DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is responsive to a continuation application filed on June 24th, 2024. In this office action:
Claims 1-39 are pending.
Claims 1-39 are rejected.
Drawings
The drawings submitted on June 24th, 2024 have been considered and accepted.
Claim Objections
Claims 5-8, 16, and 18-19 are objected to because of the following informality:
“The computer system according to any one of claims 1 …” should read (Examiner’s suggestion) “The computer system according to [[ claim 1 …”
Claim 23 is objected to because of the following informality:
“wherein system server is further configured to sends the processed information to the coaching server for storage onto a database” should read (Examiner’s suggestion) “wherein system server is further configured to [[ send the processed information to the coaching server for storage onto a database.”
Claim 29 is objected to because of the following informalities:
“forwarding, including forwarding the processed transcript of the video conference to a coaching server” should read (Examiner’s suggestion) “forwarding, including forwarding the processed transcript of the video conference to [[the coaching server.”
“requesting, by a processor, from a communication server” should read (Examiner’s suggestion) “requesting, by [the processor, from a communication server.”
“parsing, by a processor, the file containing the transcript” should read (Examiner’s suggestion) “parsing, by [the processor, the file containing the transcript.”
Claim 30 is objected to because of the following informality:
“aligning, by a processor” should read (Examiner’s suggestion) “aligning, by [the processor.”
Claim 31 is objected to because of the following informality:
“appending, by a processor” should read (Examiner’s suggestion) “appending, by [the processor.”
Claim 32 is objected to because of the following informality:
“analyzing, by a processor” should read (Examiner’s suggestion) “analyzing, by [the processor.
Appropriate correction(s) is/are required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 29-39 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 29 recites the term “the termination” in "requesting and retrieving, including monitoring, by a processor, for the termination of a video conference on a video conferencing platform through an application programming interface hook.” The term “the termination” has never been introduced in the instant claim. Therefore, there is insufficient antecedent basis for this limitation in the claim.
For the purpose of examination, the Examiner interprets the claim as: requesting and retrieving, including monitoring, by a processor, for [[a termination of a video conference on a video conferencing platform through an application programming interface hook ...
Claims 30-39 are rejected under 35 U.S.C. 112(b) as they depend on the rejected claim 29.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-14, 16-26, and 28-39 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Gomes-Casseres et al. (Pub. No. US 2024/0152848), hereinafter Gomes.
Claim 1. Gomes discloses [a] computer system for video conference analysis, the computer system comprising:
a coaching server (Fig. 1; “Server 120-3”), configured to store data associated with one or more users, including a user name and a user role identifier being at least one of a worker and a coaching staff member (See Parag. [0058]; a server 120-3 that stores demographic information for people associated with a particular organization or institution in a private data store 126 ... names of particular people can be encrypted such that participation analysis application 108 cannot access unnecessary personally identifying information associated with participants ... private data store 126 can be organized into any suitable data structure; private data store 126 can be organized as a database for multi-variate analysis of data across courses, students, and/or instructors (user role identifier being a coaching staff member). See Parag. [0086]; the demographic information can include any suitable data about each participant, such as identifying information and a value associated with each of one or more demographic characteristics of interest. See also Parag. [0082]; an instructor coach[es] students. See Parag. [0100]; “employees.” See also Parag. [0043] [0062] [0102]);
a communication server (Fig. 1; “Communication Platform Server 120-1”) configured to host communications between the one or more users (See Parag. [0053]; a server 120-1 that is associated with a communication platform can execute a communication platform server application 104 that can facilitate communications (e.g., of audio, video, text, images, etc.) between computing devices 110 executing client applications. In some embodiments, each computing device 110 participating in a meeting can transmit audio and/or video to server 120-1, and server 120-1 can transmit audio and/or video received from multiple computing devices to other computing devices 110 participating in the meeting); and
a system server (Fig. 1; “Server 120-2”), including a processor (See Parag. [0070]; server 120 can include a processor 212) configured to interface between the coaching server and the communication server, wherein the system server processor is configured to retrieve assignment information between at least one of the one or more coaching staff member users and one or more of the worker users (See Parag. [0060]; a server 120-2 can execute communication analysis application 106 and/or participation analysis application 108. In such embodiments, server 120-1 (communication server) can communicate data used by communication analysis application 106 and/or participation analysis application 108 (e.g., audio data associated with one or more individual users, video data associated with one or more individual users, transcript data, etc.) to server 120-2 (system server configured to retrieve assignment information). For example, server 120-2 can request such information via an application program interface (API) ... server 120-2 can request transcript data from an API associated with server 120-1, and can use such transcript data to perform participation analysis using participation analysis application 108. See Parag. [0062]; server 120-3 (coaching server) can communication at least a portion of demographic details associated with one or more people in response to authorized requests for demographic information from a server executing participation analysis application 108. See Parag. [0084]; receive the demographic information from a server associated with an organization or institution with which at least a portion of the participants are affiliated ... an authorized computing device (e.g., server 120-2) can request at least a portion of demographic information via an API ... See also Parag. [0061]), and
wherein the system server processor is configured to perform a first interfacing task comprising retrieving information from the coaching server and engaging with an application programming interface of the communication server in order to initiate an event based on the retrieved assignment information (See Parag. [0060]; a server 120-2 (system server) can execute communication analysis application 106 and/or participation analysis application 108. In such embodiments, server 120-1 (communication server) can communicate data used by communication analysis application 106 and/or participation analysis application 108 (e.g., audio data associated with one or more individual users, video data associated with one or more individual users, transcript data, etc.) to server 120-2 (system server retrieve information). For example, server 120-2 can request such information via an application program interface (API) ... server 120-2 can request transcript data from an API associated with server 120-1, and can use such transcript data to perform participation analysis using participation analysis application 108 ... See Parag. [0062]; server 120-3 (coaching server) can communication at least a portion of demographic details associated with one or more people in response to authorized requests for demographic information from a server executing participation analysis application 108 (system server). See Parag. [0084]; an authorized computing device (e.g., server 120-2) can request at least a portion of demographic information via an API).
