practical application because the claims (the judicial exception and the additional elements such as at least one processing device comprising a processor coupled to a memory; a non-transitory processor-readable storage medium having stored therein program code of one or more software programs; to perform natural language processing of the first data structure utilizing a first machine learning model to identify one or more topics for the given instance of the recurring meeting; the first machine learning model comprising a bi-directional recurrent neural network with long short-term memory configured to utilize a first processing sequence that processes one or more sentences of the natural language description of the given instance of the recurring meeting from a beginning point to an end point of at least a portion of the natural language description of the given instance of the recurring meeting and a second processing sequence that processes the one or more sentences of the natural language description of the given instance of the recurring meeting from the end point to the beginning point; a second machine learning model; the second machine learning model comprising a dense multi-layer neural network comprising an input layer, two or more hidden layers and an output layer, the input layer having a number of neurons associated with a number of independent variables affecting the likelihood of the one or more potential invitees attending the given instance of the recurring meeting, the independent variables comprising a date and time of the given instance of the recurring meeting, the identified one or more topics for the given instance of the recurring meeting, an organizer of the given instance of the recurring meeting, and the level of interaction of the one or more historical invitees for the one or more historical meetings associated with the identified one or more topics; to process the third data structure utilizing the second machine learning model to generate a prediction as to the likelihood of the given potential invitee attending the given instance of the recurring meeting) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment such that the claims as a whole is more than a drafting effort designed to monopolize the exception (see MPEP §§ 2106.05(a-c, e)).
Claims 2-7, 13, 16-17, and 19-20 each recite applying standard machine learning functions/architecture to the abstract idea at a high level, amounting to mere instructions to implement the abstract idea on a computer.
Dependent claims 2-14, 16-17, and 19-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations recite 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).
Regarding Step 2B of the 2019 PEG, the additional elements have been considered above in Step 2A Prong 2. The claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite 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).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, “Performing repetitive calculations,” Flook, and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing steps to “obtain” first data, “perform” natural language processing, “obtain” second data, “create” third data, “train” a second machine learning model, “process” third data, and “generate” an invitation (Example Claim 1).
By the above, the claimed computing “call[s] for performance of the claimed information collection, analysis, and display functions ‘on a set of generic computer components' and display devices” [Elec. Power Group, 830 F.3d at 1355] operating in a “normal, expected manner” [DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1245, 1258 (Fed. Cir. 2014)].
Conclusively, Applicant's invention is patent-ineligible. When viewed both individually and as a whole, Claims 1-20 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept.
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, 4-6, 10-15, 18, and 21-29 are rejected under 35 USC 103 as being unpatentable over the teachings of
Dhumal et al., US 20220383265 A1, hereinafter Dhumal in view of
Balasubramanian et al., US 20180189743 A1, hereinafter Balasubramanian, in view of
Kulkarni et al., US 20220107852 A1, hereinafter Kulkarni, in view of
Mitra et al., US 20220245446 A1, hereinafter Mitra. As per,
Claims 1, 15, 18
Dhumal teaches
An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured: /
A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device: /
A method comprising: (Dhumal [0052]; [0057])
to obtain a first data structure characterizing a natural language description of a given instance of a recurring meeting, ; (Dhumal [0062] “the scheduling engine 241 may be configured to analyze scheduling information provided by any of the communication devices 205 … The scheduling information may include, for example, scheduled calendar events associated with a user of a communication device 205 and details (e.g., content) associated with the scheduled calendar events” corresponding to a data structure characterizing a description of a meeting)
to perform natural language processing of the first data structure utilizing a first machine learning model to identify one or more topics for the given instance of the recurring meeting, […]; (Dhumal [0075] “the scheduling engine 241 may create, select, and execute processing operations. The processing operations may include, for example, content extraction, content analysis, and context analysis described herein. In some aspects, the scheduling engine 241 may support natural language processing operations” note the natural language processing for content extraction; [0077] “the contextual information may include subject matter (e.g., meeting topics) associated with each scheduled calendar event”)
