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 2/6/2024 in relation to application 18/433,800.
The instant application claims is a CIP benefit to 18/521,928 with a priority date of 11/28/2023.
Claims 1-31 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 1-31 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 computer devices and system. Thus fall within one of the four statutory categories (Step 1: YES).
Independent Claim 1 is directed to a AI case applied to a classroom environment with specific modules operating for attendance, tutoring, evaluation, presentation of learning materials. A first, second third etc. classroom computer generating attendance signal, a cloud-based processor for receiving the take attendance signal, confirming attendance generating an attendance check code in response to receiving the confirm attendance code by a second classroom computer provides the attendance check code, a timer for counting down time from a predetermined time to zero time when the attendance check code is displayed. The codes further applied on a plurality of mobile devices for providing a distinct plurality of responding attendance check codes to the cloud-based processor before the timer is at zero time, wherein each one of the plurality of mobile devices provides the distinct one of the plurality of attendance check codes to the cloud-based processor and a determination and a distinct storage of a comparison result is made. All of these involve steps drawn to concept categorized as an actions that are receiving, observing, comparing, storing, evaluating and judging of inputs. A concept that are mental processes and by including generating records for specific storing and processing of classroom activities and timings, they are like organizing of certain human activities. The use of artificial intelligence models and machine-learned modules could also be categorized as a use mathematical calculations within some mathematical concepts. They are generally categorized as a grouping of an abstract idea (Step 2A: Prong 1 YES).
The independent claim do not include additional elements that are sufficient to be significantly more than the judicial exception because the limitations of “a computer system with interface display”, “a processor’, “a memory’, "network remote storage", "databases of digital content with predetermined string of cloud-based AI engine”, “presentation of tutoring plans”, “navigational controls for the modules between computers” are merely use of generic computer functions and computer parts. That is simply selecting portions of modules input and determining from storage only a corresponding filtered session for evaluation. 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 of the instant specification indicates object movements for a hardware/ software in a standard network environment with image panel implement to process claimed here. 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 2-31 describe additional limitations that serve only to modify and further describe the abstract idea. The displaying of data, transmitting confirmation of signals, playing of transferred modules, learning objective response to selected questions, provide appropriate codes, mobile device applications for tutoring sessions are further description of elements not making abstract idea less abstract. The monitoring of activities such as of use of templates for timing and evaluating etc. are likewise an abstract idea. The server, processor activities to alert managers and /or operators, storing of comments on board, remedial action and instruction generated for another user or use of communication with any improvement resulting from the claimed invention has nothing to do with the claimed computing devices, e.g., being able to run faster, use less power, and/or be manufactured more cheaply as a result of the invention. Instead, the improvement, if there is one, is in terms of the applicant’s particular method for collecting data, analyzing data, and providing an output based on that analysis with pre or post solution activities. This may not be patent eligible improvement and hence the limitations are not patent eligible.
US 20220253963 A1 Fowler; Cameron et al. classroom (fig.2A)
US 11307667 B2 Banerjee; Ayan et al.
US 20200126438 A1 SHEHATA; Shady et al.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication Number US 20220253963 A1 to Fowler et al. (Fowler) in view of US Patent Application Publication Number US 11599836 B2 to Ahmad et al. (Ahmad)
Claim 1. Fowler teaches a system for taking classroom attendance of a plurality of students located inside a classroom (Para 0004, 0030 classroom tags and access to attendance), comprising:
a first classroom computer for generating a take attendance signal, wherein the first classroom computer is located inside a classroom; b. a cloud-based processor for receiving the take attendance signal, the cloud-based processor generating a confirm attendance code in response to the take attendance signal; c. a cloud-based artificial intelligence (Al) engine for receiving the confirm attendance code, wherein the cloud-based artificial intelligence engine generates an attendance check code in response to receiving the confirm attendance code (Para 0019, 0025 attendance signal for server cloud base and a wallet for first class counting);
d. a second classroom computer for receiving the attendance check code, wherein the second classroom computer provides the attendance check code; e. a projector for receiving the attendance check code and displaying the attendance check code to be visually seen by a plurality of students in the classroom (para 0142 check data records for requested information such as check attendance code);
