DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the reply filed September 23, 2025.
Claims 1-2, 6-10, 12, 15-16, and 18-19 have been amended.
Claims 3, 5, 11, and 17 have been cancelled.
Claims 1-2, 4, 6-10, 12-16, and 18-19 are currently pending and have been examined.
Response to Arguments
The previous claim objections have been withdrawn in response to the submitted amendments.
The previous claim interpretation of claim 17 under 35 USC 112(f) and the corresponding rejections to claim 17 under 35 USC 112(a) and 35 USC 112(b) are moot in response to the submitted amendments cancelling claim 17.
Applicant’s arguments filed September 23, 2025 have been fully considered but they are not persuasive.
Examiner notes that no arguments for claim 19 being directed toward a transitory propagating signal have been submitted and therefore the below rejection of claim 19 under 35 USC 101 is maintained.
Regarding the previous rejection under 35 USC 103, Applicant presented the following arguments:
Firstly, Basson only discloses "the career path output by the system can include one or more corresponding recommended curriculum", "According to an exemplary embodiment of the present invention, a curriculum design system uses labor market data, machine learning techniques (e.g., high-dimensional regression, sentiment learning) to develop predictive models of future market demand and trends for skills, careers, etc., over multiple time scales; combined with structured and unstructured data analytics to extract, correlate and cluster in-demand/predicted skills into recommended curriculum modules", "According to an embodiment of the present invention, from the perspective of an individual, an example of an applied career path 522 includes outputting, through the user interface of a school's online career portal, a recommended career path having a high correspondence (e.g., a best match determined by an optimization) to the individual's criteria. The user interface includes selections for the individual to modify the criteria, rerun an optimization (e.g., by selecting button 779 in FIG. 7), and obtaining an alternative recommended career path selecting using the revised criteria. This process can be repeated until the individual finds a career path fitting their needs and desires. Furthermore, the career path output by the system can include one or more corresponding recommended curriculum", that is Basson only discloses the career path output by the system can include one or more corresponding recommended curriculum, but does not disclose about teacher, and does not further disclose the feature "smart matching the first personalized course system with the knowledge and ability of the teacher user in the teacher information library, and matching the first personalized characteristic with the second personalized characteristic in the teacher information library, to determine the teacher user corresponding to the student user" of claim 1.
Examiner respectfully disagrees. As discussed in the rejection below, the smart matching limitation, previously in claim 3, is taught by the combination of Basson and Wright rather than by Basson alone, as alleged by Applicant.
Regarding the previous rejection under 35 USC 103, Applicant presented the following arguments:
Moreover, Bass only discloses the user interface includes selections for the individual to modify the criteria, rerun an optimization (e.g., by selecting button 779 in FIG. 7), and obtaining an alternative recommended career path selecting using the revised criteria, that is, the optimization is obtain through the operation of the user, which is different from the psychological portrait, does not disclose adjusting both the first personalized characteristic and adjusting the ability and knowledge system required by the target career role neither, and does not further disclose adjusting the teacher user. So Basson does not disclose the feature "adjusting the ability and knowledge system required by the target career role and the first personalized characteristics according to the results of the psychological portrait, and adjusting the first personalized course system corresponding to the student user and the teacher user according to the adjusted ability and knowledge system required by the target career role and the first personalized characteristic" of claim 1. Otherwise, Bass does not discloses "the ability knowledge system library of the career role is pre-established by collecting occupational qualification data, the occupational qualification data comprises career qualification catalogs, success paths and conditions of new career roles" of claim 1.
Examiner respectfully disagrees. Basson in [0086] does disclose assessing an individual’s aptitude, where they stand currently in the knowledge graph, modeling their aptitude, and optimizing a recommended curriculum. Further, that optimization is rerun according to Basson [0091].
Regarding the adjusting the teacher user, the claim only recites adjusting the first personalized course system. It is irrelevant whether Basson discloses “adjusting the teacher user,” as argued, because Basson does disclose adjusting the system, which is the only adjusting claimed.
