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 .
DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/10/2026 has been entered.
Response to Arguments
In communications filed on 4/10/2026, claims 1-3, 7-13, and 17-23 are presented for examination. Claims 1 and 11 are independent.
Amended claims: 1, 7, 11, 17.
Applicants’ arguments, see Applicant Arguments/Remarks filed 3/5/2026, with respect to claim(s) rejected under prior art have been considered but are moot in view of new ground(s) of rejection necessitated by Applicants’ amendment to the claim(s).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 7-13, and 17-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20170293873 A1 (hereinafter ‘Chrapo’) in view of US 20130046704 A1 (hereinafter ‘Patwa’) in view of US 20110047608 A1 (hereinafter ‘Leven’) in view of US 20170244746 A1 (hereinafter ‘Hawthorn’).
As regards claim 1, Chrapo (US 20170293873 A1) in combination with Patwa (US 20130046704 A1) teaches: A method comprising: receiving, by one or more servers via one or more application programming interfaces (APIs), information about a candidate from a job application system; (Chrapo: Figs. 1-2, 4-7, and ¶3-¶4, ¶68-¶77, ¶94-¶97, ¶134-¶137, i.e., candidate applying for a job through an application system and ¶68-¶80, ¶97, ¶101-¶102, i.e., risk scores are calculated using multiple different sets of information regarding a user including ID information such as facial recognition i.e., authentication information, messages, internet browsing etc, wherein the information is calculated using software i.e., API). Although, Chrapo does not explicitly teach the use of common software building blocks like API, Patwa, in analogous art, teaches using APIs to send/receive a job candidate related information from multiple sources. See Patwa, ¶95-¶108).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Charpo to include using basic software building blocks such as APIs to send/receive a job candidate related information from sources as taught by Patwa with the motivation to improve the efficiency and quality of the recruitment process (Patwa: ¶12)
Charpo et al in combination with Leven (US 20110047608 A1) further teaches: the information comprising an email address and a type of authentication or strength of password the candidate used to authenticate to the job application system to submit an application for a job during a job application process; (Leven: ¶57-¶58, i.e., authentication based on the strength of the authentication password and/or multifactor authentications)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Chrapo to include authentication information to include strength of the authentication password and/or multifactor authentication as taught by Leven with the motivation to make the authentication stronger (Leven: ¶14)
Charpo et al in combination with Hawthorn (US 20170244746 A1) further teaches: generating, by the one or more servers, one or more simulated phishing communications created and personalized to the candidate using the information about the candidate received from the job application system; (Chrapo: Figs. 1-2, 4-7, and ¶3-¶4, ¶6, ¶33-¶42, ¶94-¶97, i.e., candidate applying for a job through an application system and ¶70-¶71, ¶97, ¶101-¶102, i.e., risk scores are calculated using multiple different sets of information regarding a user including ID information such as facial recognition i.e., authentication information, network/online/email usage behavior of the user and so forth. See also, Hawthorn: Fig 7, item 708; Fig 9; ¶108]-¶111; ¶115 provides for the security system to obtain employee profiles, etc., from client businesses, Fig 17-18, Fig 20, step 2010; ¶237 provides for calculating a risk score; ¶226-¶230 provides for communicating risk assessment reports to the business client including user-specific risk scores)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Chrapo to include responses to a simulated phishing campaign to a user as taught by Hawthorn with the motivation to calculate trustworthiness of an individual (Hawthorn: ¶37)
communicating, by the one or more servers, one or more simulated phishing communications to one or more devices of the candidate using the email address provided by the candidate to the job application system to submit the application for the job; (Chrapo: Figs. 1-2, 4-7, and ¶3-¶4, ¶6, ¶33-¶42, ¶94-¶97, i.e., candidate applying for a job through an application system and ¶70-¶71, ¶97, ¶101-¶102, i.e., risk scores are calculated using multiple different sets of information regarding a user including ID information such as facial recognition i.e., authentication information, network/online/email usage behavior of the user and so forth. See also, Hawthorn: Fig 7, item 708; Fig 9; ¶108]-¶111; ¶115 provides for the security system to obtain employee profiles, etc., from client businesses, Fig 17-18, Fig 20, step 2010; ¶237 provides for calculating a risk score; ¶226-¶230 provides for communicating risk assessment reports to the business client including user-specific risk scores)
