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
Status the Application
The following is a Final Office Action in response to communication received on 5/22/2026. Claims 2-4 are pending in this office action. The Information Disclosure Statement (IDS) filed on behalf of this case on 5/22/2026 has been considered by the Examiner.
Response to Amendment
Applicant’s cancellation of claim 1 and addition of new claims 2-4 is acknowledged.
Response to Arguments
Applicant cancels all claims and adds all new claims and then argued on remarks page 6 that the rejections are overcome. The Examiner has applied new rejections in view of Applicant’s amendments as detailed in the office action below to Applicant’s new claims.
Claim Interpretation
Claim 2 is interpreted as a machine as the claims recite a system with processors and memory. Claim 3 is interpreted as an article of manufacture as the claims recite a computer readable medium not comprising transitory propagating signals (signals per se). Claim 4 is interpreted as a process as the claims recite a method implemented on a computing device.
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 2-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) generating improved over time (self healing) multi media employment content generative information using special purpose rules, performing quality and bias checks on employment candidates, when quality and bias checks fail continuing to improve rules (model) over time, when quality and bias checks pass publish (use or display) the determined information, use the determined information from the first candidate to publish information of the first candidate, and storing, retrieving and publishing candidate information from distributed locations. This is part of the abstract idea.
As the claims are recited at such a high level of generality they recite limitations that include observations, evaluations, judgements, and or opinions that can be performed in the human mind or with physical aid (like pen and paper). Therefore the claims recite a mental process (see MPEP 2106.04(a)).
Further as the claims recite subject matter related to employment the claims recite subject matter relating to managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), which is certain method of organizing human activity (see MPEP 2106.04(a)).
Mental processes and certain methods of organizing human activity are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Specifically as recited in the claims:
Examiner notes that the Examiner has underlined the additional elements in the claims for distinction. Limitations not bolded and underlined are considered a part of the abstract idea for this analysis.
2. (New) A self-healing multi-media employment content generative Al core system comprising:
a group of self-healing multi-media employment content generative Al core appliances including processors and memory configured to store and execute a group of self-healing multi- media employment content generative Al core applications to:
generate self-healing multi-media employment content of multiple candidates using Al, which includes special purpose software components of the self-healing multi-media employment content generative Al system;
perform a quality check on the Al generated self-healing multi-media employment content of multiple candidates;
perform a bias check on the Al generated self-healing multi-media employment content of multiple candidates;
if the quality check fails or the bias Check fails, and at least one of:
an upper limit of healing repeat count is not reached; an upper healing time limit is not reached; and an upper cost limit to generate the Al generated self-healing multi- media employment content of multiple candidates content is not reached, then self- healing of the generated content continues to feed a live payload to be processed by a specialized trained Al model to self-heal the Al generated self-healing multi-media employment content of multiple candidates;
if the quality check and bias check meet expectations, publish the self-healed Al generated multi-media employment content of multiple candidates;
a group of self-healing multi-media employment content generative Al relationship core appliances including processors and memory configured to store and execute a group of self- healing multi-media employment content generative Al relationship core application software components to integrate the published self-healed Al generated multi-media employment content from a first of the multiple candidates into integrated published self-healed Al generated multimedia employment content of the first candidate;
and a group of distributed self-healing multi-media employment content generative Al core appliances including processors and memory configured to store and execute a group of distributed self-healing multi-media employment content generative Al core application software components including instructions to record in distributed locations, retrieve in distributed locations, and package in distributed locations the integrated published self-healed Al generated multi-media employment content of the first candidate, and distribute the recorded, retrieved and packaged integrated published self-healed Al generated multi-media employment content of the first candidate.
3. (New) A computer readable storage medium, which does not include transitory propagating signals, storing computer executable instructions for controlling a computing device in a self-healing multi-media employment content generative Al core system to perform a method in the self-healing multi-media employment content generative Al core system, the method comprising:
generating self-healing multi-media employment content of multiple candidates using Al, which includes special purpose software components of the self-healing multi-media employment content generative Al system;
performing a quality check on the Al generated self-healing multi-media employment content of multiple candidates;
performing a bias check on the Al generated self-healing multi-media employment content of multiple candidates;
if the quality check fails or the bias Check fails, and at least one of: an upper limit of healing repeat count is not reached; an upper healing time limit is not reached;
and an upper cost limit to generate the Al generated self-healing multi- media employment content of multiple candidates content is not reached, then self- healing of the generated content continues to feed a live payload to be processed by a specialized trained Al model to self-heal the Al generated self-healing multi-media employment content of multiple candidates;
if the quality check and bias check meet expectations, publishing the self-healed Al generated multi-media employment content of multiple candidates;
integrating the published self-healed Al generated multi-media employment content from a first of the multiple candidates into integrated published self-healed Al generated multimedia employment content of the first candidate;
and recording in distributed locations, retrieving in distributed locations, and packaging in distributed locations the integrated published self-healed Al generated multi-media employment content of the first candidate, and distributing the recorded, retrieved and packaged integrated published self-healed Al generated multi-media employment content of the first candidate.
