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 .
DETAILED ACTION
Status of Claims
This First Office action is in reply to the application filed on 19 May 2025.
Claims 1-20 are currently pending and have been examined. The Information Disclosure Statement filed 27 August 2025 has been considered by the Examiner. A signed copy is enclosed with this Office Action.
Inventorship
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the Examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicants are advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the Examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
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 rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) because the claimed invention is directed to a judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more. The claims as a whole recite certain grouping of an abstract idea and are analyzed in the following step process:
Step 1: Claims 1-20 are each focused to a statutory category of invention, namely “system; computer device; computer-implemented method” sets.
Step 2A: Prong One: Claims 1-20 recite limitations that set forth the abstract ideas, namely, the claims as a whole recite the claimed invention as directed to an abstract idea without significantly more. The claims recite steps for dynamically generating and analyzing metadata for online meetings as:
“receive at least one stream of at least one of audio and video of an online meeting, wherein the at least one stream includes a plurality of participants participating in the online meeting;
extract a plurality of metadata from the at least one stream;
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 plurality of participants in the online meeting;
analyze the diarization information to calculate one or more key performance indicators (KPIs);
and generate visualization of the key performance indicators to be displayed to one or more participants in the online meeting”
As detailed in the MPEP 2106 and commensurate to the two-part subject matter eligibility framework decision in the Federal court decision in Alice Corp. Pty. Ltd. V. CLS Bank International et al., (Alice), 2019 revised patent subject matter eligibility guidance (2019 PEG) and the October 2019 Update: Subject Matter Eligibility (“October 2019 Update), and the new “July 2024 Guidance Update on Patent Subject Matter Eligibility Examples, including on Artificial Intelligence”, the 2019 PEG explains that the abstract idea exception includes the following groupings of subject matter. The 35 U.S.C. 101 Step 2A, Prong One analysis focuses on whether a claim recites a judicial exception by evaluating if it falls into one of three specific groupings: mathematical concepts, mental processes, or certain methods of organizing human activity. Based on the provided steps above, the analysis for Step 2A Prong One is as follows:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations. The calculation of KPIs and diarization information inherently relies on mathematical algorithms and data manipulation.
Certain methods of organizing human activity – The claimed steps are directed to an abstract idea because they represent fundamental economic/business principles and human mental processes.
Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). The steps of analyzing data, determining participation (diarization), and calculating Key Performance Indicators (KPIs) describe operations that can be performed entirely in the human mind or by using pen and paper.
See MPEP § 2106.04(a) III C. Hence, the claims are ineligible under Step 2A Prong one. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception.
Prong Two: Claims 1-20: With regard to this step of the analysis (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. The claim recites technological components such as receiving an audio/video stream and extracting metadata. Claims 1-20 recite additional elements directed to “computer device; at least one processor; memory device” (e.g., see Applicants’ published Specification ¶’s 5-8, 53-57). Therefore, the claims contain computer components that are cited at a high level of generality and are merely invoked as a tool to perform the abstract idea. Simply implementing an abstract idea on a computer is not a practical application of the abstract idea. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible. See MPEP § 2106.05(f) (h).
Step 2B: As explained in MPEP § 2106.05, Claims 1-20 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 nor recites additional elements that integrate the judicial exception into a practical application. While gathering information from a stream constitutes a technological environment, merely applying an abstract analytical or mathematical formula (calculating KPIs and generating visualizations) using generic computing devices fails to add an inventive concept, often rendering such claims invalid under 35 U.S.C. 101 unless specific, unconventional technical improvements are detailed. The additional elements of “computer device; at least one processor; memory device”, etc. are generically-recited computer-related elements that amount to a mere instruction to “apply it” (the abstract idea) on the computer-related elements (see MPEP § 2106.05 (f) – Mere Instructions to Apply an Exception). These additional elements in the claims are recited at a high level of generality and are merely limiting the field of use of the judicial exception (see MPEP §2106.05 (h) – Field of Use and Technological Environment). There is no indication that the combination of elements improves the function of a computer or improves any other technology. Furthermore, the dependent claims are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The limitations of the claims do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea using generally-recited computer components, and furthermore do not amount to an improvement to a computer or any other technology, and thus are ineligible.
