DETAILED ACTION
Status of the Application
Claims 1-6 have been examined in this application. This communication is the first action on the merits. The information disclosure statement (IDS) submitted on 08/27/2025; was filed with this application. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status
This action is a Non-Final Action on the merits in response to the application filed on 05/12/2025.
Claims 1-6 remain pending in this application.
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 1-6 are directed towards a system of which are among the statutory categories of invention.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including performing an diarization to data. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1-6, the independent claim (claim 1) are directed to managing user’s meetings online, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1,
receive at least one stream of at least one of audio and video of an online meeting, wherein the at least one stream includes one or more participants participating in the online meeting;
extract a plurality of metadata from the at least one stream;
analyze the diarization information to calculate one or more key performance indicators;
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial interaction such as business relations; managing personal behavior, interactions between people such as social activities (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction, managing personal behavior, interactions between people, then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of computer device, memory device, processor, stream, diarization. The claims recite the steps are performed by the computer device, memory device, processor, stream, diarization.
The limitations of
A system for dynamically generating and analyzing metadata for online meetings, the system comprising a computer device comprising at least one processor in communication with at least one memory device, wherein the at least one memory device stores computer-implemented instructions that cause the at least one processor to:
perform diarization on the at least one stream and the plurality of metadata the at least one stream to generate diarization information, wherein the diarization information includes information about participation for the one or more participants in the online meeting;
and generate visualization of the key performance indicators to be displayed to one or more participants in the online meeting.
are mere analyzing data and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by computer device, memory device, processor, stream, diarization. The computer device, memory device, processor, stream, diarization, memory are recited at a high level of generality. In limitation (a), the computer device, memory device, processor, stream, diarization is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The computer device, memory device, processor, stream, diarization are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites diarization. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the computer device, memory device, processor, stream, diarization. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data analyzing and outputting. Then, the machine learning techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0023 “The Data Processing and Analysis Module utilizes machine learning algorithms and statistical techniques. The module processes the captured metadata to extract relevant insights regarding participant behavior, group dynamics, group intelligence, meeting effectiveness, productivity and creativity. The module employs techniques such as diarization to segment the audio data.”]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
A system for dynamically generating and analyzing metadata for online meetings, the system comprising a computer device comprising at least one processor in communication with at least one memory device, wherein the at least one memory device stores computer-implemented instructions that cause the at least one processor to:
perform diarization on the at least one stream and the plurality of metadata the at least one stream to generate diarization information, wherein the diarization information includes information about participation for the one or more participants in the online meeting;
and generate visualization of the key performance indicators to be displayed to one or more participants in the online meeting.
are recited at a high level of generality. These elements amount to storing and retrieving information in memory; and receiving or transmitting data over a network, e.g., using the Internet to gather data are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of a computer device, memory device, processor, stream, diarization to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2-6 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 2 recite modules and machine learning for processing data; claim 4 recites diarization for processing audio data. The dependent claims 2-6 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2-6 recites computer device, memory device, processor, stream, diarization which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2-6 recites computer device, memory device, processor, stream, diarization, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2-6 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1. Therefore claims 2-6 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
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 of this title, 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 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20180046957, Yaari, et al. to hereinafter Yaari in view of United States Patent Publication US 20160371317, Sharma, et al. to hereinafter Sharma in view of United States Patent Publication US 20140029757, Aronowitz, et al.
