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 .
Application Status
Present office action is in response to application filed 05/15/2023. Claims 1-20 are currently pending in the application.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: Statutory Category?
Independent claims 1, 10 and 19 respectively recites “a method” (i.e. a process), “a system” (i.e. a machine) and “a non-transitory computer-readable storage medium” (i.e. a manufacture). As such, independent claims 1, 10 and 19 are each directed to a statutory category of invention within § 101, i.e., machine, process and manufacture. (Step 1: YES).
Step 2A – Prong 1: Judicial Exception Recited?
Independent claim 1, analyzed as representative of the claimed subject matter, is reproduced below. The limitations determined to be abstract ideas are shown in italics. The additional element(s) recited at a high level of generality are shown in bold. The limitation(s) determined to be extra-solution activity are underlined.
A method comprising:
receiving lesson information comprising a plurality of lessons;
receiving student information associated with a student;
preparing a lesson plan based in part on the lesson information and the student information;
presenting a first lesson of the plurality of lessons to the student;
determining, based on an interaction between the student and the first lesson, a student engagement information;
applying the student engagement information as input to a machine learning model, wherein applying the student engagement information to the machine learning model causes the machine learning model to output a lesson success evaluation;
determining a lesson success based on the lesson success evaluation;
modifying the lesson plan based on the lesson success to generate an adjusted lesson plan; and
presenting a second lesson of the plurality of lessons to the student based on the adjusted lesson plan.
The claim steps can be read on longstanding teaching practices including creating and presenting teaching approaches, collecting responses from students in order to determine correctness and provide further teaching approaches. The published specification discloses “[E]ducational systems may utilize computer applications to instruct a user in communicating in one or more languages …” (¶ 2). Thus, other than reciting the “machine learning model” additional non-abstract element in representative independent claim 1 above, under the broadest reasonable interpretation, at least the italicized claim limitations may be performed in the human mind, including observations, evaluations, and judgments and may also be characterized as a certain method of organizing human activity, i.e., managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claim recites an abstract idea under Step 2A: Prong 1. (Step 2A – Prong 1: YES).
Step 2A – Prong 2: Integrated into a Practical Application?
The computer component(s), namely the “machine learning model” is recited at a high level of generality (see published Specification (at least ¶ 67: … the mobile application can use an artificial intelligence model (e.g., machine learning model, neural network, etc.); ¶ 76: … a user device, such as a desktop computer, laptop, and a mobile phone. In general, the user device can be any computing device such as a desktop, laptop or tablet computer, personal computer, wearable computer, server, personal digital assistant (PDA), hybrid PDA/mobile phone, mobile phone, electronic book reader, set-top box, voice command device, camera, digital media player, and the like; ¶ 78: … a computing system that is a single computing device, or it may include multiple distinct computing devices, such as computer servers, logically or physically grouped together to collectively operate as a server system. The components of the computing system can each be implemented in application-specific hardware (e.g., a server computing device with one or more ASICs) such that no software is necessary, or as a combination of hardware and software. In addition, the modules and components of the computing system can be combined on one server computing device or separated individually or into groups on several server computing device; ¶ 142: … The student device 6210 may be running the adaptive teaching system 6215 locally and may be a mobile device, laptop computing device, desktop computing device, touchscreen monitor in communication with a remote computing device (e.g., a cloud computing environment where the adaptive teaching system 6215 is running), or any other device configured to display video lessons and accept input from the student 6205; ¶ 143: … The teacher device 6250 may be running the adaptive teaching system 6215 locally and may be a mobile device, laptop computing device, desktop computing device, touchscreen monitor in communication with a remote computing device (e.g., a cloud computing environment where the adaptive teaching system 6215 is running), or any other device configured to display instructions; ¶ 145: …. server 6220 may be a computing system that is a single computing device, or it may include multiple distinct computing devices, such as computer servers, logically or physically grouped together to collectively operate as a server system. The components of the computing system can each be implemented in application-specific hardware (e.g., a server computing device with one or more ASICs) such that no software is necessary, or as a combination of hardware and software; ¶ 162: …. computer system may, in some cases, include multiple distinct computers or computing devices (e.g., physical servers, workstations, storage arrays, cloud computing resources, etc.) that communicate and interoperate over a network to perform the described functions. Each such computing device typically includes a processor (or multiple processors) that executes