DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to claims filed on 6/11/2024 in relation to application 16/716,766.
The instant application claims benefit to provisional application #62/780,817, #62/780,823 with a priority date of 12/17/2018.
The Pre-Grant publication #US20200193859 is published on 6/18/2020.
Claims 6,16,25 are cancelled.
Claims 1-5, 7-15, 17-24 are pending.
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 1-5, 7-15, 17-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claimed invention is to a process (claim 1--19) and a computer devices (20) and thus fall within one of the four statutory categories (Step 1: YES).
Claims 1, 10, 11 and 22-24 are directed to an educational tool system having at least one student and at least one teacher engaged in on-going classroom with a personalized learning environment in which students work at their own pace. A passive system gathers objective data on the students selected from the group consisting of student-specific and of combined analytics , in a real-time. The cognitive augmentation by a teacher with a passive feedback decision making displays an analytics to augment the perceptions and decision-making during the on-going classroom activities.
All of these involve steps drawn to concept categorized as an actions that are receiving, observing, identifying, evaluating, displaying and judging from textual and image inputs. A concept that are mental processes and by including generating passive feedback, it is deemed some evaluation result communicated and processing of information by tutors are much like organizing of certain human activities of managing personal behavior or
relationship of interactions in a method of human being how, for example, to do better surgery. The use of some machine-learned model from a personalized learning environment converting real-time data into some analytics result could also be categorized as a use mathematical calculations within some mathematical concepts. These are all generally categorized as a grouping of an abstract idea (Step 2A: Prong 1 YES).
The independent claims do not include additional elements that are sufficient to be significantly more than the judicial exception because the limitations of “a personal computer system with interface display”, “a processor’, “a memory’, "network remote storage", " a classroom or desk”, “assistance to teacher to determine analytical results via use of models”, and “some wearable smart watch/device for receiving data”, The navigational control augmenting teacher’s perception or in making a decision process are merely been done by use of generic computer functions and computer parts not improving the functionality of the computer itself. Hence not indicative of integration of a practical application (Step 2A: Prong 2 No).
The steps in the recited claims that are highlighted are a well-understood, routine, and conventional activities known in art. Fig.1-3 of the instant specification depicts personal for teacher’s point-of-view using a hardware/ software of a standard AR/VR environment with image panel implement the process claimed here. Some “AR/VR” sensor or some device or “machine learning” or “artificial intelligence” or “speech recognition”, etc. are generically used in addition to the abstract idea. They are disclosed in their specification in a manner that indicates that those features are well-known, routine, and conventional. They are not dealing with actual improvements to, e.g., AR/VR, machine learning, etc. These limitations may be characterized as an abstract idea of collecting data (e.g., inputs from the teacher), analyzing those inputs and providing an output based on that analysis (e.g., allowing the student to view and manipulate the presentation based on the inputs by the teacher) As an example in case of Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, the activities of storing and retrieving of information in a memory of consumer electronic for a field of use purposes are recognized to be computer functions well-understood, routine, and conventional, when they are claimed in a merely generic manner. Further, there found to be no additional elements here in the claim recitation that improves the functioning of a computer itself to overcome the abstract idea rejection (Step 2B: No).
The dependent claims 2-9, 12-21 describe additional limitations that serve only to modify and further describe the abstract idea. The displaying of data, transmitting confirmation of signals, specifics about students, use of recording devices, and reviewing of activities are further description of elements not making abstract idea less abstract. The elements claimed in addition to the abstract idea comprise visualization devices, embodying applicant’s abstract idea as computer software, and providing computer displays (“virtual” displays). All of these are generic, well-known, and conventional uses of these devices and software and thereby do not constitute “significantly more” than the claimed abstract idea. In other words, Applicant’s claimed invention does not technologically improve the claimed “visualization devices” in terms of making them able to, e.g., run faster, use less power, and/or be manufactured more cheaply. Hence the dependent claims of the instant case, when analyzed as a whole are held to be ineligible subject matter and are rejected under 35 U.S.C. § 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5,7-15,17-24 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication Number 20180293281 A1 Kim in view of Non-Patent Literature Augmented Reality Lecture Class talk to Dufresne et al. (Dufresne).