Claim 2. Gomes discloses [t]he computer system according to claim 1,
Gomes further discloses wherein the stored data for worker users further comprises coaching data (See Parag. [0058]; a server 120-3 that stores demographic information for people associated with a particular organization or institution in a private data store 126 ... names of particular people can be encrypted such that participation analysis application 108 cannot access unnecessary personally identifying information associated with participants ... private data store 126 can be organized into any suitable data structure; private data store 126 can be organized as a database for multi-variate analysis of data across courses (coaching data), students, and/or instructors. See also Parag. [0082]; an instructor coach[es] students).
Claim 3. Gomes discloses [t]he computer system according to claim 2,
Gomes further discloses wherein the coaching data includes one or more of one or more key performance indices and one or more coaching metrics associated with each of the plurality of worker users (See Parag. [0041]; mechanisms described herein can improving online learning experiences by facilitating evaluation of one or more participant's engagement throughout the educational process. For example, mechanisms described herein can analyze data indicative of engagement to automatically (e.g., without substantial user input) generate useful output and feedback to assist instructors engagement with students, evaluation of student performance, and feedback to students on their in-class performance).
Claim 4. Gomes discloses [t]he computer system according to claim 1,
Gomes further discloses wherein the coaching server further comprises a coaching server processor for processing the stored data (See Parag. [0070]; server 120 can include a processor 212).
Claim 5. Gomes discloses [t]he computer system according to any one of claims 1,
Gomes further discloses wherein the communication server is further configured to store video conference user data for the one or more users (See Parag. [0053]; a server 120-1 (communication server) that is associated with a communication platform can execute a communication platform server application 104 that can facilitate communications (e.g., of audio, video, text, images, etc.) between computing devices 110 executing client applications. In some embodiments, each computing device 110 participating in a meeting can transmit audio and/or video to server 120-1, and server 120-1 can transmit audio and/or video received from multiple computing devices to other computing devices 110 participating in the meeting).
Claim 6. Gomes discloses [t]he computer system according to any one of claims 1,
Gomes further discloses wherein the communication server further comprises a video conferencing processor (See Parag. [0070]; server 120 can include a processor 212) configured to host video conferences between the one or more users (See Parag. [0053]; a server 120-1 (communication server) that is associated with a communication platform can execute a communication platform server application 104 that can facilitate communications (e.g., of audio, video, text, images, etc.) between computing devices 110 executing client applications. In some embodiments, each computing device 110 participating in a meeting can transmit audio and/or video to server 120-1, and server 120-1 can transmit audio and/or video received from multiple computing devices to other computing devices 110 participating in the meeting).
Claim 7. Gomes discloses [t]he computer system according to any one of claims 1,
Gomes further discloses wherein the information retrieved from the coaching server includes the user name and the user role identifier (See Parag. [0062]; server 120-3 (coaching server) can communication at least a portion of demographic details associated with one or more people in response to authorized requests for demographic information from a server executing participation analysis application 108 (system server). See Parag. [0084]; an authorized computing device (e.g., server 120-2) can request at least a portion of demographic information via an API. See Parag. [0058]; a server 120-3 that stores demographic information for people associated with a particular organization or institution in a private data store 126 ... names of particular people can be encrypted such that participation analysis application 108 cannot access unnecessary personally identifying information associated with participants ... private data store 126 can be organized into any suitable data structure; private data store 126 can be organized as a database for multi-variate analysis of data across courses, students, and/or instructors (user role identifier)).
Claim 8. Gomes discloses [t]he computer system according to any one of claims 1,
Gomes further discloses wherein the system processor is further configured to perform a second interfacing task comprising retrieving information from the communication server, processing the information, and providing the processed information to the coaching server (See Parag. [0078]; a meeting transcript can be generated by a communication platform server (e.g., by communication platform server application 104) and/or by a different server (e.g., server 120-2) that may be associated with a participation analysis service that is not affiliated with the provider of the communication platform. In some embodiments, the file generated by process 300 at 306 can be accessible by authorized computing device (e.g., server 120-2, a particular computing device 110) and/or process 300 can store the file at a particular storage location specified by an organizer of the meeting (e.g., at a particular cloud storage location specified by the meeting organizer). See also Parag. [0060] [0091]).