Referring to Claim 1, Yaari teaches a system for dynamically generating and analyzing metadata for online meetings, the system comprising a computer device comprising at least one processor in communication with at least one memory device, wherein the at least one memory device stores computer-implemented instructions that cause the at least one processor to (
Yaari teaches “a system comprising: one or more sensors configured to provide sensor data; one or more processors; and one or more computer storage media storing computer useable instructions that, when executed by the one or more processors, implement a method for providing one or more recommendations for an online meeting” (Yaari: Claim 10), thus disclosing a computer device with processors and memory storing instructions. See Yaari: Sec. 0110, FIG. 6.):
receive at least one stream of at least one of audio and video of an online meeting, wherein the at least one stream includes one or more participants participating in the online meeting (
Yaari teaches that “the sensed data may include audio or video recording(s) of the online meeting, which may be converted into text in order to deduce meeting features.” See Yaari: Sec. 0015. The audio/video recording is a stream including meeting participants.);
extract a plurality of metadata from the at least one stream (
Yaari teaches that the text derived from the recordings “may be analyzed to determine meeting features such as, without limitation, topics discussed, an identification of a presenter or contributor, an amount of time that the presenter or other meeting participant spoke,” and that engagement/activity data for participants is used to derive additional features. See Yaari: Sec. 0015. These meeting features constitute metadata extracted from the meeting streams.);
analyze the diarization (See Aronowitz) information to calculate one or more key performance indicators (
Yaari teaches that “effectiveness scores that reflect the meeting’s effectiveness may be generated… Effectiveness scores may be determined based on derived meeting effectiveness data and/or explicit meeting effectiveness data,” and may be computed at global and participant levels. See Yaari: Sec. 0016. These scores correspond to KPIs reflecting meeting effectiveness based on participation and other features.);
and generate visualization of the key performance indicators (See Aronowitz) to be displayed to one or more participants in the online meeting (
Yaari teaches a “presentation component 204” that “generally operates to render various user interfaces or otherwise provide information generated by the online meeting optimization system 200,” including “meeting management dashboard 260,” “recommended meeting features for proposed meetings,” and “live meeting recommendations.” See Yaari: Sec. 0039,. These dashboards display the analysis to meeting presenters and participants.).
Yaari and both teaches Sharma generate visualization of to be displayed to one or more participants in the online meeting
Yaari does not explicitly teach generate visualization of the key performance indicators to be displayed to one or more participants.
However, Sharma teaches generate visualization of the key performance indicators to be displayed to one or more participants (
Sharma teaches that “the generated visualization analysis is displayed on a computer generated graphical user interface (GUI)” and that “the method provides the user to get the visualization analysis (e.g., KPIs) in real-time along with a report or desired chart.” See Sharma: Sec. 0031. Sharma discloses rendering KPIs as tiles and charts, e.g., “Bar Chart rendered with dimension of quarter” See Sharma: Sec. 0040, Table 2. These charts constitute KPI visualizations displayed to users.)
Yaari and Sharma are both directed to the analysis of communications (See Yaari at 0035, 0038, 0042; Sharma at 0015, 0018, 0019). Yaari discloses that additional elements, such as the organization online meeting can be considered (See Yaari at 0081). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate KPI visualization techniques as taught by Sharma into the meeting effectiveness system of Yaari, with the motivation of presenting meeting effectiveness scores and related KPIs in graphical formats (tiles, charts) that improve interpretability and align with common business intelligence dashboards (See Sharma at 0021, 0033, 0038).
Yaari in view of Sharma does not explicitly teach perform diarization on the at least one stream and the plurality of metadata the at least one stream to generate diarization information, wherein the diarization information includes information about participation for the one or more participants in the online meeting.
However, Aronowitz teaches perform diarization on the at least one stream and the plurality of metadata the at least one stream to generate diarization information, wherein the diarization information includes information about participation for the one or more participants in the online meeting (
Aronowitz teaches the analyzing of stream information “An audio session is input in the form of an unlabelled audio stream and is divided 101 into sections. In one embodiment, the sections may be evenly spaced and may be overlapping. In another embodiment, a change-detector may be used to determine section” See Aronowitz: Sec. 0024. “receiving an audio session as unsegmented audio data; computing a spectral ratio of principal component analysis (PCA) of sections of the received audio session by a ratio between the largest eigenvalue and the second largest eigenvalue; using the PCA spectral ratio as a confidence measure for speaker diarization processing.” See Aronowitz: Sec. 0009, claim 1. Aronowitz is explicitly directed to speaker diarization for multi-speaker audio sessions such as meetings and lectures. Aronowitz discloses employs diarization techniques to segment the audio data in Claim 4. Aronowitz states that speaker diarization is “an important component in many speech applications such as… meeting and lecture summarization,” See Aronowitz: Sec. 0003 “also for online speaker diarization. In online processing, the audio from the beginning of the call may be used for building the initial diarization models of the speakers in the call” See Aronowitz: Sec. 0067 where diarization enables downstream analysis of who spoke when for multi-speaker content. This supports using diarization-based segments as the foundation for metrics regarding speaker participation and meeting dynamics. Aronowitz teaches diarization to enable downstream online meeting analytics..);
Yaari, Sharma, and Aronowitz are all directed to the analysis of communications (See Yaari at 0035, 0038, 0042; Sharma at 0015, 0018, 0019; Aronowitz at 0090). Yaari discloses that additional elements, such as the organization online meeting can be considered (See Yaari at 0081). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to include speaker diarization techniques as taught by Aronowitz in the meeting analytics pipeline of Yaari, with the motivation of more accurately answering the “Who spoke when?” question for online meetings and thereby improving the accuracy of participation-based metrics such as talk-time distribution, turn-taking patterns, and speaker balance, which are recognized in the diarization and meeting-analytics literature as important for meeting summarization and analysis. The diarization outputs from Aronowitz would be used as inputs to the effectiveness determiner and inference engine of Yaari, producing more precise meeting success indicators, and these indicators would then be visualized via the KPI visualization techniques of Sharma. (See Aronowitz at 0003, 0064, 0067).