Claim 1. Kim teaches an educational tool system having at least one student and at least one teacher engaged in on-going classroom activities (Fig.1 element 102 students and teacher; Fig.4 elements 410, 450 online activities), comprising:
a personalized learning environment in which students work at their own pace (Fig.4 element 300 learning performance geared towards individual students personalized background and prior ability to work on their own pace under augmented reality view as in para 0061, 0062 for registered first and second user personalities available public information etc.) ;
a passive system that gathers data on the students (Fig.3 element 370, para 0055 feedback UI of computing device communicating or gathering or capturing data on a passive display of an interactive virtual object in an augmented reality networking application) and converts the data in real time to at least one type of analytics (Para 0030 analytics conversion based on learning experience ) selected from the group consisting of student-specific analytics (Para 0024 users could be student specifics) and
student-combined analytics (Para 0027 could also be all student’s combination analysis); and
a real-time (Para 0025 real-time interactions) , wearable cognitive augmentation device ( Para 0022 wearable augmented reality devices and displays) that displays the analytics to the at least one teacher (Para 0025 teacher may use wearable computing devices for a display of context base information).
Kim teaches learning performance monitoring based on a non-neuro feedback imaging measurements for an intelligent tutoring evaluation with the use of input by a user without direct exposition of passive feedback system passively gathering objective data from personalized learning environment. In a non-patent literature Dufresne, in a reasonably pertinent problem of active learning in an intelligent augmented reality (AR) tutoring system, as faced by an inventor for reasonable expectation of success, provides passively gathering objective data (for example,. histogram of responses )from learning students (Fig. 2) such that in a real-time investigation for learning using AR displays related to personalized learning environment (Fig.3,student responses after delivery of learning content in AR based environment , Fig. 4 Analytics in records mode from user histogram of class responses augmenting teachers perceptions and decision-making during an on-going classroom activities and survey). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a passive objective feedback decision making system that in a real-time cognitive augmentation device displays the analytics to augment the perceptions and decision-making during the on-going classroom activities, as taught by Dufresne, into the educational tool feedback mechanism of Kim, in order to gather a passive objective feedback about the motivation and engagement level of learning environment students completing a given task.
Claim 2. Kim in combination with Dufresne teaches the educational tool system of claim 1, wherein the real-time, wearable cognitive augmentation device allows the at least one teacher to view the analytics while engaging with the students (Para 0022 viewing and video capturing for additional analytics is based on students engaging with live teacher, recorded lectures via worn computing devices).Claim 3. Kim in combination with Dufresne teaches the educational tool system of claim 1, wherein the real-time, wearable cognitive augmentation devices is selected from the group consisting of a smart watch, smart glasses, at least one earpiece, mixed reality glasses, and a heads-up display (Para 0036 electronic glasses; Kaur (¶Section 2 augmented instruments to visualize interacting participants is in real-time extracting motivational factor from questionnaire-based survey).Claim 4. Kim in combination with Dufresne teaches the educational tool system of claim 1, wherein the passive feedback system comprises an intelligent tutoring system (Para 0030 drive intelligent student based information as a passive feedback to analytical electronic tutoring modules used).Claim 5. Kim teaches the educational tool system of claim 1, wherein the passive feedback system comprises self-paced learning software (Para 0030 feedback device operatively coupled for playback self-paced learning).Claim 7. Kim teaches the educational tool system of claim 1, wherein the passive feedback system comprises at least one recording device to record the activities of students (Para 0062 audio recording student activities during learning experience).Claim 8. Kim in combination with Dufresne teaches the educational tool system of claim 1, wherein the analytics comprise student-specific details (Kim: Para 0019 specifics for purpose of illustrations include information identifying specific cognitive function areas analytics such as a student exhibiting low learning performance for teacher appropriate intervention).Claim 9. Kim teaches the educational tool system of claim 1, wherein the analytics comprise student-combined details (Fig.5 elements 502,504; Para 0067 analytics from interactive group of users’ combination of computing devices).Claim 10. Kim teaches an educational tool system having more than one student and at least one teacher (Fig.1 element 100 student, teacher), comprising: a personalized learning environment (Fig.3 element 106 learning performance for individual students); a gathering system that gathers data from the at least one student (Fig.3 element 102 device gathering data) ; a processing system that converts the data into analytics selected from the group consisting of student-specific analytics (Para 0024 users could be student specifics; working memory performance, mental effort and/or attention engagement of a self-study, interactive, physical learning experiences specific to a student) and student-combined analytics (Fig.5 elements 502,504; Para 0067 analytics from interactive group of users’ combination of computing devices; plurality of student-combined users analytics based on monitoring of learning performance of one or more users participating in same or different learning experience); and
a real-time (col.4 lines 4-7 real-time student cognitive processing), wearable cognitive augmentation device (Fig.8 element 804 determine by device learning performance feedback with respect to defined cognitive functions) that receives and displays the analytics to the at least one teacher col.8 lines 44-46 heads up display (HUD) as a wearable device).
