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 .
Status of Claims
This action is in reply to the amendment filed on 02/12/2026.
Claims 1, 7, and 13 have been amended and are hereby entered.
Claims 24-27 have been canceled.
Claims 1-2, 4-8, 10-14, 19-20, and 22-23 are currently pending and have been examined.
This action is made FINAL.
Response to Arguments
Applicant’s arguments, see Pages 9-14, filed 07/13/2026, with respect to the 35 U.S.C. 101 rejections of claims 1-2, 4-8, 10-14, 19-20, and 22-27 have been fully considered but are not persuasive. The rejections of claims 24-27 have been withdrawn because claims 24-27 have been canceled, but the 35 U.S.C. 101 rejections of claims 1-15 and 19-23 have been maintained.
After summarizing the steps of eligibility analysis on pages 9-10, Applicant discusses that a claim may be eligible when directed to an improvement in technology. Applicant discusses the Desjardins case and the 12/05/2025 memo regarding the decision. Finally, Applicant notes from the 08/04/2025 Memo that a rejection should be made only if it is more likely than not that the claim is ineligible.
Regarding the present claims, Applicant argues across pages 10-11 that the amended claims do not recite an abstract idea. Applicant argues that the generation of a transcript from audio signals, associating device identifying information with components of transcript text, extracting educational effectiveness indicators from audio and visual signals in real time, and generating reports in real time allegedly preclude the claim from reciting a judicial exception. Applicant emphasizes the “real-time” nature of these limitations as further precluding the claim from reciting a judicial exception. Examiner respectfully disagrees.
Applicant appears to be arguing that the transcript generation and device association cannot be part of the abstract idea because the generation is based on “audio signals” and the “digital construct” of a device identifier. However, Examiner notes that a human can identify particular people speaking in a meeting and generate a transcript based on the spoken words and actions of people in the meeting. In addition to the teacher aide example discussed previously, court stenographers generate transcripts of proceedings with identifies of the participants of the proceedings. The present claims reciting that the speech is in the form of audio signals and that a device identifier is being used to identify participants instead of a person’s name does not preclude the claim from reciting an abstract idea. The execution of the abstract idea in a computing environment requires further analysis at Prong Two and Step 2B, but the mere recitation of “audio signals” and “device identifying information” (which Examiner notes need not even be digital and instead be a name or number assigned to the device in a table, for example) does not automatically prevent the claim from reciting an abstract idea. Regarding the educational effectiveness indicators, Examiner notes that paragraph [0080] of the specification explicitly considers tracking of the number of times a participant has spoken in a meeting. Examiner respectfully disagrees with Applicant’s assertion that an aide or other human observer could not keep track of the number of instances participants speak in a meeting through tallies on a class roster or similar note taking method. Similarly, a human observer can note which participants have their cameras on or off during the meeting, which is another educational effectiveness indicator according to the present disclosure.
As far as the execution of the report generation and other argued limitations being done in “real-time”, Examiner notes that while the “real-time” nature of the limitations may be an additional element and will be analyzed in later steps, the mere recitation that a function is done “in real-time” does not mean that the claimed function is no longer an abstract idea. Examiner also respectfully disagrees that the argued limitations have no human activity analog. The generation of a transcript with identification of speaking participants is a human activity as discussed above, the “extraction” of educational effectiveness intervals explicitly covers counting the number of times each participant speaks, and the generation of reports can be done by taking the previously generated transcript and indicator information and manipulating the data to output a report or summary. While these features are being performed in a computing environment which will be analyzed at Step 2A Prong Two and 2B, the fact that they are being performed “in real-time” in a computing environment does not prevent the claims from reciting an abstract idea at Step 2A Prong One. Applicant’s arguments at Prong One are not persuasive, and analysis moves to Prong Two.
Next, across pages 11-13, Applicant argues that the amended claims integrate any judicial exception into a practical application at Step 2A Prong Two. Applicant argues that the limitations at issue in Prong One reflect a technological improvement to address technical constraints imposed by limited screen space and limited observational capacity. Examiner respectfully disagrees. First, as discussed in the 03/24/2026 Non-Final Rejection, the claims at issue only require one student to be a part of the class session. Accordingly, Applicant’s arguments regarding the number of students being too much information to handle and observe on a screen are not persuasive, as the claimed invention explicitly covers a scenario in which the teacher only has to manage one student. Secondly, Applicant appears to argue that the issues of class monitoring and observing are a technical challenge rooted in remote classes/learning. However, observational issues in a class are not limited to technology. Even in an in-person classroom, a single teacher would not be able to take a transcript of the class, observe each and every student continuously, and actually teach the class. Additional human aides/observers would be able to prepare such information on behalf of a teacher. Accordingly, the problems being resolved in the claimed invention are not strictly technical in nature and would be present in offline classrooms as well in the form of a limitation on the amount of observation that can be performed by a teacher while still teaching a class.
Furthermore, the alleged technical solution is not reflected in the claims such that one of ordinary skill in the art would recognize the claimed invention as an improvement to technology. Specifically, MPEP 2106.05(a) recites “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement”. Regarding the transcript generation, paragraphs [0055] and [0077]-[0078] recite that the transcript can be generated by the server and communication analysis application 106 in “any suitable” file format, but beyond the high level recitation of “analyzing” audio/video data to generate an output of who said what in the transcript, the technical process of how the audio signals are analyzed is not provided in the disclosure. Similarly, [0055] recites that the communication analysis application “can accurately attribute speech or other activity with a particular user by using audio and/or video received from a particular device associated with the user to generate a portion of a transcript”, but the only detail provided as to how this identification is accomplished is “analyzing” audio from each device. Here, instead of indicating a technological improvement, the claims and specification more closely fall into the MPEP 2106.05(f) “When determining whether a claim simply recites a judicial exception with the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, examiners may consider the following: (1) Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it"”. By merely stating that an application generates a transcript by analyzing the audio data and identifies a user speaking by analyzing the audio data from each device without providing technical detail as to how such analysis is performed , the claims and specification are indicative of applying some type of speech-to text software to generate a transcript instead of a human being recording what they hear each person in a meeting say. Regarding the effectiveness indicators, as discussed above the effectiveness indicators include indicators that can be recognized by a human like the number of times a participant speaks. Regarding the generation of reports, the reports themselves as discussed in [0090]-[0113] cover abstract data manipulation (average speech times, aggregating data across categories of participants and are displayed on a user interface. The determining of effectiveness indicators and generation of reports are abstract ideas (observation of behavior/speech, and data manipulation, respectively) that are being performed in a computing environment and using computers as a tool. See MPEP 2106.05(f) “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”.
Regarding these features being performed “in real-time”, Examiner notes that MPEP 2106.05(a) also states “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality:… ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)”. In the claimed invention, while the processes being performed “in real-time” may be done faster than a human(s) could perform them, this increased speed in which transcripts are generated, indicators extracted, and reports generated is provided by the speed of the generic computing component being used as a tool to perform the process. Paragraph [0067] of the specification as filed explicitly states that the processor “can be any suitable hardware processor or combination of processors, such as a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a field- programmable gate array (FPGA), etc.” Accordingly, the “real-time” speed of the processes at issue is provided by capabilities of a generic processor.
Regarding Applicant’s arguments on pages 12-13 regarding the teacher aide, Examiner notes that the claimed processes being argued are abstract ideas being applied using generic computing components. Examiner is not arguing that a human teacher aide could perform the analysis of digital audio signals, etc. In fact, the rejections both below and in the 03/24/2026 Office Action do not classify the claims as reciting a Mental Process. Instead the teacher aide analogy is to show that the acts of generating a transcript or noting how many times each student speaks in a class, for example, fall into the judicial exception of managing personal behavior and interactions between people. As discussed above, these abstract ideas are being applied in a computing environment instead of an analog/in-person environment (i.e. using digital audio signals instead of a human listening to another human speak in a classroom).
Regarding Applicant’s Desjardins arguments, Examiner points to the above discussion of how the claims and specification as filed do not reflect an improvement to technology and instead reflect applying a judicial exception using generic computing components. Thus, the claimed invention is not analogous to that of Desjardins.