As discussed in the rejection below, the ability knowledge library limitation, previously in claim 4, is taught by Basson. (Basson [0063] skill profiles of early professionals together with the skills needed to be eligible for these roles; [0065] required skills are mapped to curricula to detect coverage, redundancies, and gaps)
Regarding the previous rejection under 35 USC 103, Applicant presented the following arguments:
Wright only discloses: the images are further configured to assess whether an assessed teacher possesses the specific personality traits that are helpful in distinguishing between one of our eight defined teaching styles, that is Wright only discloses assessed teacher's "teaching " style, but not disclose "the knowledge and ability of the teacher", and does not further disclose the feature "smart matching the first personalized course system with the knowledge and ability of the teacher user in the teacher information library, and matching the first personalized characteristic with the second personalized characteristic in the teacher information library, to determine the teacher user corresponding to the student user; after matching the student user corresponding to the teacher user and the first personalized course system corresponding to the student user, the method further comprises: regularly carrying out psychological portrait of the student user through artificial intelligence, adjusting the ability and knowledge system required by the target career role and the first personalized characteristics according to the results of the psychological portrait, and adjusting the first personalized course system corresponding to the student user and the teacher user according to the adjusted ability and knowledge system required by the target career role and the first personalized characteristic; wherein the ability knowledge system library of the career role is pre-established by collecting occupational qualification data, the occupational qualification data comprises career qualification catalogs, success paths and conditions of new career roles" of claim 1.
Examiner respectfully disagrees. Teachers in Wright are service providers and the service offered by a teacher is knowledge and ability. In other words, if a service is available from a teacher, then they are considered to have that knowledge and ability to make a teaching service available. According to Specification [0100] a teacher’s personalized characteristic may include teaching style.
Regarding the previous rejection under 35 USC 101, Applicant presented the following arguments:
The online and offline hybrid education method based on a student's career aspiration of claim 1, comprises: searching the ability knowledge system library of the career role to determine an ability and knowledge system required by the target career role and searching the ability-knowledge-course corresponding relationship library to match a course corresponding to the ability and knowledge system required by the target career role and making the first personalized course system composed of the corresponding courses; smart matching the first personalized course system with the knowledge and ability of the teacher user in the teacher information library, and matching the first personalized characteristic with the second personalized characteristic in the teacher information library, to determine the teacher user corresponding to the student user; wherein, after matching the first personalized course system and the teacher user corresponding to the student user, the method further comprises: regularly carrying out psychological portrait of the student user through artificial intelligence, adjusting the ability and knowledge system required by the target career role and the first personalized characteristics according to the results of the psychological portrait, and adjusting the first personalized course system corresponding to the student user and the teacher user according to the adjusted ability and knowledge system required by the target career role and the first personalized characteristic. Thus the online and offline hybrid education method of claim 1 can accurately deliver the courses required for each student's career goals and match them with teachers whose competence and personality are fully compatible; the first personalized course system and assigned teachers can be dynamically adjusted through ongoing psychological profiling, and the teacher-course matching degree is continuously improved on a regular basis.
Examiner respectfully disagrees. The identified improvements argued by Applicant are really, at best, improvements to the performance of the abstract idea itself (e.g. improvements made in the underlying business method) and not in the operations of any additional elements or technology. See In re Board of Trustees of Leland Stanford Junior University, 991 F.3d 1245, 1251 (Fed. Cir. 2021) (“[T]he improvement in computational accuracy alleged here does not qualify as an improvement to a technological process; rather, it is merely an enhancement to the abstract mathematical calculation … itself.”); Parker v. Flook, 437 U.S. 584, 591-92 (1978) ("the novelty of the mathematical algorithm is not a determining factor at all")
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.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The United States Patent and Trademark Office is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO, see In re Zletz, 893 F.2d 319 (Fed. Cir 1989). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called storage medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter). The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims recite a “storage medium” and therefore are directed to a signal per se. Thus, claim 19 are rejected as being directed to non-statutory subject matter. For the purposes of compact prosecution, claim 19 has been interpreted to recite a manufacture.