Kim teaches learning performance monitoring based on a non-neuro feedback imaging measurements for an intelligent tutoring evaluation with the use of input by a user without direct exposition of passive feedback system passively gathering objective data from personalized learning environment. In a non-patent literature Dufresne, in a reasonably pertinent problem of active learning in an intelligent augmented reality (AR) tutoring system, as faced by an inventor for reasonable expectation of success, provides passively gathering objective data (for example,. histogram of responses )from learning students (Fig. 2) such that in a real-time investigation for learning using AR displays related to personalized learning environment (Fig.3,student responses after delivery of learning content in AR based environment , Fig. 4 Analytics in records mode from user histogram of class responses augmenting teachers perceptions and decision-making during an on-going classroom activities and survey). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a passive objective feedback decision making system that in a real-time cognitive augmentation device displays the analytics to augment the perceptions and decision-making during the on-going classroom activities, as taught by Dufresne, into the educational tool feedback mechanism of Kim, in order to gather a passive objective feedback about the motivation and engagement level of learning environment students completing a given task.
Claim 11. Kim teaches a method of teaching in a personalized learning environment having at least one student and at least one teacher (Fig.1 element 100 student, teacher), comprising: wherein the data
and
a real-time analytics (Para 0025 real-time interactions) , wearable cognitive augmentation device ( Para 0022 wearable augmented reality devices and displays) that displays the analytics to the at least one teacher (Para 0025 teacher wearable computing devices display).
Kim teaches learning performance monitoring based on a non-neuro feedback imaging measurements for an intelligent tutoring evaluation with the use of input by a user without direct exposition of passive feedback system passively gathering objective data from personalized learning environment. In a non-patent literature Dufresne, in a reasonably pertinent problem of active learning in an intelligent augmented reality (AR) tutoring system, as faced by an inventor for reasonable expectation of success, provides passively gathering objective data (for example,. histogram of responses )from learning students (Fig. 2) such that in a real-time investigation for learning using AR displays related to personalized learning environment (Fig.3,student responses after delivery of learning content in AR based environment , Fig. 4 Analytics in records mode from user histogram of class responses augmenting teachers perceptions and decision-making during an on-going classroom activities and survey). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a passive objective feedback decision making system that in a real-time cognitive augmentation device displays the analytics to augment the perceptions and decision-making during the on-going classroom activities, as taught by Dufresne, into the educational tool feedback mechanism of Kim, in order to gather a passive objective feedback about the motivation and engagement level of learning environment students completing a given task.
