DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The amendment filed on September 3, 2025 has been entered. The following has occurred: Claims 1, 11, 14, and 15 have been amended; and Claims 9-10 have been cancelled.
Claims 1-8 and 11-15 are currently pending and have been examined.
Response to Amendment
Claim objections have been withdrawn.
35 U.S.C. 101 rejection has been maintained in light of the amendment.
35 U.S.C. 103 rejection has been modified in light of the amendment.
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 1-8 and 11-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a process, machine, manufacture or composition of matter? (MPEP 2106.03)
In the present application, claims 1-8 and 11-13 are directed to a system (i.e. a machine), claim 14 is directed to a method (i.e. a process), and claim 15 is directed to a server system (i.e. a machine). Thus, the eligibility analysis proceeds to Step 2A. prong one.
Step 2A. prong one: Does the claim recite an abstract idea, law of nature, or natural phenomenon? (MPEP 2106.04)
The limitations of independent claim 1, which is representative of claims 14 and 15, reciting substantial similar limitations, have been denoted with letters by the Examiner for easy reference.
The abstract idea recited in claims 1, 14, and 15, is
(i) receive educational provider information from the educational providers, the educational provider information for each of the educational providers including learning opportunity information associated with at least one learning opportunity and the learning opportunity information including a learning opportunity description;
(ii) generate, at least one learning skill by parsing the learning opportunity description to extract a set of keywords by linguistic analysis, and cross-referencing the set of keywords with a keyword master list stored in a storage, the master keyword list comprising a plurality of master keywords and a corresponding learning skill;
(iii) receive learner information from the learners, the learner information for each of the learners including, location of the learner;
(iv) receive employer information from the employers, the employer information including employment position information;
(v) receive accreditation information from the accreditors, the accreditation information including at least one accreditation and at least one skill required to earn the accreditation;
(vi) provide/generate an integrated learning network based upon the received educational provider information, learner information, employer information and the accreditation information;
(vii) determine equivalent learning opportunities based on the generated learning skills and assigning an equivalency value representing how equivalent the learning opportunities are to each other; and
(viii) determine a geographic demand for one or more new learning opportunities by aggregating a plurality of learning goals from a plurality of learner profiles having location information within a defined region, and transmitting demand information, associated to the one or more new learning opportunities, to the educational providers for creation of the one or more new learning opportunities at the defined location.
The claimed invention is directed to an abstract idea of providing education and learning opportunity information.
The bolded portions of limitations above recite steps involved human judgements, observations, and evaluations that can be practically or reasonably performed in the human mind, the claims recite an abstract idea consistent with the “mental processes” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(III). Under the broadest reasonable interpretation, other than the additional elements of computer components, the claims 1, 14, and 15 recite processes that are all acts that could be performed by a human, e.g., mentally or manually, without the need of computer or any other machine. The above-mentioned limitations recite a process similar to collecting information (steps [A] and [C]-[E]) and analyzing the collected information (steps [B] and [F]-[G]). Because the limitations above closely follow the steps of collecting information and analyzing the collected information, and the steps involved human judgements, observations, and evaluations that can be practically or reasonably performed in the human mind, the claims recite an abstract idea consistent with the “mental processes” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(III).
Additionally and alternatively, the claims recite steps of managing personal behavior or relationships or interactions between people include following rules or instructions. Under the broadest reasonable interpretations, other than the additional elements of computer components, the claims recite for collecting (i.e. receiving) educational provider information; generating learning skill from learning opportunity description by identifying (i.e., cross-referencing) keywords; collecting (i.e. receiving) learner information; collecting (i.e., receiving) employer information; collecting (i.e., receiving) accreditation information; providing/generating an integrated learning network (i.e., learning map plan); determine equivalent learning opportunities; determining a geographic demand of learning opportunities (i.e., classes) based on number of learners (i.e., students) in the defined region; and providing the demand information to the educational providers to create the new learning opportunities (i.e., classes), are well-practiced steps performed by school counselors/advisors in the field of education and career planning. Human such as a school counselors, career advisors, and/or school principal have been responsible for gathering, organizing, and presenting collected information of the student for providing learning map plan and presenting classes to students, before the computers were available to support these tasks because education industry has existed longer than computers. Because the limitations above closely follow the steps for managing personal relationships or interactions between people including teaching and following rules or instructions, then the claims recite an abstract idea consistent with the “certain methods of organizing human activity” grouping of the abstract ideas, set forth in MPEP 2106.04(a)(2)(II).
