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
Claims 1, 5 – 7, 10 – 11, 14 – 18, 39 – 40 and 42 - 53 have been amended and are hereby entered.
Claims 2 - 4 were cancelled.
Claims 1 and 5 - 54 are pending and have been examined.
This action is made FINAL.
Response to Arguments
Applicant's arguments filed October 8, 2025 have been fully considered but they are not persuasive.
Amendments regarding the 112(b) rejection in the respective claims 40 and 43 – 53 have been entered. Therefore, this particular rejection has been withdrawn due to the applicant's amendments directed to the disclosed “at least one computing device” that provides antecedent for later claim limitations of “the at least one computing device…”.
Amendments regarding the 101 – step 1, rejection in the respective claims 39 and 42 have been entered. Therefore, this particular rejection has been withdrawn due to the Applicant's amendments directed to the “non-transitory computer-readable medium” now disclosed.
Regarding to Applicant's arguments against the 101 rejection of pending claims on pages 14-15: Applicant’s arguments directed to Step 2A prong 2 and Step 2B of the 101 analysis were considered. However, these arguments are not persuasive and the Examiner respectfully disagrees for the following reasons:
For Step 2A-Prong 2 and Step 2B starting in p. 14: The Applicant alleges that the claims integrate, the judicial exception identified, into a practical application and further alleges that claim 1 “provides improvements of the performance of the computer system” by using a “single storage table” to save memory space and facilitate data processing more efficiently. The Examiner also notes that the Applicant provided numbered paragraphs (i.e. “[0073] – [0082]”) from the Specification for support, but these are not consistent with the pages and numbered lines of the originally filed disclosure. However, the Examiner finds this argument unpersuasive and respectfully disagrees. Because the claims are invoking “computers merely as a tool” to perform the business process of reporting ranked results related to job postings or learning opportunities to applicants in which is merely adding the words “apply it” to the judicial exception (see MPEP 2106.05(a)(I) and 2106.05(f)(2) & (3)). Thus, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept” at Step 2B (see MPEP 2106.05(f)(2); TLC communications). Hence, for all these reasons, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding to Applicant's arguments of rejection under 35 USC § 102 for the pending claims on pages 15 – 16: Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please, refer to the Claim Rejections - 35 USC § 103 section which are maintained herein for further details.
Regarding to Applicant's arguments of rejection under 35 USC § 103 for the pending claims on pages 15 – 16: Applicant’s arguments regarding these amended limitation steps in the pending claims are not persuasive. Because Applicant's arguments fail to comply with 37 CFR 1.111(b) since they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the Jarret reference. Rather, the Applicant is focusing on each prior art teaching, rather than focusing on the actual language claimed in each claim limitation and how their corresponding limitation steps are different from the prior art teachings. The steps disclose a broader language that the prior art of Jarret, still reasonably satisfies and teaches in light of the broadest reasonable interpretation (BRI) of the claim language.
As for Jarret not disclosing the “ranking” of job postings or learning opportunities, as alleged. The Examiner respectfully disagrees since the claim language is still broad for ranking “with respect to the at least one job applicant, based at least on each respective degree of association of the at least one job applicant with the respective at least one potential association”. This limitation is still reasonably taught by Jarret. Jarret teaches that based on the comparison of “one or more of the individual score” to “the individual threshold scores associated with the particular employer and/or employment opportunity”, Jarret system may “recommend one or more additional training activities for the job seeker to improve his or her individual score(s)” which is interpreted as “training activities” being ranked (i.e. relevant lists) based on the individual threshold scores (see ¶0122; Jarret). Similarly, when these “threshold scores” are above a “predetermined threshold” or any other preferences/filters such as “GPS location, preferences, social networks, etc.” are set/used by the job seeker, the system recommends the employment opportunity recommendations that are interpreted as being based on relevance or rank due to the job seeker’s needs for improvements, level of skills or other criteria (see ¶0126 and ¶0142 – 143; Jarret). Even if the Applicant don’t concede with this prior art, such ranking or relevant listing of employment and/or training opportunities is well-known in the field, for example in ¶0081 – 86 from Nelson - U.S. Pub No. 20140379602 A1 this limitation step is satisfied. Please, refer to the Claim Rejections - 35 USC § 103 section for further details. For all these reasons, the Examiner respectfully disagrees, and maintains 35 USC § 103 rejection for these pending claims.
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 and 5 - 54 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, although the above mentioned claim(s) failed step 1, all claims were further evaluated for the purpose of compact prosecution. The analysis of this claimed invention recited in the claims begins in view of independent claim 1, as follows:
At Step 1: Claims 1, 5 – 39, 41 and 54 fall under statutory category of a process while claims 40 and 42 - 53 are directed to a machine.
At Step 2A Prong 1: claim 1 recites an abstract idea in the following limitations:
causing…to retrieve dynamically, from a storage table, a plurality of prompts associated with a role of at least one job applicant, the storage table storing prompts associated with a plurality of roles comprising the role of the at least one job applicant, wherein at least one prompt of the plurality of prompts is associated with at least some of a plurality of selectable responses stored in the storage table, the plurality of selectable responses being structured data;
causing…to cause the plurality of prompts to be presented dynamically to the at least one job applicant
causing …to receive dynamically at least one input signal representing at least a plurality of responses from the at least one job applicant, each response of the plurality of responses being responsive to a respective the plurality of prompts, at least some responses of the plurality of responses selected from the plurality of selectable responses;
causing…to, responsive to at least some of the plurality of responses from the at least one job applicant, associate the at least one job applicant with a respective at least one potential association of a plurality of potential associations, at least one of the respective at least one potential association representing a social attribute, wherein causing the …to associate the at least one job applicant with the respective at least one potential association comprises causing…to determine a respective degree of association of the at least one job applicant with each potential association of the respective at least one potential association
causing…to, responsive to at least some of the plurality of responses from the at least one job applicant, associate the at least one job applicant with a respective at least one job posting, the at least one job posting associated with the respective at least one potential association and
causing …to, responsive to at least some of the plurality of responses from the at least one job applicant, associate the at least one job applicant with a respective at least one learning opportunity, the at least one learning opportunity associated with the respective at least one potential association,
wherein causing…to associate the at least one job applicant with the at least one job posting and with the at least one learning opportunity comprises causing the at least one computing device to rank, with respect to the at least one job applicant, based at least on each respective degree of association of the at least one job applicant with the respective at least one potential association:
a plurality of job postings comprising the at least one job posting; and
a plurality of learning opportunities comprising the at least one learning opportunity.
These limitations, describe a method for receiving job applicant inputs to associate their response with a job posting and a learning opportunity. However, the abstract idea(s) of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) is recited in claim 1 in the form of “commercial or legal interactions” and “managing personal behavior or relationships or interactions between people” as these claims recite the steps of receiving job applicant’s information to associate and match the job applicant with a job posting and a learning opportunity which falls as advertising job openings or promoting business relations through the collection of social activities (i.e. interviewee responses from surveys/questionnaires) for hiring purposes (i.e. providing hiring or HR services). As disclosed in the specification in ¶1, p1, this invention facilitates “improving or using the potential of individuals”.