Regarding the arguments of page 13 that the argued functions meaningfully limit any abstract idea, Examiner respectfully disagrees. In particular, Examiner notes again that the transcript generation is recited as being performed by an application on a server, but no technical explanation of how the application “analyzes” the audio/video data to generate the transcript is provided in the claims or specification. This recitation of the goal of the server/application without reciting the details about how the transcript is generated falls into the MPEP 2106.05(f) analysis that the additional element is applying the judicial exception using generic computing components. The other argued limitations, as discussed above, are also not improvements to technology and are thus not meaningfully limiting the judicial exception.
Finally, Applicant’s arguments that the eligibility rejections of the claimed invention should be dropped because the claims are allegedly more likely than not to be eligible are not persuasive for the reasoning discussed above. Based on analysis of the limitations at issue (in combination with the other additional elements not particularly argued by Applicant), the additional elements as an ordered combination fall into the “apply it” analysis. Accordingly, the claims still do not integrate their judicial exception into a practical application. Analysis moves to step 2B.
Applicant argues on Page 14 that the Office Action’s Step 2B analysis lacks evidentiary support that the ordered combination of additional elements are well-understood, routine, and conventional, and that the lack of evidentiary support fails to satisfy the requirements of an ineligibility determination at Step 2B. Examiner respectfully disagrees. Per MPEP 2106.05 II. “Although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should: • Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two; • Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h): • Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and • Evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d). (emphasis added)”. As discussed above and in the rejection below, the ordered combination of additional elements falls under the “apply it” analysis discussed in MPEP 2106.05(f). As shown above, the MPEP’s discussion of Step 2B analysis explicitly states that the conclusions from this section are carried over as the conclusions of Step 2B. Only additional elements determined to be insignificant extra-solution activity at Step 2A prong Two are to be reevaluated, and there are no such additional elements in the claimed invention. Thus, instead of lacking required evidence that the additional elements are well-understood, routine, and conventional, the Office Action’s Step 2B analysis followed the explicit instructions of the MPEP and carried over Prong Two conclusions. Per MPEP 2016.05(f) “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".” Accordingly, Applicant’s amendments regarding the additional elements being significantly more than the judicial exception at Step 2B and Applicant’s arguments against the Office Action’s Step 2B analysis process are not persuasive.
Claims 1-2, 4-8, 10-14, 19-20, and 22-23 still stand rejected under 35 U.S.C. 101.
Applicant’s arguments, see Pages 14-16, filed 07/13/2026, with respect to the 35 U.S.C. 103 rejection of claim 1 have been fully considered but are not persuasive. The 35 U.S.C. 103 rejection of claim 1 has been maintained.
After summarizing the rejections across pages 14-15, Applicant argues that the combination of Nelson and Peters does not teach the newly amended limitation of "associate identifying information of the plurality of communication devices with components of the audio communications and of the video communications by at least generating a transcript of at least a portion of the live educational process based on the audio signals and associating the identifying information of the plurality of communication devices with text in the transcript.". Applicant argues that Nelson does not teach generating transcripts, but acknowledges that Nelson teaches logging the content of each speaker in [0059]. Applicant then argues that Peters’ transcript generation is not for real-time association of device identifying information with the transcript during a live educational process. Accordingly, Applicant argues that Nelson and Peters do not teach generating a transcript based on audio signals and associating device identifying information with text in the transcript. Examiner respectfully disagrees that the combination of Nelson and Peters does not teach this amended limitation.
First, Examiner has acknowledged that Nelson does not explicitly teach the generation of a transcript (see at least the 35 U.S.C. 103 rejection of claim 20 in the 03/24/2026 Office Action). Therefore, Examiner agrees that Nelson alone does not teach the argued limitation. However, Applicant’s argument that Peters does not teach the generation of a transcript based on audio signals and associating device identifying information with text in the transcript is unpersuasive. Peters explicitly state that the video conference occurring can be “a class, a lecture, a web-based seminar” in [0003] and providing information to a teacher in [0005]. Therefore, Applicant’s argument that Peters does not consider an educational process is unpersuasive. Furthermore, Peters explicitly teaches that transcripts are generated identifying the speaker and the words spoken by each participant in [0323]. Peters [0084]-[0085] further recite that audio recognition is used on audio signals to determine which words each participant has said as well as the speaking time of each individual. As both Nelson and Peters explicitly consider tracking the content of each speaker, the combination of Nelson and Peters teaches the amended limitations of generating a transcript.
Next, Applicant argues across pages 15-16 that Nelson and Peters allegedly do not teach the generation of reports “in real time”. Examiner respectfully disagrees. Specifically, while Examiner agrees that Nelson alone does not teach the generation of reports in real time, Examiner holds that the combination of Nelson and Peters explicitly teaches real-time report generation. Specifically, Peters [0110] teaches [0110] “an embodiment of the system can include endpoints or participant devices that communicate with one or more servers to perform analysis of participants' emotions, engagement, participation, attention, and so on, and deliver indications of the analysis results, e.g., in real-time along with video conference data or other communication session data and/or through other channels, such as in reports, dashboards, visualizations (e.g., charts, graphs, etc.)”. Paragraphs [0066]-[0067] of Peters further states “the video conference moderator may utilize facial recognition and other technology to dynamically monitor and track the emotional status and response of each participant in order to help measure and determine the level and quality of participation, which is output, in real time, as a representation (e.g., symbol, score, or other indicator) to a meeting organizer or person of authority”. Thus, Peters explicitly teaches performing real-time reporting on participants’ participation, engagement, and attention in the remote class. Applicant’s argument that Peters’ teaching of reporting the real-time participation information as an overlay on the organizer/teacher feed somehow disqualifies the reporting on educational effectiveness indicators from being “a report” is unpersuasive because the broadest reasonable interpretation of “report” does not preclude real-time information about students/participants being presented alongside the teacher video. Specification [0113], which discusses real-time reporting, only uses a dedicated dashboard as only an example of where the reports are updated, and does not preclude reporting and video feeds from being shown concurrently.
Furthermore, even if the claims somehow precluded outputting reports alongside the video feed, which Examiner does not concede, the combination of Nelson and Peters would still teach the limitation anyway. Particularly, Nelson teaches a reporting dashboard with a selectable level of granularity to review effectiveness indicators like time speaking, number of instances speaking, etc. Incorporating the real-time nature of Peters’ reporting on an ongoing class into the reporting structure of Nelson teaches the generation of reports using effectiveness indicators and demographic information at a selected level of granularity in real time as is required by the amended claim.
Therefore, Nelson and Peters, in combination, teach the ordered combination of elements that Applicant argues on page 16 (generation of transcripts from audio signals, associating identifying information with transcript text, extracting effectiveness indicators from both audio and video in real-time, accessing demographic databases, and generating multi-granularity reports based on demographics in real time. Nelson teaches the recording of spoken content from audio signals and identifying speakers, extracting effectiveness indicators such as the number of times speaking for each participant, and generating reports at the individual and group level based on demographic information. Peters teaches the generation of a transcript from audio signals for each identified participant, extracting effectiveness indicators from both audio and video signals, accessing demographic information from a database, and the real time nature of reporting. Thus, while Nelson alone does not teach all of the argued features, the combination of Nelson and Peters teaches the entirety of claim 1.
Finally, Applicant argues that the Office’s motivation for combining Nelson and Peters is impermissible hindsight. Applicant particularly argues that one of ordinary skill in the art would not have been motivated to combine Nelson’s in-person class recording system with the “complex emotional-analysis video conferencing infrastructure” of Peters because the references allegedly address different problems. Examiner respectfully disagrees. First, Examiner notes that both Nelson [0013] “although the disclosure relates to an example of recording an audio discussion, the technology described herein can apply to video discussions, such as webcast or video conferencing meetings, classes” and Peters [0005] “With a large audience, the presenter cannot reasonable read the emotional cues from each member of the audience. Detecting these cues is even more difficult with remote, device-based, interactions rather than in-person interactions. To assist a presenter and enhance the communication session, the system can provide tools with emotional intelligence, reading verbal and non-verbal signals to inform the presenter of the state of the audience” explicitly consider applying their respective technologies using remote, video classroom instruction. Furthermore, both Nelson [0010] and Peters [0005] state that their respective technologies have the goal of evaluating participation and engagement in the classroom. Therefore, one of ordinary skill in the art would have recognized that both Nelson and Peters share a similar goal in gathering feedback on the efficacy of classroom/remote classroom instruction. As cited in the motivation to combine Peters with Nelson, Peters [0005] explicitly states that the real-time feedback and analysis of the ongoing class would provide the instructor/teacher with the ability to analyze how the current class is being received and allow the instructor/teacher to modify the ongoing class to better suit the needs of the students. This motivation along with Nelson’s focus in [0003]-[0006] that educators need to be focused on ensuring participants are not being overly dominant or marginalized in a discussion would cause one of ordinary skill in the art to recognize the value in combining the real-time nature of Peters with Nelson. By incorporating Peters, the analysis of classroom engagement/participation can be done and adjusted on the fly instead of only reviewed in hindsight as in Nelson. Accordingly, one of ordinary skill in the art would have had motivation to combine the teachings of Nelson and Peters without impermissible hindsight. Applicant’s arguments against the combination of Nelson and Peters teaching claim 1 are unpersuasive. Claim 1 still stands rejected under the combination of Nelson and Peters.