program instructions or modules stored in a memory or other non-transitory computer-readable storage medium or device (e.g., solid state storage devices, disk drives, etc.). The various functions disclosed herein may be embodied in such program instructions, or may be implemented in application-specific circuitry (e.g., ASICs or FPGAs) of the computer system. Where the computer system includes multiple computing devices, these devices may, but need not, be co-located …; ¶ 165: …. machine, such as a processor device, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor device can be a microprocessor, but in the alternative, the processor device can be a controller, microcontroller, or state machine, combinations of the same, or the like. A processor device can include electrical circuitry configured to process computer-executable instructions … processor device includes an FPGA or other programmable device that performs logic operations without processing computer-executable instructions. A processor device can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor device may also include primarily analog components … some or all of the algorithms described herein may be implemented in analog circuitry or mixed analog and digital circuitry. A computing environment can include any type of computer system, including, but not limited to, a computer system based on a microprocessor, a mainframe computer, a digital signal processor, a portable computing device, a device controller, or a computational engine within an appliance, to name a few. The lack of details about the “machine learning model” indicates that the additional element(s) is/are generic, or part of generic computer elements performing or being used in performing the generic functions claimed. The additional elements beyond the abstract idea are the recited “receiving lesson information” (data gathering), “receiving student information” (data gathering), “presenting a first lesson” (data presentation), “input to a machine learning model…output a lesson success evaluation” (data gathering and outputting), and “presenting a second lesson” (data presentation) which are each a form of extra-solution activity. The claim limitations do not purport to improve the functioning of the “machine learning model”, do not improve the technology of the technical field, and do not require a “particular machine.” Rather, they are performed using generic computer components. Further, the claim fails to effect any particular transformation of an article to a different state. The recited steps in the claim fail to provide meaningful limitations to limit the judicial exception. In this case, the claim merely uses the claimed computer elements as a tool to perform the abstract idea.
Considering the elements of the claim both individually and as “an ordered combination” the functions implemented on the “machine learning model” at each step of the method are purely conventional. Each step performed in the claim does no more than require a generic computer to perform a generic computer function. Thus, the claimed elements have not been shown to integrate the judicial exception into a practical application as set forth in the Revised Guidance which references the Manual of Patent Examining Procedure (“MPEP”) §§ 2106.04(d) and 2106.05(a)–(c) and (e)–(h). Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong Two: NO).
Step 2B: Claim provides an Inventive Concept?
As discussed with respect to Step 2A Prong Two, the “machine learning model” in the claim amounts to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the published Specification, as noted above (for example, (¶¶ 67, 76, 78, 142, 143, 145, 162, 165)) describes the “machine learning model” in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting conventional computer components and techniques, particularly in light of the published Specification sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional element(s) “in a manner that indicates that the additional element(s) is/are sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.” The generic description of the “machine learning model” indicates the steps are well-known enough that no further description is required for a skilled artisan to understand the process and that these computer components are all used in a manner that is well-understood, routine, and conventional in the field. In particular, the recited data gathering (“receiving lesson information”; “receiving student information”; “input to a machine learning model…”), data outputting (“output a lesson success evaluation”), and data presentation (“presenting a first lesson”; “presenting a second lesson”) are nothing more than well-understood, routine, and conventional activity because these limitations are not distinguished from generic, conventional data gathering, outputting and data presentation with a computer. See Elec. Power Grp., 830 F.3d at 1356 (claims to gathering, analyzing, and displaying data in real time using conventional, generic technology do not have an inventive concept); Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Hence, the additional element(s) is/are generic, well-known, and conventional computing element(s). The use of the additional element(s) either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept, and thus the claims are patent ineligible. (Step 2B: NO).
In regard to independent Claim 10:
Independent claim 10 recites a system comprising: a memory storing computer-executable instruction; and a processor in communication with the memory, wherein the computer-executable instructions, when executed by the processor, cause the processor to perform steps comparable to those of representative claim 1. Accordingly, independent claim 10 is rejected for reasons similar to those previously explained when addressing representative claim 1.