receiving, by the one or more servers, one or more responses from the one or more devices of the candidate, the one or more responses identifying one or more failure actions in the candidate's interaction with the one or more simulated phishing communications; (Chrapo: Figs. 1-2, 4-7, and ¶3-¶4, ¶6, ¶33-¶42, ¶94-¶97, i.e., candidate applying for a job through an application system and ¶70-¶71, ¶97, ¶101-¶102, i.e., risk scores are calculated using multiple different sets of information regarding a user including ID information such as facial recognition i.e., authentication information, network/online/email usage behavior of the user and so forth. See also, Hawthorn: Fig 7, item 708; Fig 9; ¶52-¶53, i.e., user behavior response is received in response to a simulated phishing campaign; ¶108-¶111; ¶115 provides for the security system to obtain employee profiles, etc., from client businesses, Fig 17-18, Fig 20, step 2010; ¶237 provides for calculating a risk score; ¶226-¶230 provides for communicating risk assessment reports to the business client including user-specific risk scores)
determining, by the one or more servers responsive to receiving one or more responses to the one or more simulated phishing communications from the one or more devices of the candidate, a risk score of the candidate based at least on the one or more failure actions of the one or more responses and the type of authentication or the strength of password the candidate used to authenticate to the job application system to submit the application for the job; and (Chrapo: Figs. 1-2, 4-7, and ¶3-¶4, ¶94-¶97, i.e., candidate applying for a job through an application system and ¶70-¶71, ¶97, ¶101-¶102, i.e., risk scores are calculated using multiple different sets of information regarding a user including ID information such as facial recognition i.e., authentication information, network/online/email usage behavior of the user and so forth. See also, Hawthorn: Fig 7, item 708; Fig 9; ¶108]-¶111; ¶115 provides for the security system to obtain employee profiles, etc., from client businesses, Fig 17-18, Fig 20, step 2010; ¶237 provides for calculating a risk score; ¶226-¶230 provides for communicating risk assessment reports to the business client including user-specific risk scores)
communicating, by the one or more servers via the one or more APIs, the risk score to the job application system to use in the job application process. (Chrapo: Figs. 4-7, and ¶3-¶4, ¶33-¶42, ¶94-¶97, ¶134-¶137, i.e., hiring decision determination based on calculating risk scores regarding a potential candidate. Hawthorn: Fig 7, item 708; Fig 9; ¶108]-¶111; ¶115 provides for the security system to obtain employee profiles, etc., from client businesses, Fig 17-18, Fig 20, step 2010; ¶237 provides for calculating a risk score; ¶226-¶230 provides for communicating risk assessment reports to the business client including user-specific risk scores)
Claim 11 recites substantially the same features recited in claim 1 above and is rejected based on the aforementioned rationale discussed in the rejection.
As regards claim 2, Chrapo et al combination teaches the method of claim 1, further comprising receiving, by the one or more servers, the information about the candidate during the job application process. (Chrapo: Figs. 4-7, and ¶3-¶4, ¶33-¶42, ¶94-¶97, ¶134-¶137, i.e., the job application process)
Claim 12 recites substantially the same features recited in claim 2 above and is rejected based on the aforementioned rationale discussed in the rejection.
As regards claim 3, Chrapo et al combination teaches the method of claim 1, further comprising generating, by the one or more servers, the one or more simulated phishing communications to include content personalized by the one or more servers based at least on the information about the candidate. (Chrapo: Figs. 1-2, 4-7, and ¶3-¶4, ¶94-¶97, i.e., candidate applying for a job through an application system and ¶70-¶71, ¶97, ¶101-¶102, i.e., risk scores are calculated using multiple different sets of information regarding a user including ID information such as facial recognition i.e., authentication information, network/online/email usage behavior of the user and so forth. See also, Hawthorn: ¶64-¶65, i.e., generating trustworthiness level of an individual based on the response to the results of one or more simulated phishing campaigns received from the individual’s device)
Claim 13 recites substantially the same features recited in claim 3 above and is rejected based on the aforementioned rationale discussed in the rejection.
As regards claim 7, Chrapo et al combination teaches the method of claim 1, wherein the type of authentication comprises one-factor authentication or two-factor authentication. (Leven: ¶57-¶58, i.e., authentication based on the strength of the authentication password and/or multifactor authentications)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Chrapo to include authentication information to include strength of the authentication password and/or multifactor authentication as taught by Leven with the motivation to make the authentication stronger (Leven: ¶14)
Claim 17 recites substantially the same features recited in claim 7 above and is rejected based on the aforementioned rationale discussed in the rejection.