Accordingly, the above-mentioned limitations are considered as a single abstract idea, therefore, the claims recite an abstract idea and the analysis proceeds to Step 2A. prong two.
Step 2A. prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? (MPEP 2106.04)
This judicial exception is not integrated into a practical application because the additional elements merely add instructions to apply the abstract idea to a computer and insignificant extra-solution activity.
The additional elements considered include:
Claim 1: “an educational information system, the system comprising: (a) at least one server; (b) a plurality of computers in data communication with the at least one server, the computers being usable by educational providers, learners, employers and accreditors to connect to the server; (c) wherein the server is configured to:” “by executing a natural language processing algorithm configured to” and “a storage device”.
Claim 14: “by executing a natural language processing algorithm configured to” and “a storage device”.
Claim 15: “a server for providing educational information, the server comprising at least one processor configured to:” “by executing a natural language processing algorithm configured to” and “a storage device”.
The additional element of a system comprising generic computer elements are found to recite mere instructions to apply a generic computer and technology to execute the method in the recited claim limitations, as merely using a computer transmit, manipulate and display information is not an improvement to a technology or technical field. The additional elements merely recite computer elements to receive, generate, and provide information.
The computer in the steps is recited at a high-level of generality (i.e., as generic computer components performing a generic computer function; See Applicant’s Specification at least at paragraphs [0023], [0031], and [0032], “computing devices 20 are electronic devices that include one or more hardware data processors that can be used to access one or more servers 31. The computing devices, for example, include laptops 20 a, a personal digital assistant (PDA) 20 b or smartphone, a personal computer 20 c or terminal, a tablet computer 20 d and a game console 20 e.” and “The system 10 in this embodiment includes servers 32. Each of the servers 32 may include one or more hardware processors configured to provide the learning network as described herein. For example, the servers 32 may include hardware computer processors such as processors produced by Intel Inc. or AMD Inc. The servers 32 are configured to send information (e.g. electronic files such as web pages) to be displayed on one or more computing devices 20 in association with the learning network. In some embodiments, a server 32 may be a computing device 20 (e.g. a laptop or personal computer).” These are all generic computer components and generic computing devices) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014) (“[A]fter Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.”).
That is, the limitations in [A] and [C]-[E] are merely steps of transmitting, receiving, and presenting data which are insignificant extra-solution activity to the judicial exception as discussed in MPEP 2106.05(g). The function of limitations [A]-[H] are steps of adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer.
Accordingly, even in combination, these additional element(s) do not integrate the abstract idea into a practical application because they do not improve a computer or other technology, do not transform a particular article, do not recite more than a general link to a computer, and do not invoke the computer in any meaningful way; the general computer is effectively part of the preamble instruction to “apply” the exception by the computer. Therefore, the claims are directed to an abstract idea and the analysis proceeds to Step 2B.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? (MPEP 2106.05)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the bold portions of the limitations recited above, were all considered to be an abstract idea in Step2A-Prong Two. The additional elements and analysis of Step2A-Prong two is carried over. For the same reason, these elements are not sufficient to provide an inventive concept. Applicant has merely recited elements that instruct the user to apply the abstract idea to a computer or other machinery. When considered individually and in combination the conclusion, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the above-mentioned limitations of transmitting and providing information in [A]-[H] amount to no more than mere instructions to apply the function of the limitations to the exception using generic computer component, as discussed in MPEP 2106.05(f). The claim as a whole merely describes how to generally “apply” the concept for providing education and learning opportunity information. Further, the claims simply append well-understood, routine, and conventional (WURC) activities previously known to the industry, specified at a high level of generality, to the judicial exception, in the form of the extra-solution activity. The courts have recognized that the computer functions claimed (the “receive”, “provide”, and “transmit” limitations) as WURC (see 2106.05(d)(II), identifying, receiving or transmitting data over a network as WURC, as recognized by Symantec). Thus, viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. For these reasons there is no inventive concept in the claims and thus are ineligible.