Claims 1-2, 4, 6-10, 12-16, and 18-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Alice/Mayo Framework Step 1:
Claims 1-2, 4, 6-10, and 12-16 recite a series of steps and therefore recite a process.
Claims 18 recite a combination of devices and therefore recite a machine.
Claims 19 recites a tangible article given properties through artificial means and therefore recite a manufacture.
Alice/Mayo Framework Step 2A – Prong 1:
Claims 1, 9, and 18-19, as a whole, are directed to the abstract idea of matching a student user and a teacher user based on the career role and personality of both users, which is a method of organizing human activity and mental process. The claims recite a method of organizing human activity because the identified idea is managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) by reciting instructions for analyzing a student’s career role and personality and matching a student to a teacher based on that analysis. See MPEP 2106.04(a)(2)(II)(C). The claims recite a mental process because the identified idea contains limitations that can practically be performed in the human mind (including an observation, evaluation, judgement, or opinion) by reciting evaluating students and teachers and providing an opinion of a matching teacher based on an ideal career role. See MPEP 2106.04(a)(2)(III). The method of organizing human activity and mental process of “matching a student user and a teacher user based on the career role and personality of both users,” is recited by claiming the following limitations: obtaining a target career role, obtaining teacher characteristics, matching a teacher user to the student, providing the student with teaching of a course, storing knowledge, collecting occupational qualification data, searching for a matching course, matching a teacher, and adjusting the system based on a psychological portrait of a student. The mere nominal recitation of a processor, a memory, a storage medium, and artificial intelligence does not take the claim of the method of organizing human activity or mental process groupings. Thus, the claim recites an abstract idea.
With regards to Claims 2, 4, 6-7, 10, 12-15, the claims further recite the above-identified judicial exception (the abstract idea) by reciting the following limitations: analyzing student psychological profile, determining an ideal career role, selecting a career role, making a portrait of a teacher, receiving a career role change request, matching a new teacher, recording the status of completed courses, providing uncompleted courses, and signing a student teacher contract.
Alice/Mayo Framework Step 2A – Prong 2:
Claims 1, 9, and 18-19 recite the additional elements: a processor, a memory, a storage medium, and artificial intelligence. These processor, memory, storage medium, and artificial intelligence limitations are no more than mere instructions to apply the exception using a generic computer component. Claims 2, 4, 10, and 12-13 recite artificial intelligence limitations which are no more than mere instructions to apply the exception using a generic computer component. Claims 7-8, 15-16 recite blockchain, and audio and video data at a high level of generality and amounts to selecting a particular data source or type of data to be manipulated, or an insignificant application, which is a form of insignificant extra-solution activity. Taken individually these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Considering the limitations containing the judicial exception as well as the additional elements in the claim besides the judicial exception does not amount to a practical application of the abstract idea. The claim as a whole does not improve the functioning of a computer or improve other technology or improve a technical field. The claim as a whole is not implemented with a particular machine. The claim as a whole does not effect a transformation of a particular article to a different state. The claim as a whole is not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. The claim as a whole merely describes how to generally “apply” the concept of career counseling in a computer environment. The claimed computer components are recited at a high level of generality and are merely invoked as tools to perform an existing higher education process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. The claim is directed to the abstract idea.