to obtain a second data structure characterizing one or more potential invitees for the given instance of the recurring meeting; (Dhumal [0025] “machine learning techniques described herein may support automatically learning activity patterns (also referred to herein as a working patterns) of multiple users (e.g., one or more meeting hosts, one or more invitees)” noting the invitees)
to create a third data structure characterizing the identified one or more topics of the given instance of the recurring meeting and a given one of the one or more potential invitees for the given instance of the recurring meeting; (Dhumal [0077] “contextual information may include subject matter (e.g., meeting topics) associated with each scheduled calendar event;” [0110] “the machine learning network may support recognition of key words (e.g., names) associated with the contextual information;” [0111] “the machine learning network may generate an output inclusive of candidate temporal periods … based on presence data, time zone information, scheduling information and/or contextual information” note the topics corresponding to contextual information and the model recognizing names associated with contextual information to generate an output)
[…];
to process the third data structure utilizing the second machine learning model to generate a prediction as to the likelihood of the given potential invitee attending the given instance of the recurring meeting; and (Dhumal [0029] noting the discussion of the device utilizing machine learning models to generate various attendance probabilities; “[0074] “the processing decisions may include predicting available temporal periods for a meeting invitation (e.g., time slots associated with a high availability for an invitee and/or meeting host), displaying suggested temporal periods for a meeting invitation (e.g., based on probability scores and/or confidence scores associated with the predictions)” note the probability score associated with the generated prediction for the potential invitee
to generate an invitation to the given instance of the recurring meeting for the given potential invitee based at least in part on the prediction of the likelihood of the given potential invitee attending the given instance of the recurring meeting, […]. (Dhumal [0128] “based on a user input selecting a temporal period 315 (e.g., temporal period 315-a) from among temporal periods 315 (e.g., temporal period 315-a, temporal period 315-b, temporal period 315-d) suggested by the communication device, the communication device 205 may transmit the meeting invitation to a communication devices 205 associated other users” note the generation of the invitation based on the suggested temporal periods)
wherein the method is performed by at least one processing device comprising a processor coupled to a memory (Claim 18). (Dhumal [0052]; [0057])
Dhumal does not explicitly teach, Balasubramanian however in the analogous art of scheduling teaches
“recurring meeting,” as recited throughout the claim; (Balasubramanian [0017] “the topic of discussion inferred or directly read including the objectives so that the above factors can be evaluated to best suit the topic, the context and background of the meeting, any related communications of an event, action or meeting, previous meetings on the same topic and the previous meeting outcomes, and/or a combination thereof” noting the system optimizing the scheduling of meetings based previous meetings on the same topic corresponding to the “recurring” nature of the meetings)
to train, utilizing information characterizing a level of interaction of one or more historical invitees for one or more historical meetings associated with the one or more topics, a second machine learning model to generate predictions as to a likelihood of the one or more potential invitees attending the given instance of the recurring meeting, […] affecting the likelihood of the one or more potential invitees attending the given instance of the recurring meeting, the independent variables comprising a date and time of the given instance of the recurring meeting, the identified one or more topics for the given instance of the recurring meeting, an organizer of the given instance of the recurring meeting, and the level of interaction of the one or more historical invitees for the one or more historical meetings associated with the identified one or more topics; (Balasubramanian [0020] “One or more machine learning models may be invoked and applied to learning over time a level of attendee contribution (e.g., speaking, communicating via electronic devices, interaction with other attendees, etc.) at certain types of events, activities, or meetings;” [0065] “a machine learning module as illustrated in block 502 may be used to learn and/or train a machine learning model information relating to a user such as, for example, an attendee of a meeting … an attendance confidence level for each user may be assigned for each selected time slot (e.g., assigning the attendance confidence level as a percentage that the user will attend and/or actively engage);” [0071] “the event, action, and/or meeting scheduling may be based on a highest level of determined attention of one or more participants gathered through historical patterns as well as a user profile given logistics and engagement situations and interpreted in the context of the topic of discussion and the context;” [0073] “ a role or title of the user may be included in a profile of a user and may be used to identify the context and/or associated with the context” Note the training of machine learning models utilizing historical interaction data from users associated with a topic, the time, the engagement level, and role in a meeting to generate attendance predictions)