As for dependent claim 2, the claim recites the use of same additional element of server to generate a learning goal, at a high level of generality (i.e., as a generic computer system performing generic computer function of generate information) such that it amounts no more than mere instructions to apply the exception using a generic computer component, see MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claim is ineligible.
As for dependent claim 3, the claim recites limitation that further define the abstract idea noted in the independent claim. The claims further recite additional descriptive information of receiving information for the generating of learning goal, which does not change abstract idea of the independent claim. The step of receiving information via computer system has been considered insignificant extra-solution activity to the judicial exception as discussed in MPEP 2106.05(g) and WURC, as discussed MPEP 2106.05(d)(II), for steps of identifying, receiving or transmitting data over a network as WURC, as recognized by Symantec. The further details of the claim limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technology environment. The claim is ineligible.
As for dependent claim 4, the claim recites limitation that further define the abstract idea noted in the independent claim. The claims further recite additional descriptive information of learner information, which does not change abstract idea of the independent claim. The further details of the claim limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technology environment. The claim is ineligible.
As for dependent claim 5, the claim recites the use of same additional element of server to generate a learning map, at a high level of generality (i.e., as a generic computer system performing generic computer function of generate information) such that it amounts no more than mere instructions to apply the exception using a generic computer component, see MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claim is ineligible.
As for dependent claim 6, the claim recites the use of same additional element of server to generate a personalized learning map, at a high level of generality (i.e., as a generic computer system performing generic computer function of generate information) such that it amounts no more than mere instructions to apply the exception using a generic computer component, see MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claim is ineligible.
As for dependent claims 7 and 8, the claims recite the use of same additional element of server to recommend learning opportunity, at a high level of generality (i.e., as a generic computer system performing generic computer function of recommend or present information) such that it amounts no more than mere instructions to apply the exception using a generic computer component, see MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
As for dependent claim 11, the claim recites limitation that further define the abstract idea noted in the independent claim. The claims further recite additional descriptive information of equivalency value, which does not change abstract idea of the independent claim. The further details of the claim limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technology environment. The claim is ineligible.
As for dependent claim 12, the claim recites the use of same additional element of server to determine demand information, at a high level of generality (i.e., as a generic computer system performing generic computer function of determine information) such that it amounts no more than mere instructions to apply the exception using a generic computer component, see MPEP 2106.05(f). Even in combination, the additional element does not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claim is ineligible.
As for dependent claim 13, the claim recites limitation that further define the abstract idea noted in the independent claim. The claims further recite additional descriptive information of demand information, which does not change abstract idea of the independent claim. The further details of the claim limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technology environment. The claim is ineligible.
In summary, the dependent claims considered both individually and as ordered combination do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims do not recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. Therefore, claims 1-8 and 11-15 are rejected under 35 U.S.C. 101.
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 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 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 1-8 and 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Philips (US 20040219493 A1) in view of Menon et al (US 20150127567 A1, hereinafter “Menon”), in view of Parija et al. (US 20050106549 A1, hereinafter “Parija”), and further in view of Pelt (US 20130346335 A1, hereinafter, “Pelt”).