The Examiner interprets that the steps of the claimed invention both individually and as an ordered combination result in Mere Instructions to Apply a Judicial Exception (see MPEP §2106.05 (f)). These claims recite only the idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism used for accomplishing the result. Here, the claims utilize a computer or other machinery (e.g., see Applicants’ published Specification ¶’s 5-8, 53-57) regarding using existing computer processors as well as program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored. “process 100” in its ordinary capacity for performing tasks (e.g., to receive, analyze, transmit and display data) and/or use computer components after the fact to an abstract idea (e.g., a fundamental economic practice and certain methods of organization human activities) and does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)). Software implementations are accomplished with standard programming techniques with logic to perform connection steps, processing steps, comparison steps and decisions steps. These claims are directed to being a commonplace business method being applied on a general-purpose computer (see Alice Corp. Pty, Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357, 110 USPQ2d 1976, 1983 (2014)); Versata Dev. Group, Inc., v. SAP Am., Inc., 793 D.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) and require the use of software such as via a server to tailor information and provide it to the user on a generic computer. Based on all these, Examiner finds that when viewed either individually or in combination, these additional claim element(s) do not provide meaningful limitation(s) that raise to the high standards of eligibility to transform the abstract idea(s) into a patent eligible application of the abstract idea(s) such that the claim(s) amounts to significantly more than the abstract idea(s) itself. Accordingly, Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception (i.e. abstract idea exception) without significantly more.
Claim Rejections - 35 USC § 112
The terms “weather symbol analogy; centralities S; gravitational pull; centrality” in Claims 7, 8, 19 are relative terms which renders the claim indefinite. The terms are not defined by the claim and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For examination purposes the Examiner will interpret these indefinite terms as broadly and reasonably interpreted. Clarification is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian et al. (Subramanian) (US 2024/0304205) in view of Yaari et al. (Yaari) (US 2018/0046957).
With regard to Claims 1, 3, 5, 7, 11, 17-20, Subramanian in view of Yaari teaches a system/computer device/computer-implemented method for dynamically (interaction dynamics) generating and analyzing metadata for online meetings (In the case of sound separation systems required in modern day settings, such as for meeting transcription, the sound separation task is not only concerned with separating the speakers, but also transcribing the audio recordings of meetings into machine readable text and also with enriching the transcription with detailed information of the speakers. Such information is known as diarization information, that indicates “who spoke when” in the audio mixture of the meeting) (see at least paragraphs 2-5), the system comprising a computer device comprising at least one processor in communication with at least one memory device (see at least paragraphs 44-54), wherein the at least one memory device stores computer-implemented instructions that cause the at least one processor to:
receive at least one stream of at least one of audio and video of an online meeting (In the case of sound separation systems required in modern day settings, such as for meeting transcription, the sound separation task is not only concerned with separating the speakers, but also transcribing the audio recordings of meetings into machine readable text and also with enriching the transcription with detailed information of the speakers. Such information is known as diarization information, that indicates “who spoke when” in the audio mixture of the meeting. As is conventionally known, diarization is the process of separating an audio stream into different segments according to an identity of a speaker. Speaker diarization is thus, a combination of speaker segmentation and speaker clustering, where speaker segmentation deals with identification of speaker change points in the audio stream, and speaker clustering deals with grouping together speech segments based on speaker characteristics), wherein the at least one stream includes a plurality of participants participating (speakers) in the online meeting (online meeting transcript applications) (see at least paragraphs 3, 73);
extract a plurality of metadata from the at least one stream (the extraction module 118 may be used to combine the extracted time-frequency mask with the audio mixture 102 and generate the output 106 for each single target speaker from the multiple speakers. This output 106 is then rendered in one or more output modalities, including, but not limited to: text, speech, audio, video, audio-video, multi-media, or a combination thereof. For example, the text modality includes speech transcription data of each speaker of the multiple speakers, along with corresponding identity of the speaker. The output 106 generated by the deep neural network 110 is very accurate and of high quality and provides reliable speech transcription data, specifically in cases of online meeting transcription applications) (see at least paragraph 73);