As regards claim 8, Chrapo et al combination teaches the method of claim 1, further comprising determining, by the one or more servers, the risk score using the one or more responses to the simulated phishing communications. (Chrapo: Figs. 4-7, and ¶3-¶4, ¶33-¶42, ¶94-¶97, ¶160-¶161)
Claim 18 recites substantially the same features recited in claim 8 above and is rejected based on the aforementioned rationale discussed in the rejection.
As regards claim 9, Chrapo et al combination teaches the method of claim 1, wherein the job application system uses the risk score in making a hiring decision on the candidate. (Chrapo: Figs. 1-2, 4-7, and ¶3-¶4, ¶68-¶77, ¶94-¶97, ¶134-¶137
Claim 19 recites substantially the same features recited in claim 9 above and is rejected based on the aforementioned rationale discussed in the rejection.
As regards claim 10, Chrapo et al combination teaches the method of claim 1, wherein the job application system executes on a second one or more servers. (Chrapo: Figs. 1-2, 4-7. Patwa: Fig. 1, ¶37)
Claim 20 recites substantially the same features recited in claim 10 above and is rejected based on the aforementioned rationale discussed in the rejection.
As regards claim 21, Chrapo et al combination teaches the method of claim 1, wherein generating the one or more simulated phishing communications comprises personalizing content of the one or more simulated phishing communications based on job profile information of the candidate received from the job application system. (Chrapo: Figs. 1-2, 4-7, and ¶3-¶4, ¶6, ¶33-¶42, ¶94-¶97, i.e., candidate applying for a job through an application system and ¶70-¶71, ¶97, ¶101-¶102, i.e., risk scores are calculated using multiple different sets of information regarding a user including ID information such as facial recognition i.e., authentication information, network/online/email usage behavior of the user and so forth. See also, Hawthorn: Fig 7, item 708; Fig 9; ¶108]-¶111; ¶115 provides for the security system to obtain employee profiles, etc., from client businesses, Fig 17-18, Fig 20, step 2010; ¶237 provides for calculating a risk score; ¶226-¶230 provides for communicating risk assessment reports to the business client including user-specific risk scores)
As regards claim 22, Chrapo et al combination teaches the method of claim 1, wherein generating the one or more simulated phishing communications comprises personalizing content of the one or more simulated phishing communications based on information identifying one or more social media platforms associated with the candidate. (Chrapo: Figs. 1-2, 4-7, and ¶3-¶4, ¶6, ¶33-¶42, ¶94-¶97, i.e., candidate applying for a job through an application system and ¶70-¶71, ¶97, ¶101-¶102, i.e., risk scores are calculated using multiple different sets of information regarding a user including ID information such as facial recognition i.e., authentication information, network/online/email usage behavior of the user and so forth. See also, Hawthorn: Fig 7, item 708; Fig 9; ¶108]-¶111; ¶115 provides for the security system to obtain employee profiles, etc., from client businesses, Fig 17-18, Fig 20, step 2010; ¶237 provides for calculating a risk score; ¶226-¶230 provides for communicating risk assessment reports to the business client including user-specific risk scores)
As regards claim 23, Chrapo et al combination teaches the method of claim 1, wherein generating the one or more simulated phishing communications comprises configuring the simulated phishing communications to appear as originating from the job application system used by the candidate to submit the application. (Chrapo: Figs. 1-2, 4-7, and ¶3-¶4, ¶6, ¶33-¶42, ¶94-¶97, i.e., candidate applying for a job through an application system and ¶70-¶71, ¶97, ¶101-¶102, i.e., risk scores are calculated using multiple different sets of information regarding a user including ID information such as facial recognition i.e., authentication information, network/online/email usage behavior of the user and so forth. See also, Hawthorn: Fig 7, item 708; Fig 9; ¶108]-¶111; ¶115 provides for the security system to obtain employee profiles, etc., from client businesses, Fig 17-18, Fig 20, step 2010; ¶237 provides for calculating a risk score; ¶226-¶230 provides for communicating risk assessment reports to the business client including user-specific risk scores)
Conclusion
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/SYED A ZAIDI/Primary Examiner, Art Unit 2432
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