Claim 9. Gomes discloses [t]he computer system according to claim 8,
Gomes further discloses wherein the retrieved information includes a transcript of a video conference held between two or more users, and the stored user data associated with the two or more users which held the video conference (See Parag. [0060]; a server 120-2 can execute communication analysis application 106 and/or participation analysis application 108. In such embodiments, server 120-1 can communicate data used by communication analysis application 106 and/or participation analysis application 108 (e.g., audio data associated with one or more individual users, video data associated with one or more individual users, transcript data, etc.) to server 120-2. For example, server 120-2 can request such information via an application program interface (API). In such embodiments, communication analysis application n 106 and/or participation analysis application 108 may be omitted from server 120-1 ... server 120-2 can request transcript data from an API associated with server 120-1, and can use such transcript data to perform participation analysis using participation analysis application 108. See also Parag. [0043] [0055]).
Claim 10. Gomes discloses [t]he computer system according to claim 8,
Gomes further discloses wherein the system server processor is further configured to communicate with the communication server to monitor for termination of a video conference in order to trigger the second interfacing task (See Parag. [0075]; an authorized computing device (e.g., server 120-2) can request at least a portion of a meeting record via the API, and data included in the meeting record can be provided to the authorized computing device using any suitable technique or combination of techniques (e.g., via one or more JavaScript Object Notation (JSON) data objects). In some embodiments, data can be generated at 304 in real time or near real time (e.g., before a meeting has ended), and can be provided as a stream of data during the meeting).
Claim 11. Gomes discloses [t]he computer system according to claim 8,
Gomes further discloses wherein the second interfacing task further comprises requesting and receiving information of a video conference held between two or more users, and processing the video conference information (See Parag. [0060]; a server 120-2 can execute communication analysis application 106 and/or participation analysis application 108. In such embodiments, server 120-1 (communication server) can communicate data used by communication analysis application 106 and/or participation analysis application 108 (e.g., audio data associated with one or more individual users, video data associated with one or more individual users, transcript data, etc.) to server 120-2 (system server configured to retrieve assignment information). For example, server 120-2 can request such information via an application program interface (API)).
Claim 12. Gomes discloses [t]he computer system according to claim 11,
Gomes further discloses wherein processing the video conference information further comprises extracting text from a transcript of the video conference, assigning user identity information to the sections of the transcript, and formatting the information in a manner suitable for delivery to the coaching server (See Parag. [0055]; analyze audio data and/or video data received from different devices (e.g., computing devices 110, and/or any other suitable devices, such as telephones) to generate a transcript of the meeting (e.g., including times and identifying information of participants in the meeting). In such an example, server 120-1 can associate identifying information associated with a particular computing device 110 with received audio, such as by associating a username of a user that is logged in to a computing device 110 with text in the transcript. See Parag. [0078]; a meeting transcript can be generated by a communication platform server (e.g., by communication platform server application 104) and/or by a different server (e.g., server 120-2) that may be associated with a participation analysis service that is not affiliated with the provider of the communication platform. In some embodiments, the file generated by process 300 at 306 can be accessible by authorized computing device (e.g., server 120-2, a particular computing device 110) and/or process 300 can store the file at a particular storage location specified by an organizer of the meeting (e.g., at a particular cloud storage location specified by the meeting organizer). See also Parag. [0091]).
Claim 13. Gomes discloses [t]he computer system according to claim 12,
Gomes further discloses wherein processing the video conference information further comprises analyzing the extracted text in order to identify one or more of a promise, an observation, assessment, commitment and a challenge for the two or more users (See Parag. [0042]; extract data from digital recordings of an online meeting (e.g., video, text, other records from online meetings, etc.), and use the data to analyze how participants in the meeting related to each other during the meeting. For example, the pattern of engagement in the meeting of individuals and categories of individuals can be analyzed. Such patterns of engagement can be used by meeting participants and/or organizers to improve products, services, and/or personal development. See Parag. [0043]; mechanisms described herein can use information from a transcript of a meeting to analyze behavior of participants (e.g., students, instructors, organizers, employees, etc.). For example, a technology platform used to facilitate the meeting (e.g., via video conferencing, via audio conferencing, etc.) can generate a transcript indicative of when each participant spoke and/or what each participant said (e.g., via a transcript). As another example, a technology platform used to facilitate the meeting can generate a record of when each participant was speaking even if the platform did not record what each participant said via a transcript. See also Parag. [0055]).
Claim 14. Gomes discloses [t]he computer system according to claim 13,
Gomes further discloses wherein the second interfacing task further comprises sending the identified user commitments to the coaching server, the coaching server further configured to store the identified user commitments as data associated with the one or more users which held the video conference (See Parag. [0048]; generate results formatted as one or more output files that can be used to evaluate participation and/or educational effectiveness ... generate one or more reports, one or more dashboards, etc., that can be presented to a non-participant (e.g., an administrator, a supervisor, a consultant, etc.) to provide insight into engagement in one or more meetings. See Parag. [0078]; a meeting transcript can be generated by a communication platform server (e.g., by communication platform server application 104) and/or by a different server (e.g., server 120-2) that may be associated with a participation analysis service that is not affiliated with the provider of the communication platform. In some embodiments, the file generated by process 300 at 306 can be accessible by authorized computing device (e.g., server 120-2, a particular computing device 110) and/or process 300 can store the file at a particular storage location specified by an organizer of the meeting (e.g., at a particular cloud storage location specified by the meeting organizer). See also Parag. [0091]).