The steps directed in part to “causing” the computer to “…associate the at least one job applicant with a respective at least one potential association…”, that further “…determine a respective degree of association of the at least one job applicant with each potential association…”, “….associate the at least one job applicant with a respective at least one job posting…”, “…associate the at least one job applicant with a respective at least one learning opportunity…”, “…associate the at least one job applicant with the at least one job posting and with the at least one learning opportunity…” and further “…to rank, with respect to the at least one job applicant, based at least on each respective degree of association of the at least one job applicant with the respective at least one potential association” fall under the abstract idea of mental processes that can be practically be performed in the human mind or in pen and paper (See MPEP 2106.04(a)(2), subsection III). Because associating job applicants with potential associations (that. represents “social attributes”), a job posting and/or a learning opportunity, determine a “degree of association of the at least one job applicant with each potential association” and further rank the job applicant based on their “respective degree of association” (i.e. job posting or learning opportunities) encompasses evaluation, judgement and opinion. Also, these steps can either be done with the help of physical aid such as pen and paper or can be performed by humans without or with the assistance (e.g. tool) a computer. Thus, the steps do not negate and further still reads in the mental nature of the limitation(s), when processing such associations and determining their association degrees per job applicant, as well as the concept is merely claimed to be performed on a generic computer and is merely using a computer as a tool to perform the concept of associating job applicants with potential associations (i.e. job posting or learning opportunities) to then rank the results (see MPEP 2106.04(a)(2)(III)(B & C)).
Step 2A Prong 2: For independent claim 1, This judicial exception is not integrated into a practical application (see MPEP 2106.04 (d)). Because the claim steps and their additional feature element(s) of at least one computing device, individually and in combination, merely is used as a tool to perform the abstract idea (refer to MPEP 2106.05(f)). This element feature including the computer is recited in the limitations at a high level of generality and are performed generally to apply the abstract idea without placing any limits on how these steps are performed distinctively from other generic computer components and how this technological component of the computer is being improved, while distinguishing in the claim language, the performing limitations from functions that generic computer components can perform.
As for the steps of “…retrieve dynamically, from a storage table, a plurality of prompts associated with a role of at least one job applicant…”, “…cause the plurality of prompts to be presented dynamically to the at least one job applicant” and “…receive at least one input signal…” in all claims are really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)).
Step 2B: For independent claim 1, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: at least one computing device is not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 5-54, these claims cover or fall under the same abstract idea of a method of organizing human activity and mental processes. They describe additional limitations steps of:
Claims 5 - 54: further describes the abstract idea of the data-processing method and the prompts that are presented and associated to the job applicant, based on degrees of association and similarity determined, rankings for each job applicant and their associated job postings, learning opportunities, attributes, employment benefits, skills, occupation, industry job sector, social capabilities, learning provider, etc. Thus, being directed to the abstract idea group of “commercial or legal interactions” and “managing personal behavior or relationships or interactions between people” as it is encompassing the advertisement of job openings and/or promotion of business relations through the collection of social activities (i.e. interviewee responses from surveys/questionnaires) for hiring purposes (i.e. providing hiring or HR services).
Step 2A Prong 2 and Step 2B: For dependent claims 35 – 36, 39, 41 – 42 and 44 – 54, these claims recite the additional elements: a machine-learning algorithm (from claims 35 – 36); At least one computer-readable medium and at least one processor (from claims 39 and 42); at least one user device (from claim 41); a personal computer (from claim 44); a laptop (from claim 45); a tablet computer (from claim 46); a smartphone (from claim 47); a smart watch (from claim 48); glasses (from claim 49); a mobile activity tracker (from claim 50); a wearable activity tracker (from claim 51); a haptic glove (from claim 52); at least one sensor wearable on a body and operable to measure movement of the body (from claim 53); an interactive user interface (from claim 54); which are also recited to be merely used as a tool to perform the abstract idea to receive and transmit the user input data and display data. Thus, it amounts no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). Therefore, these claim limitations amount to no more than mere instructions to apply the exception using generic computer components and or computing technologies (e.g. that are merely deployed to be used as a tool; see MPEP 2106.05 (f)).
Also, these elements and their limitations as mentioned above are “merely indicating a field of use or technological environment” (i.e. computer environments) in which “to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” (MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 5-54 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jarrett (U.S. Pub No. 20160196534 A1).
Regarding claim 1:
Jarrett teaches:
causing at least one computing device to retrieve dynamically, from a storage table, a plurality of prompts associated with a role of at least one job applicant, the storage table storing prompts associated with a plurality of roles comprising the role of the at least one job applicant, wherein at least one prompt of the plurality of prompts is associated with at least some of a plurality of selectable responses stored in the storage table, the plurality of selectable responses being structured data; (In ¶0055 -- 56; Fig. 1 (118 and 126): teaches that “the diagnostic module 118 may generate, store, and/or provide diagnostic assessments for completion by the job seekers and may determine a job seeker's competence score representing a competency of the job seeker with respect to performing individual skills in a skill set, compatibility (e.g., fitness) with respect to particular employers, and/or interest specific to industry sectors (e.g., job tracks) and/or roles” and may include “a database 126” that further includes “a document style database (e.g., XML, etc.), a graph database, and/or an ontological map” which is directed to a storage table storing prompts with selectable responses. Refer to ¶0070 – 72 for more general details of the types of prompts that the “diagnostic module” can provide to the job seeker.)
causing the at least one computing device to cause the plurality of prompts to be presented dynamically to the at least one job applicant (In ¶0076 – 77; Fig. 2 (204), Figs. 21 and 27 – 29: teaches that the system’s “diagnostic module 118 may prompt a job seeker for input regarding an employer that he or she is interested in working for” and the “job seeker may elect to participate in the compatibility assessment by actuating a control (e.g., 204 on user interface 200) or by providing some other indication that he or she would like to participate in the compatibility assessment to identify employers that may be of interest to the job seeker”. Refer to ¶0139 and ¶0144 for more details regarding Figs. 21 and 27 – 29.)
causing at least one computing device to receive dynamically at least one input signal representing at least a plurality of responses from the at least one job applicant, each response of the plurality of responses being responsive to a respective prompt of the plurality of prompts, at least some responses of the plurality of responses selected from the plurality of selectable responses; (In ¶0071; Fig. 1 (102, 110 and 108); Fig. 18 (1802 – 1804); Fig. 20 (118, 2002 and 2004); Fig. 22: teaches that the system’s “competency module 2002 may prompt a job seeker with a plurality of questions to determine how the job seeker performs with respect to each of the skills in the skill set”. Refer to ¶0076 – 77 wherein the “diagnostic module 118 may prompt a job seeker for input regarding an employer that he or she is interested in working for” and the “job seeker may elect to participate in the compatibility assessment by actuating a control (e.g., 204 on user interface 200) or by providing some other indication that he or she would like to participate in the compatibility assessment to identify employers that may be of interest to the job seeker”. Refer to ¶0045 wherein the system comprises of a “service provider 102 may include one or more server(s) and other machines 110, any of which may include one or more processing unit(s) 112 and computer-readable media 114.” But also, different “devices 108” (see ¶0049).)