Claim 12. Kim in combination teaches the method of teaching of claim 11, wherein the real-time, wearable cognitive augmentation device allows the at least one teacher to view the analytics while engaging with the at least one student (Para 0022 viewing and video capturing for additional analytics is based on student engaging with teacher, recorded lectures via worn computing devices).Claim 13. . Kim in combination with Dufresne teaches the method of teaching of claim 11, wherein the real-time, wearable cognitive augmentation devices is selected from the group consisting of a smart watch, smart glasses, at least one earpiece, mixed reality glasses, and a heads-up display (Para 0036 electronic glasses).Claim 14. Kim in combination with Dufresne teaches the method of teaching of claim 11, wherein the data is passively gathered using an intelligent tutoring system (Para 0030 drive intelligent student based information as a feedback to analytical electronic tutoring modules used)Claim 15. Kim in combination with Dufresne teaches the method of teaching of claim 11, wherein the data is passively gathered using self-paced learning software (Para 0030 feedback device operatively coupled for playback self-paced learning self-study that includes feedback system).Claim 17. Kim in combination with Dufresne teaches the method of teaching of claim 11, wherein the data is passively gathered using at least one recording device to record the activities of the at least one student (col.5 line 5 recording students during learning experience).Claim 18. Kim in combination with Dufresne teaches the method of teaching of claim 11, wherein the analytics comprise student-specific details (Fig.5 elements 502,504; Para 0067 analytics from interactive group of users’ combination of computing devices information identifying specific cognitive function areas i.e. analytics in which a student exhibiting low learning performance for teacher appropriate intervention).Claim 19. Kim in combination with Dufresne teaches the method of teaching of claim 11, wherein the analytics comprise student-combined details (Fig.5 elements 502,504; Para 0067 analytics from interactive group of users’ combination of computing devices).Claim 20. Kim teaches a method of teaching in a personalized learning environment having at least one student and at least one teacher (Fig.3 learning performance geared towards individual students ;Fig.1 element 100 student, teacher), comprising: wherein the data
Kim teaches learning performance monitoring based on a non-neuro feedback imaging measurements for an intelligent tutoring evaluation with the use of input by a user without indicating of at least one teacher wearable cognitive device to augment the teacher’s perceptions and decision-making during on-going classroom activities. In a non-patent literature Dufresne, in a reasonably pertinent problem of active learning in an intelligent augmented reality (AR) tutoring system, as faced by an inventor for reasonable expectation of success, provides at least one teacher wearable cognitive device to augment the teacher’s perceptions and decision-making during on-going classroom activities
(Page 12 paragraph 2 teacher perception augmented after class talk software analyzes the passive responses from on-going classroom activity and further analysis and examination could be done a subsequent time) . Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a passive objective feedback decision making system that in a real-time cognitive augmentation device provides at least one teacher wearable cognitive device to augment the teacher’s perceptions and decision-making during on-going classroom activities, as taught by Dufresne, into the educational tool feedback mechanism of Kim, in order to augment teacher leading the classroom activities.
Claim 21. Kim teaches a system for use in educational settings of students and teacher in a classroom comprising: a personalized learning environment( Fig.3 elements 302 learning performance geared towards individual students); wherein the data environment (Fig.3 element 308b,320 learning performance geared towards individual students).
Kim teaches learning performance monitoring based on a non-neuro feedback imaging measurements for an intelligent tutoring evaluation with the use of input by a user without wherein the data is from the self-paced software. Dufresne, in a reasonably pertinent problem of active learning in an intelligent augmented reality (AR) tutoring system, as faced by an inventor for reasonable expectation of success, provides wherein the data is from the self-paced software (for example,. histogram of responses )from learning students (Page 10 self-paced used of record mode) such that in a real-time investigation for learning using AR displays related to personalized learning environment (Fig.3,student responses after delivery of learning content in AR based environment , Fig. 4 Analytics in records mode from user histogram of class responses augmenting teachers perceptions and decision-making during an on-going classroom activities and survey). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a passive objective feedback decision making system that in a real-time cognitive augmentation device wherein the device provides real-time analytics of data gathered from the personalized learning environment, wherein the data is from the self-paced software, as taught by Dufresne, into the educational tool feedback mechanism of Kim, in order to gather a passive objective feedback about the motivation and engagement level of learning environment students completing a given task.
Claim 22. Kim in combination with Dufresne teaches a method for use in educational settings comprising: gathering and processing student data from a personalized learning environment (Fig.3 element 306 learning performance geared towards individual students) to generate analytics; and displaying the analytics on a real-time, wearable cognitive augmentation device (Fig.4 elements 450 glasses, goggles).
a real-time (Para 0025 real-time interactions) , wearable cognitive augmentation device ( Para 0022 wearable augmented reality devices and displays) that displays the analytics to the at least one teacher (Para 0025 teacher wearable computing devices display).