In regard to independent Claim 19
Independent claim 19 recites a non-transitory computer-readable storage medium comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a computer system, cause the computer system to perform steps comparable to those of representative claim 1. Accordingly, independent claim 19 is rejected for reasons similar to those previously explained when addressing representative claim 1.
In regard to the dependent claims:
Dependents claims 2-9, 11-18 and 20 include all the limitations of corresponding independent claims 1, 10 and 19 from which they depend and, as such, recite the same abstract idea(s) noted above for corresponding independent claims 1, 10 and 19. The dependent claims do not appear to remedy the issues noted above. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 2-5 and 9-14 integrates the judicial exception into a practical application. Additionally, while dependent claims 2-9, 11-18 and 20 may have a narrower scope than corresponding independent claims 1, 10 and 19, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 2-9, 11-18 and 20 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more. Claim Rejections - 35 USC § 102/103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1, 10 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Brinton et al. (US 20210049923 A1) (Brinton).
Re claims 1, 10 and 19:
[Claim 1] Brinton discloses a method comprising: receiving lesson information comprising a plurality of lessons; receiving student information associated with a student; preparing a lesson plan based in part on the lesson information and the student information; presenting a first lesson of the plurality of lessons to the student; determining, based on an interaction between the student and the first lesson, a student engagement information (at least ¶ 5: educationally, customize a collection of content comprising a course and to individualize or adapt its sequence of delivery for a particular student, even as the course is being delivered); applying the student engagement information as input to a machine learning model, wherein applying the student engagement information to the machine learning model causes the machine learning model to output a lesson success evaluation; determining a lesson success based on the lesson success evaluation; modifying the lesson plan based on the lesson success to generate an adjusted lesson plan; and presenting a second lesson of the plurality of lessons to the student based on the adjusted lesson plan (at least ¶ 5: selection of material from the collection which is better attuned to the student's understood comprehension and/or preferred learning style, determined typically during the course; tracks a learner's behavior, actions, and/or performance as he/she interacts with those materials, analyzes these interactions even before a quiz or exam is offered, and makes corresponding individualized adjustments in course delivery; ¶ 7: describe an action or a set of actions exhibited by a student that may either positively or negatively be associated with learning outcomes in a statistical sense …Behaviors are used to model a student, compare the student to one or more other students, or compare the student to the same student's behaviors in prior courses, or both, and adjust course delivery based, at least in part, on known success approaches for different behaviors … Behaviors are used to model a student, compare the student to one or more other students, or compare the student to the same student's behaviors in prior courses, or both, and adjust course delivery based, at least in part, on known success approaches for different behaviors; ¶ 21: the decisions of whether switching to an alternate sequence is likely to be beneficial and, if so, which content this alternate sequence will consist of, is determined … through artificial intelligence and machine learning methods … a “best fit” model is established, based at least on a comparison with stored data on student performance, vis-a-vis paths taken and actions, student-by-student, and refined as more students are involved, and the present student's attributes are used to determine his/her best next Module, File, and/or Segments …; ¶ 22: various interactions are captured by the system … processed into “behaviors” so as to determine the student's overall strengths and weaknesses and specific positives and negatives relative to the topic material …; ¶ 40: decision of whether it is necessary to branch to an alternate sequence is preferably determined at least in part through machine learning associated with the student and triggers in the User Model. From the set of potential sequences, an at-that-time optimal one is determined by generating a prediction of the student's knowledge and/or preferences on the course topics after the processor … in a modeling sense, traverses each of the potential paths, and chooses the one with the highest value; ¶ 56: To the extent students follow known motifs, modules may be selected conformant to success with similar modules of students that have followed similar motifs; ¶ 105: Machine learning algorithms, among others, are implemented on these inputs so as to discern and categorize the types of human interaction … concepts … are extracted through machine learning to find the set of concepts that are optimal in the sense of identifying the key factors affecting user performance … if students are spending an inordinate amount of time on one lecture, the system may recommend a number of alternatives to that lecture; ¶ 130: … leverage correlations identified between behavioral information (e.g., fraction of time or number of pauses registered for that user on a video related to the assessment) to enhance the proficiency determination, by applying a supervised learning algorithm such as a Support Vector Machine (SVM) that can readily identify such correlations and apply them to prediction when they exist; ¶ 203: a final score measuring how effective each Segment is at helping remediate the behavior at hand, and with it, the system now has the information necessary to select candidate sequences to show; ¶ 209: determine the efficacy of the remediation Module, and more specifically, the remediation Segment selection and sequence for the user).