causing the at least one computing device to, responsive to at least some of the plurality of responses from the at least one job applicant, associate the at least one job applicant with a respective at least one potential association of a plurality of potential associations, at least one of the respective at least one potential association representing a social attribute, (In ¶0068: teaches that the “information module 116 may receive, access, and/or store data associated with competence scores representative of job seekers' competencies with respect to performing the skills in the skill set and other information for comparing and/or matching one or more of the users 106 (e.g., job seekers and/or recruiters and/or employers)” which is directed to potential associations. Refer to ¶0077 wherein “compatibility module 2004 may ask the job seeker a plurality of questions to determine which employer best matches the job seeker's preferences based at least in part on the employer's description of its work space” including “answer[s] that most appropriately describes the job seeker's preferences and/or personality traits” (i.e. preferences interpreted to social attributes).)
wherein causing the at least one computing device to associate the at least one job applicant with the respective at least one potential association comprises causing the at least one computing device to determine a respective degree of association of the at least one job applicant with each potential association of the respective at least one potential association (In ¶0071: teaches that the system’s “competency module 2002” may utilize an “ontological map” wherein “the ontological map may identify relationships between data associated with the job seekers and individual skills and/or specify a nature of association between the data associated with the job seekers and individual skills in the ontological map”, in accordance to last paragraph from p. 19 in Applicant specs. Also, refer to ¶0084 – 85 wherein the “matching module” uses “the competence score and/or scores with respect to individual skills of the skill set” to “match a job seeker to a recruiter and/or employer or a job seeker to a specific employment opportunity and/or industry sector offered by an employer” that is directed to determining a respective degree of association of the at least one job applicant with each potential association, as claimed.)
causing the at least one computing device to, responsive to at least some of the plurality of responses from the at least one job applicant, associate the at least one job applicant with a respective at least one job posting, the at least one job posting associated with the respective at least one potential association and (In ¶0084 – 85; Fig. 20 (118, 2002 and 2004); Fig. 26 (2608) : teaches that the system’s “matching module 122 may access the data” such as “data associated with the job seeker's competence score determined from the competence assessment or updated after completion of one or more exercises, the job seeker's compatibility data, and/or the job seeker's interest data” to “match the job seeker with one or more of the recruiters and/or employers” and/or “a job seeker to a specific employment opportunity and/or industry sector offered by an employer”.)
causing the at least one computing device to, responsive to at least some of the plurality of responses from the at least one job applicant, associate the at least one job applicant with a respective at least one learning opportunity, the at least one learning opportunity associated with the respective at least one potential association, (In ¶0099; Fig. 18 (1806 – 1808); Fig. 24 (2416): teaches that “based at least in part on the diagnostic assessment, the training module 120 may determine one or more training activities for strengthening one or more of the skills in the skill set” to the job seeker. Refer to ¶0081 – 82 for more training program recommendation details.)
wherein causing the at least one computing device to associate the at least one job applicant with the at least one job posting and with the at least one learning opportunity comprises causing the at least one computing device to rank, with respect to the at least one job applicant, based at least on each respective degree of association of the at least one job applicant with the respective at least one potential association: (In ¶0122; Fig. 16; Fig. 18 (1808); Fig. 19 (1912); Fig 22; Fig. 24 (2416); Fig. 25 and Fig. 26 (2616): teaches that based on “one or more of the individual score is less than the individual threshold scores associated with the particular employer and/or employment opportunity”, “the training module 120 may recommend one or more additional training activities for the job seeker to improve his or her individual score(s), as illustrated in Block 2512” which means that these relevant “training activities” are ranked based on the individual threshold scores. As another example, when “the job seeker's competence score may be at least equal to the threshold score but one or more of the job seeker's individual scores corresponding to the individual skills may not be equal to the threshold scores associated with the employer and/or employment opportunity”, “the matching module 122 may recommend the job seeker to the employer and the employer may consider the job seeker's electronic portfolio and other user information to decide whether to offer the job seeker an interview”. Moreover, in ¶0126, “the matching module 122 may recommend employment opportunities to job seekers and/or job seekers to recruiters and/or employers based at least in part on a particular job seeker's competence score and/or scores with respect to individual skills of the skill set being above a predetermined threshold” wherein other preferences/filters such as “GPS location, preferences, social networks, etc.” can be used for the ranking/relevance of the employment recommendations. See ¶0096 for “some examples, user interface 300 may also provide information with respect to industry sectors and/or roles that may be of interest to the job seeker based at least in part on competence scores of other job seekers, as illustrated in box 308.” Refer to ¶0142 – 143 for more opportunity recommendations based on minimum threshold competence scores and scores from other types of criteria such as “schedule flexibility, U.S. citizenship, etc.”, that when the threshold is not satisfied, in Block 2616 relevant training activities are recommended to the job seeker instead. See ¶0129 – 132 for more details of matches based on fitness percent and threshold scores.)
a plurality of job postings comprising the at least one job posting; and a plurality of learning opportunities comprising the at least one learning opportunity. (In ¶0055; Fig. 1 (120, 122 and 126): teaches that the “training module 120 may recommend and facilitate the training activities” (i.e. learning opportunities) and the “matching module 122 may leverage determined competence scores, compatibility with respect to particular companies, interest specific to industry sectors and/or roles, and other relevant information to match two or more users 106 (e.g., job seekers and recruiters and/or employers) and/or recommend one or more employment opportunities to job seekers” (i.e. job postings). Refer to ¶0081 for the training activities stored and retrieved from the “database 126” (see ¶0066 – 68) and used by the “training module 120”. Similarly, refer to ¶0084 for the employment opportunities which include job postings retrieved from the “database 126” (see ¶0066 – 68) and used by the “matching module 122”.)
Regarding claim 2:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device to cause at least some of the plurality of prompts to be presented to the at least one job applicant. (In ¶0076 – 77; Fig. 2 (204), Figs. 21 and 27 – 29: teaches that the system’s “diagnostic module 118 may prompt a job seeker for input regarding an employer that he or she is interested in working for” and the “job seeker may elect to participate in the compatibility assessment by actuating a control (e.g., 204 on user interface 200) or by providing some other indication that he or she would like to participate in the compatibility assessment to identify employers that may be of interest to the job seeker”. Refer to ¶0139 and ¶0144 for more details regarding Figs. 21 and 27 – 29.)