Applicant’s arguments against the prior art rejections of independent claims 7 and 13 on page 17 of Remarks are unpersuasive for similar reasoning as discussed above regarding the rejection of claim 1.
Applicant’s arguments against the prior art rejections of the dependent claims, namely that the claims are over the prior art by virtue of their dependence on their respective independent claims, are also unpersuasive for the reasoning discussed above. Applicant’s arguments against the Christ and Bixler references in regards to the transcript generation, device to transcript association, and real-time reporting are moot because the Christ and Bixler references are not being used to teach these limitations. Accordingly, all of claims 1-2, 4-8, 10-14, 19-20, and 22-23 still stand rejected under 35 U.S.C. 103.
Claim Objections
Claim 13 is objected to because of the following informalities:
Claim 13 recites “…at least the audio signals, the video signals communications, and an operation of the…” when it appears Applicant intended the claim to recite “…at least the audio signals, the video signals
Appropriate correction is required.
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 1-2, 4-8, 10-14, 19-20, and 22-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite determining the effectiveness of an educational process.
As an initial matter, claims 1-2, 4-6, and 19-20 fall into at least the machine category of statutory subject matter. Claims 7-8, 10-12, and 22 fall into at least the process category of statutory subject matter. Finally, claims 13-14 and 23 fall into at least the manufacture category of statutory subject matter. Therefore, all claims fall into at least one of the statutory categories. Eligibility analysis proceeds to Step 2A.
Claim 1 recites the concept of determining the effectiveness of an educational process which is a certain method of organizing human activity including Managing Personal Behavior or Relationships or Interactions Between People. Managing education processes in a distributed education environment, comprising: receive from a plurality of one or more students, information about a live educational process being experienced in a distributed education environment where at least an educator is remotely located from the one or more students and the distributed education environment facilitates communication between the educator and the one or more students including at least audio communications; receive, from the educator, a selection of a level of granularity; associate identifying information with components of the audio communications and of the video communications by at least generating a transcript of at least a portion of the live educational process and associating the identifying information with text in the transcript; extract one or more educational effectiveness indicators from at least the audio and an operation of the distributed education environment during the live educational process, wherein the one or more educational effectiveness indicators include at least one of a number of audio communications by each of the one or more students during the live educational process, length of audio communications by each of the one or more students during the live educational process, number of audio interactions by each of the one or more students during the live educational process, an on/off status of respective cameras of each of the one or more students during the live educational process, or a gaze direction of each of the one or more students during the live educational process; access demographic information about the one or more students and correlate the demographic information with the one or more students; and generate a plurality of reports with regard to the live educational process about individual students of the one or more students and groups within the one or more students using the one or more educational effectiveness indicators and the demographic information, corresponding to the selected level of granularity all, as a whole, fall under the category of Managing Personal Behavior or Relationships or Interactions Between People. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a system, a computer system, at least one processor, a plurality of communication devices respectively associated with one or more students, generating a transcript based on audio signals, receiving and extracting indicators from audio signals, receiving and extracting indicators from video signals, a communication device associated with the educator, and at least one database. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Further, the additional elements of the communications, extraction of indicators, and report generation being “real-time” or “near real time” also amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a system, a computer system, at least one processor, a plurality of communication devices respectively associated with one or more students, generating a transcript based on audio signals, receiving and extracting indicators from audio signals, receiving and extracting indicators from video signals, a communication device associated with the educator, and at least one database amounts to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional element of the communications, extraction of indicators, and report generation being “real-time” or “near real time” also amounts to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 2 and 4 further limit the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 5 further limits the abstract idea of claim 4 while introducing the additional element of a registration database. The claim does not integrate the abstract idea into a practical application because the element of a registration database is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 4 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 6 further limits the abstract idea of claim 4 while introducing the additional element of a registration database. The claim does not integrate the abstract idea into a practical application because the element of a registration database is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 4 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 7 recites the concept of determining the effectiveness of an educational process which is a certain method of organizing human activity including Managing Personal Behavior or Relationships or Interactions Between People. A method for managing education processes in a distributed education environment, comprising: receiving, from a plurality of one or more students, information about a live educational process being experienced in a distributed education environment where at least an educator is remotely located from the one or more students and the distributed education environment facilitates communication between the educator and the one or more students including at least audio communications; receiving, from the educator, a selection of a level of granularity; associating identifying information of the plurality of communication devices with components of the audio communications and of the video communications by at least generating a transcript of at least a portion of the live educational process and associating the identifying information with text in the transcript; extracting one or more educational effectiveness indicators from at least the audio and an operation of the distributed education environment during the live educational process, wherein the one or more educational effectiveness indicators include at least one of a number of audio communications by each of the one or more students during the live educational process, length of audio communications by each of the one or more students during the live educational process, number of audio interactions by each of the one or more students during the live educational process, an on/off status of respective cameras of each of the one or more students during the live educational process, or a gaze direction of each of the one or more students during the live educational process; accessing demographic information about the one or more students and correlating the demographic information with the one or more students; and generating a plurality of reports with regard to the live educational process about individual students of the one or more students and groups within the one or more students using the one or more educational effectiveness indicators and the demographic information, corresponding to the selected level of granularity all, as a whole, fall under the category of Managing Personal Behavior or Relationships or Interactions Between People. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a plurality of communication devices respectively associated with one or more students, generating a transcript based on audio signals, receiving and extracting indicators from audio signals, receiving and extracting indicators from video signals, a communication device associated with the educator, and at least one database. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Further, the additional elements of the communications, extraction of indicators, and report generation being “real-time” or “near real time” also amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a plurality of communication devices respectively associated with one or more students, generating a transcript based on audio signals, receiving and extracting indicators from audio signals, receiving and extracting indicators from video signals, a communication device associated with the educator, and at least one database amounts to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional elements of the communications, extraction of indicators, and report generation being “real-time” or “near real time” also amount to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claims 8 and 10 further limit the abstract idea of claim 7 without adding any new additional elements. Therefore, by the analysis of claim 7 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 11 further limits the abstract idea of claim 10 while introducing the additional element of a registration database. The claim does not integrate the abstract idea into a practical application because the element of a registration database is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 10 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 12 further limits the abstract idea of claim 10 while introducing the additional element of a registration database. The claim does not integrate the abstract idea into a practical application because the element of a registration database is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 10 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 13 recites the concept of determining the effectiveness of an educational process which is a certain method of organizing human activity including Managing Personal Behavior or Relationships or Interactions Between People. A method for managing education processes in a distributed education environment, the method comprising: receiving information from a plurality of sources about a live educational process being experienced in a distributed education environment where at least an educator is remotely located from one or more students and the distributed education environment facilitates communication between the educator and the one or more students including at least audio communications and video communications; receiving a selection of a level of granularity from a source associated with the educator; associating identifying information of the plurality of sources with components of the audio communications and of the video communications by at least generating a transcript of at least a portion of the live educational process and associating the identifying information of the plurality of sources with text in the transcript; extracting one or more educational effectiveness indicators from at least the audio, the video communications, and an operation of the distributed education environment during the live educational process, wherein the one or more educational effectiveness indicators include at least one of a number of audio communications by each of the one or more students during the live educational process, length of audio communications by each of the one or more students during the live educational process, number of audio interactions by each of the one or more students during the live educational process, an on/off status of respective cameras of each of the one or more students during the live educational process, or a gaze direction of each of the one or more students during the live educational process; accessing demographic information about the one or more students and correlating the demographic information with the one or more students; generating a plurality of reports with regard to the live educational process about individual students of the one or more students and groups within the one or more students using the one or more educational effectiveness indicators and the demographic information, corresponding to the selected level of granularity; receiving information from the plurality of sources about a plurality of live educational processes across an educational institution being experienced in the distributed education environment; and aggregating one or more educational effectiveness indicators and the plurality of reports across the plurality of live educational processes, wherein the demographic information includes the educational institution or a part of the educational institution all, as a whole, fall under the category of Managing Personal Behavior or Relationships or Interactions Between People. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a non-transitory computer readable medium containing computer executable instructions, a processor, generating a transcript based on audio signals, receiving and extracting indicators from audio signals, receiving and extracting indicators from video signals, at least one database, and a registration database. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Further, the additional elements of the communications, extraction of indicators, and report generation being “real-time” or “near real time” also amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a non-transitory computer readable medium containing computer executable instructions, a processor, generating a transcript based on audio signals, receiving and extracting indicators from audio signals, receiving and extracting indicators from video signals, at least one database, and a registration database amounts to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional elements of the communications, extraction of indicators, and report generation being “real-time” or “near real time” also amount to no more than mere instructions to apply the exception using generic computer components. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 14 further limits the abstract idea of claim 13 without adding any new additional elements. Therefore, by the analysis of claim 13 above, claim 14 does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claims 19-20 further limit the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claims 22 further limits the abstract idea of claim 7 without adding any new additional elements. Therefore, by the analysis of claim 7 above this claim does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claim 23 further limits the abstract idea of claim 13 while introducing the additional element of extracting indicators from audio signals and video signals. The claim does not integrate the abstract idea into a practical application because the element of extracting indicators from audio signals and video signals is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional elements from claim 13 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 7-8, 19-20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson (U.S. Pre-Grant Publication No. 2018/0033332, hereafter known as Nelson) in view of Peters et al. (U.S. Pre-Grant Publication No. 2021/0076002, hereafter known as Peters).