f. a timer for counting down time from a predetermined time to zero time when the attendance check code is displayed ( Para 0035 opening of time period) ;
g. A plurality of mobile devices for providing a distinct plurality of responding attendance check codes to the cloud-based processor before the timer is at zero time, wherein each one of the plurality of mobile devices provides the distinct one of the plurality of attendance check codes to the cloud-based processor, wherein at least one of the plurality of mobile devices is being activated by a distinct one of the plurality of students in the classroom to send the distinct one of the plurality of responding attendance check codes, wherein the cloud-based processor compares the distinct one of the plurality of responding attendance check codes to the attendance check code to produce a comparison result (para 0008 mobile devices; Para 0205 comparison) ; and
h. Fowler does not teach a cloud-based relational database for receiving the comparison result and storing the comparison result in the cloud-based relational database, wherein the cloud-based relational database includes at least one storage area corresponding to the each one of the plurality of students in the classroom, wherein storing the comparison result includes storing the comparison result in a distinct storage area of the cloud-based relational database corresponding to at least one of the plurality of students in the classroom. (Para 0253 relational data). Ahmad et al. however, teaches the receiving the comparison result and storing the comparison result in the cloud-based relational database (col.10 lines 43- 51 an object may be mapped to a vector based on one or more properties, attributes, or features of the object, relationships of the object with other objects, or any other suitable information associated with the object). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the comparison result and storing the comparison result in the cloud-based relational database, wherein the cloud-based relational database includes at least one storage area corresponding to the each one of the plurality of students in the classroom, wherein storing the comparison result includes storing the comparison result in a distinct storage area of the cloud-based relational database corresponding to at least one of the plurality of students in the classroom, as taught by Ahmad, into the system of Fowler , in order to provide quick control on parameters of each phases of classroom activity, irrespective of which phase the intended activity is in, so that phases could efficiently contribute towards an overall goal.
Fowler in combination with Ahmad teaches Claim(s) 2-10
2. A system according to claim 1, wherein the comparison result includes data indicating that at least one of the plurality of students is not in the classroom when comparison result indicates that the distinct one of the plurality of responding attendance codes does not match the attendance check code (Para 0126 pattern matching technique).
3. A system according to claim 2, wherein the cloud-based Al engine generates a student absent notice and a personalized tutoring plan for the at least one of the plurality of students based on the comparison result.
4. A system according to claim 3, wherein the cloud-based Al engine provides the student absent notice and tutoring plan to the at least one of the plurality of mobile device.
5. A system according to claim 4, wherein the cloud-based Al engine receives natural language communication using webhooking from the at least one of the mobile devices to facilitate a tutoring session.
6. A system according to claim 5, wherein the cloud-based Al engine generates a random attendance check code.
7. A system according to claim 2, wherein the at least one of the plurality of mobile devices generates a student excused code, and wherein the cloud-based processor receives the student excused code from at least one of the plurality of mobile devices.
8. A system according to claim 7, wherein the cloud-based processor provides the student excused code to the cloud-based relational database, wherein the cloud-based-relational database stores the student excused code in the at least one storage area corresponding to the at least one of the plurality of mobile devices.
9. A system according to claim 8, wherein the cloud-based Al engine generates a personalized tutoring plan, wherein the tutoring plan is personalized for the at least one mobile devices that provided the student excused code.
10. A system according to claim 9, wherein the cloud-based Al engine provides the student absent notice and tutoring plan to the at least one of the plurality of mobile device that provided the student excused code.
{Para 0044 a plurality of machine-readable codes (MRCs), each machine-readable code (MRC) in the plurality having a unique MRC identifier; (b) a server having a computer processor and a computer memory; (c) a database operatively connected to the server, the database containing information relating to each MRC in the plurality of MRCs, the information relating to each MRC including: (i) the unique MRC identifier associated with the MRC; and (ii) a pointer to a template for the interactive digital student platform associated with the unique MRC identifier; and (d) wherein the computer memory of the server stores executable code which when executed enables the server to perform a process }
Fowler in combination with Ahmad teaches Claim(s) 11-21:
11. system according to claim 10, wherein the at least one of the plurality of mobile device provides a natural language communication to the cloud-based Al engine using webhooking to facilitate a tutoring session.
Claim 12. A system according to step 11, wherein the student excused code is a natural language communication.