Claim 16. Gomes discloses [t]he computer system according to any one of claims 1,
Gomes further discloses wherein the coaching server is a server for a coaching management software for contact center staff (See Parag. [0058]; a server 120-3 that stores demographic information for people associated with a particular organization or institution in a private data store 126 ... See also Parag. [0082]; an instructor coach[es] students. See Parag. [0100]; “employees.”), and the communication server is a server for a video conferencing platform (See Parag. [0053]; a server 120-1 that is associated with a communication platform can execute a communication platform server application 104 that can facilitate communications (e.g., of audio, video, text, images, etc.) between computing devices 110 executing client applications).
Claim 17. Gomes discloses [t]he computer system according to claim 3,
Gomes further discloses wherein the coaching server is further configured to process key performance indices data for worker users and generate a notification for a worker user and a coaching staff member user associated with the worker user when key performance indices data falls below a predetermined threshold (See Parag. [0048]; mechanisms described herein can generate results formatted as one or more output files that can be used to evaluate participation and/or educational effectiveness. For example, mechanisms described herein can generate one or more reports (notification), one or more dashboards, etc., that can be presented to a participant (e.g., a student, an instructor, a presenter, an audience member, etc.) to provide insight into engagement in one or more meetings. See Parag. [0127]; report can include bar graphs showing a percentage of total speech time attributed to students, total speech time attributed to an instructor(s), total speech time attributed to a guest speaker, and/or total speech time attributed to a student presentation. The instructor speech time can be further reported as speech time that represents lecturing (e.g., instructor speech times over 180 seconds of continuous speech), or discussion leadership (e.g., instructor speech times of less than 180 seconds). See Parag. [0128]; chart 702 can provide a basis for determining overall participation by demographic group and can illustrate changes in engagement over time. Such a chart can provide feedback to an instructor to help the instructor understand if certain classes elicited more or less engagement from students in particular demographic groups, and identify trends in engagement from students in particular demographic groups. See also Parag. [0129-0132]).
Claim 18. Gomes discloses [t]he computer system according to any one of claims 1,
Gomes further discloses wherein the coaching server is further configured to instruct the system server to initiate a video conference event (See Parag. [0062]; server 120-3 (coaching server) can communication at least a portion of demographic details associated with one or more people in response to authorized requests for demographic information from a server executing participation analysis application 108 (system server). See Parag. [0084]; an authorized computing device (e.g., server 120-2) can request at least a portion of demographic information via an API. See Parag. [0060]; a server 120-2 can execute communication analysis application 106 and/or participation analysis application 108).
Claim 19. Gomes discloses [t]he computer system according to any one of claims 1,
Gomes further discloses wherein the system server processor is further configured to: provide user data to the communication server in order to initiate a video conference event, to send a notification to the users associated with the video conference event, to monitor entry of all users associated with the video conference event into the video conference event, and to monitor the video conference event to determine when the video conference event concludes (See Parag. [0053]; communication platform server application 104 can execute one or more portions of process 300 described below in connection with FIG. 3 (e.g., receiving content at 302, generating data identifying users that participated in a meeting and/or other information related to a meeting at 304). See Parag. [0083]; receive demographic information about participants in a meeting. As described below, the demographic information can be used to aggregate individual educational effectiveness indicators for groups of participants that share one or more demographic characteristics. Patterns that emerge in aggregated educational effectiveness indicators can be used for a variety of purposes. For example, patterns in aggregated indicators can be used by an instructor to monitor their own performance (e.g., to monitor whether they exhibit a calling pattern, to monitor the pace of their class, and to monitor their own speech time, which may or may not be classified as speech when instructor is lecturing and speech when the instructor is leading, and/or participating in, a discussion. Patterns and trends in the data can act as feedback to an instructor on the effectiveness of their in-class pedagogy, which can lead to improvements in the instructor's behavior).
Claim 20. Gomes discloses [t]he computer system according to claim 12,
Gomes further discloses wherein system server processor is further configured to processes the transcript text to assign a user identifier and time stamp (See Parag. [0060]; a communication analysis application executed by server 120-1 can analyze audio associated with a meeting to generate a transcript of the meeting in which words spoken during the meeting are associated with particular participants, and a time stamp identifies the times of when those words were spoken, and a communication analysis application executed by server 120-2 can analyze the transcript and can identify speech instances attributable to a particular participant).
Claim 21. Gomes discloses [t]he computer system according to claim 12,
Gomes further discloses wherein system server processor is further configured to process the transcript text and to identify sections of the text as important (See Parag. [0043]; mechanisms described herein can use information from a transcript of a meeting to analyze behavior of participants (e.g., students, instructors, organizers, employees, etc.). For example, a technology platform used to facilitate the meeting (e.g., via video conferencing, via audio conferencing, etc.) can generate a transcript indicative of when each participant spoke and/or what each participant said (e.g., via a transcript). See also Parag. [0055]).