Alternatively, in the event Brinton is viewed as disclosing all the claim limitations but the claim limitations are viewed as not being part of a single embodiment and/or the claim elements are not disclosed as claimed, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Brinton as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 10] The claim is a system comprising: a memory storing computer-executable instruction; and a processor in communication with the memory, wherein the computer-executable instructions, when executed by the processor, cause the processor to perform steps comparable to those of representative claim 1. Accordingly, independent claim 10 is rejected for reasons similar to those previously explained when addressing representative claim 1.
[Claim 19] The claim is a non-transitory computer-readable storage medium comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a computer system, cause the computer system to perform steps comparable to those of representative claim 1. Accordingly, independent claim 19 is rejected for reasons similar to those previously explained when addressing representative claim 1.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as obvious over Brinton, as applied to claim 1 above, and further in view of Vining et al. (US 11113983 B1) (Vining).
Re claims 2 and 11:
[Claims 2 and 11] Brinton appears to be silent on but Vining teaches or at least suggests wherein the lesson information further comprises a lesson level associated with a difficulty of the plurality of lessons (at least col 4, lines 40-46: Each individual course 210 within courses 208 might comprise a number of difficulty levels 212, such as beginner, intermediate, or advances, according to the needs of users. Each difficulty level 214 in turn comprises specific course material 216 tailored to that difficulty level. Course 210 might also comprise an exam 218 with corresponding questions 220 appropriate for the difficulty level 214 in question). Hence, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have used Vining’s course difficulty levels and modify Brinton as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 3-4, 9, 12, 16-18 and 20 are rejected under 35 U.S.C. 103 as obvious over Brinton, as applied to claim 1, and further in view of Fieldman (US 11113983 B1).
Re claims 3-4 and 12:
[Claims 3-4 and 12] Brinton teaches or at least suggests receiving content from the teacher; generating a lesson based in part on the received content to create a new lesson; and adding the new lesson to the plurality of lessons, wherein the request is transmitted in response to the determination of the lesson success (at least ¶ 5: educationally, customize a collection of content comprising a course and to individualize or adapt its sequence of delivery for a particular student, even as the course is being delivered; ¶ 7: Behaviors are used to model a student, compare the student to one or more other students, or compare the student to the same student's behaviors in prior courses, or both, and adjust course delivery based, at least in part, on known success approaches for different behaviors; ¶ 11: the course instructor manually specify which topics appear in which Files and in which Segments; ¶ 21: determine whether remediation or adjustment will be beneficial to the student, and if so, identifying the corresponding combination of topics that needs remediation or adjustment. Then, they include selecting a plurality of alternate potential learning paths for the student by mathematically comparing the topics in need of remediation in the current user model with the content tags of the course files from the Content Tagging step, searching for the most relevant sets of material, and putting these sets of material together in appropriate sequences that could be delivered to the student; ¶ 42: An instructor … can create and/or add new content files for the course and replace old ones; ¶ 45: deliver any type of learning mode (i.e., content files) to end-users, including but not limited to one or more of videos, textbooks, articles, PDFs, slides, interactive presentations, animations, and/or simulations; ¶ 68: an online course for which an author has provided both recorded lecture videos and excerpts from a textbook for the students to learn from) or individualized (i.e., the content from the author is adapted to each individual user, either through machine or human intelligence or a combination thereof) … the user interface can … deliver specific suggestions for course adjustments by the instructor …; ¶ 84: determine how to adjust material delivered to a student. Of course, at least some of those changes are automatically implemented and the instructor is given indication of those changes as well as recommendations for other changes; ¶ 89: … an instructor may be recommended to divide content into chunks according to the play events where this motif occurs, and then create additional content within these chunks …; ¶ 90: generate recommendations about how content can be modified to create a more effective learning experience; ¶ 105: he system may suggest recommendations to the instructor … if students are spending an inordinate amount of time on one lecture, the system may recommend a number of alternatives to that lecture and, as appropriate, the lecture could be replaced in the repository (e.g., indicating that the lecture itself has room for improvement); ¶ 203: a final score measuring how effective each Segment is at helping remediate the behavior at hand, and with it, the system now has the information necessary to select candidate sequences to show; ¶ 209: determine the efficacy of the remediation Module, and more specifically, the remediation Segment selection and sequence for the user).