[…], the generated invitation for the given instance of the recurring meeting utilizing an invitation type for the given potential invitee based at least in part on post-meeting feedback for one or more previous instances of the recurring meeting. (Balasubramanian [0014] “the present invention provides a solution to … take into account the context and/or importance of the meeting or persons that are required to attend the event/meeting;” [0065] “a machine learning module as illustrated in block 502 may be used to learn and/or train a machine learning model information relating to a user such as, for example, an attendee of a meeting … one or more meeting attendees may be identified based on a level of required attendance such as, for example, required attendees, optional attendees, and/or “key, active participants” that are both required and mandatory” note the system learning based on attended meetings by the invitee and classifying invitees as required, optional, and/or key/active participants corresponding to the invitation type for the invitee; for additional post-meeting feedback relating to the meeting and user see [0061] “the mechanisms of the illustrated embodiments implement a machine learning/rule learning system which, based on the particular feedback/reaction, infers new contextual rules or adjusts the user profile”)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Dhumal’s predictive meeting management to include training a model on historical user interaction data for predicting a likelihood of a user’s attendance in view of Balasubramanian in an effort to improve the effectiveness of a scheduled meeting (see Balasubramanian ¶ [0014] & MPEP 2143G).
Dhumal / Balasubramanian do not explicitly teach, Kulkarni however in the analogous art of activity prediction teaches
[…] the first machine learning model comprising a bi-directional recurrent neural network with long short-term memory configured to utilize a first processing sequence that processes one or more sentences of the natural language description of the given instance of the recurring meeting from a beginning point to an end point of at least a portion of the natural language description of the given instance of the recurring meeting and a second processing sequence that processes the one or more sentences of the natural language description of the given instance of the recurring meeting from the end point to the beginning point; (Kulkarni [0043] “in one or more embodiments, the natural language model 202 is a recurrent neural network (RNN). To illustrate, in one or more implementations, the natural language model 202 includes one or more a long short-term memory (LSTM) network layers and/or one or more gated recurrent unit (GRU) network layers. Additionally or alternatively, the natural language model 202 in some embodiments comprises one or more transformer layers and/or neural attention mechanisms (or layers). For example, the natural language model 202 in one or more implementations utilizes bidirectional encoder representations from transformers”)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Dhumal’s meeting management system and Balasubramanian’s predicted attendance values based on historical interaction and topic data to include a recurrent neural network in view of Kulkarni in an effort to improve the accuracy of the predicted user activity (see Kulkarni ¶ [0026] & MPEP 2143G).
Dhumal / Balasubramanian / Kulkarni do not explicitly teach, Mitra however in the analogous art of activity prediction teaches
[…], the second machine learning model comprising a dense multi-layer neural network comprising an input layer, two or more hidden layers and an output layer, the input layer having a number of neurons associated with a number of independent variables […]. (Mitra fig. 4; [0118]; [0138] “ Architecture 400 includes an input layer 410, an embedding layer 412, and hidden layers 414, 424, and 434;” [0139] “After embedding, the embedded input data is passed through a first set of hidden layers 414, that may include dense layers and drop out layers … While the input of the second and third sets of hidden layers 424 and 434 is the same, the outputs correspond to different tasks. In example embodiments, the second set of hidden layers 424 of the first branch 420 outputs an open_hour prediction 426, which is a time at which a recipient is most likely to open the electronic communication, and the third set of hidden layers 434 of the second branch 430 outputs a click_hour prediction 436, which is a time at which the recipient is most likely to click on content” noting the neural network with an input layer and at least three hidden layers)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Dhumal’s meeting management system, Balasubramanian’s predicted attendance values based on historical interaction and topic data, and Kulkarni’s recurrent neural network to include a dense artificial neural network in view of Mitra in an effort to optimize communication times (see Mitra ¶ [0016]-[0018] & MPEP 2143G).