Claim 13. A system according to claim 12, further comprising:
a. a plurality of course modules stored in the cloud-based relational database, wherein each one of the plurality of course modules includes at least one course objective; b. a plurality of distinct quiz questions, wherein each one of the plurality of distinct quiz questions is assigned to at least one of the at least one course objectives; c. a distinct quiz answer assigned to each one of plurality of distinct quiz questions, wherein the first classroom computer generates a select course module signal, wherein the select course module signal corresponds to at least one of the plurality of course modules; d. wherein the cloud-base processor receives the generated select a course module signal and retrieves at least one of the plurality of course modules from the cloud-based relational database which corresponds to the generated select a course module signal; e. wherein the cloud-based processor provides the retrieved at least one of the course modules including the at least one course objective to the second classroom computer; f. wherein the second classroom computer provides the at least one course objective to the projector, wherein the projector displays the at least one course objective to be seen by a plurality of students; g. wherein the first classroom computer selects at least one of the plurality of learning objectives upon which to quiz the plurality of students; h. wherein the first classroom computer selects at least one of the plurality of distinct quiz questions corresponding to the selected one of the at least one of the plurality of course objectives and provides the selected one of the plurality of distinct quiz questions to the projector, wherein the projector displays the selected one the plurality of distinct quiz questions for the plurality of students to see; i. wherein the timer counts down from a predetermined time to a zero time when projector displays the selected one of the plurality of distinct quiz questions; j. wherein at least one of the plurality of mobile devices provides a quiz response data to the cloud-based processor via webhook before the timer counts down to zero time; k. wherein the cloud-based processor provides the received quiz response data to the cloud- based Al engine.
14. A system according to claim 13, wherein the cloud-based Al engine compares the received quiz response data to the stored quiz answer assigned to the selected one of the plurality of quiz questions and produces a quiz comparison result, wherein the cloud-based Al engine provides the quiz comparison result to the cloud-based relational database, and wherein the cloud-based Al engine provides the quiz comparison result to the first classroom computer.
15. A system according to claim 14, wherein the cloud-based Al engine prepares a personalized quiz tutoring plan when the quiz comparison result indicates that the quiz answer assigned to the selected one of the plurality of quiz questions does not match the received quiz response data, wherein the cloud-based Al engine provides the personalized quiz tutoring plan to the /at least one of the plurality of mobile devices that provided the received quiz response data.
16. A system according to claim 15, wherein the at least one of the plurality of mobile devices that provided the received quiz response data provides natural language communication via webhook to initiate a personalized quiz tutoring session relative to the personalized quiz tutoring plan.
17. A system according to claim 16, wherein the cloud-based relational database stores a plurality of brainstorming modules, wherein the plurality of brainstorming modules includes a plurality of distinct brainstorming questions.
18. A system according to claim 17, wherein the first classroom computer sends a select brainstorming module signal to the cloud-based processor, wherein the first computer sends a brainstorming question signal to the cloud-based Al engine, wherein the cloud-based Al engine generates a brainstorming question in response to the receiving the brainstorming question signal, wherein the cloud-based Al engine generates the brainstorming question relative to at least one of the distinct brainstorming questions.
19. A system according to claim 18, wherein the cloud-based Al engine provides the generated brainstorming question to the second classroom computer, wherein the second classroom computer provides the brainstorming question the projector.
20. A system according to claim 19, wherein the at least one of the plurality of mobile devices provides a brainstorming question answer to the cloud-based processor via webhook.
21. A system according to claim 20, wherein the cloud-based processor provides the brainstorming question answer to the second classroom computer, wherein the second classroom computer provides the brainstorming question answer to the cloud-based processor, wherein the second classroom computer provides the brainstorming question answer to the projector.
{ Para 0061 Code used to direct a user device, browser, Web app, progressive Web app, administrator device, server, database, API, tool, software, etc., to a resource within the system or a networ; Para 0215 codes, natural language processing, or other now known or developed methodologies to identify language}
Fowler in combination with Ahmad teaches Claim(s) 22-31:
22. A system according to claim 21, wherein the first classroom computer provides a plurality of learning course modules to the cloud-based processor, wherein the cloud-based processor provides the plurality of learning course modules to the cloud-based relational database, wherein the cloud-based relational database stores the plurality of learning course modules, wherein each one of the plurality of learning modules includes at least one learning course objective, wherein each one of the plurality of learning course objectives includes at least one learning course quiz question, and wherein each one of the learning course quiz questions is assigned at least learning course objective question answer.