Alice/Mayo Framework Step 2B:
Claims 1, 9, and 18-19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims recite a generic computer performing generic computer function by reciting a processor, a memory, and a storage medium. See Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1341 (describing a “processor” as a generic computer component); Mortg. Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324–25 (Fed. Cir. 2016) (claims reciting an “interface,” “network,” and a “database” are nevertheless directed to an abstract idea); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347–48 (discussing the same with respect to “data” and “memory”). The claims recite the following computer functions recognized by the courts as generic computer functions by reciting receiving and transmitting information (See MPEP 2106.05(d)(II) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec; TLI Communications LLC; OIP Techs.; buySAFE, Inc.), processing information (See MPEP 2106.05(d)(II) performing repetitive calculations, Flook; Bancorp Services), presenting information (See MPEP 2106.05(d)(II), MPEP 2106.05(g) presenting offers gathering statistics, OIP Technologies), and storing and retrieving information (See MPEP 2106.05(d)(II) storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc.; OIP Technologies). The specification demonstrates the well-understood, routine, conventional nature of the following additional elements because they are described in a manner that indicates the elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a): a processor (Specification [0165]), a memory (Specification [00165]), a storage medium (Specification [0165]), and artificial intelligence (Specification [0091]). See MPEP 2106.05(d)(I)(2). The claims add the words “apply it” or words equivalent to “apply the abstract idea” such as instructions to implement the abstract idea on a computer by reciting a processor, a memory, a storage medium, and artificial intelligence. See MPEP 2106.05(f). The claims limit the field of use by reciting teaching online. See MPEP 2106.05(h). Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See MPEP 2106.05(a). Their collective functions merely provide conventional computer implementation. See MPEP 2106.05(b). Therefore, the claims do not include additional elements alone, and in combination, that are sufficient to amount to significantly more than the recited judicial exception.
With regards to Claims 2, 4, 7-8, 10, 12-13, and 15-16, the additional elements do not amount to significantly more than the judicial exception. Claims 2, 4, 10, and 12-13 add the words “apply it” or words equivalent to “apply the abstract idea” such as instructions to implement the abstract idea on a computer by reciting artificial intelligence. See MPEP 2106.05(f). Claims 7-8, 15-16 recite insignificant extrasolution activity (i.e. selecting a particular data source or type of data to be manipulated, or an insignificant application) by reciting blockchain, and audio and video data. See MPEP 2106.05(g). Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. See MPEP 2106.05(a). Their collective functions merely provide conventional computer implementation. See MPEP 2106.05(b). Therefore, the claims do not include additional elements that are sufficient to amount to significantly more than the recited judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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 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.
Claim(s) 1-6, 8-14, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Basson et al. (U.S. P.G. Pub. 2018/0075765 A1), hereinafter Basson, in view of Wright et al. (U.S. P.G. Pub. 2013/0108997 A1), hereinafter Wright.
Claim 1.
Basson discloses an online and offline hybrid education method based on a student's career aspiration, applied in a student user terminal comprising:
obtaining a target career role and a first personalized characteristic of a student user (Basson [0064], [0086] students’ career aspirations; [0091] recommended career path determined by an optimization using an individual’s criteria);
Regarding the following limitation:
according to the target career role and the first personalized characteristic, matching a first personalized course system and a teacher user corresponding to the student user from a course module library of a pre-established career role and a teacher information library;
Basson discloses according to the target career role and the first personalized characteristic, matching a first personalized course system and an institution user corresponding to the student user from a course module library of a pre-established career role and a institution information library (Basson [0063] skill profiles of early professionals together with the skills needed to be eligible for these roles; [0065] required skills are mapped to curricula to detect coverage, redundancies, and gaps; [0092] institution curricula). However, Basson does not disclose matching a first personalized course system and a teacher user corresponding to the student user from a course module library of a pre-established career role and a teacher information library, but Wright does (Wright [0036], [0052] the pairing or matching of consumers and providers/businesses can occur when the present inventions system of artificial and interpretive intelligence analyzes, from a personality trait perspective, the various consumer and the things they are seeking and algorithmically computes the best matches; [0111] assess whether a teacher possesses the specific personality traits that are helpful in distinguishing between one of our eight defined teaching styles).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the teachers of Wright for the institution of Basson. Both teachers and institutions are authorities offering educational services. Thus, the simple substitution of one known element in the art of providing educational services for another producing a predictable result renders the claim obvious. Specifically, one of ordinary skill in the art would have recognized that only routine engineering would be required to substitute the above features and yield predictable result of Basson’s system with the improved functionality to provide a more precise match for a student thereby increasing the likelihood they have a positive educational experience.