Claim 4
Dhumal teaches
wherein the first machine learning model is trained utilizing a corpus of meeting topics associated with an enterprise for which the given instance of the recurring meeting is scheduled. (Dhumal [0063] “the scheduling engine 241 may be configured to analyze content provided by any of the communication devices 205 … The content may include historical content (e.g., previously communicated by a communication device 205 and/or the application server 210);” [0133] “the communication device 205 may support forward learning based on past actions of a user (e.g., a meeting host, meeting co-host, invitee) with respect to previous meeting invitations”)
Claim 5
Dhumal / Balasubramanian / Mitra do not explicitly teach, Kulkarni however in the analogous art of activity prediction teaches
wherein the second machine learning model comprises a binary classification model that provides, as output, a prediction of whether or not the given potential invitee will attend the given instance of the recurring meeting. (Kulkarni [0046] “the natural language model 202 can generate respective probability scores or rankings for candidate sequences (as described more below in relation to the following figures) that include various different candidate activity events. Using the respective probability scores or rankings, the user activity sequence system 104 can then determine predicted activity events 206 (e.g., as described more in relation to FIG. 4 based on a comparison of the probability scores or rankings to each other and/or to a predetermined threshold);” [0047] “the predicted activity events 206 can include a prediction for what a user will do next on a particular digital content item given the user's activity events associated with the particular digital content item”)
The motivation/rationale to combine Dhumal / Balasubramanian / Mitra with Kulkarni persists.
Claim 6
Dhumal teaches
wherein training the second machine learning model utilizes information characterizing the one or more historical meetings of an enterprise for which the given instance of the recurring meeting is scheduled, the information characterizing the one or more historical meetings including, for each historical meeting, one or more meeting topics, one or more organizers, one or more historical attendees, and the level of interaction of each of the one or more historical attendees. (Dhumal [0027] “the device (e.g., machine learning network, scheduling application) may predict working patterns of invitees based on historical presence information (e.g., presence information associated with one or more prior temporal periods) associated with the invitees” corresponding to the historical level of interaction; [0077] “the contextual information may include subject matter (e.g., meeting topics) associated with each scheduled calendar event;” [0133] “the communication device 205 may support forward learning based on past actions of a user (e.g., a meeting host, meeting co-host, invitee) with respect to previous meeting invitations.” corresponding to the training of the model using historical meeting information)
Claim 10
Dhumal / Balasubramanian / Kulkarni do not explicitly teach, Mitra however in the analogous art of activity prediction teaches
wherein each of the one or more hidden layers comprises a set of neurons utilizing a first activation function, and wherein the output layer comprises a single neuron utilizing a second activation function. (Mitra fig. 4; [0139] “the embedded input data is passed through a first set of hidden layers 414, that may include dense layers and drop out layers. Dense layers are fully-connected layers … the second set of hidden layers 424 of the first branch 420 outputs an open_hour prediction 426”)
The motivation/rationale to combine Dhumal / Balasubramanian / Kulkarni with Mitra persists.
Claim 11
Dhumal / Balasubramanian / Kulkarni do not explicitly teach, Mitra however in the analogous art of activity prediction teaches
wherein the first activation function comprises a Rectified Linear Unit (ReLU) activation function and the second activation function comprises a sigmoid activation function. (Mitra fig. 4; [0139] “the embedded input data is passed through a first set of hidden layers 414, that may include dense layers and drop out layers. Dense layers are fully-connected layers … the second set of hidden layers 424 of the first branch 420 outputs an open_hour prediction 426”)
The motivation/rationale to combine Dhumal / Balasubramanian / Kulkarni with Mitra persists.
Claim 12
Dhumal teaches
wherein the generated invitation to the given instance of the recurring meeting for the given potential invitee specifies an attendee class for the given potential invitee based at least in part on the prediction of the likelihood of the given potential invitee attending the given instance of the recurring meeting, the attendee class comprising one of a required attendee and an optional attendee. (Dhumal [0123] “the communication device 205 may modify indicators 320 (e.g., modify respective line thicknesses, colors, patterns, etc.) based on a configurable priority associated with the users (e.g., prioritizing according to a meeting host, required attendees, optional attendees, etc.).”)