Referring to Claim 2, Yaari teaches the system of Claim 1, further comprising:
a meeting metadata capture module configured to collect data generated during online meetings, including participant speaking patterns and audio characteristics (
Yaari teaches “data collection component 202” that “collects online meeting data and user data,” including “meeting data elements (or meeting features)” and “data that is sensed, recorded, or tracked during a meeting,” where “the sensed data may include audio or video recording(s) of the online meeting.” See Yaari: Sec. 0034. It further teaches that such data is used to determine “an identification of a presenter or contributor, an amount of time that the presenter or other meeting participant spoke,” and engagement/activity features. See Yaari: Sec. 0015. This corresponds to capturing audio characteristics and speaking patterns.);
a data processing and analysis module configured to process the captured metadata using machine learning algorithms and statistical techniques to extract insights regarding participant behavior and group dynamics and generate meeting success indicators (
Yaari teaches “effectiveness determiner 220” and “inference engine 230” that “determine effectiveness scores” Yaari: Sec. 0065 and “meeting patterns,” including “participant-specific effectiveness scores” Yaari: Sec. 0069 and “global meeting patterns” determined by identifying semantically related features and correlations. See Yaari: Sec. 0069. “Thus, logic 291 may comprise pattern recognition classifier(s), fuzzy logic, neural network, finite state machine, support vector machine, logistic regression, clustering, or machine learning techniques, similar statistical classification processes or, combinations of these to identify meetings from user data.” Yaari: Sec. 0042 These operations process meeting features and user data (using rules/heuristics, clustering, and pattern inference) to generate meeting success indicators based on participant behavior and patterns.);
a reporting and visualization module configured to generate reports and visualizations summarizing the findings from the data analysis (
Yaari teaches “presentation component 204” that “render[s] various user interfaces” and a “meeting management dashboard 260 interface,” which present recommended features, live recommendations, and other meeting analytics to users. See Yaari: Sec. 0039. This is a reporting/visualization module summarizing analytical results.).
a recommendation module configured to provide recommendations to increase the overall success of the meeting based on scientific findings in real time during the meeting and / or after the meeting as a summary report (
Yaari teaches that “recommended meeting features may be generated that optimize the inferred effectiveness score for the future meeting” See Yaari: Sec. 0068 (future meetings) and that “ongoing meetings may be monitored… [and] data… analyzed to provide recommendations/insights to meeting presenters and participants in real-time, or near real-time,” with recommendations communicated during the meeting. See Yaari: Sec. 0022. These recommendations are explicitly intended to optimize meeting effectiveness.).
Referring to Claim 3, Yaari teaches the system of claim 2, wherein the meeting metadata capture module further captures metadata related to participant location and date/time of participation (
Yaari teaches that “features relating to a time and day of the meeting, a meeting subject, a meeting organizer, among others, may be detected from the meeting invitation.” See Yaari: Sec. 0015. Yaari teach features including the date/time of the online meeting, which correspond to date/time of participant participation. Yaari teaches capturing metadata related to date/time of participation.