Regarding claim 1, Nelson teaches:
A system for managing education processes in a (see [0062] "the system 10 can a user interface 12 displaying a menu 14 where users may select an existing group 16 or create a new group" and [0099]-[0104], particularly [0100] for the system comprising a PC with a CPU with a memory storing instructions for execution to the CPU. See [0096] and [0064] and [0010] for the system managing a classroom environment)
receive, from a plurality of communication devices audio signals representing audio communications (see [0059] "the system can automatically identify the different participants by voice, wherein the system can automatically record and log the duration of time spoken, number of times spoken, and/or content of each speaker in real time. The system can include optional cameras, microphones, and/or audio speakers, among other recording and replay technologies" for receiving audio signals representing audio communications from a variety of communication devices of the classroom environment. See [0061] and [0065] for students and teachers being participants in the environment)
receive, from a communication device associated with the educator, a selection of a level of granularity (see [0082] “As shown in FIG. 17, the result summary page 44 may include topic headings to navigate the user to the metrics relating to the discussion. These topics may include group analytics…individual participant analytics”, [0084] “upon selecting a specific participant, the system can visually highlight 50 the selected participant's portion of the visual representation of participation in the discussion. FIG. 19 depicts a schematic of a user interface illustrating the screen in FIG. 18 with one of the participant results highlighted to illuminate that participant's level of participation in various charts appearing on the screen”, [0093] “FIG. 28 depicts a schematic of a user interface illustrating data for a single group participant 18. A menu in the left of the screen allows the user to view the data for other participants. The screen includes a field for taking notes about the participant 18”, and [0094] “the discussion of a group of participants can be compared to various other groups of different (or partially different) groups of participants in previously recorded discussions” for the selection of a group/class-level of granularity and a selection of an individual level of granularity by the user. See [0061] for the user of the device navigating based on granularity level being a teacher. Examiner notes that Applicant’s specification [0090] explicitly considers an individual and particular class/meeting as examples of levels of granularity)
extract one or more educational effectiveness indicators from at least the audio signals, (see [0059] "the system can automatically identify the different participants by voice, wherein the system can automatically record and log the duration of time spoken, number of times spoken, and/or content of each speaker in real time" for extracting the number of times, duration of speech from identified participants in the classroom in real time. See [0076]-[0078] for the operation of the classroom environment including input from the moderator/teacher that there is a period of silence or chaos, multiple people speaking, small group discussions)
wherein the one or more educational effectiveness indicators include at least one of a number of audio communications by each of the one or more students during the live educational process, length of audio communications by each of the one or more students during the live educational process, number of audio interactions by each of the one or more students during the live educational process, an on/off status of respective cameras of each of the one or more students during the live educational process, or a gaze direction of each of the one or more students during the live educational process (see [0059] "the system can automatically identify the different participants by voice, wherein the system can automatically record and log the duration of time spoken, number of times spoken, and/or content of each speaker in real time" for extracting the number of times, duration of speech from identified participants in the classroom. Examiner notes that only one of the indicators listed is required to teach the limitation as a whole)
access at least (see Claim 16 "wherein the controller is further configured to receive demographic information related to each of the plurality of participants, wherein the results summary includes a graphic visually representing the participation of a group of participants within a demographic" for receiving and correlating demographic information with participants. See [0065] for the receipt of gender data for each participant during group/class setup)
and generate a plurality of reports (see Fig. 22 and [0085] "The results summary can be configured to summarize any number of metrics. For example, FIG. 22 is a schematic of a user interface illustrating data relating to the gender equity in the group discussion, including a comparison of how often and how long females and males spoke" for a report on number of times spoken and time spoken broken out by gender at the group level. See Fig. 28 and [0093] "FIG. 28 depicts a schematic of a user interface illustrating data for a single group participant 18. A menu in the left of the screen allows the user to view the data for other participants. The screen includes a field for taking notes about the participant 18" for reports on individual participants' number of times spoken and duration of time spoken (at the individual level of granularity). Examiner also notes that the icon 18 displayed in the individual report in Fig. 28 is generated according to demographic information per [0065] “Further, the icon can be gender specific for females and males. As the individuals in the group are named, the software may create icons for them and display the name above the icon representing the participant 18. These participant icons may be displayed in on the user interface” so the individual reporting is also done according to demographic information)
As discussed above, Nelson teaches the speaking occurrence and duration data being collected in an in-person classroom environment, and while Nelson contemplates the utility of its invention in a video class meeting in [0013], Nelson does not explicitly teach the distributed classroom environment in which a teacher is remotely located from students who each have communication devices associated with them. Nelson accordingly does not explicitly teach the reception of video signals and the extraction of educational effectiveness indicators from the video signals. While Nelson teaches the receiving of demographic/gender data of the participants in the class, Nelson also does not explicitly teach the demographic data being received from at least one database. Nelson also does not explicitly teach associating identifying information of the communication devices with components of the audio communications and video communications by at least generating a transcript of at least a portion of the live educational process based on the audio signals and associating the identifying information of the plurality of communication devices with text in the transcript. Finally, while Nelson teaches the generation of reports, the report generation is not explicitly taught as taking place in “real time”. However, Peters teaches:
receive, from a plurality of communication devices respectively being associated with one or more students, information about a live educational process being experienced in a distributed education environment where at least an educator is remotely located from the one or more students and the distributed education environment facilitates real-time communication between the educator and the one or more students including at least audio signals representing audio communications and video signals representing video communications (see 0115] "The monitoring of emotion and feedback about emotion can be performed during remote interactions, shared-space interactions, or hybrid interactions having both local and remote participants...examples of remote interactions include various forms of video conferencing, such as...streamed lectures... Examples of shared-space interactions include in-class instruction in school" and [0145] "This information can be output to a teacher's device, for example, overlaid or incorporated into a video feed showing a class, with the emotional states of different students indicated near their faces. The same information can be provided in remote learning (e.g., electronic learning or e-learning) scenarios, where the emotional states and engagement of individuals are provided in association with each remote participant's video feed" for the education environment being distributed with students communicating with teachers over video feed and located remotely. See [0068] "Each of the endpoints 12a-f communicates a source of audio and/or video and transmits a resulting media stream to the moderator module 20", Fig. 3, [0081], and [0109] for each of the endpoint devices associated with each of the participants/students communicating a stream of video and audio signals to the moderator/teacher device. See [0008] and [0118] for the conferencing being performed in real-time)
associate identifying information of the plurality of communication devices with components of the audio communications and of the video communications (see [0084] “the analysis processor 30 is configured to derive a raw score for each participant endpoint 12a-f for each displayed characteristic relating to each participant's visual and audio media stream input 46”, [0085] “throughout the analysis processor 30, the audio input media stream is analyzed by audio recognition technology in order to detect individual speaking/participation time, keyword recognition, and intonation and tone which indicate certain characteristics of each participants collaborative status” and [0312]-[0318] creating and storing an emotional response profile that identifies a user and their endpoint devices and is associated with components/features of the audio and video communications like speaking time and keywords as well as facial expression)