Claim 13
Dhumal teaches
wherein the at least one processing device is further configured to obtain post-meeting feedback for the given instance of the recurring meeting, and to utilize the post-meeting feedback for updating a training of the second machine learning model. (Dhumal [0057] “the memory 240 may be configured to store program instructions (instruction sets) that are executable by the processor 230 and provide functionality of a scheduling engine 241 described herein. The memory 240 may also be configured to store data or information that is usable or capable of being called by the instructions stored in memory 240. One example of data that may be stored in memory 240 for use by components thereof is a data model(s) 242 (also referred to herein as a neural network model) and/or training data 243 (also referred to herein as a training data and feedback).” Note the feedback and training data for the models; [0058] “the data model(s) 242 may be built and updated by the scheduling engine 241 based on the training data 243;” [0133] “communication device 205 may support forward learning based on past actions of a user (e.g., a meeting host, meeting co-host, invitee) with respect to previous meeting invitations”)
Claim 14
Dhumal teaches
wherein the post-meeting feedback characterizes at least one of: whether the given potential invitee attended the given instance of the recurring meeting; and a level of interaction of the given potential invitee during the given instance of the recurring meeting. (Dhumal [0133] “communication device 205 may support forward learning based on past actions of a user (e.g., a meeting host, meeting co-host, invitee) with respect to previous meeting invitations. In some examples, the past actions may include proposed meeting times, meeting acceptances, meeting rejections”)
Claims 21, 24, 27
Dhumal / Kulkarni / Mitra do not explicitly teach, Balasubramanian however in the analogous art of scheduling teaches
wherein the at least one processing device is further configured: to obtain post-meeting feedback for the given instance of the recurring meeting, the post- meeting feedback characterizing whether the given potential invitee attended the given instance of the recurring meeting; and (Balasubramanian [0074] “The meeting outcome analyzer 612 may be responsible for monitoring the emotional state and mood and participation of the attendees and at the end of the meeting will collect data for a learning module 614 to update the learning module and/or profiles based on the collected results of the meeting” noting the post-meeting results analysis)
The motivation/rationale to combine Dhumal / Kulkarni / Mitra with Balasubramanian persists.
Dhumal / Balasubramanian / Kulkarni do not explicitly teach, Mitra however in the analogous art of activity prediction teaches
to update the training of the second machine learning model responsive to […] and (ii) the post-meeting feedback indicates that the given potential invitee attended the given instance of the recurring meeting. (Mitra [0130] “ Output of task predictor 322 is compared to ground truth data … Generally, the comparisons to ground truth data indicate errors (or losses), which are used to adjust the neural network system 334 to reduce the amount of error in future iterations;” [0136] noting the comparison of a prediction and adjusting the network when the prediction is erroneous)
The motivation/rationale to combine Dhumal / Balasubramanian / Kulkarni with Mitra persists.
Dhumal teaches
[…](i) the generated prediction of the likelihood of the given potential invitee attending the given instance of the recurring meeting is below a designated threshold […]. (Dhumal [0101] “The predicted availabilities may be indicated by the machine learning network, for example, as a “high availability” (e.g., a predicted availability above a first threshold), a “medium availability” (e.g., a predicted availability above a second threshold but below the first threshold), a “low availability” (e.g., a predicted availability above a third threshold but below the second threshold), and “no availability” (e.g., a predicted availability below a fourth threshold)” noting the thresholds)
Claims 22, 25, 28
Dhumal teaches
wherein generating the invitation to the given instance of the recurring meeting for the given potential invitee is further based at least in part on whether the given potential invitee is also an invitee for one or more additional meetings that conflict with the given instance of the recurring meeting. (Dhumal [0105] “the scheduling information may include scheduled calendar events associated with any of the identified users and another meeting invitation (e.g., a confirmed calendar event);” [0108] “the machine learning network may extract and analyze the scheduling information (e.g., scheduled calendar events, attendees associated with the scheduled calendar events, temporal periods associated with the scheduled calendar event), based on which the machine learning network may identify any temporal periods 315 for which a user (e.g., meeting host, invitee) already has a scheduled calendar event. In an example, the machine learning network may identify scheduled calendar events that at least partially overlap a candidate temporal period”)
Claims 23, 26, 29
Dhumal / Kulkarni / Mitra do not explicitly teach, Balasubramanian however in the analogous art of scheduling teaches