Brinton’s recommendations to modify/create/add content is interpreted to read on transmitting a request to record content to a teacher. Additionally, the function of transmitting a request is merely a basic computer function which is an obvious feature of every computing device. Hence, in the event the above interpretation is viewed as not being reasonable, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Brinton as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
Brinton appears to be silent on but Fieldman teaches or at least suggests the content is video (at least col 3, lines 7-25: a video presentation production system … cause … capture, using the first camera component, a video media of a presenter delivering a first live mobile video presentation, and further being configured to generate a presenter video feed of the presenter delivering the first mobile video presentation; generate a first presentation content feed relating to the first mobile video presentation, the first presentation content feed including a first portion of presentation content; and generate a composite video presentation feed comprising a Chroma key composite video image of the presenter video feed overlaid or superimposed over a portion of the first presentation content feed; col 3, lines 49-58: enable the presenter to selectively add, in real-time and while the presenter is delivering the first mobile video presentation, at least one annotation to the first portion of presentation content; and enable the presenter to view the annotated presentation content on the display screen in substantially real-time, while the presenter is delivering the first mobile video presentation; col 4, lines 61-67: causing the first video presentation to be displayed in a manner such that the video image of the presenter is superimposed over a first region of displayed presentation content associated with the presentation content feed; and enabling the end user to dynamically move the video image of the presenter over a second region of displayed presentation content associated with the presentation content feed; col 23, lines 37-40: Because of the placement of the video camera 222, the video frame includes an image of the remote participant's body (usually from the chest up) with a background behind the remote participant; col 23, line 62 – col 24, line 20: … the Interface portions 710 and 750 may include features and/or functionality for enabling the Teacher user to initiate and/or perform one or more of the following operation(s)/action(s) (or combinations thereof): … Record, edit, upload and post video content (712); col 43, lines 47-54: the Presenter is able to observe (e.g., in real-time) the composite video feed showing his image overlaid in front of the presentation whiteboard content (e.g., as the video presentation may appear to end users), and is able to angle his body movement(s) accordingly for interacting with portions (e.g., graphs, text, images, etc.) of the displayed presentation whiteboard content). It would have been obvious, when faced with the issue of delivering online course customized to student, one of ordinary skill in the art would have looked to take advantage of and incorporate the video presentation and digital compositing features of Fieldman and modify Brinton as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Re claims 9, 16 and 20:
[Claims 9, 16 and 20] Brinton discloses allowing the teacher record the first lesson (at least ¶ 42: An instructor … can create and/or add new content files for the course and replace old ones … as new course files are added, replaced, or removed … are restarted, especially for the new content); displaying to the teacher one or more instructions, the instructions indicating a set of recommendations for recording a first video segment (at least ¶ 71: instructor interface application, for both visualization and recommendation purposes; ¶ 89: an instructor may be recommended to divide content into chunks according to the play events where this motif occurs, and then create additional content within these chunks; ¶ 90: recommendations about how content can be modified to create a more effective learning experience; ¶ 104: generate recommendations from the data to assist the instructor; ¶ 105: the system may suggest recommendations to the instructor; ¶ 110: Recommendations to revisit specific portions of a learning mode where the level of focus).