Basson, as modified above by Wright, teaches:
according to the first personalized course system, providing the student user with online or offline teaching of a corresponding course taught by the corresponding teacher user (Basson [0026]-[0027] custom designed curriculum; [0060] virtual classroom education delivery; [0065] detect skills required and map skills to curricula; [0092] institution curricula);
wherein, the course module library of the career role comprises an ability knowledge system library of the career role and an ability-knowledge-course-corresponding-relationship library, the teacher information library stores a knowledge ability and the second personalized characteristics of the teacher user (Basson [0065] required skills are mapped to curricula to detect coverage, redundancies, and gaps; [0066] assess curricula to determine the readiness for a career);
wherein the ability knowledge system library of the career role is pre-established by collecting occupational qualification data, the occupational qualification data comprises career qualification catalogs, success paths and conditions of new career roles (Basson [0063] skill profiles of early professionals together with the skills needed to be eligible for these roles; [0065] required skills are mapped to curricula to detect coverage, redundancies, and gaps);
wherein, the step of according to the target career role and the first personalized characteristic, matching the first personalized course system and the teacher user corresponding to the student user from the course module library of the pre-established career role and the teacher information library comprises:
searching the ability knowledge system library of the career role to determine an ability and knowledge system required by the target career role and searching the ability-knowledge-course corresponding relationship library to match a course corresponding to the ability and knowledge system required by the target career role and making the first personalized course system composed of the corresponding courses (Basson [0091] career path output by the system can include one or more corresponding recommended curriculum);
Basson does not teach the following limitation, but Wright does:
smart matching the first personalized course system with the knowledge and ability of the teacher user in the teacher information library, and matching the first personalized characteristic with the second personalized characteristic in the teacher information library, to determine the teacher user corresponding to the student user (Wright [0036], [0052] the pairing or matching of consumers and providers/businesses can occur when the present inventions system of artificial and interpretive intelligence analyzes, from a personality trait perspective, the various consumer and the things they are seeking and algorithmically computes the best matches; [0111] assess whether a teacher possesses the specific personality traits that are helpful in distinguishing between one of our eight defined teaching styles);
One of ordinary skill in the art would have been motivated to include the teachings of Wright in the system of Basson for the same reasons discussed above.
Basson, as modified above by Wright, teaches:
wherein, after matching the first personalized course system and the teacher user corresponding to the student user, the method further comprises:
regularly carrying out psychological portrait of the student user through artificial intelligence, adjusting the ability and knowledge system required by the target career role and the first personalized characteristics according to the results of the psychological portrait, and adjusting the first personalized course system corresponding to the student user and the teacher user according to the adjusted ability and knowledge system required by the target career role and the first personalized characteristic (Basson Fig. 4 Item 403 recommend actions of hierarchy include curriculum adjustments; [0075], [0082] machine learning; [0086] students personal career aspirations and aptitudes are incorporated into the curricula design; [0091] rerun optimization).
Claim 2.
Basson in view of Wright teaches all the elements of claim 1, as shown above. Additionally, Basson discloses wherein, the step of obtaining the target career role and the first personalized characteristic of a student user comprises:
according to a student user information, analyzing the student user's psychological profile or behavior trajectory through artificial intelligence, so as to identify the first personalized characteristic of the student user (Basson [0082] curriculum design uses machine learning models; [0086] students personal career aspirations and aptitudes are incorporated into the curricula design);
determining and recommending an ideal career role for the student user for selection according to the first personalized characteristic (Basson [0091] recommended career path determined by an optimization using an individual’s criteria);
determining the target career role of the student user according to a selection operation of the student user (Basson [0064], [0086] students’ career aspirations; [0091] individual criteria).
Claim 4.
Basson in view of Wright teaches all the elements of claim 1, as shown above. However, Basson does not disclose wherein, the teacher information library is established in the following way:
making a portrait of the teacher user by artificial intelligence, identifying the knowledge ability and the second personalized characteristic possessed by the teacher user, and establishing the teacher information library based on the identified knowledge ability and the second personalized characteristic (Wright [0036], [0052] the pairing or matching of consumers and providers/businesses can occur when the present inventions system of artificial and interpretive intelligence analyzes, from a personality trait perspective, the various consumer and the things they are seeking and algorithmically computes the best matches; [0111] assess whether a teacher possesses the specific personality traits that are helpful in distinguishing between one of our eight defined teaching styles).