Regarding claim 1, Philips discloses an educational information system (Abstract, "A system and method for providing a user with information to enhance the user's learning and development."), the system comprising:
(a) at least one server (Para. [0009], [0060] and Fig. 1, server means 6);
(b) a plurality of computers in data communication with the at least one server, the computers being usable by educational providers, learners, employers and accreditors to connect to the server (Para. [0060]-[0061] and Fig. 1, discloses processing devices 3, 5, and 7; server means 6; communication network 8; PC 4 and 21; Para. [0241] discloses on-line education provider and learners; Para. [0004]-[0006], [0238], [0241], [0256]-[0263] learner; Para. [0049], [0097], [0099], [0130] discloses employer; Para. [0048], [0072], [0088], [0125], [0143] discloses educational institution which can be both education provider and accreditors as described in paragraph [0028] of the Specification, "employers may also be acting as accreditors or educational providers.");
(c) wherein the server is configured to (Fig. 1, server means 6):
(i) receive educational provider information from the educational providers, the educational provider information for each of the educational providers including learning opportunity information associated with at least one learning opportunity and the learning opportunity information including a learning opportunity description (Para. [0143], "there is also provided links to educational institutions that conduct the required courses in order to qualify for the occupation (where specific provider, course information, entry requirements, location, pathway options and specialist courses are provided) and lists or provides links to employers within the locality and region of the user giving information about each business, induction materials, work experience details, job offers and contact details as shown in screen 100 in FIG. 11." This discloses receiving educational provider (i.e. educational institutions) with learning opportunity information (i.e. course information) with learning opportunity description (see para. [0197] for relevant course identification with training skillset of Microsoft Office));
(ii) generate, by executing a processing algorithm configured to generate learning skills, at least one learning skill by parsing the learning opportunity description to extract a set of keywords by linguistic analysis, and cross-referencing the set of keywords with a keyword master list stored in a storage, the master keyword list comprising a plurality of master keywords and a corresponding learning skill (Note: the claim limitation is interpreted based on the applicant’s specification in paragraphs [0042]: “For example, a natural language analysis may be performed to the description of the learning opportunity to obtain non-trivial keywords from the description. These keywords may then be cross referenced against a master list of keywords to determine the keywords that are referring to the skills and knowledge being offered by the learning opportunity.” [0047]: “The learning opportunity profile 80 also includes skills 94 that will be provided through the learning opportunity. The skills 94, as noted above, may be provided by the educational provider or extracted from the description of the learning opportunity through keyword analysis.” [0065] “The employment position profile 100 also includes skills required 120 indicatives of one or more skills or accreditations required or recommended for the position. The information about the skills required may be provided by the employer, extracted from a description associated with the position, or a combination of both. If the skills required are extracted from the description, a keyword extraction algorithm similar to the algorithm described above for extracting skills being offered by the learning opportunity may be employed.” The generating of learning skill using processing algorithm is a computer function step of extracting keywords from learning opportunity description. Philips in Claim 35 and paragraph [0085], “a personalised on-line website 15 developed specifically for a particular user and includes access to a number of modules including a performance manager module 16, a career manager module 18, a learning manager and learning tool box module 20, a personal profile module and database 22 and a matching agent module (or career match module) 72. Each of the modules 16, 18, 20, 22 and 24 may be stored at server 6. Links 25 to each of the modules 16, 18, 20 and 22 is also available. Linked to the personalised on-line website 15 is an interface, more particularly a desk top management interface 26 which has at its core a software program known as a desk top mentor or guru (DTG) or a knowledge application manager (KAM) 28. The KAM 28 and desk top toolset integrate with the on-line website 15 to maintain a database of information off-line for individual user learning and career planning referral and reference. The software module 28 links to the various databases and other modules within the system and searches, extracts and processes relevant information for the user. The software module 28 has the ability to intelligently interact with a website and the number of databases to install its specific information and resource tools upon the users desk top PC 4 relevant to the needs of that particular individual.” Which software module links to the various database and other modules within the system to searches, extracts, and processes relevant information for the user. Specifically, in paragraph [0195] disclosing the linked to modules within the system specifically seeks and identifies on-line and off-line training opportunities (i.e. learning opportunity) for individual (i.e. learner) by using a search engine based on keywords extracted from data input to the databases and modules. The search engine based on keyword searched, extracted, and processed to be presented to the user is consistent to the description of the function of natural language processing based analysis or keyword extraction algorithm descripted in the applicant’s specification paragraphs [0042] and [0047]. The examiner asserts Knowledge application manager KAM performs the function of the natural language processing-based analysis software as intended to achieve the same result as the applicant’s invention. Also in paragraph [0099], “performance manager module 16 assists the user to develop a personal mission statement and aligns this with their job description. It also assists the user to undertake induction processes, performance appraisals, identifies professional training and development opportunities, builds employability via the user's skill bank, to be hereinafter described and develops a competency based resumé aligned with suitable career pathways, it provides a link between education and work enabling the user to manage their own performance to meet specific job relevant key performance criteria.” Specifically in paragraphs [0195]-[0199] disclosing the specific course identification for providing course that includes keywords such as “Microsoft Office®” required of skills and knowledge that is required of scores of 95.5 in Chemistry, and provide links to training required keywords of “Cyto-Publishers Network.”);
(iii) receive learner information from the learners, the learner information for each of the learners including, location of the learner (Para. [0016], "The personal profile may be entered and stored in a user profile database and include information on said user, such as biographical information, academic information, personal interests, etc." discloses database receiving user (i.e. learner) information. The user information would include the location of the users as it is indicated in paragraphs [0064], [0068], and [0158] "groupings of individuals were the groupings are dependent on geography." In addition to paragraph [0099], "The personal profile manager module and database 22 captures the users biographic, demographic, education/experience information, achievements and psychometric information required for careers and education management.");
(iv) receive employer information from the employers, the employer information including employment position information (Para. [0130] discloses employer information. In addition, Fig. 8 and Para. [0143] discloses job profile (i.e. employment position information) and provides links to employers (i.e. employer information));
(v) receive accreditation information from the accreditors, the accreditation information including at least one accreditation and at least one skill required to earn the accreditation (Fig. 8 discloses accreditation information from the accreditors (i.e. Universities: Bachelor of Business (Accountancy), TAFE Colleges: Certificate III Accountancy [… ]Online Courses: Accountancy) further with Key Skills required: Numeracy and Mathematics, Communication, Teamwork, Technology and Software. Further in para. [0048] and Fig. 10 provides further details on an educational institution (i.e. accreditation information). Furthermore, Fig. 9, 17 and para. [0047], [0144], [0055], [0206] discloses the skill bank competency and competency statement (i.e. earn the accreditation)); and
(vi) providing an integrated learning network based upon the received educational provider information, learner information, employer information and the accreditation information (Abstract, Para. [0009], [0011], [0020]-[0021], [0026]-[0027], [0032]-[0033] discloses a communication network as platform for providing users, employers, and institution to enhance user’s learning and development).
(vii) aggregating a plurality of learning goals from a plurality of learner profiles having location information within a defined region (Para. [0016], [0064], [0068], [0158] disclosing groupings of individuals dependent of geography and collection of personal profile including location information, which is representative of aggregating a plurality of learning goals from a plurality of learner profiles having location information within a defined region).
However, Philips does not explicitly teach (italic emphasis):
(ii) generate, by executing a natural language processing algorithm configured to generate learning skills, at least one learning skill by parsing the learning opportunity description to extract a set of keywords by linguistic analysis, and cross-referencing the set of keywords with a keyword master list stored in a storage, the master keyword list comprising a plurality of master keywords and a corresponding learning skill;
(vii) determine equivalent learning opportunities based on the generated learning skills and assigning an equivalency value representing how equivalent the learning opportunities are to each other; and
(vii) determine a geographic demand for one or more new learning opportunities by aggregating a plurality of learning goals from a plurality of learner profiles having location information within a defined region, and transmitting demand information, associated to the one or more new learning opportunities, to the educational providers for creation of the one or more new learning opportunities at the defined location.