wherein generating the invitation to the given instance of the recurring meeting for the given potential invitee comprises changing the invitation type for the given potential invitee for the given instance of the recurring meeting relative to an invitation type used for the given potential invitee for at least one of the one or more previous instances of the recurring meeting based at least in part on the post-meeting feedback for the one or more previous instances of the recurring meeting. (Balasubramanian [0065] “one or more meeting attendees may be identified based on a level of required attendance such as, for example, required attendees, optional attendees, and/or “key, active participants” that are both required and mandatory;” [0088] “he methods 400, 600, and/or 700 may identify as the identified contextual factors a user profile, data relating to a calendar of each one of the one or more users, information relating to the scheduled meeting, topics of discussion of previously attended meetings, one or more previous meetings on a similar topic relating to the meeting topic and objective, a plurality of communication or documentation relating previously attended meetings by the one or more users, use an analyzer device (e.g., a processor device or a module controlled by a processor device) to cognitively identify the one or more time slots for scheduling the meeting; collect and update data relating to the identified contextual factors upon completion of previously attended meeting to update a user profile of the one or more users, and/or apply one or more rules for using the identified contextual factors based on learned historical patterns” noting the required/optional invitation and post-meeting feedback that changes the profile or rules)
The motivation/rationale to combine Dhumal / Kulkarni / Mitra with Balasubramanian persists.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2024/0259446 A1: A system and method for improving event and user communications and experiences. (e.g., managing meetings and their participants) is disclosed. By treating events such as meetings and their participants (meeting leaders and invited attendees) as a managed system, the present invention improves the quality and outcomes of meetings and the satisfaction of the participants. The present invention also makes joining of this intelligently-managed system particularly simple for new as well as returning participants, by providing a unique approach to the issues at the pre-meeting, meeting, and post-meeting phases in order to generate scores for the particular meeting, each participant (meeting leader and attendees), and a meeting graph linking the performance of each participant (meeting leader and attendees). This score is used to adjust and predict participant (meeting leader and attendees) behavior in subsequent meetings. The system advantageously is not intrusive into the meeting (and meeting set up), yet is configured to obtain useful changes in behavior in meetings.
WO 2017/184468 A1: Meeting scheduling resources are provided including systems and methods for optimizing proposed meeting details using historical information derived from meeting invitees. A statistical analysis, which may employ machine-learning techniques, may be used to determine a meeting attendance model based on past meetings and/or events, user activity, or other information associated with a user. Meeting patterns and availability for the user also may be used to generate the meeting attendance model. A meeting manager service may implement the meeting attendance models to facilitate schedule future meetings. The meeting manager service may also determine an attendance importance for invitees, a likelihood of attending the proposed meeting, given specific meeting features of the proposed meeting (such as time, location, or other meeting features) and recommend optimal meeting features for the proposed meeting.
Xu et al., Exploiting the Dynamic Mutual Influence for Predicting Social Event Participation, 2019: It is commonly seen that social events are organized through online social network services (SNSs), and thus there are vested interests in studying event-oriented social gathering through SNSs. The focus of existing studies has been put on the analysis of event profiles or individual participation records. While there is significant dynamic mutual influence among target users through their social connections, the impact of dynamic mutual influence on the people's social gathering remains unknown. To that end, in this paper, we develop a discriminant framework, which allows to integrate the dynamic mutual dependence of potential event participants into the discrimination process. Specifically, we formulate the group-oriented event participation problem as a two-stage variant discriminant framework to capture the users' profiles as well as their latent social connections. The validation on real-world data sets show that our method can effectively predict the event participation with a significant margin compared with several state-of-the-art baselines. This validates the hypothesis that dynamic mutual influence could play an important role in the decision-making process of social event participation. Moreover, we propose the network pruning method to further improve the efficiency of our technical framework. Finally, we provide a case study to illustrate the application of our framework for event plan design task
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/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624
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