Regarding claim 3:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device to, responsive to at least some of the plurality of responses from the at least one job applicant, associate the at least one job applicant with a respective at least one potential association of a plurality of potential associations. (In ¶0068: teaches that the “information module 116 may receive, access, and/or store data associated with competence scores representative of job seekers' competencies with respect to performing the skills in the skill set and other information for comparing and/or matching one or more of the users 106 (e.g., job seekers and/or recruiters and/or employers)” which is directed to potential associations. Refer to ¶0077 wherein “compatibility module 2004 may ask the job seeker a plurality of questions to determine which employer best matches the job seeker's preferences based at least in part on the employer's description of its work space”.)
Regarding claim 4:
Jarrett, as shown in the rejection above, discloses the limitations of claim 3.
Jarrett further teaches:
wherein causing the at least one computing device to associate the at least one job applicant with the respective at least one potential association comprises causing the at least one computing device to determine a respective degree of association of the at least one job applicant with each potential association of the respective at least one potential association. (In ¶0071: teaches that the system’s “competency module 2002” may utilize an “ontological map” wherein “the ontological map may identify relationships between data associated with the job seekers and individual skills and/or specify a nature of association between the data associated with the job seekers and individual skills in the ontological map”, in accordance to last paragraph from p. 19 in applicant specs. Also, refer to ¶0084 – 85 wherein the “matching module” uses “the competence score and/or scores with respect to individual skills of the skill set” to “match a job seeker to a recruiter and/or employer or a job seeker to a specific employment opportunity and/or industry sector offered by an employer”.)
Regarding claim 5:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein causing the at least one computing device to determine the respective degree of association of the at least one job applicant with each potential association of the respective at least one potential association comprises causing the at least one computing device to determine the respective degree of association of the at least one job applicant with each potential association of the respective at least one potential association responsive to at least a count of the plurality of responses from the at least one job applicant that are positively associated with the potential association. (In ¶0071: teaches that “based at least in part on a job seeker's answer, response, choice, etc., the competency module 2002 may positively or negatively adjust one or more baseline scores that each corresponds to a skill in the skill set”. For example, “the competency module 2002 may utilize the document style database (e.g., XML, etc.), the graph database, and/or the ontological map associated with the database 126 to determine relationships between questions asked, job seeker responses, and individual skills in the skill set for positively or negatively adjusting one or more baseline scores that each corresponds to a skill in the skill set” wherein the ”ontological map may identify relationships between questions asked, job seeker responses, and individual skills and/or specify a nature of association between the questions asked, job seeker responses, and individual skills in the ontological map”. Refer to ¶0134 wherein “the matching module 122 determining that a job seeker's qualifications (e.g., competence score, geographic location, etc.) meet or exceed predetermined criteria associated with the recruiters and/or employers, the matching module 122 may automatically present information about the recruiter and/or employer to the job seeker (via a user profile in the user interface, etc.) and/or information about the job seeker to the recruiter and/or employer” and the module can determine that “job seeker's qualifications satisfy the recruiter's and/or employer's threshold score, the matching module 122 may provide information about one or more job seekers to the recruiter and/or employer for further examination”.)
Regarding claim 6:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein causing the at least one computing device to determine the respective degree of association of the at least one job applicant with each potential association of the respective at least one potential association comprises causing the at least one computing device to determine the respective degree of association of the at least one job applicant with each potential association of the respective at least one potential association responsive to at least a count of the plurality of responses from the at least one job applicant that are negatively associated with the potential association. (In ¶0071: teaches that “based at least in part on a job seeker's answer, response, choice, etc., the competency module 2002 may positively or negatively adjust one or more baseline scores that each corresponds to a skill in the skill set”. For example, “the competency module 2002 may utilize the document style database (e.g., XML, etc.), the graph database, and/or the ontological map associated with the database 126 to determine relationships between questions asked, job seeker responses, and individual skills in the skill set for positively or negatively adjusting one or more baseline scores that each corresponds to a skill in the skill set” wherein the ”ontological map may identify relationships between questions asked, job seeker responses, and individual skills and/or specify a nature of association between the questions asked, job seeker responses, and individual skills in the ontological map”)
Regarding claim 7:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device to determine a degree of similarity of the respective at least one potential association associated with the at least one job applicant with a respective at least one potential association of the plurality of potential associations and associated with the at least one job posting, wherein causing the at least one computing device to associate the at least one job applicant with the at least one job posting comprises causing the at least one computing device to associate the at least one job applicant with the at least one job posting responsive to at least the degree of similarity of the respective at least one potential association associated with the at least one job applicant with the respective at least one potential association associated with the at least one job posting. (In ¶0069; Fig. 16 (1604 and 1606): teaches that the system’s “information module 116” uses the “the data associated with the competence scores, compatibility data”, (directed to degree of similarity) and/or “interest data” to map it with the “user profiles corresponding to individual job seekers”, in accordance to ¶2 from p. 20 in applicant specs. Refer to ¶0084 – 86 wherein the “matching module 122 may leverage the determined competency of the job seekers, compatibility (e.g., fitness) with respect to particular recruiters and/or employers, interest specific to industry sectors (e.g., job tracks), and other relevant information to match two or more users 106 (e.g., job seekers and recruiters and/or employers) and/or recommend one or more employment opportunities to a job seeker” including “threshold scores” that are compared to employer criteria/requirements/qualifications for an “employment opportunity” which is also directed to the degree of similarity for the potential association for a job posting. Finally, refer to ¶0130 for more details about Fig, 16.)
Regarding claim 8:
Jarrett, as shown in the rejection above, discloses the limitations of claim 7.
Jarrett further teaches:
wherein: causing the at least one computing device to associate the at least one job applicant with the respective at least one potential association comprises causing the at least one computing device to determine a respective degree of association of the at least one job applicant with each potential association of the respective at least one potential association; and (In ¶0071: teaches that the system’s “competency module 2002” may utilize an “ontological map” wherein “the ontological map may identify relationships between data associated with the job seekers and individual skills and/or specify a nature of association between the data associated with the job seekers and individual skills in the ontological map”. Also, refer to ¶0084 – 85 wherein the “matching module” uses “the competence score and/or scores with respect to individual skills of the skill set” to “match a job seeker to a recruiter and/or employer or a job seeker to a specific employment opportunity and/or industry sector offered by an employer”.)
causing the at least one computing device to determine the degree of similarity of the respective at least one potential association associated with the at least one job applicant with the respective at least one potential association associated with the at least one job posting comprises causing the at least one computing device to determine the degree of similarity of the respective at least one potential association associated with the at least one job applicant with the respective at least one potential association associated with the at least one job posting comprises responsive to each respective degree of association of the at least one job applicant with the respective at least one potential association associated with the at least one job posting. (In ¶0069; Fig. 16 (1604 and 1606): teaches that the system’s “information module 116” uses the “the data associated with the competence scores, compatibility data”, (directed to degree of similarity) and/or “interest data” to map it with the “user profiles corresponding to individual job seekers”. Refer to ¶0084 – 86 wherein the “matching module 122 may leverage the determined competency of the job seekers, compatibility (e.g., fitness) with respect to particular recruiters and/or employers, interest specific to industry sectors (e.g., job tracks), and other relevant information to match two or more users 106 (e.g., job seekers and recruiters and/or employers) and/or recommend one or more employment opportunities to a job seeker” including “threshold scores” that are compared to employer criteria/requirements/qualifications for an “employment opportunity” which is also directed to the degree of similarity for the potential association for a job posting. Finally, refer to ¶0130 for more details about Fig, 16.)