wherein the data
Kim teaches learning performance monitoring based on a non-neuro feedback imaging measurements for an intelligent tutoring evaluation with the use of input by a user without direct exposition of passive feedback system passively gathering objective data from personalized learning environment. In a non-patent literature Dufresne, in a reasonably pertinent problem of active learning in an intelligent augmented reality (AR) tutoring system, as faced by an inventor for reasonable expectation of success, provides passively gathering objective data (for example,. histogram of responses )from learning students (Fig. 2) such that in a real-time investigation for learning using AR displays related to personalized learning environment (Fig.3,student responses after delivery of learning content in AR based environment , Fig. 4 Analytics in records mode from user histogram of class responses augmenting teachers perceptions and decision-making during an on-going classroom activities and survey). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a passive objective feedback decision making system that in a real-time cognitive augmentation device displays the analytics to augment the perceptions and decision-making during the on-going classroom activities, as taught by Dufresne, into the educational tool feedback mechanism of Kim, in order to gather a passive objective feedback about the motivation and engagement level of learning environment students completing a given task.
Claim 23. Kim teaches a system for use in educational settings having at least one student and at least one teacher in an on-going classroom activities (Para 0022 viewing and video capturing for additional analytics is based on student engaging with live teacher, recorded lectures via worn computing devices); and a wearable device that processes (col.9 lines 16-18 wearable glasses, goggles)and displays real-time analytics gathered from the computer-based personalized learning environment (Fig.3 element 106 learning performance geared towards individual students).
Kim teaches learning performance monitoring based on a non-neuro feedback imaging measurements for an intelligent tutoring evaluation with the use of input by a user without indicating of at least one teacher wearable cognitive device wherein the analytics are based upon real-time, objective data from the intelligent tutoring system. In a non-patent literature Dufresne, in a reasonably pertinent problem of active learning in an intelligent augmented reality (AR) tutoring system, as faced by an inventor for reasonable expectation of success, wherein the analytics are based upon real-time, objective data from the intelligent tutoring system (Page 25 paragraph 2,3 effective classroom management leading to intelligent tutoring system) . Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a passive objective feedback decision making system wherein the analytics are based upon real-time, objective data from the intelligent tutoring system, as taught by Dufresne, into the educational tool feedback mechanism of Kim, in order to augment teacher leading the classroom activities.
wherein the analytics are based upon real-time, objective data from the intelligent tutoring system.
Claim 24. Kim teaches a system for use in education settings comprising: a wearable device (Para 0022 wearable glasses, goggles) that presents a teacher with rich, real-time analytics gathered based on one or more student's ongoing interactions (Para 0025 real-time interactions; Para 0022 wearable augmented reality devices and displays the analytics to the at least one teacher) within a computer-based learning environment (Fig.3 element 302 learning performance geared towards individual students), whereby the analytics are presented to the teacher continuously and in real-time to augment the teacher's perceptions and decision making (Para 0022 teacher continuous real-time interactions) .
Kim teaches learning performance monitoring based on a non-neuro feedback imaging measurements for an intelligent tutoring evaluation with the use of input by a user without direct exposition of passive feedback system passively gathering objective data from personalized learning environment. In a non-patent literature Dufresne, in a reasonably pertinent problem of active learning in an intelligent augmented reality (AR) tutoring system, as faced by an inventor for reasonable expectation of success, provides passively gathering objective data (for example,. histogram of responses )from learning students (Fig. 2) such that in a real-time investigation for learning using AR displays related to personalized learning environment (Fig.3,student responses after delivery of learning content in AR based environment , Fig. 4 Analytics in records mode from user histogram of class responses augmenting teachers perceptions and decision-making during an on-going classroom activities and survey). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a passive objective feedback decision making system that in a real-time cognitive augmentation device displays the analytics to augment the perceptions and decision-making during the on-going classroom activities, as taught by Dufresne, into the educational tool feedback mechanism of Kim, in order to gather a passive objective feedback about the motivation and engagement level of learning environment students completing a given task.
Response to Arguments/Remarks
Applicant's arguments/amendments filed on December 16, 2024 have been considered.
Applicant's assertion on pages 6-7 of arguments/remark on 12/16/2024 against secondary art Kaur et al. not a prior art is convincing. Examiner has withdrawn the art and the rejection.
Upon further consideration, a new ground(s) of rejection is made (above) as necessitated by argument clarifications provided here.
Following traversals/Remark are retained as a summarized form from prior comments so as to address apriority varied interpretations. This is also answering proactively some of the new questions that may arise because of current arguments.