One of ordinary skill in the art would have been motivated to include the teachings of Wright in the system of Basson for the same reasons discussed in claim 1 above.
Basson discloses:
wherein the ability knowledge system library of the career role is pre-established by collecting occupational qualification data (Basson [0063] skill profiles of early professionals together with the skills needed to be eligible for these roles; [0065] required skills are mapped to curricula to detect coverage, redundancies, and gaps).
Claim 6.
Basson in view of Wright teaches all the elements of claim 1, as shown above. Additionally, Basson discloses after matching the first personalized course system and the teacher user corresponding to the student user, the method further comprises:
receiving a career role change request of the student user, and changing the target career role of the student user according to the career role change request (Basson [0091] individual may modify the criteria);
according to the changed target career role and the first personalized characteristic, matching a second personalized course system and a new teacher user corresponding to the student user from the course module library of the career role and the teacher information library (Basson [0091] rerun an optimization);
comparing the courses that have been taught in the first personalized course system with the second personalized course system, and recording the status of the course in the second personalized course system that is the same as the course that has been taught as completed, and storing the course that has been taught but is not comprised in the second personalized course system as the competency addition of the student user (Basson [0086] student’s personal career aspirations and aptitudes, where they stand currently in the knowledge graph, are incorporated into the curricula design and recommendations);
providing the student user with teaching in the uncompleted course in the second personalized course system under the teaching of the new teacher user (Basson [0082] course selection; Fig. 4 Item 403 recommend actions of hierarchy include curriculum adjustments; [0091] rerun optimization).
Claim 8.
Basson in view of Wright teaches all the elements of claim 1, as shown above. Additionally, Basson discloses wherein, the first personalized course system comprises an online course;
the step of according to the first personalized course system, providing the student user with online or offline teaching of a corresponding course taught by the corresponding teacher user comprises: providing the online course teaching resources to the student user based on audio and video technology or holographic image technology (Basson [0060] virtual classroom; [0086] what type of material a student responds to e.g. visual, audio).
Claim 9.
Basson discloses an online and offline hybrid education method based on student's career aspiration, applied in a teacher user terminal, comprising:
Regarding the following limitation:
obtaining a knowledge ability and a second personalized characteristic of a teacher user;
Basson discloses obtaining a knowledge ability and a second personalized characteristic of an institution (Basson [0065] intuitions curricula). However, Basson does not teach obtaining a knowledge ability and a second personalized characteristic of a teacher user, but Wright does (Wright [0111] assess whether a teacher possesses the specific personality traits that are helpful in distinguishing between one of our eight defined teaching styles).
One of ordinary skill in the art would have been motivated to include the teachings of Wright in the system of Basson for the same reasons discussed in claim 1 above.
Regarding the following limitation:
according to the knowledge ability and the second personalized characteristic of the teacher user, matching a student user corresponding to the teacher user and a first personalized course system corresponding to the student user from a course module library of a pre-established career role and a student information library, wherein, the course module library of the career role comprises an ability knowledge system library of the career role and an ability-knowledge-course corresponding relationship library, the student information library stores a target career role and a first personalized characteristic of the student user, wherein the ability knowledge system library of the career role is pre-established by collecting occupational qualification data, the occupational qualification data comprises career qualification catalogs, success paths and conditions of new career roles;
Basson discloses according to the knowledge ability and the second personalized characteristic of the institution, matching a student user corresponding to the institution and a first personalized course system corresponding to the student user from a course module library of a pre-established career role and a student information library, wherein, the course module library of the career role includes an ability knowledge system library of the career role and an ability-knowledge-course corresponding relationship library, the student information library stores a target career role and a first personalized characteristic of the student user, wherein the ability knowledge system library of the career role is pre-established by collecting occupational qualification data, the occupational qualification data comprises career qualification catalogs, success paths and conditions of new career roles (Basson [0026]-[0027] custom designed curriculum; [0060] virtual classroom education delivery; [0063] skill profiles of early professionals together with the skills needed to be eligible for these roles; [0065] detect skills required and map skills to curricula; [0086] students personal career aspirations and aptitudes are incorporated into the curricula design; [0092] institution curricula). However, Basson does not teach matching a teacher user, but Wright does (Wright [0036], [0052] the pairing or matching of consumers and providers/businesses can occur when the present inventions system of artificial and interpretive intelligence analyzes, from a personality trait perspective, the various consumer and the things they are seeking and algorithmically computes the best matches; [0111] teacher).