23. A system according to claim 22, wherein the first classroom computer generates a select learning course module signal and provides the generated select learning course module signal to the cloud-based processor, wherein the select learning course module signal corresponds to at least one of the stored plurality of learning course modules.
24. A system according to claim 23, wherein the cloud-based processor retrieves from the cloud- based relational database at least one of the plurality of learning course modules corresponding to the select learning course module signal, wherein the cloud-based processor provides the retrieved at least one of the plurality of learning course modules including the at least one learning course quiz questions to the first classroom computer.
Claim 25. A system according to claim 24, wherein the first classroom computer selects a first learning question and a second learning question from the at least one of the plurality of learning course modules, wherein the first classroom computer provides the selected first learning question to a first one of the plurality of mobile devices via webhook, and wherein the first computer provides the second learning question to a second one of plurality of mobile devices via webhook, where the first learning question is distinct from the second learning question.
Claim 26. A system according to claim 25, wherein the timer counts down from a predetermined time to a zero time when the first classroom computer provides the selected first learning question to the first one of the plurality of mobile devices and when the first classroom computer provides the selected second learning question to the second one of the plurality of mobile devise.
Claim 27. A system according to claim 26, wherein the first one of the plurality of mobile devices provides a learning objective response to the cloud-based processor via webhook before the time counts down to zero time, wherein the learning objective response corresponds to the first learning question.
Claim 28. A system according to claim 27, wherein the cloud-based processor provides the received learning objective response to the cloud-based Al engine, wherein the cloud-based Al engine compares the learning objective response to the selected first learning question to produce a learning objective comparison response.
Claim 29. A system according to claim 28, wherein the cloud-based processor provides the learning objective comparison response to the cloud-based Al engine, wherein the cloud-based Al engine generates a learning objective tutoring plan relative to the learning objective comparison ( ) .
Claim 30. A system according to claim 29, wherein the cloud-based Al engine provides the learning objective tutoring plan to the first one of the plurality of mobile devices via webhook.
Claim 31. A system according to claim 1, further including:
a. a third classroom computer for generating a second take attendance signal, wherein the third classroom computer is located inside a second classroom, wherein the cloud-based processor receives the second take attendance signal, the cloud-based processor generating a second confirm attendance code in response to the second take attendance signal, wherein the cloud-based artificial intelligence (Al) engine receives the second confirm attendance code, wherein the cloud-based artificial intelligence engine generates a second attendance check code in response to receiving the second confirm attendance code;
b. a third classroom computer for receiving the attendance check code, wherein the third classroom computer provides the second attendance check code;
c. a second projector for receiving the second attendance check code and displaying the second attendance check code to be visually seen by a second plurality of students in the second classroom;
d. a second timer for counting down time from a predetermined time to zero time when the second attendance check code is displayed; e. a second plurality of mobile devices for providing a second distinct plurality of responding attendance check codes to the cloud-based processor before the second timer is at zero time, wherein each one of the second plurality of mobile devices provides the second distinct one of the plurality of attendance check codes to the cloud-based processor, wherein at least one of the second plurality of mobile devices is being activated by a distinct one of the second plurality of students in the classroom to send the second distinct one of the plurality of responding attendance check codes, wherein the cloud-based processor compares the second distinct one of the plurality of responding attendance check codes to the second attendance check code to produce a second comparison result, wherein the cloud-based relational database for receives the second comparison result and stores the second comparison result in the cloud-based relational database, wherein the cloud-based relational database includes at least one storage area corresponding to the each one of the second plurality of students in the second classroom, wherein storing the second comparison result includes storing the second comparison result in a distinct storage area of the cloud-based relational database corresponding to at least one of the second plurality of students in the classroom, wherein the cloud-based processor aggregates the first comparison result and the second comparison result into an aggregated comparison result, wherein the cloud-based relational database receives the aggregate comparison result and stores the aggregate comparison result in the cloud- based relational database, wherein storing the aggregate comparison result includes storing the aggregate comparison result in the distinct storage area of the cloud-based relational database corresponding to the at least one of the second plurality of students in the classroom.
{Para0195 The educational institution will designate the number of unique identifying information questions that must be confirmed in order satisfy the rule. The system (10) will check external data sources to confirm the unique identifying information supplied by the user matches the unique identifying information in the verified data source. A second set of classroom under this situation is just an alternative}
Conclusion
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/S.Z/Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715