by at least generating a transcript of at least a portion of the live educational process based on the audio signals and associating the identifying information of the plurality of communication devices with text in the transcript (see [0313] “Various types of data can be collected for a communication session, such as (1) a transcript of the conversation (entire or key-word summary), (2) facial expression data, emotional responses, cognitive attributes, etc., (3) voice stress analysis, and (4) speaking times for participants” for the generation of a transcript as part of the analysis of audio and visual data. See [0323] “Elements 1902a-1902n represent the facial and/or emotion data gathered for n different individuals during a communication session. Each data gathering element 1902 represents collection of some or all of the data dimensions shown in element 1904, such as a transcript (e.g., at least key word or topic)… In general, the collected data can include, for example, speaking times, words (e.g., a full transcript or keyword summary)” for the association of words spoken in the transcript to their corresponding individual n. See [0084]-[0085] above for identifying the individual based on the data stream from their respective device)
extracting one or more educational effectiveness indicators from the audio signals, the video signals in a distributed education environment (see [0072] “module 110a can determine a frequency and duration that the participant is speaking. Similarly, the module 110a can determine a frequency and duration that the participant is listening. The module 110b determines eye gaze direction of the participant and head position of the participant, allowing the module to determine a level of engagement of the participant at different times during the video conference. This information, with the information about when the user is speaking, can be used by the modules 110a, 110b to determine periods when the participant is actively listening (e.g., while looking toward the display showing the conference) and periods when the user is distracted and looking elsewhere” for educational effectiveness indicators of speaking time and gaze direction. Also see [0073])
access at least one database of demographic information about the one or more students and correlate the demographic information with the one or more students (see [0323] "Each data gathering element 1902 represents collection of some or all of the data dimensions shown in element 1904, such as a...demographic attributes (e.g., age, gender, ethnicity estimation)...geographic location (e.g., city, state, region, economic micro-zone), occupational or economic data (e.g., industry, income level, education level), and so on. Information may be captured from a user profile of the user" and [0204] "The system can store profile set or database of participant information" for user profiles of a database comprising demographic data being accessed for each of the students)
generate a plurality of reports in real time or near real time with regard to the live educational process (see [0066]-[0067] and [0110] “an embodiment of the system can include endpoints or participant devices that communicate with one or more servers to perform analysis of participants' emotions, engagement, participation, attention, and so on, and deliver indications of the analysis results, e.g., in real-time along with video conference data or other communication session data and/or through other channels, such as in reports, dashboards, visualizations (e.g., charts, graphs, etc.)”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the distributed learning environment with remotely located students, each associated with communication devices, associating the identity of the student/device with audio and video communications, and real-time generation of reports of Peters into the system of Nelson. As Peters states in [0005] “The system's ability to gauge and indicate the emotional and cognitive state of the participants as a group can be very valuable to a teacher, lecturer, entertainer, or other type of presenter… With a large audience, the presenter cannot reasonable read the emotional cues from each member of the audience. Detecting these cues is even more difficult with remote, device-based, interactions rather than in-person interactions… the system can provide a presenter or other user with information about the overall state of the audience which the presenter otherwise would not have. For example, the system can be used to assist teachers, especially as distance learning and remote educational interactions become more common. The system can provide feedback, during instruction, about the current emotions and engagement of the students in the class, allowing the teacher determine how well the instruction is being received and to better customize and tailor the instruction to meet students' needs”. As Peters states, distance learning is becoming more common. One of ordinary skill in the art would have recognized that the effectiveness evaluation capabilities of Peters would have aided teachers of Nelson who have large class sizes and have students who are at least a mix of in-person and remote learners. Therefore, it would have been obvious to incorporate these evaluation capabilities from Peters into Nelson to aid teachers in conducting productive classes, especially as remote and distance learning proliferates.
Regarding the demographic data being accessed from a database instead of received from a user, since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of receiving student demographic data from a database of Peters for the receiving of demographic data from a user of the system of Nelson.
Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 2, the combination of Nelson and Peters teaches all of the limitations of claim 1 above. Nelson further teaches:
wherein the at least one processor is further programmed to: receive information from the plurality of communication devices about a plurality of live educational processes being experienced in the distributed education environment (see [0062] "the system can save a plurality of conversations previously recorded for each group…In addition, the system 10 can store and recall various information graphics for each conversation" for receiving information from a plurality of classes via the sources/communication devices taught in [0059]. The educational environment is distributed in the combination of Nelson and Peters)
and aggregate one or more educational effectiveness indicators and the plurality of reports across the plurality of live educational processes (see [0062] "the system 10 can store and recall various information graphics for each conversation, and information graphics summarizing multiple conversations for each group. For example, the system can compare the most recent conversation of a specific group to the average results from all of the previously recorded discussions for the specific group, a different group, or average of different groups" for aggregating the participation results of time spoken and number of times spoken as discussed in [0059] across multiple meetings of the group as an average)
Regarding claim 7, Nelson teaches:
A method for managing education processes in a (see [0059], [0065], and [0085] for receiving input from a plurality of sources including the number of times a student speaks, receiving demographic information, and reporting out the duration and number of times each student has spoken in the class)
Regarding the remaining limitations of claim 7, see the rejection of claim 1 above.
Regarding claim 8, the combination of Nelson and Peters teaches all of the limitations of claim 7 above. Regarding the limitations introduced in claim 8, see the rejection of claim 2 above.
Regarding claim 19, the combination of Nelson and Peters teaches all of the limitations of claim 1 above. Nelson further teaches:
wherein the at least one processor is further programmed to: analyze the audio signals to generate a record of the live educational process, wherein the at least one processor is programmed to extract the one or more educational effectiveness indicators based on the record (see [0059] “the system can automatically identify the different participants by voice, wherein the system can automatically record and log the duration of time spoken, number of times spoken, and/or content of each speaker in real time” for the system analyzing the audio signals to identify each speaker and extracting effectiveness indicators like the speaking time duration and number of times spoken of each speaker)
Regarding claim 20, the combination of Nelson and Peters teaches all of the limitations of claim 19 above. While Nelson teaches automatically recording the time each speaker is speaking in [0059], Nelson does not explicitly teach the record of the educational process including a transcript. However, Peters further teaches:
wherein the record includes a transcript (see [0313] “Various types of data can be collected for a communication session, such as (1) a transcript of the conversation (entire or key-word summary)…and (4) speaking times for participants”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the recording of a complete transcript of the educational process as taught by Peters in the system of Nelson, 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.
Specifically, Peters explicitly teaches that a transcript can be recorded along with the recording of speaking times of each participant, which Nelson already teaches are being captured. Accordingly, one of ordinary skill in the art would have recognized that the capturing of the transcript alongside the speaking times of Nelson would have had predictable results and performed the same function as they had done separately.