Regarding claim 9:
Jarrett, as shown in the rejection above, discloses the limitations of claim 7.
Jarrett further teaches:
wherein causing the at least one computing device to associate the at least one job applicant with the at least one job posting comprises causing the at least one computing device to rank a plurality of job applicants comprising the at least one job applicant in association with the at least one job posting according to the respective degrees of similarity. (In ¶0096; Fig. 3 (306); Fig. 16 (1604): teaches that “in FIG. 3, the job seeker's competence score ranks her in the top 76% of other job seekers who are subscribing to the services offered by the service provider 102, as shown in box 306”, such as “industry sectors and/or roles that may be of interest to the job seeker based at least in part on competence scores of other job seekers” which is directed to job postings. Also, in Fig. 16 and ¶0129 – 130 applicants are ranked based on percent of fitness for a specific criterion determined/filtered by the employer such as by applicant or by job posting.)
Regarding claim 10:
Jarrett, as shown in the rejection above, discloses the limitations of claim 7.
Jarrett further teaches:
wherein causing the at least one computing device to associate the at least one job applicant with the at least one job posting comprises causing the at least one computing device to rank the plurality of job postings comprising the at least one job posting in association with the at least one job applicant according to the respective degrees of similarity. (In ¶0129 – 130; Figs. 15 – 16: teaches, in Fig. 16 and ¶0129 – 130 applicants are ranked based on percent of fitness for a specific criterion determined/filtered by the employer such as by applicant or by job posting. Refer to ¶0092 for Fig. 15 details.)
Regarding claim 11:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device to determine a degree of similarity of the respective at least one potential association associated with the at least one job applicant with a respective at least one potential association of the plurality of potential associations and associated with the at least one learning opportunity, wherein causing the at least one computing device to associate the at least one job applicant with the at least one learning opportunity comprises causing the at least one computing device to associate the at least one job applicant with the at least one learning opportunity responsive to at least the degree of similarity of the respective at least one potential association associated with the at least one job applicant with the respective at least one potential association associated with the at least one learning opportunity. (In ¶0102; Figs. 5 – 8; Fig. 18 (1804 – 1808); Fig. 24 (2414 – 2416): teaches that “the recommendation module 2008 may recommend different training activities to job seekers who have the same competence scores and have indicated interests in a same employer and/or role but have different scores with respect to one or more of the individual skills in the skill set.” Refer to ¶0114 – 115 for more details about Fig. 24 when determining and recommending “training activities” based on “competence scores with the threshold scores associated with the role, industry sector, employer, etc.”)
Regarding claim 12:
Jarrett, as shown in the rejection above, discloses the limitations of claim 11.
Jarrett further teaches:
wherein; causing the at least one computing device to associate the at least one job applicant with the respective at least one potential association comprises causing the at least one computing device to determine a respective degree of association of the at least one job applicant with each potential association of the respective at least one potential association; and (In ¶0114; Fig. 18 (1804 – 1808); Fig. 24 (2414 – 2416): teaches that the system can compare “the job seeker's competence score and individual scores corresponding to the individual skills in the skill set with the threshold score and respective individual threshold scores that are associated with the determined role, industry sector, employer, etc.” to determine “training activities” in Block 2416, including “gap analysis” of job seeker’ skills (see ¶0115).)
causing the at least one computing device to determine the degree of similarity of the respective at least one potential association associated with the at least one job applicant with the respective at least one potential association associated with the at least one learning opportunity comprises causing the at least one computing device to determine the degree of similarity of the respective at least one potential association associated with the at least one job applicant with the respective at least one potential association associated with the at least one learning opportunity comprises responsive to each respective degree of association of the at least one job applicant with the respective at least one potential association associated with the at least one learning opportunity. (In ¶0102; Figs. 5 – 8; Fig. 18 (1804 – 1808); Fig. 24 (2414 – 2416): teaches that “the recommendation module 2008 may recommend different training activities to job seekers who have the same competence scores and have indicated interests in a same employer and/or role but have different scores with respect to one or more of the individual skills in the skill set.” Refer to ¶0114 – 115 for more details about Fig. 24 when determining and recommending “training activities” based on “competence scores with the threshold scores associated with the role, industry sector, employer, etc.”)
Regarding claim 13:
Jarrett, as shown in the rejection above, discloses the limitations of claim 11.
Jarrett further teaches:
wherein causing the at least one computing device to associate the at least one job applicant with the at least one learning opportunity comprises causing the at least one computing device to rank a plurality of job applicants comprising the at least one job applicant in association with the at least one learning opportunity according to the respective degrees of similarity. (In ¶0130 – 131; Fig. 16 (1604); Fig. 17 (1704): teaches that in Fig. 16 applicants are ranked based on percent of fitness for a specific criterion determined/filtered by the employer such as by applicant or by job posting, but also by “training activities” earned or completed or any other “employer's predetermined criteria and information relevant to the matches”. Refer to ¶0129 – 132 for more details.)
Regarding claim 14:
Jarrett, as shown in the rejection above, discloses the limitations of claim 11.
Jarrett further teaches:
wherein causing the at least one computing device to associate the at least one job applicant with the at least one learning opportunity comprises causing the at least one computing device to rank the plurality of learning opportunities comprising the at least one learning opportunity in association with the at least one job applicant according to the respective degrees of similarity. (In ¶0103; Fig. 5; Fig. 18 (1808): teaches that the “skill building module 2010 may select one or more online training activities for the presentation module 124 to cause to be presented to the job seeker to strengthen one or more skills of the set of skills, as illustrated in FIG. 5” which under the broadest reasonable interpretation (BRI) such ranking basis is satisfied since such interface is personalized by user and their skills and needs, which can be prioritized.)
Regarding claim 15:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device receive at least one input signal representing a contribution, by the at least one job applicant, of at least one new potential association to the plurality of potential associations. (In ¶0061; Fig. 18 (1802); Figs. 21 and 27 – 29: teaches that “job seekers may upload resumes and/or other data items into the training, tracking, and placement system and the information module 116 may extract data from the resumes and/or other data items”, but also the “information module 116 may also access and/or retrieve information from social media accounts…online banking accounts, geolocation devices, etc., associated with the job seekers” to store “a combination of data input by the job seeker and accessed and/or retrieved data associated with the job seekers in the profile corresponding to the job seeker”. Refer to ¶0091 for more details.)