One of ordinary skill in the art would have been motivated to include the teachings of Wright in the system of Basson for the same reasons discussed in claim 1 above.
Basson, as modified above by Wright, teaches:
allowing the teacher user teaching the student user a corresponding course of the first personalized course system by online or offline taught (Basson [0060] virtual classroom education delivery);
wherein the step of according to the knowledge ability and the second personalized characteristic, matching the student user corresponding to the teacher user and a first personalized course system corresponding to the student user from the course module library of the pre-established career role and the student information library comprises:
searching the ability knowledge system library of the career role to determine an ability and knowledge system required by the target career role and searching the ability-knowledge-course corresponding relationship library to match a course corresponding to the ability and knowledge system required by the target career role and making the first personalized course system composed of the corresponding courses (Basson [0091] career path output by the system can include one or more corresponding recommended curriculum);
Basson does not teach the following limitation, but Wright does:
smart matching the first personalized course system with the knowledge and ability of the teacher user in the teacher information library, and matching the first personalized characteristic with the second personalized characteristic in the teacher information library, to determine the teacher user corresponding to the student user (Wright [0036], [0052] the pairing or matching of consumers and providers/businesses can occur when the present inventions system of artificial and interpretive intelligence analyzes, from a personality trait perspective, the various consumer and the things they are seeking and algorithmically computes the best matches; [0111] assess whether a teacher possesses the specific personality traits that are helpful in distinguishing between one of our eight defined teaching styles);
One of ordinary skill in the art would have been motivated to include the teachings of Wright in the system of Basson for the same reasons discussed in claim 1 above.
Basson, as modified above by Wright, teaches:
after matching the student user corresponding to the teacher user and the first personalized course system corresponding to the student user, the method further comprises:
regularly carrying out psychological portrait of the student user through artificial intelligence, adjusting the ability and knowledge system required by the target career role and the first personalized characteristics according to the results of the psychological portrait, and adjusting the first personalized course system corresponding to the student user and the teacher user according to the adjusted ability and knowledge system required by the target career role and the first personalized characteristic (Basson Fig. 4 Item 403 recommend actions of hierarchy include curriculum adjustments; [0075], [0082] machine learning; [0086] students personal career aspirations and aptitudes are incorporated into the curricula design; [0091] rerun optimization).
Claim 10.
Basson in view of Wright teaches all the elements of claim 9, as shown above. Additionally, Basson discloses wherein, the step of obtaining a knowledge ability and a second personalized characteristic of a teacher user comprises:
making a portrait of the teacher user by artificial intelligence, identifying the knowledge ability and the second personalized characteristic possessed by the teacher user (Wright [0111] assess whether a teacher possesses the specific personality traits that are helpful in distinguishing between one of our eight defined teaching styles).
One of ordinary skill in the art would have been motivated to include the teachings of Wright in the system of Basson for the same reasons discussed in claim 1 above.
Claim 12.
Basson in view of Wright teaches all the elements of claim 9, as shown above. Additionally, Basson discloses:
according to the student user information, using artificial intelligence to analyze the student user's psychological profile or behavior trajectory, so as to identify the first personalized characteristic of the student user (Basson [0082] curriculum design uses machine learning models; [0086] students personal career aspirations and aptitudes are incorporated into the curricula design);
determining and recommending an ideal career role for the student user for selection according to the first personalized characteristic (Basson [0091] recommended career path determined by an optimization using an individual’s criteria);
determining the target career role of the student user according to a selection operation of the student user (Basson [0064], [0086] students’ career aspirations; [0091] individual criteria);
establishing the student information library based on the target career role of the student user and a first personalized characteristic (Basson [0086] student’s personal career aspirations and aptitudes, where they stand currently in the knowledge graph, are incorporated into the curricula design and recommendations);
wherein the ability knowledge system library of the career role is pre-established by collecting occupational qualification data (Basson [0063] skill profiles of early professionals together with the skills needed to be eligible for these roles; [0065] detect skills required and map skills to curricula).