Yaari teaches that user data streams may include location information: “user data… may include data that is sensed or determined from one or more sensors… such as location information of mobile device(s)… smartphone data (such as… date/time), global positioning system (GPS) data… and nearly any other source of data that may be sensed or determined as described herein.” See Yaari: Sec. 0038. This user data is collected via data collection component 202 and associated with meetings via meeting monitor 210 and user profile 240. See Yaari: Sec. 0037.). Yaari teaches capturing metadata related to participant location.
Referring to Claim 4, Yaari teaches the system of claim 2, wherein the data processing and analysis module employs diarization techniques to segment the audio data. Based on this data metrics are being calculated that have shown to be key meeting success indicators in scientific research in a variety of meeting contexts (
Yaari teaches processing audio data to derive speaker-specific participation metrics.
Yaari teaches that “the sensed data may include audio or video recording(s) of the online meeting, which may be converted into text in order to deduce meeting features,” and that “the text may be analyzed to determine meeting features such as… an identification of a presenter or contributor, an amount of time that the presenter or other meeting participant spoke.” See Yaari: Sec. 0015. Yaari teach features are derived from audio data and correspond to speaker participation metrics.
Yaari teaches calculating meeting success metrics based on participation and other features.
Yaari teaches generating “effectiveness scores that reflect the meeting’s effectiveness” from derived and explicit meeting effectiveness data, including participant-specific and global effectiveness scores. See Yaari: Sec. 0003. These are meeting success indicators driven by measured features.).
Yaari in view of Sharma does not explicitly teaches diarization techniques to segment the audio data.
However, Aronowitz teaches diarization techniques to segment the audio data (
Aronowitz teaches employing diarization techniques to segment audio data.
“speaker diarization answers the ‘Who spoke when?’ question for a given audio signal,” See Aronowitz: Sec. 0002 and that “a speaker diarization system usually consists of a speech/non-speech segmentation component, a speaker segmentation component, and a speaker clustering component.” See Aronowitz: Sec. 0004. Aronowitz teaches a method “for providing a confidence measure for speaker diarization” that includes “receiving an audio session as unsegmented audio data; computing a spectral ratio of principal component analysis (PCA) of sections of the received audio session by a ratio between the largest eigenvalue and the second largest eigenvalue; using the PCA spectral ratio as a confidence measure for speaker diarization processing.” See Aronowitz: Sec. 0009, claim 1. Aronowitz is explicitly directed to speaker diarization for multi-speaker audio sessions such as meetings and lectures.
Aronowitz discloses employs diarization techniques to segment the audio data in Claim 4.
Aronowitz states that speaker diarization is “an important component in many speech applications such as… meeting and lecture summarization,” See Aronowitz: Sec. 0003 where diarization enables downstream analysis of who spoke when for multi-speaker content. This supports using diarization-based segments as the foundation for metrics regarding speaker participation and meeting dynamics. Aronowitz teaches diarization to enable downstream meeting analytics.)
Yaari, Sharma, and Aronowitz are all directed to the analysis of communications (See Yaari at 0035, 0038, 0042; Sharma at 0015, 0018, 0019; Aronowitz at 0090). Yaari discloses that additional elements, such as the organization online meeting can be considered (See Yaari at 0081). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to include speaker diarization techniques as taught by Aronowitz in the meeting analytics pipeline of Yaari, with the motivation of more accurately answering the “Who spoke when?” question for online meetings and thereby improving the accuracy of participation-based metrics such as talk-time distribution, turn-taking patterns, and speaker balance, which are recognized in the diarization and meeting-analytics literature as important for meeting summarization and analysis. The diarization outputs from Aronowitz would be used as inputs to the effectiveness determiner and inference engine of Yaari, producing more precise meeting success indicators, and these indicators would then be visualized via the KPI visualization techniques of Sharma. (See Aronowitz at 0003, 0064, 0067).
Referring to Claim 5, Yaari teaches the system of claim 2, wherein the reporting and visualization module generates visualizations such as graphs and charts (See Sharma) to present the analyzed data in an easily interpretable format (
Yaari teaches “presentation component 204” that “generally operates to render various user interfaces or otherwise provide information generated by the online meeting optimization system 200,” including “meeting management dashboard 260.” See Yaari: Sec. 0039.