Regarding claim 16:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device receive at least one input signal representing a contribution, by at least one employer, of at least one new potential association to the plurality of potential associations. (In ¶0093; Fig. 22: teaches that a “user interface 2200 personalized for recruiters and/or employers that that may be caused to be presented by the presentation module 124 to provide recruiters and/or employers the functionality to input predetermined criteria and additional criteria for a particular employment opportunity, role, and/or industry sector” wherein “recruiters and/or employers may also input information about the recruiter and/or employer, a description of the specific employment opportunity, responsibilities and skills associated with the particular specific activity, as illustrated in box 2210”.)
Regarding claim 17:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device receive at least one input signal representing a contribution, by at least one learning institution, of at least one new potential association to the plurality of potential associations. (In ¶0060; Fig. 22: teaches under BRI, that “information may be input by users 106 via an application programming interface associated with the service provider 102” wherein the users include “universities or other academic institutions”.)
Regarding claim 18:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device receive at least one input signal representing a contribution, by at least one educator, of at least one new potential association to the plurality of potential associations. (In ¶0130; Fig. 17: teaches that the system allows “mentor (e.g., coach) feedback” to be inputted and included as shown in Fig. 17. Refer to ¶0083 for general details.)
Regarding claim 19:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of potential associations are associated with respective different industry job trends. (In ¶0130; Fig. 15; Fig. 16 (1600); Fig. 30: teaches an example wherein in Fig. 16, “User interface 1600 may also include quick links to additional information related to interviewing, hiring, and other statistics (e.g., data analytics) as illustrated in box 1614” for a particular industry sector and their respective applicants)
Regarding claim 20:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of potential associations are associated with respective different industry job sectors. (In ¶0097; Fig. 4 (402); Figs. 15; Fig. 16 (1600); Fig. 30: teaches an example wherein “in FIG. 4, the job seeker's scores with respect to the skills rigor, polish, and ownership are below the threshold for being eligible for a business, finance, and data analytics industry sector as identified in box 402”.)
Regarding claim 21:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of potential associations are associated with respective different occupations. (In Fig. 16: teaches that applicants for a particular industry sector have a degrees of “Economics (BA)” and “Operation and information management (BS)” which is directed to applicant associations with respect to different occupations.)
Regarding claim 22:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of potential associations are associated with respective different employment benefits. (In ¶0126; Figs. 12 – 13 and 15 – 16; Fig. 22; Fig. 30: teaches, under BRI, that the system can “match a job seeker to a recruiter and/or an employer or a job seeker to a specific employment opportunity offered by an employer” which under BRI can include different employment benefits described in the job description from “job order” or final job offer as shown in Fig. 22. Also, in Fig. 16 and ¶0130 can include a “User interface 1600” with “quick links to additional information related to interviewing, hiring, and other statistics (e.g., data analytics) as illustrated in box 1614”.)
Regarding claim 23:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of potential associations are associated with respective different skills. (In ¶0045; Fig. 1 (102); Fig. 15 (1504); Fig. 17: teaches that the system’s “service provider 102 may perform diagnostic assessments to determine competence scores associated with job seekers (e.g., users 106) that represent the job seekers' performances of individual skills in a skill set and recommend and provide training programs, exercises, and/or projects to improve the job seekers' competence scores”. Refer to ¶0036 for general details about skills.)
Regarding claim 24:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of potential associations are associated with respective social attributes. (In ¶0076 – 77; Figs. 21 and 23: teaches that the system’s “compatibility module 2004 may provide an additional assessment for the job seeker to determine which employer best matches the job seeker's preferences and/or personality traits”, in accordance to ¶3, p.10 from applicant specs.)
Regarding claim 25:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of potential associations are associated with respective social capabilities. (In ¶0097; Fig. 4: teaches that “in FIG. 4, the job seeker's scores with respect to the skills rigor, polish, and ownership are below the threshold for being eligible for a business, finance, and data analytics industry sector as identified in box 402”, in accordance to ¶3, p.10 from applicant specs. Refer to ¶0036 for details regarding “skills rigor, polish, and ownership”, wherein “polish” is the “ability to communicate professionally, confidently, authentically, etc.”)
Regarding claim 26:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of plurality of learning opportunities are respective different learning institutions. (In ¶0040 – 41; Fig. 5: teaches, under BRI, that “universities or other educational institutions may also utilize aggregated data collected by the training, tracking, and placement system to determine strengths and/or weaknesses with respect to course offerings, extracurricular offerings, programming, etc. and/or determine activities for improving competence scores of their students, etc.” to further be recommended as “training activities (e.g., training programs, exercises, and/or projects)” (see ¶0040 and ¶0055).)
Regarding claim 27:
Jarrett, as shown in the rejection above, discloses the limitations of claim 26.
Jarrett further teaches:
wherein at least some of the learning institutions are provided independently from a provider of the method. (In ¶0045 - 46; Fig. 1 (102, 106, 108 and 110): teaches that the users which include “universities or other educational institutions” is provided independently via the user “device 108” from the system’s provider or “service provider 102”, in accordance to ¶2, p.11 and ¶1, p12 from applicant specs.)
Regarding claim 28:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of plurality of learning opportunities are respective different educators. (In ¶0045; Fig. 5: teaches, under BRI, that the system can offer different learning opportunities from different educators or “universities or other educational institutions” as shown in Fig. 5. See ¶0047 – 48 for more details)
Regarding claim 29:
Jarrett, as shown in the rejection above, discloses the limitations of claim 28.
Jarrett further teaches:
wherein at least some of the educators are independent from a provider of the method. (In ¶0045 - 46; Fig. 1 (102, 106, 108 and 110): teaches under BRI, that the users which include “universities or other educational institutions” which can include educators, is provided independently via the user “device 108” from the system’s provider or “service provider 102”, in accordance to ¶2, p.11 and ¶1, p12 from applicant specs.)
Regarding claim 30:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of plurality of learning opportunities are respective different learning courses. (In ¶0045; Fig. 5: teaches, under BRI, that the system can offer different learning opportunities from different educators or “universities or other educational institutions” as shown in Fig. 5 that shows different learning courses or “training programs, exercises, and/or projects”. See ¶0047 – 48 for more details.)
Regarding claim 31:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of plurality of learning opportunities are respective different learning programs of respective pluralities of learning courses. (In ¶0045; Fig. 5: teaches, under BRI, that the system can offer different learning opportunities from different educators or “universities or other educational institutions” as shown in Fig. 5 that shows different learning courses or “training programs, exercises, and/or projects”. See ¶0047 – 48 for more details.)
Regarding claim 32:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein at least some of the plurality of plurality of learning opportunities are respective different electronic media resources. (In ¶0068; Figs. 5 – 8: teaches that the user can access different media resources such as a “course introduction” video as shown in Fig. 6. Refer to ¶0068 – 69 wherein sources and “work product (e.g., from completed training activities), videos of presentations, documents associated with completed activities and/or tasks, etc.” are included in an “electronic portfolio” for user access.)