Claim 13.
Basson in view of Wright teaches all the elements of claim 9, as shown above. Additionally, Basson discloses:
wherein, the first personalized characteristic of the student user in the student information library is adjusted according to the results of regularly performing psychological portraits of the student user through artificial intelligence (Basson Fig. 4 Item 403 recommend actions of hierarchy include curriculum adjustments; [0091] rerun optimization).
Claim 14.
Basson in view of Wright teaches all the elements of claim 9, as shown above. Additionally, Basson discloses:
wherein, the target career role of the student user of the student information library is changed according to the student user's career role change request (Basson [0091] individual may modify the criteria).
Claim 16.
Basson in view of Wright teaches all the elements of claim 9, as shown above. Additionally, Basson discloses:
wherein, the first personalized course system comprises an online course (Basson [0060] virtual classroom; [0090] online career portal);
the step of allowing the teacher user teaching the student user a corresponding course of the first personalized course system by online or offline taught comprises: providing the online course teaching resources to the student user based on audio and video technology or holographic image technology (Basson [0060] virtual classroom; [0086] what type of material a student responds to e.g. visual, audio).
Claim 18.
Basson discloses an electronic device, comprising:
a processor (Basson Fig. 1, [0049] processor); and
a memory for storing instructions executable by the processor (Basson Fig. 1, [0049] memory);
As shown above in claim 1, Basson in view of Wright teaches the following limitation:
wherein the processor is configured to execute the instructions, so as to implement the online and offline hybrid education method based on student's career aspiration as claim 1.
Claim 19.
Basson discloses:
a storage medium, when the instructions in the storage medium are executed by a processor of an electronic device (Basson Fig. 1, [0049]),
As shown above in claim 1, Basson in view of Wright teaches the following limitation:
the electronic device can execute the online and offline hybrid education method based on the student's career aspiration claim 1.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Basson in view of Wright further in view of Liu (U.S. P.G. Pub. 2021/0117925 A1), hereinafter Liu.
Claim 7.
Basson in view of Wright teaches all the elements of claim 1, as shown above. However, Basson does not teach the following limitation, but Liu does:
wherein, before according to the first personalized course system, providing the student user with online or offline teaching of the corresponding course taught by the corresponding teacher user, the method also comprises: signing an electronic contract between the student user, the first personalized course system and the corresponding teacher user through block chain technology (Liu [0006], [0012], [0028], [0042], [0044], [0047], [0061] the target student selects a course of one teacher to study, the entire process from course selection, fee payment, the end of the course, to course evaluation is recorded in the blockchain nodes).
It would have been obvious to one of ordinary skill in the art before the effective filing date to include managing educational transactions on a blockchain as taught by Liu in the system of Basson, since the claimed invention is merely a combination of old elements in the art of providing educational services, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Specifically, one of ordinary skill in the art would have recognized that only routine engineering would be required to incorporate the above features and yield predictable result of Basson’s system with the improved functionality to securely share a student’s educational records.
Claim 15.
Basson in view of Wright teaches all the elements of claim 9, as shown above. However, Basson does not teach the following limitation, but Liu does:
wherein, before allowing the teacher user teaching the student user the corresponding course of the first personalized course system by online or offline taught, the method further comprises:
signing an electronic contract between the teacher user, the corresponding student user and the first personalized course system through block chain technology (Liu [0006], [0012], [0028], [0042], [0044], [0047], [0061] the target student selects a course of one teacher to study, the entire process from course selection, fee payment, the end of the course, to course evaluation is recorded in the blockchain nodes).
One of ordinary skill in the art would have been motivated to include the teachings of Liu in the system of Basson for the same reasons discussed in claim 7 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SCOTT M TUNGATE/Primary Examiner, Art Unit 3628
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