Claim 22. Gomes discloses [t]he computer system according to claim 8,
Gomes further discloses wherein system server is further configured to display the processed information on a graphical user interface, and is further configured to receive a user selection from the graphical user interface to select information from the text transcript and associating one or more of a promise, a commitment and a challenge with a user (See Parag. [0104]; one or more reports can be presented using a dashboard user interface that a user can navigate to cause reports related to different individuals and/or groups to be presented at various levels of granularity. For example, process 300 can cause reports to be presented using an instructor dashboard user interface, which can include data for one or more courses and/or one or class within a course. An instructor dashboard can include data for all classes and all students in a course. A visual representation of participation patterns and rankings by student performance can serve as aids in grading and feedback. Such information can be useful in evaluating pedagogy across courses and over time. In some embodiments, student names can be identified to the instructor in such a dashboard (e.g., to facilitate use of the information for grading). See Parag. [0126]; report 500 can include user interface elements 502 that graphically illustrate average shares of participants having various demographic characteristics, and user interface elements 504 that graphically illustrate how much of each class was recorded and not recorded (and how much was recorded silence). For example, user interface elements 502 can illustrate demographic patterns of students in the course. As another example, user interface elements 504 can illustrate the share of class time that was recorded and submitted for analysis (e.g., used to generate a transcript and/or other information indicative of engagement as described above in connection with 306 of FIG. 3). See also Parag. [0048] [0091]).
Claim 23. Gomes discloses [t]he computer system according to claim 8,
Gomes further discloses wherein system server is further configured to sends the processed information to the coaching server for storage onto a database (See Parag. [0058]; private data store 126 can be organized as a database (e.g., a relational database, a non-relational database). In a more particular example, private data store 126 can be organized as a database for multi-variate analysis of data across courses, students, and/or instructors).
Claim 24. Gomes discloses [t]he computer system according to claim 23,
Gomes further discloses wherein the system server is further configured to retrieve the processed information stored on the database of the coaching server, to display the processed information on a graphical user interface, and to receive a user selection from the graphical user interface to amend the processed information, wherein the amended processed information is updated on the database and stored on the coaching server (See Parag. [0104]; one or more reports can be presented using a dashboard user interface that a user can navigate to cause reports related to different individuals and/or groups to be presented at various levels of granularity. For example, process 300 can cause reports to be presented using an instructor dashboard user interface, which can include data for one or more courses and/or one or class within a course. An instructor dashboard can include data for all classes and all students in a course. A visual representation of participation patterns and rankings by student performance can serve as aids in grading and feedback. Such information can be useful in evaluating pedagogy across courses and over time. In some embodiments, student names can be identified to the instructor in such a dashboard (e.g., to facilitate use of the information for grading). See Parag. [0058]; private data store 126 can be organized as a database (e.g., a relational database, a non-relational database). In a more particular example, private data store 126 can be organized as a database for multi-variate analysis of data across courses, students, and/or instructors. See Parag. [0078]; a meeting transcript can be generated by a communication platform server (e.g., by communication platform server application 104) and/or by a different server (e.g., server 120-2) that may be associated with a participation analysis service that is not affiliated with the provider of the communication platform. In some embodiments, the file generated by process 300 at 306 can be accessible by authorized computing device (e.g., server 120-2, a particular computing device 110) and/or process 300 can store the file at a particular storage location specified by an organizer of the meeting (e.g., at a particular cloud storage location specified by the meeting organizer). See also Parag. [0091]).
Claim 25. Gomes discloses [t]he computer system according to claim 8,
Gomes further discloses wherein system server is further configured to display the processed information on a graphical user interface, and is further configured to receive a user selection from the graphical user interface to navigate through the processed information based on a selection criterion (See Parag. [0104]; one or more reports can be presented using a dashboard user interface that a user can navigate to cause reports related to different individuals and/or groups to be presented at various levels of granularity. For example, process 300 can cause reports to be presented using an instructor dashboard user interface, which can include data for one or more courses and/or one or class within a course. An instructor dashboard can include data for all classes and all students in a course. A visual representation of participation patterns and rankings by student performance can serve as aids in grading and feedback. Such information can be useful in evaluating pedagogy across courses and over time. In some embodiments, student names can be identified to the instructor in such a dashboard (e.g., to facilitate use of the information for grading)).
Claim 26. Gomes discloses [t]he computer system according to claim 25,
Gomes further discloses wherein the selection criterion comprises one or more of a timestamp, a user, or a keyword (See Parag. [0104]; one or more reports can be presented using a dashboard user interface that a user can navigate to cause reports related to different individuals and/or groups to be presented at various levels of granularity. For example, process 300 can cause reports to be presented using an instructor dashboard user interface, which can include data for one or more courses and/or one or class within a course. An instructor dashboard can include data for all classes and all students in a course. A visual representation of participation patterns and rankings by student performance can serve as aids in grading and feedback. Such information can be useful in evaluating pedagogy across courses and over time. In some embodiments, student names can be identified to the instructor in such a dashboard (e.g., to facilitate use of the information for grading) (selection criterion comprises a user)).