Regarding claims 33 – 34:
Jarrett, as shown in the rejection above, discloses the limitations of claim 30.
Jarrett further teaches:
wherein at least some of the learning courses are provided independently from a provider of the method. (In ¶0045 - 46; Fig. 1 (102, 106, 108 and 110); Fig. 5: teaches that the users which include “universities or other educational institutions” is provided independently via the user “device 108” from the system’s provider or “service provider 102”, wherein the user can include or link the courses offerings such as “training programs, exercises, and/or projects to improve the job seekers' competence scores” as shown in Fig. 5.)
Regarding claim 35:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein causing the at least one computing device to associate the at least one job applicant with the respective at least one job posting comprises causing the at least one computing device to associate the at least one job applicant with the respective at least one job posting according to at least a machine-learning algorithm. (In ¶0056: teaches that “machine learning algorithms (e.g., supervised learning algorithms, unsupervised learning algorithms, deep learning algorithms, etc.) may access the document style database, the graph database, and the ontological map for learning new machine learning algorithms for inferring relationships between new data associated with the users 106 and/or experience associated with the users 106 and the users 106.”)
Regarding claim 36:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
wherein causing the at least one computing device to associate the at least one job applicant with the respective at least one learning opportunity comprises causing the at least one computing device to associate the at least one job applicant with the respective at least one learning opportunity according to at least a machine-learning algorithm. (In ¶0056: teaches, under BRI, that “machine learning algorithms (e.g., supervised learning algorithms, unsupervised learning algorithms, deep learning algorithms, etc.) may access the document style database, the graph database, and the ontological map for learning new machine learning algorithms for inferring relationships between new data associated with the users 106 and/or experience associated with the users 106 and the users 106.”)
Regarding claim 37:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device to produce at least one output signal responsive to association of the at least one job applicant with the respective at least one job posting. (In ¶0084; Fig. 1 (102, 106, 108 and 110); Fig. 3 (308); Fig. 15: teaches that “matching module 122 may leverage the determined competency of the job seekers, compatibility (e.g., fitness) with respect to particular recruiters and/or employers, interest specific to industry sectors (e.g., job tracks), and other relevant information to match two or more users 106 (e.g., job seekers and recruiters and/or employers) and/or recommend one or more employment opportunities to a job seeker”.)
Regarding claim 38:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device to produce at least one output signal responsive to association of the at least one job applicant with the respective at least one learning opportunity. (In ¶0081 – 82; Fig. 1 (102, 106, 108 and 110); Figs. 18 – 19, 24, 25, and 26: teaches that the “recommendation module 2008 may determine and recommend to the job seeker one or more training activities for strengthening one or more of the skills in the skill set”. Refer to ¶0040 for general details.)
Regarding claim 39:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
At least one non-transitory computer-readable medium storing thereon program codes that, when executed by at least one processor, cause the at least one processor to implement the method of any one of claim1 (In ¶0045; Fig. 1 (110): teaches a “service provider 102 may include one or more server(s) and other machines 110, any of which may include one or more processing unit(s) 112 and computer-readable media 114”.)
Regarding claim 40:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
A computer system programmed to implement the method of any one of claim 1, the computer system comprising the at least one computing device. (In ¶0045; Fig. 1 (110): teaches a “service provider 102 may include one or more server(s) and other machines 110, any of which may include one or more processing unit(s) 112 and computer-readable media 114” which “may perform diagnostic assessments to determine competence scores associated with job seekers (e.g., users 106) that represent the job seekers' performances of individual skills in a skill set and recommend and provide training programs, exercises, and/or projects to improve the job seekers' competence scores”.)
Regarding claim 41:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing at least one user device to transmit the at least one input signal to the at least one computing device (In ¶0045; Fig. 1 (102, 106, 108 and 110); Fig. 18 (1802 – 1804); Fig. 20 (118, 2002 and 2004); Fig. 22: teaches “the service provider 102 may receive data from one or more job seekers and leverage the data for recommending industry sectors (e.g., job tracks), roles, employment opportunities, recruiters, and/or employers”. Refer to ¶0076 – 77 wherein the “diagnostic module 118 may prompt a job seeker for input regarding an employer that he or she is interested in working for” and the “job seeker may elect to participate in the compatibility assessment by actuating a control (e.g., 204 on user interface 200)” which is directed to transmitting input signals from the user device to the computing device.)
Regarding claim 42:
Jarrett, as shown in the rejection above, discloses the limitations of claim 41.
Jarrett further teaches:
At least one non-transitory computer-readable medium storing thereon program codes that, when executed by at least one processor, cause the at least one processor to implement the method of claim 41. (In ¶0045; Fig. 1 (110): teaches a “service provider 102 may include one or more server(s) and other machines 110, any of which may include one or more processing unit(s) 112 and computer-readable media 114”.)
Regarding claim 43:
Jarrett, as shown in the rejection above, discloses the limitations of claim 41.
Jarrett further teaches:
A computer system programmed to implement the method of claim 41, the computer system comprising the at least one computing device. (In ¶0045; Fig. 1 (110): teaches a “service provider 102 may include one or more server(s) and other machines 110, any of which may include one or more processing unit(s) 112 and computer-readable media 114” which “may perform diagnostic assessments to determine competence scores associated with job seekers (e.g., users 106) that represent the job seekers' performances of individual skills in a skill set and recommend and provide training programs, exercises, and/or projects to improve the job seekers' competence scores”.)
Regarding claim 44:
Jarrett, as shown in the rejection above, discloses the limitations of claim 43.
Jarrett further teaches:
wherein the at least one computing device is or comprises a personal computer. (In ¶0049; Fig. 1 (108): teaches that the “device(s) 108” can include “personal computers”.)
Regarding claim 45:
Jarrett, as shown in the rejection above, discloses the limitations of claim 43.
Jarrett further teaches:
wherein the at least one computing device is or comprises a laptop. (In ¶0049; Fig. 1 (108): teaches that the “device(s) 108” can include “laptop computers”.)
Regarding claim 46:
Jarrett, as shown in the rejection above, discloses the limitations of claim 43.
Jarrett further teaches:
wherein the at least one computing device is or comprises a tablet computer. (In ¶0049; Fig. 1 (108): teaches that the “device(s) 108” can include “tablet computers”.)
Regarding claim 47:
Jarrett, as shown in the rejection above, discloses the limitations of claim 43.
Jarrett further teaches:
wherein the at least one computing device is or comprises a smartphone. (In ¶0049; Fig. 1 (108): teaches that the “device(s) 108” can include “mobile computers” which under the broadest reasonable interpretation (BRI) can be smartphones or “telecommunication devices” as shown in Fig 1 and its “device(s) 108”.)
Regarding claim 48:
Jarrett, as shown in the rejection above, discloses the limitations of claim 43.
Jarrett further teaches:
wherein the at least one computing device is or comprises a smart watch. (In ¶0049; Fig. 1 (108): teaches that the “device(s) 108” can include “wearable computers” which can be a smart watch under BRI.)