Nonetheless, Menon in the field of data mining including processing natural language text and machine learning to infer competencies of candidates for employment, which specifically teaches the use of natural language processing-based analysis with skills offered and skill required from job description.
Specifically, Menon teaches (italic emphasis):
(ii) generate, by executing a natural language processing algorithm configured to generate learning skills, at least one learning skill by parsing the learning opportunity description to extract a set of keywords by linguistic analysis, and cross-referencing the set of keywords with a keyword master list stored in a storage, the master keyword list comprising a plurality of master keywords and a corresponding learning skill (Para. [0027]-[0031] disclosing the extracting of skill terms from job description, skills offered by a person, and skills taught by a course. The extracted information using algorithms enables the system to automate the comparison between documents. “For example, by extracting the information in a résumé and a job description one can determine the relevance of a résumé to the job. Similarly, by extracting information in a course description one can compare the competencies required by a job with one course or a set of courses. Finally, extracting information from jobs helps the system to understand common skills across occupations.” “extract skills from a job description automatically use a curated dictionary of skills to guide the extraction and have limitations. Curating a valid dictionary of skills is expensive and limiting. For example, as the market requires new skills, the dictionary needs to be constantly updated in order to stay relevant. Such approaches do not consider context and can therefore extract inappropriate skills as being appropriate. For example, the job description of an accounting job at Intel® will include information about Intel and its primary business, which is semiconductor. A keyword-based extraction may extract “semiconductor” as a skill required by the job when the job may not require such a skill. This approach focuses on extracting just the skill terms but not on how those skills are being applied.” In paragraphs [0068], [0113]-[0117], [0120], and [0128] teaching the use of natural language processing to extract sentences (i.e. keywords). Claim 18, “wherein the instructions further comprise instructions that, when executed by the one or more processors, cause the computer system to apply the detected at least one pattern to extract competencies from unseen job descriptions.” Claim 18, “generate one or more rules for configuring a structured document for assessing an outcome of a comparison between the job candidate and the job opening data.” Para. [0054], “With such example embodiments, a host of applications may be built to address the planning gap, skill gaps and degree gaps. Using data sciences and assessments as foundation, the data mining system 411 may operate an education-to-career place connecting individuals, employers, and education solutions; all built on top of the competency databases 409.” Para. [0119], “As for the degree gap, with access to an accurate picture of what the labor market values today and the future (through predictive analytics and/or the like), institutions (or providers in general, including employers) are able to create solutions (degrees, courses, certificates, etc.), to address those needs. Outside of the institutions, using the analytics provided by the system that quantify gaps seen in skill-profiles, experts may create content and assessments to impart and validate competencies.” and [0120], “As for the planning gap, by providing information about what the labor market values today (and in the future), and with access to information about solutions and their alignment to the labor market needs, individuals (e.g., students, workers looking to skill up, etc.) are better able to plan their specific pathway to their goals.”).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the education information system of Philips for the knowledge application manager software in extracting keyword for presenting information that is relevant to user learning and development to include the feature of using natural language processing algorithm for keyword extraction relating to skills offered by learning opportunity and skills required from job description of employment to identify the skill/education gap as taught by Menon for the advantage of effectively compare candidates skillsets to open job position with skills demands and identifying shortcoming of skill gap in a large scale which saves the time and energy in the manual process of checking thousands of candidates (para. [0005]-[0006]).
The combination of Philips and Menon fail to expressly teach (italic emphasis included):
(vii) determine equivalent learning opportunities based on the generated learning skills and assigning an equivalency value representing how equivalent the learning opportunities are to each other;
(vii) determine a geographic demand for one or more new learning opportunities by aggregating a plurality of learning goals from a plurality of learner profiles having location information within a defined region, and transmitting demand information, associated to the one or more new learning opportunities, to the educational providers for creation of the one or more new learning opportunities at the defined location.