Claim 28. Gomes discloses [t]he computer system according to claim 8,
Gomes further discloses wherein the communication server is further configured to display the processed information on a graphical user interface, and the system server is further configured to receive a user selection from the graphical user interface to select information from the text transcript and associating one or more of a promise, a commitment and a challenge with a user (See Parag. [0104]; one or more reports can be presented using a dashboard user interface that a user can navigate to cause reports related to different individuals and/or groups to be presented at various levels of granularity. For example, process 300 can cause reports to be presented using an instructor dashboard user interface, which can include data for one or more courses and/or one or class within a course. An instructor dashboard can include data for all classes and all students in a course. A visual representation of participation patterns and rankings by student performance can serve as aids in grading and feedback. Such information can be useful in evaluating pedagogy across courses and over time. In some embodiments, student names can be identified to the instructor in such a dashboard (e.g., to facilitate use of the information for grading). See Parag. [0126]; report 500 can include user interface elements 502 that graphically illustrate average shares of participants having various demographic characteristics, and user interface elements 504 that graphically illustrate how much of each class was recorded and not recorded (and how much was recorded silence). For example, user interface elements 502 can illustrate demographic patterns of students in the course. As another example, user interface elements 504 can illustrate the share of class time that was recorded and submitted for analysis (e.g., used to generate a transcript and/or other information indicative of engagement as described above in connection with 306 of FIG. 3). See also Parag. [0048] [0091]).
Claim 29. Gomes discloses [a] method for video conferencing analysis comprising the steps of:
requesting and retrieving, including monitoring, by a processor (See Parag. [0070]; server 120 can include a processor 212), for the termination of a video conference on a video conferencing platform through an application programming interface hook (See Parag. [0074]; process 300 can generate data identifying participants in the meeting and details of the meeting. For example, process 300 can generate a file that includes details about the meeting, such as identifying information associated with the meeting (e.g., a semantically meaningful meeting name, a link used to join the meeting, a programmatically generated meeting identifier, etc.), the time the meeting started, the time the meeting ended (termination of a video conference), the length of the meeting, identifying information associated with participants in the meeting. See Parag. [0053]; a server 120-1 that is associated with a communication platform can execute a communication platform server application 104 that can facilitate communications (e.g., of audio, video, text, images, etc.) between computing devices 110 executing client applications ... communication platform server application 104 can maintain data related to users that participated in a meeting ... communication platform server application 104 can execute one or more portions of process 300 ), requesting, by a processor, from a communication server, a file containing a transcript of the video conference, and receiving, by a processor, from the communication server, the file containing the transcript of the conversation between one or more participants of the video conference (See Parag. [0060]; a server 120-2 can execute communication analysis application 106 and/or participation analysis application 108. In such embodiments, server 120-1 (communication server) can communicate data used by communication analysis application 106 and/or participation analysis application 108 (e.g., audio data associated with one or more individual users, video data associated with one or more individual users, transcript data, etc.) to server 120-2. For example, server 120-2 can request such information via an application program interface (API) ... server 120-2 can request transcript data from an API associated with server 120-1, and can use such transcript data to perform participation analysis using participation analysis application 108);
parsing, by a processor, the file containing the transcript of the conversation of the video conference to identify the one or more participants in the video conference, dividing the transcript into fragments, and assigning time stamps to each of said fragments (See Parag. [0060]; communication analysis application 106 implemented by server 120-2 can be an instance of the same communication analysis application that is executed by server 120-1, or can be an instance of a different application. For example, a communication analysis application executed by server 120-1 can analyze audio associated with a meeting to generate a transcript of the meeting in which words spoken during the meeting are associated with particular participants, and a time stamp identifies the times of when those words were spoken, and a communication analysis application executed by server 120-2 can analyze the transcript and can identify speech instances (fragments) attributable to a particular participant);
processing, including conducting, by the processor, further processing of the transcript of the video conference such that the transcript information is in a format comprehensible by a coaching server (See Parag. [0048]; generate results formatted as one or more output files that can be used to evaluate participation and/or educational effectiveness. For example, mechanisms described herein can generate one or more reports, one or more dashboards, etc., See Parag. [0099]; a report for an individual participant can include aggregated data that can be used as a basis for comparison between the individual's engagement and engagement by other participants. For example, a report can include total speech time by a participant in each meeting and average speech time for all participants, average speech time for participants in a same demographic category as the individual, etc. In some embodiments, a report for an individual participant can include text representing the student's participation. For example, all text in transcripts for classes that is attributed to a student can be included in a report for that student);
forwarding, including forwarding the processed transcript of the video conference to a coaching server (See Parag. [0048]; generate one or more reports, one or more dashboards, etc., that can be presented to a participant (e.g., a student, an instructor, a presenter, an audience member, etc.) to provide insight into engagement in one or more meetings. See Parag. [0104-0105]; one or more reports can be presented using a dashboard user interface that a user can navigate to cause reports related to different individuals and/or groups to be presented at various levels of granularity. For example, process 300 can cause reports to be presented using an instructor dashboard user interface, which can include data for one or more courses and/or one or class within a course. An instructor dashboard can include data for all classes and all students in a course ... an instructor dashboard can include data on each student for each class (e.g., speech time for individual students and a class average, speech instances and class average, etc.). A visual representation of participation patterns and text of student speech in each class can serve as aids in grading and feedback. See also Parag. [0082]; an instructor coach[es] students); and
entering the processed transcript of the video conference into the coaching server (See Parag. [0078]; a meeting transcript can be generated by a communication platform server (e.g., by communication platform server application 104) and/or by a different server (e.g., server 120-2) that may be associated with a participation analysis service that is not affiliated with the provider of the communication platform. In some embodiments, the file generated by process 300 at 306 can be accessible by authorized computing device (e.g., server 120-2, a particular computing device 110) and/or process 300 can store the file at a particular storage location specified by an organizer of the meeting (e.g., at a particular cloud storage location specified by the meeting organizer). See also Parag. [0091]. Examiner’s interpretation: Storing the file, including a transcript, at a particular storage location specified by an organizer of the meeting (e.g., at a particular cloud storage location specified by the meeting organizer) is interpreted as storing the file in a server (coaching server) to be accessed by authorized computing devices (instructor)).