4. (New) A self-healing multi-media employment content generative Al core system computing device implemented method comprising:
generating self-healing multi-media employment content of multiple candidates using Al, which includes special purpose software components of the self-healing multi-media employment content generative Al system;
performing a quality check on the Al generated self-healing multi-media employment content of multiple candidates;
performing a bias check on the Al generated self-healing multi-media employment content of multiple candidates;
if the quality check fails or the bias Check fails, and at least one of:
an upper limit of healing repeat count is not reached; an upper healing time limit is not reached; and an upper cost limit to generate the Al generated self-healing multi- media employment content of multiple candidates content is not reached, then self- healing of the generated content continues to feed a live payload to be processed by a specialized trained Al model to self-heal the Al generated self-healing multi-media employment content of multiple candidates;
if the quality check and bias check meet expectations, publishing the self-healed AI generated multi-media employment content of multiple candidates;
integrating the published self-healed AI generated multi-media employment content from a first of the multiple candidates into integrated published self-healed AI generated multimedia employment content of the first candidate;
and recording in distributed locations, retrieving in distributed locations, and packaging in distributed locations the integrated published self-healed AI generated multi-media employment content of the first candidate, and distributing the recorded, retrieved and packaged integrated published self-healed AI generated multi-media employment content of the first candidate.
Specifically as recited in the claims:
As per claim 2, the claims recite mental process and certain methods of organizing human activities steps of generating improved over time (self-healing) multimedia employment content generative information using special purpose rules (models), performing quality and bias checks on candidates, when quality and bias checks fail continuing to improve rules (model) over time, when quality and bias checks pass publish (use or display) the determined information, use the determined information from the first candidate to published information of the first candidate, and storing, retrieving and publishing candidate information from distributed locations. The rules can be special purpose as they are used for this specific application and models can be trained as broadly and generally recited herein if for example they are formed or created based on past data. These are mental process and certain methods of organizing human activities steps and are therefore part of the abstract idea.
The additional elements that these mental process and certain method of organizing human activities are instead being performed by computers and software (and software running on a computer), specifically as recited in the claims the limitations of “AI”, “appliances including processors and memory configured to… execute…applications”, and “software” components, merely results in apply it.
Here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a compute or other machinery in its ordinary capacity for economic or other tasks or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application and or provide significantly more. Further here the claims recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. Here Applicant does not recite as broadly recited in the claims an improvement in AI rather Applicant is merely reciting using the above additional elements of “AI”, “appliances including processors and memory configured to… execute…applications”, and “software” components to implement the abstract idea at a result-oriented apply it level recitation of additional elements.
Further additionally or alternatively the additional elements that these mental process and certain method of organizing human activities are instead being performed by computers and software, specifically as recited in the claims the limitations of “AI”, “appliances including processors and memory configured to… execute…applications”, and “software” components, merely results in generally linking it to the field of computers.
As per claim 3, the claims recite substantially the same subject matter as discussed above in claim 1 and is accordingly rejected under the same grounds. It is noted as recited in claim 3 the additional element of “a computer readable storage medium, which does not include transitory propagating signals, storing computer executable instructions for controlling a computer device… to perform a method” is interpreted as again as a recitation of software running on a computer and accordingly results in merely apply it or generally linking it to the field of computers as discussed above in claim 1.
As per claim 4, the claims recite substantially the same subject matter as discussed above in claim 1 and is accordingly rejected under the same grounds. It is noted as recited in claim 4 the additional element of “computing device” is interpreted as again as abstract idea limitations being implemented by a computer and accordingly results in merely apply it or generally linking it to the field of computers as discussed above in claim 1.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed above with respect to the practical application step.
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.
Claim(s) 2-4 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable over Scarborough et al. (United States Patent Application Publication Number: US 2012/0078804).
As per claim 2, Scarborough et al. teaches A self-healing (see paragraphs 0016, 0097, 0114, and 0121, Examiner's note: system is adaptive so ineffective predictors can be removed (see paragraph 0016), can adapt to changing
conditions like demographics, economic, job content, or job effectiveness (see
paragraph 0097), further teaches an old algorithm is archived and a new model is
deployed (see paragraph 0114). Examiner notes that "self-healing" is interpreted
as updating the model or AI over time).
multi-media (see paragraph 0070, Examiner's note: multi- media or text).