However, Parija, which is directed to a system and method for optimization of class scheduling under demand uncertainty, does teach:
(vii) determine a geographic demand for one or more new learning opportunities by aggregating a plurality of learning goals from a plurality of learner profiles having location information within a defined region, and transmitting demand information, associated to the one or more new learning opportunities, to the educational providers for creation of the one or more new learning opportunities at the defined location (para. [0004], [0005], and [0115] teaching determining and presenting demand for classes of each curriculum based on class requests at location city. In Claims 2, 9, and 11, recites generating and outputting planned/scheduled classes (i.e., curriculum identification) at location city based on class demand for optimal profit).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the educational information system of Phillips for providing educational opportunity to user/learners based on location information to include the features of the determining geographic demand information based on the learner location information for creating or scheduling new learning opportunities (i.e., classes) as taught by Parijia with the education information system and method of Philips for the advantage of maximizing school revenue/profit and reducing the chances of class cancellation (Parija, Para. [0005]).
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Philips to include, as disclosed in Parija, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. The rationale to support a conclusion that the claim would have been obvious is that all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination yielded nothing more than predictable results to one of ordinary skill in the art. KSR, 550 U.S. at, 82 USPQ2d at 1395; Sakraida v. AG Pro, Inc., 425 U.S. 273, 282, 189 USPQ 449, 453 (1976); Anderson's-Black Rock, Inc. v. Pavement Salvage Co., 396 U.S. 57, 62-63, 163 USPQ 673, 675 (1969); Great Atlantic & P. Tea Co. v. Supermarket Equipment Corp., 340 U.S. 147, 152, 87 USPQ 303, 306 (1950).
However, the combination of Philips, Menon, and Parija fail to expressly teach,
(vii) determine equivalent learning opportunities based on the generated learning skills and assigning an equivalency value representing how equivalent the learning opportunities are to each other.
Pelt is directed to similar field of academy course planning, which specifically teaches,
(vii) determine equivalent learning opportunities based on the generated learning skills and assigning an equivalency value representing how equivalent the learning opportunities are to each other (Abstract: “providing indications of equivalence for courses at and among institutions of higher education. Embodiments may be used to calculate and assign various scores and metrics to one or more course which allow for automated determination of whether a proposed equivalent course should be accepted as an equivalent of a base course at an institution.” Also, see para. [0026]-[0032], and [0051]).
Therefore, it would have been obvious for one of ordinary skill in the art, before the effective filling of the invention to modify the educational information system and method of Philips for providing educational opportunity to user/learners based on location information and learning opportunity to include the feature of determining equivalent learning opportunities based on the generated learning skills and assigning an equivalency value representing how equivalent the learning opportunities are to each other as taught by Pelt for the motivation of planning student educational path to meet goals such as reduced cost or shortest time to a particular degree with advance knowledge of which course is transferrable and serve the need of workforce and employers (Pelt, para. [0002]-[0004]).