Applicant’s argument 6/11/2024 from Pages 12-17 for Claim 1 as amended distinguishing from prior art is as follows:
Claim indicates that “a passive feedback system that passively gathers objective data” is not taught or disclosed or suggested by Kim and/or Zarraonandia.
Claim reciting “Real-time analytics selected from the group consisting of student-specific analytics and student-combined analytics, wherein the data is from the personalized learning environment;" are not taught disclosed or suggested by Kim and/or Zarraonandia.
Examiner respectfully traverses and adds that the previous secondary prior art Zarraonandia in abstract clearly indicating that in order to overcome this problem a system is proposed whereby lecturers receive immediate and private feedback only for both individualized student as well as aggregated for the whole class. Hence a person with ordinary skills in art could figure out that the AR system here teaches “a passive feedback system that passively gathers objective data”. The art further illustrates that “With that purpose the lecturer, who is equipped with a head mounted AR display, can visualize symbols that student select to represent their status in relation to the lecture content. Hence “the Real-time analytics selected from the group consisting of student-specific analytics and student-combined analytics, wherein the data is from the personalized learning environment” as recited by the claim.
Nevertheless examiner has cited different secondary reference Kaur et al. in this office action describing a use of augmented reality for interactive individualized learning in an educational and learning environment. Analytics on motivational factors are determined.
Applicants’ other argument on narrowed down dependent claims become moot since the prior art combined references and citations in the rejection statement has changed.
Previously applicant’s amendment and remarks made 35USC112 rejection were withdrawn.
Applicant on pages 10-15 respectfully submits that the Examiner's description of what is taught by Kim goes beyond the text and figures of Kim, and, as a result, Kim is not directed to a tool that gathers data for use by a teacher as specifically set forth in independent claims 1, 11, and 24. Examiner traverses and have provided a new ground of rejection based on non-patent literature when the combination explicitly indicate the use of augmented reality devices by teacher to gather specific evaluation data.
Applicant continues as on pages 15-22 that art Kim only discloses a system and method that gathers data so that the system and method can determine what information to provide to the student without the teacher making those decisions. Stated differently, the teacher is not part of the system or method disclosed. Examiner respectfully traverses and finds that both the current art provides ample evidence that all students and teachers can have wearable cognitive augmentation devices to determine student-specific analytics and student-combined analytics. It is further observed that non-patent literature provides explanation of teacher wearing a cognitive augmentation device to receive passive feedback from students performing work in the classroom while the teacher is engaged with the student (section 3.2 of lecturer’s perspective during question rounds and explanations).
Applicant on pages 11 argument/remarks 5/11/2022 indicates prior art previously teaches neurofeedback includes neuroimaging measurements captured via one or more NIRS sensors included on or within the device worn by the student. For example, the device can include one or more NIRS sensors that capture quantitative hemodynamic and metabolic information from one or more areas of the brain. The hemodynamic and metabolic information can be correlated to mental performance with respect to one or more defined cognitive function areas. Though the prior art monitors learning performance with numerous generic sensors biometric ones are in focus. Information gathering is about quantitative hemodynamic and metabolic information from one or more areas of the brain, electrical activity of the brain, neurofeedback, neuroimaging sensors, NIRS sensors and/or EEG sensors. The instant specification is found not explicitly indicating this non-neuro imaging has not been categorically excluded to indicate that use of quantitative hemodynamic and metabolic information from one or more areas of the brain or information about the electrical activity of the brain cannot be collected. Examiner has now referred to another prior art Kim that more focused on Augmented reality classroom applications. Therefore, Applicant’s those specific arguments are moot.
Applicant on pages 12- 19 of argument/remarks has argued about the secondary prior art Bazanov not teaching “feedback measurements that are based on non-neuro-imaging data.” Examiner respectfully traverses and finds the secondary art Bazanov is depicting a classroom learning art with teacher and students feedback measurement but new art combination clearly indicates the using of augmented devices. The feedback measurements that are generated is used to highlight non-neuro but quality of presentation in imaging data for sensors of various inputs.