employment content generative Al core system comprising: (see paragraphs 0012 and 0046, Examiner's note: automated employee selection system and method (see paragraph 0012), the predictive model is an artificial intelligence model (see paragraph 0046), and the model can be implemented by software running on a computer (See paragraph 0046))
a group of self-healing multi-media employment content generative Al core appliances including processors and memory configured to store and execute a group of self-healing multi- media employment content generative Al core applications to: (see paragraphs 0044, 0046, 0122, and 0131, Examiner’s note: teaches multiple models, processors, devices, software running on a computer to perform operations in Scarborough et al.).
generate self-healing multi-media employment content of multiple candidates using Al, (see paragraphs 0016, 0097, and 0114, Examiner's note: system is adaptive so ineffective predictors can be removed (see paragraph 0016), can adapt to changing conditions like demographics, economic, job content, or job effectiveness (see paragraph 0097), further teaches an old algorithm is archived and a new model is deployed (see paragraph 0114)).
which includes special purpose software components of the self-healing multi-media employment content generative Al system; (see paragraphs 0121 and 0206-0207, Examiner’s note: Further teaches the model is converted to software code, which is interpreted as “special purpose software components”).
perform a quality check on the Al generated self-healing multi-media employment content of multiple candidates; (see paragraphs 0016 and 0149-0150, Examiner’s note: teaches using a quality check of information based on experts or data or ineffectiveness).
perform a bias check on the Al generated self-healing multi-media employment content of multiple candidates; (see paragraphs 0161, 0205, and 0249, Examiner’s note: teaches determining biases by comparing them to groups and removing them through various ways like filters or dropping them when deemed improper).
if the quality check fails or the bias Check fails, and at least one of: an upper limit of healing repeat count is not reached; an upper healing time limit is not reached; and an upper cost limit to generate the Al generated self-healing multi- media employment content of multiple candidates content is not reached, then self- healing of the generated content continues to feed a live payload to be processed by a specialized trained Al model to self-heal the Al generated self-healing multi-media employment content of multiple candidates; if the quality check and bias check meet expectations, publish the self-healed Al generated multi-media employment content of multiple candidates; (see paragraphs 0085, 0088, 0095, 0100, and 0114, Examiner’s note: teaches this limitation as the system updates over time and removes ineffective information or questions or bias from models and then deploys a new model with improved performance).
a group of self-healing multi-media employment content generative Al relationship core appliances including processors and memory configured to store and execute a group of self- healing multi-media employment content generative Al relationship core application software components to integrate the published self-healed Al generated multi-media employment content from a first of the multiple candidates into integrated published self-healed Al generated multimedia employment content of the first candidate; (see paragraphs 0044-0045, 0047, 0096, 0100, and 0114, Examiner’s note: teaches using prehire and post hire information from users to generate models (see paragraphs 0044-0045 and 0047) and updating models over time based on that collected prehire and post hire information to generate more effective models (see paragraphs 0096, 0100, and 0114).
and a group of distributed self-healing multi-media employment content generative Al core appliances including processors and memory configured to store and execute a group of distributed self-healing multi-media employment content generative Al core application software components including instructions to record in distributed locations, retrieve in distributed locations, and package in distributed locations the integrated published self-healed Al generated multi-media employment content of the first candidate, and distribute the recorded, retrieved and packaged integrated published self-healed Al generated multi-media employment content of the first candidate. (see paragraphs 0066, 0070-0071, 0130, 0200, and 0122, Examiner’s note: teaches the data that is processed as well as the processors (computers) that process that information can be distributed or remote, further teaches this may be collected at a remote location and aggregated at a central location).
Further as per claim 3, claim 3 recites substantially the same subject matter as discussed above with respect to claim 2 in a corresponding computer readable medium claim, therefore the claims are rejected under the same grounds as discussed above with respect to claim 2. It is noted Scarborough et al. teaches this invention can be a computer readable (see paragraph 0110, Examiner’s note: removable computer readable medium (see paragraph 0110) and the computer readable medium can be a disk (see paragraph 0044)).
Further as per claim 4, claim 4 recites substantially the same subject matter as discussed above with respect to claim 2 in a computer implemented method claim, therefore the claims are rejected under the same grounds as discussed above with respect to claim 2. It is noted Scarborough et al. teaches this invention can be a computer implemented method (see paragraphs 0052 and 0057).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Danson et al. (United States Patent Application Publication Number: US 2015/0046357) teaches a system and method for evaluating job candidates, which include machine learning (see paragraph 0032) of multi media content (see paragraph 0030) which updates based on feedback (see paragraph 0046)
Pattabirman et al. (United States Patent Application Publication Number: US 2018/0322463) teaches a system for increasing a user’s interaction with job posting (see abstract) based on machine learning (see paragraph 0091) and updating over time based on real time feedback (see paragraph 0023)
Mondal et al. (United States Patent Application Publication Number: US 2020/0184422) teaches a system for screening potential candidates for employment (see abstract) based on machine learning (see paragraphs 0039-0041) where the models control bias (see paragraph 0031)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626
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