Regarding claim 49:
Jarrett, as shown in the rejection above, discloses the limitations of claim 43.
Jarrett further teaches:
wherein the at least one computing device is or comprises glasses. (In ¶0049; Fig. 1 (108): teaches that the “device(s) 108” can include “wearable computers” which can be glasses under BRI.)
Regarding claim 50:
Jarrett, as shown in the rejection above, discloses the limitations of claim 43.
Jarrett further teaches:
wherein the at least one computing device is or comprises a mobile activity tracker. (In ¶0049; Fig. 1 (108): teaches that the “device(s) 108” that can be “mobile computers” and include “wearable computers” which can be a mobile activity tracker under BRI.)
Regarding claim 51:
Jarrett, as shown in the rejection above, discloses the limitations of claim 43.
Jarrett further teaches:
wherein the at least one computing device is or comprises a wearable activity tracker. (In ¶0049; Fig. 1 (108): teaches that the “device(s) 108” that can be “mobile computers” and include “wearable computers” which can be a wearable activity tracker under BRI.)
Regarding claim 52:
Jarrett, as shown in the rejection above, discloses the limitations of claim 43.
Jarrett further teaches:
wherein the at least one computing device is or comprises a haptic glove. (In ¶0049; Fig. 1 (108): teaches that the “device(s) 108” that can be “mobile computers” and include “wearable computers” as well as “implanted devices” which can be a haptic glove under BRI.)
Regarding claim 53:
Jarrett, as shown in the rejection above, discloses the limitations of claim 43.
Jarrett further teaches:
wherein the at least one computing device comprises at least one sensor wearable on a body and operable to measure movement of the body. (In ¶0049; Fig. 1 (108): teaches that the “device(s) 108” that can be “mobile computers” and include “wearable computers” as well as “implanted devices” which can be at least one sensor wearable on a body under BRI.)
Regarding claim 54:
Jarrett, as shown in the rejection above, discloses the limitations of claim 1.
Jarrett further teaches:
further comprising causing the at least one computing device to control an interactive user interface of a user computing device, wherein causing the at least one computing device to control the interactive user interface of the user computing device comprises causing the interactive user interface of the user computing device to, at least: present the plurality of prompts; (In ¶0076 – 77; Fig. 2 (204), Figs. 21 and 27 – 29: teaches that the system’s “diagnostic module 118 may prompt a job seeker for input regarding an employer that he or she is interested in working for” and the “job seeker may elect to participate in the compatibility assessment by actuating a control (e.g., 204 on user interface 200) or by providing some other indication that he or she would like to participate in the compatibility assessment to identify employers that may be of interest to the job seeker”. Refer to ¶0139 and ¶0144 for more details regarding Figs. 21 and 27 – 29.)
receive the plurality of responses; (In ¶0045; Fig. 1 (102, 106, 108 and 110); Fig. 18 (1802 – 1804); Fig. 20 (118, 2002 and 2004); Fig. 22: teaches “the service provider 102 may receive data from one or more job seekers and leverage the data for recommending industry sectors (e.g., job tracks), roles, employment opportunities, recruiters, and/or employers”. Refer to ¶0076 – 77 wherein the “diagnostic module 118 may prompt a job seeker for input regarding an employer that he or she is interested in working for” and the “job seeker may elect to participate in the compatibility assessment by actuating a control (e.g., 204 on user interface 200)” which is directed to transmitting input signals from the user device to the computing device.)
present the at least one job posting; and (In ¶0084; Fig. 1 (102, 106, 108 and 110); Fig. 3 (308); Fig. 15: teaches that “matching module 122 may leverage the determined competency of the job seekers, compatibility (e.g., fitness) with respect to particular recruiters and/or employers, interest specific to industry sectors (e.g., job tracks), and other relevant information to match two or more users 106 (e.g., job seekers and recruiters and/or employers) and/or recommend one or more employment opportunities to a job seeker”.)
present at least one identification of the at least one learning opportunity. (In ¶0081 – 82; Fig. 1 (102, 106, 108 and 110); Figs. 18 – 19, 24, 25, and 26: teaches that the “recommendation module 2008 may determine and recommend to the job seeker one or more training activities for strengthening one or more of the skills in the skill set”. Refer to ¶0040 for general details.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nelson (U.S. Pub No. 20140379602 A1) is pertinent because it “generally relates to an online computer system for education opportunities, careers, and/or job opportunities, and, more specifically, to presenting education opportunities, careers, and/or job opportunities to a particular user based, at least in part, on a skill set that is identified for the user.”
Talbot (U.S. Pub No. 20210357868 A1) is pertinent because it is a “system and process for communicating job skills requirements to job market participants, including job seekers, employers, and educators.”
Desjardins (U.S. Pub No. 20120265770 A1) is pertinent because it “relates generally to the field of computer implemented systems and methods to facilitate automated job searching, recruitment and placement via the Internet or other communication networks.”
Ramo (U.S. Pub No. 20180005163 A1) is pertinent because it “relate in general to a system and method for connecting a user to an employment resource tailored to the user.”
Lunardi (U.S. Pub No. 20150227632 A1) is pertinent because it “provide[s] a system and method for creating online groups/spaces to network users together based on shared past job experiences, education, and future career interests, that translates or decodes specific inputted codes (e.g., employment related codes) of profiles into plain language to support intelligent group creation and matching services between job candidates, employers, education programs, and third parties, and other useful content.”
Rafaty (U.S. Pub No. 20140279637 A1) is pertinent because it is “computer-implemented systems, especially web-based systems, that enable job seekers to apply for work and employers to post jobs and job skills and other types of training sponsored or provided by the employers and available to job seekers. More specifically the disclosed invention is directed to systems that serve a defined community that is a subset of the entire community of job seekers and is one comprising job seekers who self-identify as persons having a disability as required by the Terms Of Use of the system and its application.”
Forman (U.S. Pub No. 20140164271 A1) is pertinent because it is about “system and methods described herein provide a web-based application, and/or plug-in, to aid a job-seeker in tracking, managing, and applying for potential employment positions.”
Bass (U.S. Pub No. 20030009742 A1) is pertinent because it “relates to the field of knowledge engineering and an automated job training and performance tool, and particularly to a computer software program which provides an architecture and an infrastructure/framework for enabling organizations to design, develop, implement, evaluate and administer Web based instructional and training aids for members of their organization”
DaCosta-Paul (U.S. Pub No. 20200219218 A1) is pertinent because it is “relates to a technique for matching potential employees with potential employers over a network, and more particularly to matching employers needs with potential employees using traditional employment information”
Grover (U.S. Pub No. 20190043017 A1) is pertinent because it “pertain to online systems for job posting marketplaces.”
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ivonnemary Rivera Gonzalez whose telephone number is (571)272-6158. The examiner can normally be reached Mon - Fri 9:00AM - 5:30PM.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626