Claim 30. Gomes discloses [t]he method according to claim 29,
Gomes further discloses wherein the processing step further includes aligning, by a processor, portions of the transcript of the video conference according to each of the one or more participants in the video conference (See Parag. [0060]; communication analysis application 106 implemented by server 120-2 can be an instance of the same communication analysis application that is executed by server 120-1, or can be an instance of a different application. For example, a communication analysis application executed by server 120-1 can analyze audio associated with a meeting to generate a transcript of the meeting in which words spoken during the meeting are associated with particular participants, and a time stamp identifies the times of when those words were spoken, and a communication analysis application executed by server 120-2 can analyze the transcript and can identify speech instances attributable to a particular participant).
Claim 31. Gomes discloses [t]he method according to claim 30,
Gomes further discloses wherein the processing step further includes appending, by a processor, time stamps to each portion of the transcript of the video conference (See Parag. [0060]; a communication analysis application executed by server 120-1 can analyze audio associated with a meeting to generate a transcript of the meeting in which words spoken during the meeting are associated with particular participants, and a time stamp identifies the times of when those words were spoken, and a communication analysis application executed by server 120-2 can analyze the transcript and can identify speech instances attributable to a particular participant).
Claim 32. Gomes discloses [t]he method according to claim 31,
Gomes further discloses wherein the processing step further includes analyzing, by a processor, the transcript of the video conference to identify information according to predetermined criteria (See Parag. [0060]; a communication analysis application executed by server 120-1 can analyze audio associated with a meeting to generate a transcript of the meeting in which words spoken during the meeting are associated with particular participants, and a time stamp identifies the times of when those words were spoken, and a communication analysis application executed by server 120-2 can analyze the transcript and can identify speech instances attributable to a particular participant).
Claim 33. Gomes discloses [t]he method according to claim 32,
Gomes further discloses wherein the predetermined criteria include keywords associated with coaching objectives (See Parag. [0103]; reports that illustrate a comparison of the predicted participation rate and each student's observed participation rate, can be used by students and/or instructors to monitor progress and goals for each student, which can lead to increased student engagement).
Claim 34. Gomes discloses [t]he method according to claim 29,
Gomes further discloses wherein the processing step further includes identifying, by the processor, actions, such as commitments, promises, or actionable items communicated by the one or more participants of the video conference (See Parag. [0042]; extract data from digital recordings of an online meeting (e.g., video, text, other records from online meetings, etc.), and use the data to analyze how participants in the meeting related to each other during the meeting. For example, the pattern of engagement in the meeting of individuals and categories of individuals can be analyzed. Such patterns of engagement can be used by meeting participants and/or organizers to improve products, services, and/or personal development. See Parag. [0043]; mechanisms described herein can use information from a transcript of a meeting to analyze behavior of participants (e.g., students, instructors, organizers, employees, etc.). For example, a technology platform used to facilitate the meeting (e.g., via video conferencing, via audio conferencing, etc.) can generate a transcript indicative of when each participant spoke and/or what each participant said (e.g., via a transcript). As another example, a technology platform used to facilitate the meeting can generate a record of when each participant was speaking even if the platform did not record what each participant said via a transcript. See also Parag. [0055]).
Claim 35. Gomes discloses [t]he method according to claim 34,
Gomes further discloses wherein the forwarding step further comprises sending the processed transcript of the video conference, including identified actions, to the coaching server (See Parag. [0078]; a meeting transcript can be generated by a communication platform server (e.g., by communication platform server application 104) and/or by a different server (e.g., server 120-2) that may be associated with a participation analysis service that is not affiliated with the provider of the communication platform. In some embodiments, the file generated by process 300 at 306 can be accessible by authorized computing device (e.g., server 120-2, a particular computing device 110) and/or process 300 can store the file at a particular storage location specified by an organizer of the meeting (e.g., at a particular cloud storage location specified by the meeting organizer). See also Parag. [0091]. Examiner’s interpretation: Storing the file, including a transcript, at a particular storage location specified by an organizer of the meeting (e.g., at a particular cloud storage location specified by the meeting organizer) is interpreted as storing the file in a server (coaching server) to be accessed by authorized computing devices (instructor). See Parag. [0048]).
Claim 36. Gomes discloses [t]he method according to claim 29,
Gomes further discloses wherein the forwarding step further comprises sending the transcript with modifications embedded within the transcript See Parag. [0060]; a communication analysis application executed by server 120-1 can analyze audio associated with a meeting to generate a transcript of the meeting in which words spoken during the meeting are associated with particular participants, and a time stamp identifies the times of when those words were spoken, and a communication analysis application executed by server 120-2 can analyze the transcript and can identify speech instances attributable to a particular participant.