Brinton appears to be silent on presenting to a teacher a prompt and also silent on receiving, from the teacher, a start indication indicating a request to start. However, menu options/command prompts and/or buttons/icons for presenting a prompt and receiving a start indication are functions that are each merely a basic computer function which is at least an obvious, if not inherent, feature of every computing device. Hence, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Brinton as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
Brinton appears to be silent on but Fieldman teaches or at least suggests presenting, to the teacher, a video recording interface comprising a position indicator indicating a head position in a video frame, a body position indicator indicating a body position in the video frame, and instructions; recording the first video segment; receiving from the teacher a stop indication indicating the teacher has completed recording the first video segment; terminating recording the first video segment; presenting to the teacher an editing interface comprising a trim option, the trim option allowing the teacher to edit at least a portion of the first video; receiving from the teacher a completion indication indicating the teacher has completed editing the first video segment; and combining the first video segment with a second video segment wherein the first video segment and the second video segment are associated with a topic of the first lesson to generate the first lesson (at least col 3, lines 7-25: a video presentation production system … cause … capture, using the first camera component, a video media of a presenter delivering a first live mobile video presentation, and further being configured to generate a presenter video feed of the presenter delivering the first mobile video presentation; generate a first presentation content feed relating to the first mobile video presentation, the first presentation content feed including a first portion of presentation content; and generate a composite video presentation feed comprising a Chroma key composite video image of the presenter video feed overlaid or superimposed over a portion of the first presentation content feed; col 3, lines 49-58: enable the presenter to selectively add, in real-time and while the presenter is delivering the first mobile video presentation, at least one annotation to the first portion of presentation content; and enable the presenter to view the annotated presentation content on the display screen in substantially real-time, while the presenter is delivering the first mobile video presentation; col 4, lines 61-67: causing the first video presentation to be displayed in a manner such that the video image of the presenter is superimposed over a first region of displayed presentation content associated with the presentation content feed; and enabling the end user to dynamically move the video image of the presenter over a second region of displayed presentation content associated with the presentation content feed; col 23, lines 37-40: Because of the placement of the video camera 222, the video frame includes an image of the remote participant's body (usually from the chest up) with a background behind the remote participant; col 23, line 62 – col 24, line 20: … the Interface portions 710 and 750 may include features and/or functionality for enabling the Teacher user to initiate and/or perform one or more of the following operation(s)/action(s) (or combinations thereof): … Record, edit, upload and post video content (712); col 43, lines 47-54: the Presenter is able to observe (e.g., in real-time) the composite video feed showing his image overlaid in front of the presentation whiteboard content (e.g., as the video presentation may appear to end users), and is able to angle his body movement(s) accordingly for interacting with portions (e.g., graphs, text, images, etc.) of the displayed presentation whiteboard content). As noted earlier, functions like “stop indication” and “terminating recording” are functions that are each merely a basic computer function which is at least an obvious, if not inherent, feature of every computing device. As a result, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Brinton as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense. Subsequently, it would have been obvious, when faced with the issue of delivering online course customized to student, one of ordinary skill in the art would have looked to take advantage of and incorporate the video presentation and digital compositing features of Fieldman and modify Brinton as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Re claim 17:
[Claim 17] Brinton in view of Fieldman appears to be silent on wherein the video recording interface further comprises a recording status indicator. The Examiner takes official notice that the concept and advantages of a recording status indicator were old and well known to one of ordinary skill in the art before the effective filing date of the invention. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Brinton in view of Fieldman as claimed because this would amount to no more than applying a known technique to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Re claim 18:
[Claim 18] Brinton in view of Fieldman discloses English course (Brinton: ¶ 11). However, Brinton in view of Fieldman appears to be silent on wherein the instructions comprise one of a script or a pronunciation. The Examiner takes official notice that the concept and advantages of using a script or a pronunciation in an English course were old and well known to one of ordinary skill in the art before the effective filing date of the invention. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Brinton in view of Fieldman as claimed because this would amount to no more than applying a known technique to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 5-6 and 13-14 are rejected under 35 U.S.C. 103 as obvious over Brinton in view of Fieldman, as applied to claim 3, and further in view of Romney et al. (US 20140017653 A1) (Romney).