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 plurality of participants in the online meeting (In the case of sound separation systems required in modern day settings, such as for meeting transcription, the sound separation task is not only concerned with separating the speakers, but also transcribing the audio recordings of meetings into machine readable text and also with enriching the transcription with detailed information of the speakers. Such information is known as diarization information, that indicates “who spoke when” in the audio mixture of the meeting. As is conventionally known, diarization is the process of separating an audio stream into different segments according to an identity of a speaker. Speaker diarization is thus, a combination of speaker segmentation and speaker clustering, where speaker segmentation deals with identification of speaker change points in the audio stream, and speaker clustering deals with grouping together speech segments based on speaker characteristics) (see at least paragraphs 2-4);
analyze the diarization information (in the sound processing system designed for multi-talker conversation analysis, two separate tasks of: speaker separation and speaker diarization, can be performed using two different neural networks, with each neural network trained separately for the task of interest. However, this will increase the overall complexity and computing requirements of the sound processing system, as separate effort would then need to be expended on training of each of the two neural networks) (see at least paragraph 11);
and generate visualization to be displayed to one or more participants (speakers are displayed) in the online meeting (meeting) (see at least paragraph 140; FIG. 1B, FIG. 5A);
Subramaniam does not specifically teach to calculate one or more key performance indicators (KPIs); of the key performance indicators. Yaari teaches to calculate one or more key performance indicators (KPIs); of the key performance indicators (the meeting management dashboard 260 may be responsible for generating managerial reports associated with online meetings. For example, the meeting management dashboard may generate key performance indicators (KPIs) associated with a given meeting, all meetings by meeting features, or any other features or variables associated with online meeting optimization system 200) in analogous art of online meetings for the purposes of: “determining effectiveness of online meetings and providing actionable recommendations/insights based, in part, on the determined effectiveness. The features may be detected from correlated meeting data, such as a meeting invitation, or may be determined from data that was sensed, recorded, or tracked during the meeting; for generating managerial reports associated with online meetings. For example, the meeting management dashboard may generate key performance indicators (KPIs) associated with a given meeting, all meetings by meeting features, or any other features or variables associated with online meeting optimization system 200” (see at least paragraphs 3, 88).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include online meeting optimization as taught by Yaari in the system of Subramanian, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With regard to Claim 2, Subramanian teaches wherein the online meeting is occurring in real-time (see at least paragraph 18).
With regard to Claim 3, Subramanian teaches wherein a group interaction intensity based on an average number of interactions for the plurality of participants in the online meeting (see at least paragraphs 4,59).
With regard to Claim 4, Subramanian teaches wherein the at least one processor is further programmed to generate a user interface to display the group interaction intensity as a weather symbol analogy (images; characteristics) (see at least paragraph 6).
With regard to Claim 5, Subramanian teaches wherein a number of times that each participant spoke, a total number of turns taken by all participants, and an average distance of each participant from the maximum number of turn taking performed by a participant (see at least paragraphs 4, 28).
With regard to Claim 6, Subramanian teaches wherein the at least one processor is further programmed to generate a user interface to display a visual representation of reverse average distance to the maximum of turn taking as a weather symbol analogy (see at least paragraphs 4-6, 28).
With regard to Claims 7, 18, Subramanian teaches wherein include a conversational gravity for all of the plurality of participants based on a plurality of centralities S for each participant of the plurality of participants and a gravitational pull for each participant based on that participant's centrality and the conversational gravity (see at least paragraphs 3-7, 28, 55-59, 124-130).
With regard to Claim 8, Subramanian teaches wherein the at least one processor is further programmed to generate a user interface to display at least one of a relative centrality of the plurality of participants of the online meeting and a visualization of strength and frequency of interactions (see at least paragraphs 3-7, 28, 55-59).
With regard to Claim 9, Subramanian teaches wherein the at least one processor is further programmed to determine a strength and frequency of interactions between each of the plurality of participants (see at least paragraphs 3-7, 28, 55-59).
With regard to Claim 10, Subramanian teaches wherein the at least one processor is further programmed to calculate a relative speaking time for each participant (see at least paragraphs 3-7, 28, 55-59).
With regard to Claim 11, Subramanian teaches subsequent to completion of the online meeting and transmitted to one or more participants of the online meeting (see at least paragraphs 3-7, 28, 55-59).