Regarding claim 22, the combination of Nelson and Peters teaches all of the limitations of claim 7 above. Nelson teaches the generation of a plurality of reports as discussed above in Figs. 22, 28 and [0085] and [0093]. However, Nelson’s reports in these Figures and paragraphs teach a summary of a particular communication session and overview of a particular participant. Nelson does not explicitly teach the reports indicating a trend in educational effectiveness indicators over time. However, Peters further teaches:
wherein the plurality of reports includes an indication of a trend in the one or more educational effectiveness indicators over time (see [0262] for tracking trends in engagement during a session and providing warnings that the engagement levels will be undesirable within the next 5-10 minutes if trends continue. Also see [0272] for reports showing trends during a presentation and [0296] and [0378] for recognizing patterns over the course of multiple communication sessions)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the reports indicating trends in educational effectiveness indicators over time of Peters into the system of Nelson. As Peters states in [0262] “the system may detect a progression of the distribution of emotional or cognitive states from balanced among various categories toward a large cluster of low-engagement states, and can provide an alert or warning that the audience may reach an undesirable distribution or engagement level in the next 5 minutes if the trend continues” and [0272] “As the communication session proceeds, those curves are extended, allowing the presenter to see the changes over time and the trends in emotional or cognitive states among the audience…As a result, the user interface 1600 can show how the audience is responding to, and has responded to, different content of the communication session”. One of ordinary skill in the art would have recognized that the incorporation of the Peters reports into Nelson would have allowed a presenter/teacher in the combined system to have advance warning of flagging engagement and be able to alter the lessen/session to include more features that are shown to increase engagement. Thus, the combined system would allow teachers/presenters to incorporate immediate feedback into the session to keep engagement higher than in Nelson alone.
Claims 4 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson in view of Peters and Christ et al. (U.S. Pre-Grant Publication No. 2020/0020242, hereafter known as Christ).
Regarding claim 4, the combination of Nelson and Peters teaches all of the limitations of claim 1 above. Nelson further teaches:
wherein the at least one processor is further programmed to: receive information from the plurality of communication devices about a plurality of live educational processes (see [0062] "the system can save a plurality of conversations previously recorded for each group…In addition, the system 10 can store and recall various information graphics for each conversation...the system can compare the most recent conversation of a specific group to the average results from all of the previously recorded discussions for the specific group, a different group, or average of different groups" for receiving information from a plurality of class sessions, and from a variety of different class groups, via the sources taught in [0059])
While Nelson teaches aggregating effectiveness indicators across multiple class sessions of a group as discussed above regarding claim 2, Nelson does not explicitly teach receiving information across classes of an educational institution and aggregating these indicators across the classes of an educational institution. While Peters implies the use of classroom monitoring across a school, college, or university in [0154] and [0183], Peters likewise does not explicitly teach aggregating effectiveness indicators from classes across an educational institution. Christ teaches:
receive information from the plurality of communication devices about a plurality of live educational processes across an educational institution…and aggregate one or more educational effectiveness indicators and the plurality of reports across the plurality of live educational processes (see Fig. 4 and [0079] "referring to FIGS. 4-5, an example group screening report 400 is shown. The group screening report 400 includes a section 404 that includes various summary information about a group of students, such as students in a particular school, school district... The group screening report 400 also includes a control 402 that allows the user to modify the demographics of the students shown in the report 400" for a report that aggregates individual student information of Fig. 3 and [0072] across an entire school or school district. In combination with Nelson, the individual and class level data of Nelson can be aggregated to present school-wide results)
One of ordinary skill in the art would have recognized that applying the known technique of aggregating student assessment information across an educational institution of Christ to the combination of Nelson and Peters would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Christ to the teaching of the combination of Nelson and Peters would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such aggregating student assessment information across an educational institution. Further, applying aggregating student assessment information across an educational institution to the combination of Nelson and Peters would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient presentation of lesson participation indicators to school administrators. As Peters teaches in [0387], administrators can access parameters from a set of class sessions to determine how students respond to different factors during class, evaluate teacher performance, and how to best reach students. By incorporating the school-wide aggregation and reporting of Christ, the administrator of Peters can view data for their entire school to perform teacher evaluation and educational effectiveness analysis without needing to select various groups of students as in Peters. One of ordinary skill in the art would have recognized that school-level aggregation would have had predictable results while providing this ease of use improvement to the administrator.
Regarding claim 10, the combination of Nelson and Peters teaches all of the limitations of claim 7 above. Regarding the limitations introduced in claim 10, see the rejection of claim 4 above.
Claims 5-6, 11-14, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson in view of Peters, Christ, and Bixler et al. (U.S. Patent No. 11,805,130; hereafter known as Bixler).
Regarding claim 5, the combination of Nelson, Peters, and Christ teaches all of the limitations of claim 4 above. The database comprising student demographic information taught in Peters is a database of the video conference management system and is not explicitly taught as a registration database of the educational institution. Accordingly, the combination of Nelson, Peters, and Christ does not explicitly teach the database with demographic information being a registration database of the educational institution. Bixler teaches:
wherein the at least one database of demographic information includes a registration database of the educational institution (see Col. 10 lines 19-29 “the student database 34 may store electronic student profiles for each student associated with a particular university… In particular, the student profile may include fields for… a norm group identification for which subset of a population the student belongs” and Col. 19 lines 10-22 “a norm group is established for…each of a set of demographics, for each year in college, for each gender” for a student database of a particular university storing demographic information of students that can be accessed)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the student demographic database being of the educational institution of Bixler for the student demographic database being of the video conference management system of the combination of Nelson, Peters, and Christ.
Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 6, the combination of Nelson, Peters, and Christ teaches all of the limitations of claim 4 above. The database comprising student demographic information taught in Peters is a database of the video conference management system and is not explicitly taught as a registration database of a part of the educational institution. Accordingly, the combination of Nelson, Peters, and Christ does not explicitly teach the database with demographic information being a registration database of a part of the educational institution. Bixler teaches:
wherein the at least one database of demographic information includes a registration database of part of the educational institution (see Col. 10 lines 19-29 and Col. 19 lines 10-22 citations above regarding claim 5, also see Col. 25 lines 23-28 “While the exemplary inventive student evaluation system 2 of FIG. 9 may be described with reference to universities, the exemplary inventive student evaluation system 2 may be equally applicable to students from, e.g.….university or college departments” for the database with demographic information of students being of a department (a part of) of a university/college)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the student demographic database being of a department of the educational institution of Bixler for the student demographic database being of the video conference management system of the combination of Nelson, Peters, and Christ.
Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 11, the combination of Nelson, Peters, and Christ teaches all of the limitations of claim 10 above. Regarding the limitations introduced in claim 11, see the rejection of claim 5 above.
Regarding claim 12, the combination of Nelson, Peters, and Christ teaches all of the limitations of claim 10 above. Regarding the limitations introduced in claim 12, see the rejection of claim 6 above.