Claim 37. Gomes discloses [t]he method according to claim 29,
Gomes further discloses wherein the forwarding step further comprises sending a separate document containing the modifications, and the coaching server further processes the transcript of the video conference (See Parag. [0078]; a meeting transcript can be generated by a communication platform server (e.g., by communication platform server application 104) and/or by a different server (e.g., server 120-2) that may be associated with a participation analysis service that is not affiliated with the provider of the communication platform. In some embodiments, the file (document) generated by process 300 at 306 can be accessible by authorized computing device (e.g., server 120-2, a particular computing device 110) and/or process 300 can store the file at a particular storage location specified by an organizer of the meeting (e.g., at a particular cloud storage location specified by the meeting organizer). See also Parag. [0048] [0091]).
Claim 38. Gomes discloses [t]he method according to claim 35,
Gomes further discloses wherein the entering step further comprises entering identified actions associated with one or more of the participants (See Parag. [0078]; a meeting transcript can be generated by a communication platform server (e.g., by communication platform server application 104) and/or by a different server (e.g., server 120-2) that may be associated with a participation analysis service that is not affiliated with the provider of the communication platform. In some embodiments, the file generated by process 300 at 306 can be accessible by authorized computing device (e.g., server 120-2, a particular computing device 110) and/or process 300 can store the file at a particular storage location specified by an organizer of the meeting (e.g., at a particular cloud storage location specified by the meeting organizer). See also Parag. [0091]).
Claim 39. Gomes discloses [t]he method according to claim 38,
Gomes further discloses wherein the method further comprises the step of monitoring coaching outcomes, wherein the coaching server compares identified actions for one or more of the participants to evaluate participants (See Parag. [0039]; mechanisms described herein can facilitate analysis of engagement by participants in an interactive remote meeting environment, such as a distributed educational environment. For example, as described below, mechanisms described herein can use indicators of engagement extracted from user-generated media content representing real-time communication between participants in an interactive remote meeting environment to evaluate the engagement of various participants in the remote meeting. In some embodiments, mechanisms described herein can utilize data related to participation by various participants to generate new and more accurate metrics for evaluating engagement in an interactive remote meeting environment).
Claim Rejections - 35 USC § 103
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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 15 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Gomes-Casseres et al. (Pub. No. US 2024/0152848), hereinafter Gomes; in view of Oh (Pub. No. US 2023/0135196).
Claim 15. Gomes discloses [t]he computer system according to claim 14,
Gomes doesn’t explicitly disclose wherein the computer system is further configured to trigger a subsequent video conference based on an identified user commitment.
However, Oh discloses wherein the computer system is further configured to trigger a subsequent video conference based on an identified user commitment (See Parag. [0095-0107] and Fig. 14; the video conferencing application 350, during the video conference, receives meeting information associated with a follow-up meeting ... the video conferencing application 350 may obtain availability from the subset of participants. To obtain their availability, the video conferencing application 350 may access one or more electronic calendars associated with one or more participants ... the video conferencing application 350 schedules the follow-up meeting with the video conference provider 310. To schedule the follow-up meeting, the video conferencing application 350 provides the time for the meeting and either a duration or and ending time for the meeting. In this example, the video conferencing application 350 also identifies each of the participants invited to the follow-up meeting, such as by providing email addresses, usernames, etc).
It would be obvious to one of ordinary skill in the art at the time before the effective filling date of the claimed invention to modify the computer system, taught by Gomes, to trigger a subsequent video conference based on an identified user commitment, as taught by Oh. This would be convenient to discuss topics raised during a video conference (Oh, Parag. [0011]).
Claim 27. Gomes discloses [t]he computer system according to claim 24,
Gomes doesn’t explicitly disclose wherein the coaching server is further configured schedule a video conference based on the amended processed information.
However, Oh discloses wherein the coaching server is further configured schedule a video conference based on the amended processed information (See Parag. [0095-0107] and Fig. 14; the video conferencing application 350, during the video conference, receives meeting information associated with a follow-up meeting ... the video conferencing application 350 may obtain availability from the subset of participants. To obtain their availability, the video conferencing application 350 may access one or more electronic calendars associated with one or more participants ... the video conferencing application 350 schedules the follow-up meeting with the video conference provider 310. To schedule the follow-up meeting, the video conferencing application 350 provides the time for the meeting and either a duration or and ending time for the meeting. In this example, the video conferencing application 350 also identifies each of the participants invited to the follow-up meeting, such as by providing email addresses, usernames, etc).
It would be obvious to one of ordinary skill in the art at the time before the effective filling date of the claimed invention to modify the computer system, taught by Gomes, schedule a video conference based on the amended processed information, as taught by Oh. This would be convenient to discuss topics raised during a video conference (Oh, Parag. [0011]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Stoops (US 2023/0080724) – Related art in the area of utilizing conversational artificial intelligence to train contact center agents, (Abstract; A system for utilizing conversational artificial intelligence (AI) to train contact center agents according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored therein that, in response to execution by the at least one processor, causes the system to place a virtual call from an automated training system to an agent device of an agent, connect the virtual call to a chatbot in response to establishing a communication connection with the agent device, transmit one or more statements from the chatbot, receive, from the agent device, one or more agent responses of the agent corresponding to the one or more statements, and analyze the one or more agent responses to determine one or more training characteristics associated with AI-based contact center training of the agent).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDELBASST TALIOUA whose telephone number is (571)272-4061. The examiner can normally be reached on Monday-Thursday 7:30 am - 5:30 pm.
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/Abdelbasst Talioua/Examiner, Art Unit 2445