With regard to Claim 12, Subramanian teaches:
a meeting metadata capture module configured to collect data generated during online meetings, including participant speaking patterns and audio characteristics (see at least paragraphs 3-7, 28, 55-59);
a data processing and analysis module configured to process captured metadata using machine learning algorithms and statistical techniques to extract insights regarding participant behavior and group dynamics and generate meeting success indicators (see at least paragraphs 55-59, 124-130, 148);
a reporting and visualization module configured to generate reports and visualizations summarizing findings from data analysis (see at least paragraphs 55-59, 124-130, 148);
a recommendation module configured to provide recommendations to increase overall success of the online meeting based on scientific findings at least one of in real time during the online meeting and after the online meeting as a summary report (transcription data) (see at least paragraphs 14, 25).
With regard to Claim 14, Subramanian teaches wherein the data processing and analysis module employs diarization techniques to segment at least one stream of audio data (see at least paragraphs 80-82).
With regard to Claim 16, Subramanian teaches wherein the reporting and visualization module furnishes meeting participants and third-parties with real-time guidance or analysis after the online meeting, aiding in enhancing meeting success rates (see at least paragraphs 4, 82, 94).
With regard to Claim 18, Subramanian teaches wherein at least one of a group interaction intensity based on an average number of interactions for the plurality of participants in the online meeting, a number of times that each participant spoke, a total number of turns taken by all participants, and an average distance of each participant from the maximum number of turn taking performed by a participant (see at least paragraphs 59, 82-85, 94).
With regard to Claims 13, 15, Subramanian does not specifically teach:
wherein the meeting metadata capture module further captures metadata related to participant location and date/time of participation; wherein the reporting and visualization module generates visualizations such as graphs and charts to present the analyzed data in an easily interpretable format.
Yaari teaches wherein the meeting metadata capture module further captures metadata related to participant location and date/time of participation (user data, particularly in the form of event data and/or location data can be received by data collection (component 202 from one or more computing devices associated with a user; may construct a complementary or shadow calendar for a user, as described herein, which may be stored in user account(s) and activity data 242. As discussed hereinabove, user devices 244 may include data elements produced by user devices 102a-102b including, but not limited to, real-time user device location data and past user device location data related to prior meetings) (see at least paragraphs 37-38, 60);
wherein the reporting and visualization module generates visualizations such as graphs and charts to present the analyzed data in an easily interpretable format (user account(s) and activity data 242 can include data regarding user emails, texts, instant messages, calls, and other communications; social network accounts and data, such as news feeds; online activity; calendars, appointments, or other user data that may have relevance for determining meeting patterns, attendance models, or related meeting information; user availability; and importance, urgency, or notification logic. Embodiments of user account(s) and activity data 242 may store information across one or more databases, knowledge graphs, or data structures. In one example, user account(s) and activity data 242 may be determined using calendar information from one or more user calendars, such as office calendars, personal calendars, social media calendars, or even calendars from family members or friends of the user; Organizational profile) in analogous art of for the purposes of: “Organizational profile 246 may include organizational data related to the user (title, role, hierarchy, etc.). Organizational data may comprise any data relating to the user, particularly within the context of a the user's place of work, including an organizational group or department, an area of expertise or specialization, frequent contacts, networks (including business-related social networks or connections, such as Jammer, Lync, etc.), and reporting relationships, among others. User patterns 248 may include information relating to the user and meeting patterns, behavior, or models. For example, as will be discussed in more detail below, meeting patterns for the user determined by the inference engine 230 and effectiveness scores generated by effectiveness determiner 220 may be stored in user patterns 246a and/or 246b. Effectiveness determiner 220 is generally responsible for generating effectiveness scores that reflect an online meeting's effectiveness, and may be based, at least in part, on the meeting features determined by the meeting monitor 210” (see at least paragraphs 60-62).
It would have been obvious to one of ordinary skill in the art at the time of the invention to include online meeting optimization as taught by Yaari in the system of Subramanian, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
Tandon et al. (US 8890926)
Park et al. US 2025/0029632)
Wang et al. (US 11922951)
Xue, Yawen, et al. "Online streaming end-to-end neural diarization handling overlapping speech and flexible numbers of speakers." arXiv preprint arXiv:2101.08473 (2021).
Subramanian et al. (WO 2024185903 A1)
De La Rey Emile et al. (AU 2022377385 A1)
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THOMAS L. MANSFIELD
Examiner
Art Unit 3623
/THOMAS L MANSFIELD/Primary Examiner, Art Unit 3624
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