Applicant has highlighted dependent claim elements such as temporal student engagement, wearable devices worn by analytical teachers, intelligent drive electronic capabilities, near-infrared spectroscopy sensor, self-study, wearable sensor in a personalized learning environment to be an exclusive element of the prior art may need further evaluation in view of scope and contents of the prior art combination as taught by Kim et al. in view of Bazanov. Each prior art is found to be analogous since they tend to solve a reasonably pertinent problem belonging to personalized learning environment in an intelligent tutoring system with temporal interactions among tutors and students as faced by the applicant for reasonable expectation of success. Though, the claims recite both a generic computer element—a processor—and a series of computer and teaching tools and features, those claimed components merely restate performing many common functions. The highlighted dependent claim elements are understood to be commonly present and used by a person with ordinary skills in art at the time of invention.
The prior art Kim also teaches educational tool wherein the feedback system comprises sensors placed throughout the learning environment to gather data on the at least one student's activities (Para 0029 sensing modules e.g. camera includes provision to gather data, as in cancelled claim 6)
The reference Kim also teaches the method wherein the data is passively gathered using sensors placed throughout the learning environment to gather information on the at least one student's activities (Fig.7 element 802 captured from sensor worn by user as in cancelled claim 16).
The reference Kim teaches the system wherein the data is from a source selected from the group consisting of a sensor that is external to the at least one student that monitors the body language and/or facial expressions of the at least one student, an intelligent tutoring system, a personalized learning software, an educational software, a camera, an accelerometer, an instrumented classroom, an instrumented learning environment, a computer, a tool with a sensor, a device with a sensor, a video, a recording device, an AIED, an educational tool system, and an instrumented makerspace (Para 0025, 0029, 00300032, 0042 facilitated classroom, plurality of proximity, altitude sensors, cameras, accelerometers, videos) . as in cancelled claim 25).
The prior art combination further teaches a system wherein the data
The present instant invention is a use of analytics into classrooms that are equipped with education technologies, including, without limitation AIED systems and ITSs, to enable human teachers to amplify their abilities and to leverage the complementary strengths of both the human teachers and the educational technologies.
In the present invention as claimed, the "user" is the teacher, not the student. The claimed present invention passively gathers objective data on the students, whereby the students merely work within the personalized learning environment and the data is gathered from that environment. The students do not have to actively push information into the system to be shown to the teacher.
In summary, some of the prior art combination has some distinction from the teachings of instant disclosure suggesting: (1) its applicability to non-lecture-based learning, (2) gathering any type of input other than subjective, student-self-reported data in response to questions from the lecturer, (3) incorporating a personalized learning environment and/or personalized learning software for self- paced education, and/or (4) the passive gathering of objective data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 11617559 B2 Samec; Nicole Elizabeth et al.
Augmented reality systems and methods for user health analysis
US 9923980 B2 Tatourian; Igor et al.
Embodiments of apparatus and methods for providing recommendations based on environmental data and associated contextual information.
Durfresne R.J., Gerace, W.J., Leonard, W.J., Mestre, J.P. and Wenk L. (1996), Classtalk: A Classroom Communication System for Active Learning, Journal of Computing in Higher Education, Vol 7, No. 2, pp. 3 – 47
The focus promotion of active learning and participation of the student rather than providing the lecturer with continuous feedback on their status and acquired knowledge.
Chen, G-D and Chao, P-Y (2008), Augmenting Traditional Books with Context-Aware Learning Supports from Online Learning Communities, Journal of Educational Technology and Society, Volume 11, Issue 2, 27-40
An apparatus includes a data collector to receive environmental data and an analysis module to identify a behavioral model of the first user based at least in part on the environmental data associated contextual information.
US 20180293281 A1 to Kim
The art describes a system and method that gathers data so that the system and method can determine what information to provide to the student without the teacher making those decisions. Stated differently, the teacher is not part of the system or method disclosed.
Non-Patent Literature Augmented Lecture Feedback System to Kaur et al 1. (Kaur).
The art provides feedback decision making measurements (Fig. 1) such that in a real-time investigation for learning using AR displays related to personalized learning environment (Table 2 analytics of student responses after delivery of learning content in AR based environment , Fig. 2 analytics from user information of personalized learning environment in an active tasks mode in stages associated with feedback response; the analytics augmenting teachers perceptions and decision-making during an on-going classroom activities and survey.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SADARUZ ZAMAN whose telephone number is (571)270-3137. The examiner can normally be reached M-F 9am to 5pm CST.
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/S.Z/Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715