Re claims 5 and 13:
[Claims 5 and 13] Brinton in view of Fieldman appears to be silent on but Romney teaches or at least suggests applying the lesson plan as input to a machine learning model, wherein application of the lesson plan to the machine learning model causes the machine learning model to output first information; comparing the first information to the plurality of lessons; and determining, based on the comparison of the first information to the plurality of lessons, the first information is not contained in the plurality of lessons, wherein the new lesson comprises the first information (at least ¶ 87: a crawler module 148 may discovery new academic material automatically … a crawler module 148 may determine that a subject is missing from a content object and may recommend to an academic administrator to include missing material; ¶ 166: a crawler module 148 may generate a set of educational material based on accreditation resources available from third party accreditation institutions, and may automatically recommend educational materials to be included in a deficient curriculum … a crawler module 148 may utilize various artificial intelligence algorithms to determine material that may be missing from a curriculum … a crawler module 148 may utilize a neural network to measure a completeness of a curriculum, use a genetic algorithm to construct a complete curriculum, or other, or the like. In response to determining a deficient curriculum, a crawler module 148 may recommend that missing material be included in the curriculum for the virtual instruction cloud). Hence, it would have been obvious, when faced with the issue of delivering online course customized to student, one of ordinary skill in the art would have looked to take advantage of and incorporate the missing material features of Romney and modify Fieldman in view of Brinton as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Re claims 6 and 14:
[Claims 6 and 14] Brinton in view of Fieldman appears to be silent on but Romney teaches or at least suggests applying a third lesson to the machine learning model, wherein application of the third lesson to the machine learning model causes the machine learning model to output a lesson completeness; and determining the third lesson is an incomplete lesson based on the lesson completeness (at least ¶ 87: a crawler module 148 may discovery new academic material automatically … a crawler module 148 may determine that a subject is missing from a content object and may recommend to an academic administrator to include missing material; ¶ 166: a crawler module 148 may generate a set of educational material based on accreditation resources available from third party accreditation institutions, and may automatically recommend educational materials to be included in a deficient curriculum … a crawler module 148 may utilize various artificial intelligence algorithms to determine material that may be missing from a curriculum … a crawler module 148 may utilize a neural network to measure a completeness of a curriculum, use a genetic algorithm to construct a complete curriculum, or other, or the like. In response to determining a deficient curriculum, a crawler module 148 may recommend that missing material be included in the curriculum for the virtual instruction cloud). Hence, it would have been obvious, when faced with the issue of delivering online course customized to student, one of ordinary skill in the art would have looked to take advantage of and incorporate the missing material features of Romney and modify Fieldman in view of Brinton as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 7-8 and 15 are rejected under 35 U.S.C. 103 as obvious over Brinton in view of Fieldman, as applied to claim 3, and further in view of AHN et al. (US 20210243407 A1) (AHN).
Re claims 7-8 and 15:
[Claims 7-8 and 15] Brinton in view of Fieldman appears to be silent on but AHN teaches or at least suggests applying a third lesson to the machine learning model, wherein application of the third lesson to the machine learning model causes the machine learning model to output an inappropriate/incorrect lesson element; and determining the third lesson is an inappropriate lesson based on the inappropriate lesson element, wherein the inappropriate/incorrect lesson is not suitable for presentation to the student, wherein the inappropriate/incorrect lesson element is one of a position of a teacher, an extraneous sensory stimuli, or an inappropriate/incorrect information item (at least ¶ 102: the processor 150 may be provided with a classification model (e.g., machine learning model) which is trained using classification information on whether the inappropriate element is included in a specific image (e.g., video and/or sound). The processor 150 may predict whether the video, which is acquired through the camera 111 after the specific event occurs, includes the inappropriate element using the classification model trained using the classification information related to whether the inappropriate element is included in the image; ¶ 103: processor 150 of the first terminal 100 may use a learning model according to at least one of techniques of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), region-based convolutional neural networks (R-CNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), and deep Q-networks in order to predict whether the video, which is received by the camera 111 after the specific event occurs, includes the inappropriate element; ¶ 107: In operation S140, the processor 150 of the first terminal 100 may stop transmitting the video or sound which is received by the camera 111 after the specific event occurs to the second terminal 200 according to a result of the determination; ¶ 108). Because inappropriate is a synonym of unsuitable, AHN’s inappropriate element is interpreted to read on incorrect limitation not suitable for presentation to the student. In the event this interpretation is viewed as not being reasonable, a person skilled in the art would have found it obvious to modify AHN’s classification model/machine learning model to determine incorrect information instead of inappropriate information because doing so would have been little more than substituting one element for another known in the art to yield a predictable result. See KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 416 (2007). Hence, it would have been obvious to one of ordinary skill in the art at the time before the effective filing date of the invention to use the classification model/machine learning model features of AHN and modify Brinton in view of Fieldman as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Conclusion
The prior art made of record and not relied upon is listed in the attached PTO
Form 892 and is considered pertinent to applicant's disclosure.
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