Regarding claim 13, Nelson teaches:
A non-transitory computer readable medium containing computer executable instructions that, when executed by a processor, cause the processor to perform a method for managing education processes in a (see [0104] "Hence aspects of the systems and methods provided herein encompass hardware and software for controlling the relevant functions. Software may take the form of code or executable instructions for causing a controller or other programmable equipment to perform the relevant steps, where the code or instructions are carried by or otherwise embodied in a medium readable by the controller or other machine. Instructions or code for implementing such operations may be in the form of computer instruction in any form (e.g., source code, object code, interpreted code, etc.) stored in or carried by any tangible readable medium")
receiving information from a plurality of sources about a live educational process being experienced in a audio signals representing audio communications (see [0059] "the system can automatically identify the different participants by voice, wherein the system can automatically record and log the duration of time spoken, number of times spoken, and/or content of each speaker in real time. The system can include optional cameras, microphones, and/or audio speakers, among other recording and replay technologies" for receiving audio signals representing audio communications from a variety of communication devices of the classroom environment. See [0061] and [0065] for students and teachers being participants in the environment)
receiving a selection of a level of granularity from a source associated with the educator (see [0082] “As shown in FIG. 17, the result summary page 44 may include topic headings to navigate the user to the metrics relating to the discussion. These topics may include group analytics…individual participant analytics”, [0084] “upon selecting a specific participant, the system can visually highlight 50 the selected participant's portion of the visual representation of participation in the discussion. FIG. 19 depicts a schematic of a user interface illustrating the screen in FIG. 18 with one of the participant results highlighted to illuminate that participant's level of participation in various charts appearing on the screen”, [0093] “FIG. 28 depicts a schematic of a user interface illustrating data for a single group participant 18. A menu in the left of the screen allows the user to view the data for other participants. The screen includes a field for taking notes about the participant 18”, and [0094] “the discussion of a group of participants can be compared to various other groups of different (or partially different) groups of participants in previously recorded discussions” for the selection of a group/class-level of granularity and a selection of an individual level of granularity by the user. See [0061] for the user of the device navigating based on granularity level being a teacher. Examiner notes that Applicant’s specification [0090] explicitly considers an individual and particular class/meeting as examples of levels of granularity)
extracting one or more educational effectiveness indicators from at least the audio signals, in real time or near real time (see [0059] "the system can automatically identify the different participants by voice, wherein the system can automatically record and log the duration of time spoken, number of times spoken, and/or content of each speaker in real time" for extracting the number of times, duration of speech from identified participants in the classroom in real-time. See [0076]-[0078] for the operation of the classroom environment including input from the moderator/teacher that there is a period of silence or chaos, multiple people speaking, small group discussions)
wherein the one or more educational effectiveness indicators include at least one of a number of audio communications by each of the one or more students during the live educational process, length of audio communications by each of the one or more students during the live educational process, number of audio interactions by each of the one or more students during the live educational process, an on/off status of respective cameras of each of the one or more students during the live educational process, or a gaze direction of each of the one or more students during the live educational process (see [0059] "the system can automatically identify the different participants by voice, wherein the system can automatically record and log the duration of time spoken, number of times spoken, and/or content of each speaker in real time" for extracting the number of times, duration of speech from identified participants in the classroom. Examiner notes that only one of the indicators listed is required to teach the limitation as a whole)
accessing at least (see Claim 16 "wherein the controller is further configured to receive demographic information related to each of the plurality of participants, wherein the results summary includes a graphic visually representing the participation of a group of participants within a demographic" for receiving and correlating demographic information with participants. See [0065] for the receipt of gender data for each participant during group/class setup)
generating a plurality of reports (see Fig. 22 and [0085] "The results summary can be configured to summarize any number of metrics. For example, FIG. 22 is a schematic of a user interface illustrating data relating to the gender equity in the group discussion, including a comparison of how often and how long females and males spoke" for a report on number of times spoken and time spoken broken out by gender at the group level. See Fig. 28 and [0093] "FIG. 28 depicts a schematic of a user interface illustrating data for a single group participant 18. A menu in the left of the screen allows the user to view the data for other participants. The screen includes a field for taking notes about the participant 18" for reports on individual participants' number of times spoken and duration of time spoken (at the individual level of granularity). Examiner also notes that the icon 18 displayed in the individual report in Fig. 28 is generated according to demographic information per [0065] “Further, the icon can be gender specific for females and males. As the individuals in the group are named, the software may create icons for them and display the name above the icon representing the participant 18. These participant icons may be displayed in on the user interface” so the individual reporting is also done according to demographic information)
receiving information from the plurality of sources about a plurality of live educational processes (see [0062] "the system can save a plurality of conversations previously recorded for each group…In addition, the system 10 can store and recall various information graphics for each conversation...the system can compare the most recent conversation of a specific group to the average results from all of the previously recorded discussions for the specific group, a different group, or average of different groups" for receiving information from a plurality of class sessions, and from a variety of different class groups, via the sources taught in [0059])
As discussed above, Nelson teaches the speaking occurrence and duration data being collected in an in-person classroom environment, and while Nelson contemplates the utility of its invention in a video class meeting in [0013], Nelson does not explicitly teach the distributed classroom environment in which a teacher is remotely located from students who each have communication devices associated with them. Nelson accordingly does not explicitly teach the reception of video signals and the extraction of educational effectiveness indicators from the video signals. Nelson also does not explicitly teach associating identifying information of the communication devices with components of the audio communications and video communications by at least generating a transcript of at least a portion of the live educational process based on the audio signals and associating the identifying information of the plurality of communication devices with text in the transcript. While Nelson teaches the receiving of demographic/gender data of the participants in the class, Nelson also does not explicitly teach the demographic data being received from at least one database. Finally, Nelson also does not explicitly teach generating reports in real time or near real time and aggregating educational effectiveness indicators and reports across an educational institution’s processes and that the database of demographic information includes a registration database of the educational institution. However, Peters teaches:
receiving information from a plurality of sources about a live educational process being experienced in a distributed education environment where at least an educator is remotely located from the one or more students and the distributed education environment facilitates real-time communication between the educator and the one or more students including at least audio signals representing audio communications and video signals representing video communications (see 0115] "The monitoring of emotion and feedback about emotion can be performed during remote interactions, shared-space interactions, or hybrid interactions having both local and remote participants...examples of remote interactions include various forms of video conferencing, such as...streamed lectures... Examples of shared-space interactions include in-class instruction in school" and [0145] "This information can be output to a teacher's device, for example, overlaid or incorporated into a video feed showing a class, with the emotional states of different students indicated near their faces. The same information can be provided in remote learning (e.g., electronic learning or e-learning) scenarios, where the emotional states and engagement of individuals are provided in association with each remote participant's video feed" for the education environment being distributed with students communicating with teachers over video feed and located remotely. See [0068] "Each of the endpoints 12a-f communicates a source of audio and/or video and transmits a resulting media stream to the moderator module 20", Fig. 3, [0081], and [0109] for each of the endpoint devices associated with each of the participants/students communicating a stream of video and audio signals to the moderator/teacher device. See [0008] and [0118] for the conferencing being performed in real-time)
associating identifying information of the plurality of sources with components of the audio communications and of the video communications (see [0084] “the analysis processor 30 is configured to derive a raw score for each participant endpoint 12a-f for each displayed characteristic relating to each participant's visual and audio media stream input 46”, [0085] “throughout the analysis processor 30, the audio input media stream is analyzed by audio recognition technology in order to detect individual speaking/participation time, keyword recognition, and intonation and tone which indicate certain characteristics of each participants collaborative status” and [0312]-[0318] creating and storing an emotional response profile that identifies a user and their endpoint devices and is associated with components/features of the audio and video communications like speaking time and keywords as well as facial expression)
by at least generating a transcript of at least a portion of the live educational process based on the audio signals and associating the identifying information of the plurality of sources with text in the transcript (see [0313] “Various types of data can be collected for a communication session, such as (1) a transcript of the conversation (entire or key-word summary), (2) facial expression data, emotional responses, cognitive attributes, etc., (3) voice stress analysis, and (4) speaking times for participants” for the generation of a transcript as part of the analysis of audio and visual data. See [0323] “Elements 1902a-1902n represent the facial and/or emotion data gathered for n different individuals during a communication session. Each data gathering element 1902 represents collection of some or all of the data dimensions shown in element 1904, such as a transcript (e.g., at least key word or topic)… In general, the collected data can include, for example, speaking times, words (e.g., a full transcript or keyword summary)” for the association of words spoken in the transcript to their corresponding individual n. See [0084]-[0085] above for identifying the individual based on the data stream from their respective device)
extracting one or more educational effectiveness indicators from the audio signals, the video signals communications, and an operation of the distributed education environment during the live educational process (see [0072] “module 110a can determine a frequency and duration that the participant is speaking. Similarly, the module 110a can determine a frequency and duration that the participant is listening. The module 110b determines eye gaze direction of the participant and head position of the participant, allowing the module to determine a level of engagement of the participant at different times during the video conference. This information, with the information about when the user is speaking, can be used by the modules 110a, 110b to determine periods when the participant is actively listening (e.g., while looking toward the display showing the conference) and periods when the user is distracted and looking elsewhere” for educational effectiveness indicators of speaking time and gaze direction. Also see [0073])
accessing at least one database of demographic information about the one or more students and correlate the demographic information with the one or more students (see [0323] "Each data gathering element 1902 represents collection of some or all of the data dimensions shown in element 1904, such as a...demographic attributes (e.g., age, gender, ethnicity estimation)...geographic location (e.g., city, state, region, economic micro-zone), occupational or economic data (e.g., industry, income level, education level), and so on. Information may be captured from a user profile of the user" and [0204] "The system can store profile set or database of participant information" for user profiles of a database comprising demographic data being accessed for each of the students)
generating a plurality of reports in real time or near real time with regard to the live educational process (see [0066]-[0067] and [0110] “an embodiment of the system can include endpoints or participant devices that communicate with one or more servers to perform analysis of participants' emotions, engagement, participation, attention, and so on, and deliver indications of the analysis results, e.g., in real-time along with video conference data or other communication session data and/or through other channels, such as in reports, dashboards, visualizations (e.g., charts, graphs, etc.)”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the distributed learning environment with remotely located students, each associated with communication devices, associating the identity of the student/device with audio and video communications, and generating reports in real-time of Peters into the system of Nelson. As Peters states in [0005] “The system's ability to gauge and indicate the emotional and cognitive state of the participants as a group can be very valuable to a teacher, lecturer, entertainer, or other type of presenter… With a large audience, the presenter cannot reasonable read the emotional cues from each member of the audience. Detecting these cues is even more difficult with remote, device-based, interactions rather than in-person interactions… the system can provide a presenter or other user with information about the overall state of the audience which the presenter otherwise would not have. For example, the system can be used to assist teachers, especially as distance learning and remote educational interactions become more common. The system can provide feedback, during instruction, about the current emotions and engagement of the students in the class, allowing the teacher determine how well the instruction is being received and to better customize and tailor the instruction to meet students' needs”. As Peters states, distance learning is becoming more common. One of ordinary skill in the art would have recognized that the effectiveness evaluation capabilities of Peters would have aided teachers of Nelson who have large class sizes and have students who are at least a mix of in-person and remote learners. Therefore, it would have been obvious to incorporate these evaluation capabilities from Peters into Nelson to aid teachers in conducting productive classes, especially as remote and distance learning proliferates.