Regarding claims 14 and 15, Philips discloses a method and server for providing education information (Abstract, method and server for providing user with information to enhance the user’s learning and development), comprising:
(a) receiving educational provider information from educational providers, the educational provider information for each of the educational providers including learning opportunity information associated with at least one learning opportunity and the learning opportunity information including a learning opportunity description (Para. [0143], "there is also provided links to educational institutions that conduct the required courses in order to qualify for the occupation (where specific provider, course information, entry requirements, location, pathway options and specialist courses are provided) and lists or provides links to employers within the locality and region of the user giving information about each business, induction materials, work experience details, job offers and contact details as shown in screen 100 in FIG. 11." This discloses receiving educational provider (i.e. educational institutions) with learning opportunity information (i.e. course information) with learning opportunity description (see para. [0197] for relevant course identification with training skillset of Microsoft Office));
(b) generating, by executing a processing algorithm configured to generate learning skills, at least one learning skill by parsing the learning opportunity description to extract a set of keywords by linguistic analysis, and cross-referencing the set of keywords with a keyword master list stored in a storage, the master keyword list comprising a plurality of master keywords and a corresponding learning skill (Note: the claim limitation is interpreted based on the applicant’s specification in paragraphs [0042]: “For example, a natural language analysis may be performed to the description of the learning opportunity to obtain non-trivial keywords from the description. These keywords may then be cross referenced against a master list of keywords to determine the keywords that are referring to the skills and knowledge being offered by the learning opportunity.” [0047]: “The learning opportunity profile 80 also includes skills 94 that will be provided through the learning opportunity. The skills 94, as noted above, may be provided by the educational provider or extracted from the description of the learning opportunity through keyword analysis.” [0065] “The employment position profile 100 also includes skills required 120 indicatives of one or more skills or accreditations required or recommended for the position. The information about the skills required may be provided by the employer, extracted from a description associated with the position, or a combination of both. If the skills required are extracted from the description, a keyword extraction algorithm similar to the algorithm described above for extracting skills being offered by the learning opportunity may be employed.” The generating of learning skill using processing algorithm is a computer function step of extracting keywords from learning opportunity description. Philips in Claim 35 and paragraph [0085], “a personalised on-line website 15 developed specifically for a particular user and includes access to a number of modules including a performance manager module 16, a career manager module 18, a learning manager and learning tool box module 20, a personal profile module and database 22 and a matching agent module (or career match module) 72. Each of the modules 16, 18, 20, 22 and 24 may be stored at server 6. Links 25 to each of the modules 16, 18, 20 and 22 is also available. Linked to the personalised on-line website 15 is an interface, more particularly a desk top management interface 26 which has at its core a software program known as a desk top mentor or guru (DTG) or a knowledge application manager (KAM) 28. The KAM 28 and desk top toolset integrate with the on-line website 15 to maintain a database of information off-line for individual user learning and career planning referral and reference. The software module 28 links to the various databases and other modules within the system and searches, extracts and processes relevant information for the user. The software module 28 has the ability to intelligently interact with a website and the number of databases to install its specific information and resource tools upon the users desk top PC 4 relevant to the needs of that particular individual.” Which software module links to the various database and other modules within the system to searches, extracts, and processes relevant information for the user. Specifically, in paragraph [0195] disclosing the linked to modules within the system specifically seeks and identifies on-line and off-line training opportunities (i.e. learning opportunity) for individual (i.e. learner) by using a search engine based on keywords extracted from data input to the databases and modules. The search engine based on keyword searched, extracted, and processed to be presented to the user is consistent to the description of the function of natural language processing based analysis or keyword extraction algorithm descripted in the applicant’s specification paragraphs [0042] and [0047]. The examiner asserts Knowledge application manager KAM performs the function of the natural language processing-based analysis software as intended to achieve the same result as the applicant’s invention. Also, in paragraph [0099], “performance manager module 16 assists the user to develop a personal mission statement and aligns this with their job description. It also assists the user to undertake induction processes, performance appraisals, identifies professional training and development opportunities, builds employability via the user's skill bank, to be hereinafter described and develops a competency based resumé aligned with suitable career pathways, it provides a link between education and work enabling the user to manage their own performance to meet specific job relevant key performance criteria.” Specifically in paragraphs [0195]-[0199] disclosing the specific course identification for providing course that includes keywords such as “Microsoft Office®” required of skills and knowledge that is required of scores of 95.5 in Chemistry, and provide links to training required keywords of “Cyto-Publishers Network.”);
(c) receiving learner information from learners, the learner information for each of the learners including location of the learner and at least one learning goal selected by the learner (Para. [0016], "The personal profile may be entered and stored in a user profile database and include information on said user, such as biographical information, academic information, personal interests, etc." discloses database receiving user (i.e. learner) information. The user information would include the location of the users as it is indicated in para. [0064