Yaari teaches a presentation module that generates visual interfaces presenting analytic results. Yaari discloses a reporting/visualization module that presents analyzed data to users.).
Yaari and Sharma both teach generates visualizations
Yaari does not explicitly teach generates visualizations such as graphs and charts.
However, Sharma teaches generates visualizations such as graphs and charts (
Sharma teaches that “the generated visualization analysis is displayed on a computer generated graphical user interface (GUI)” and that “the method provides the user to get the visualization analysis (e.g., KPIs) in real-time along with a report or desired chart.” See Sharma: Sec. 0031. Sharma discloses rendering KPIs in tile and chart templates, including a “Bar Chart rendered with dimension of quarter,” and highlights how such charts can be tweaked in real time. See Sharma: Sec. 0040, Table 2. Sharma teaches generating graphs and charts to present analyzed data in an easily interpretable format.)
Yaari and Sharma are both directed to the analysis of communications (See Yaari at 0035, 0038, 0042; Sharma at 0015, 0018, 0019). Yaari discloses that additional elements, such as the organization online meeting can be considered (See Yaari at 0081). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate KPI visualization techniques as taught by Sharma into the meeting effectiveness system of Yaari, with the motivation of presenting meeting effectiveness scores and related KPIs in graphical formats (tiles, charts) that improve interpretability and align with common business intelligence dashboards (See Sharma at 0021, 0033, 0038).
Referring to Claim 6, Yaari teaches the system of claim 2, wherein the reporting and visualization module furnishes meeting participants and third-parties (See Sharma) with real-time guidance or analysis after the meeting, aiding in enhancing meeting success rates (
Yaari teaches that “ongoing meetings may be monitored in real-time and data associated with the meetings may be analyzed to provide recommendations/insights to meeting presenters and participants in real-time, or near real-time,” and that “recommendations/insights for presenters and passive participants can be generated and communicated in real-time while the meeting is ongoing.” See Yaari: Sec. 0022,. Yaari teaches furnishing real-time guidance to meeting participants during meetings, such as examples including private messages to moderators to engage specific participants and notifications to passive participants when relevant speakers are presenting.
Yaari teaches that “features of a proposed/future meeting may be detected” and that “recommended meeting features may be generated that optimize the inferred effectiveness score for the future meeting.” See Yaari: Sec. 0005. These recommendations are based on prior meeting effectiveness patterns and are presented via the presentation component and dashboard. See Yaari: Sec. 0103. Yaari teaches furnishing analysis after the meeting (e.g., for future meetings) to aid in enhancing meeting success. This corresponds to post-meeting analysis used to improve future meeting success rates.).
Yaari does not explicitly teach third-parties with real-time guidance.
However, Sharma teaches third-parties with real-time guidance (
Sharma teaches KPI dashboards and scorecards used by managers and other organizational roles, stating that “while working with a sales related KPI, manager’s dashboard or a business score card can be augmented with various contextual KPIs…” (emphasis added). See Sharma: Sec. 0033. Sharma shows KPI analyses made available to third-party viewers (e.g., managers), beyond the direct operational actors.)
Yaari and Sharma are both directed to the analysis of communications (See Yaari at 0035, 0038, 0042; Sharma at 0015, 0018, 0019). Yaari discloses that additional elements, such as the organization online meeting can be considered (See Yaari at 0081). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate KPI visualization techniques as taught by Sharma into the meeting effectiveness system of Yaari, with the motivation of presenting meeting effectiveness scores and related KPIs in graphical formats (tiles, charts) that improve interpretability and align with common business intelligence dashboards (See Sharma at 0021, 0033, 0038).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Morris, et al., U.S. Patent Number, 12182768, (discussing the managing of online meetings participation).
Yaari, et al., W.O. Pub. WO 2018031377, (discussing the managing of online meetings).
Vaananen, W.O. Pub. WO 2020212649, (discussing the managing for scheduling of meetings).
Anguera et al., Speaker diarization: A review of recent research, Speaker diarization: A review of recent research, IEEE Transactions on audio, speech, and language processing, 2012 (discussing the use of speaker diarization).
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