Regarding the demographic data being accessed from a database instead of received from a user, since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of receiving student demographic data from a database of Peters for the receiving of demographic data from a user of the system of Nelson.
Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
While Nelson teaches aggregating effectiveness indicators across multiple class sessions of a group as discussed above regarding claim 2, Nelson does not explicitly teach receiving information across classes of an educational institution and aggregating these indicators across the classes of an educational institution. While Peters implies the use of classroom monitoring across a school, college, or university in [0154] and [0183], Peters likewise does not explicitly teach aggregating effectiveness indicators from classes across an educational institution. The combination of Nelson and Peters also does not explicitly teach that the database of demographic information includes a registration database of the educational institution. Christ teaches:
receiving information from the plurality of sources about a plurality of live educational processes across an educational institution…and aggregating one or more educational effectiveness indicators and the plurality of reports across the plurality of live educational processes (see Fig. 4 and [0079] "referring to FIGS. 4-5, an example group screening report 400 is shown. The group screening report 400 includes a section 404 that includes various summary information about a group of students, such as students in a particular school, school district... The group screening report 400 also includes a control 402 that allows the user to modify the demographics of the students shown in the report 400" for a report that aggregates individual student information of Fig. 3 and [0072] across an entire school or school district. In combination with Nelson, the individual and class level data of Nelson can be aggregated to present school-wide results)
One of ordinary skill in the art would have recognized that applying the known technique of aggregating student assessment information across an educational institution of Christ to the combination of Nelson and Peters would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Christ to the teaching of the combination of Nelson and Peters would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such aggregating student assessment information across an educational institution. Further, applying aggregating student assessment information across an educational institution to the combination of Nelson and Peters would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more efficient presentation of lesson participation indicators to school administrators. As Peters teaches in [0387], administrators can access parameters from a set of class sessions to determine how students respond to different factors during class, evaluate teacher performance, and how to best reach students. By incorporating the school-wide aggregation and reporting of Christ, the administrator of Peters can view data for their entire school to perform teacher evaluation and educational effectiveness analysis without needing to select various groups of students as in Peters. One of ordinary skill in the art would have recognized that school-level aggregation would have had predictable results while providing this ease of use improvement to the administrator.
The database comprising student demographic information taught in Peters is a database of the video conference management system and is not explicitly taught as a registration database of the educational institution. Accordingly, the combination of Nelson, Peters, and Christ does not explicitly teach the database with demographic information being a registration database of the educational institution. Bixler teaches:
wherein the at least one database of demographic information includes a registration database of the educational institution or a part of the educational institution (see Col. 10 lines 19-29 “the student database 34 may store electronic student profiles for each student associated with a particular university… In particular, the student profile may include fields for… a norm group identification for which subset of a population the student belongs” and Col. 19 lines 10-22 “a norm group is established for…each of a set of demographics, for each year in college, for each gender” for a student database of a particular university storing demographic information of students that can be accessed)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the student demographic database being of the educational institution of Bixler for the student demographic database being of the video conference management system of the combination of Nelson, Peters, and Christ.
Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding claim 14, the combination of Nelson, Peters, Christ, and Bixler teaches all of the limitations of claim 13 above. Nelson further teaches:
the method further comprising: receiving information from the plurality of sources about a plurality of live educational processes being experienced in the distributed education environment (see [0062] "the system can save a plurality of conversations previously recorded for each group…In addition, the system 10 can store and recall various information graphics for each conversation" for receiving information from a plurality of classes via the sources/communication devices taught in [0059]. The educational environment is distributed in the combination of Nelson, Peters, Christ, and Bixler)
and aggregating one or more educational effectiveness indicators and the plurality of reports across the plurality of live educational processes (see [0062] "the system 10 can store and recall various information graphics for each conversation, and information graphics summarizing multiple conversations for each group. For example, the system can compare the most recent conversation of a specific group to the average results from all of the previously recorded discussions for the specific group, a different group, or average of different groups" for aggregating the participation results of time spoken and number of times spoken as discussed in [0059] across multiple meetings of the group as an average)
Regarding claim 23, the combination of Nelson, Peters, Christ, and Bixler teaches all of the limitations of claim 13 above. Regarding the limitations introduced in claim 23, Nelson further teaches:
wherein extracting the one or more educational effectiveness indicators is performed on at least one audio signal including the audio communications (see [0059] "the system can automatically identify the different participants by voice, wherein the system can automatically record and log the duration of time spoken, number of times spoken, and/or content of each speaker in real time. The system can include optional cameras, microphones, and/or audio speakers, among other recording and replay technologies" for receiving audio signals representing audio communications from a variety of communication devices including microphones and recording devices that are then automatically analyzed to extract educational effectiveness indicators including the speaking duration of each speaker in a session)
While Nelson contemplates the utility of its invention in a video class meeting in [0013], Nelson does not explicitly teach the reception of video signals and the extraction of educational effectiveness indicators from the video signals. However Peters further teaches:
wherein extracting the one or more educational effectiveness indicators is performed on at least one audio signal including the audio communications and at least one video signal including the video communications (see [0072] “module 110a can determine a frequency and duration that the participant is speaking. Similarly, the module 110a can determine a frequency and duration that the participant is listening. The module 110b determines eye gaze direction of the participant and head position of the participant, allowing the module to determine a level of engagement of the participant at different times during the video conference. This information, with the information about when the user is speaking, can be used by the modules 110a, 110b to determine periods when the participant is actively listening (e.g., while looking toward the display showing the conference) and periods when the user is distracted and looking elsewhere” for educational effectiveness indicators of speaking time and gaze direction. Also see [0073])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate extracting educational effectiveness indicators from video communications of the distributed learning environment in real time of Peters into the system of Nelson. As Peters states in [0005] “The system's ability to gauge and indicate the emotional and cognitive state of the participants as a group can be very valuable to a teacher, lecturer, entertainer, or other type of presenter… With a large audience, the presenter cannot reasonable read the emotional cues from each member of the audience. Detecting these cues is even more difficult with remote, device-based, interactions rather than in-person interactions… the system can provide a presenter or other user with information about the overall state of the audience which the presenter otherwise would not have. For example, the system can be used to assist teachers, especially as distance learning and remote educational interactions become more common. The system can provide feedback, during instruction, about the current emotions and engagement of the students in the class, allowing the teacher determine how well the instruction is being received and to better customize and tailor the instruction to meet students' needs”. As Peters states, distance learning is becoming more common. One of ordinary skill in the art would have recognized that the video communication capabilities of Peters would have aided teachers of Nelson who have students who are at least a mix of in-person and remote learners. As Peters states in [0012], video and audio analysis can be used together to evaluate participation and engagement of students. Therefore, it would have been obvious to incorporate video communication capabilities from Peters into Nelson to aid teachers in conducting productive classes, especially as remote and distance learning proliferates.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chasen et al. (U.S. Pre-Grant Publication No. 2022/0013027) teaches generating grades and providing engagement reports to students in a remote learning environment
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C MORONEY whose telephone number is (571)272-4403. The examiner can normally be reached Mon-Fri 8:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached at (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.C.M./Examiner, Art Unit 3626
/EMMETT K. WALSH/Primary Examiner, Art Unit 3626
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