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 .
This communication is in response to application 19/386,290 filed 11/12/2025. Claims 1-25 are pending and hereby entered. No claims are allowed.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957). A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-25 are provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claim 1-25 of co-pending Application No. 19/451,378 (reference application). This is a provisional statutory double patenting rejection because the claims directed to the same invention have not in fact been patented.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) with no practical application and without significantly more.
The claimed invention is directed to an abstract idea in that the instant application is directed to a mental process (See MPEP 2106.04(a)(2)(III)). The independent claims (1, 14 and 24) recite a method and systems to evaluate job and candidate data to make hiring recommendations based on the processed data. These claim elements are being interpreted as concepts performed in the human mind (including observation, evaluation, judgement, and opinion). Using job and candidate data to evaluate candidates during a hiring process can equivalently be achieved by human observation and evaluation of data. The claims recite an abstract idea consistent with the “mental process” grouping set forth in the MPEP 2106.04(a)(2)(III).
Additionally, the claimed invention is directed to an abstract idea in that the instant application is directed to certain methods of organizing human activity (see MPEP 2106.04(a)(2)(II)). The independent claims (1, 14 and 24) recite a method and systems to interview candidates for job roles in a recruiting process. These claim elements are being interpreted as managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Making decisions and conducting interviews in a hiring process recite an abstract idea consistent with the certain methods of organizing human activity grouping set forth in the MPEP 2106.04(a)(2)(II).
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites an “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea. The instant application is directed towards a method and systems to implement the identified abstract idea of receiving information, processing information, and displaying the result of the analysis (i.e. processing job and candidate data to assess potential hires) and managing relationships between people (i.e. conducting interviews in a hiring process and the like) in a general computer environment. The claims do not include additional elements that integrate the judicial exception into practical application or amount to significantly more than the judicial exception. The independent claims recite the additional elements “a computer”, “hardware processors”, and “analysis agents” These claim elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a general computer environment. The machines merely act as a modality to implement the abstract idea and are not indicative of integration into a practical application (i.e., the additional elements are simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A Prong Two analysis, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
In regards to the dependent claims, claims 2-13, 15-23, and 25 recite no new additional elements or new abstract ideas and do not impact analysis under 35 USC 101. The dependent claims simply further define the existing abstract ideas or use the additional elements to perform the abstract ideas.
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.
Claims 1-2, 4-5, 7, 10-16, 18-19, and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Kaushik (US 20250252403 A1) in view of Colter (US 20250053928 A1).
Regarding Claims 1, 14, and 24, Kaushik teaches:
A computer-implemented method for orchestrating and automating a multi-stage human-resources workflow or talent acquisition process without continuous human supervision, the method comprising, by one or more hardware processors: [see at least Kaushik: (Para 0015) “The computer program product a non-transitory computer readable medium having stored thereon computer executable instructions, which when executed by one or more processors, cause the one or more processors to conduct operations.”]
receiving, via a network interface, job-requirement data for an open role; [see at least Kaushik: (Para 0035) “While hiring a candidate for an organization, a shortlisting process needs to be focused on various criteria outlined in a job description. The first step of the shortlisting process includes a thorough review of resumes of the candidates based on predefined criteria such as education, experience, and skills outlined in the job description”, (Para 0057) “In operation, the system 102 may be configured to retrieve a given job description, as described in FIG. 3B.”]
parsing the job-requirement data to generate a structured job description (JD) schema comprising prioritized skills, competency weightings, and decision thresholds; [see at least Kaushik: (Para 0057) “The system 102 may be further configured to analyze, using the AI model 110, the job description to generate assessment data. The assessment data comprises at least one of: a plurality of assessment parameters, and a weightage score 314c associated with each of the plurality of assessment parameters, as described in FIG. 3B”, (Para 0059) “In an example, the system 102 may employ the AI model 110 to match the skills and keywords mentioned in the job description with those found in the candidate's profile or resume”, (Para 0061) “In an example, if the initial score for the resume of the candidate is above a threshold value, then the resume may be shortlisted”]
receiving candidate resume data associated with a candidate; [see at least Kaushik: (Para 0055) “At 302, resume data may be retrieved”]
parsing the candidate resume data to extract structured fields including skills, employment history, education, and certifications; [see at least Kaushik: (Para 0035) “The first step of the shortlisting process includes a thorough review of resumes of the candidates based on predefined criteria such as education, experience, and skills outlined in the job description”, (Para 0056) “In an example, the resume of the candidate may include information, such as, but not limited to, personal details, educational background, professional experience, skills, achievements, and the like.”]
dispatching the structured fields of a resume data to [see at least Kaushik: (Para 0035) “The first step of the shortlisting process includes a thorough review of resumes of the candidates based on predefined criteria such as education, experience, and skills outlined in the job description”, (Para 0059) “In operation, the system 102 may be configured to provide, as an input, the resume data”]
aggregating outputs from the plurality of analysis agents into a unified candidate profile dataset conforming to a shared schema; [(Para 0036) “The disclosed system 102 may be configured to generate, using a trained AI model 110, an aggregated score for assessing skills of candidates, thereby providing optimum candidate shortlisting, and mitigating bias”]
generating a structured candidate summary based on the unified candidate profile dataset and the JD schema; [see at least Kaushik: (Para 0010) “According to additional system embodiments, the processor is configured to retrieve a resume of the candidate, analyze, using the trained AI model, the resume of the candidate to generate an initial score”, (Para 0036) “The disclosed system 102 may be configured to generate, using a trained AI model 110, an aggregated score for assessing skills of candidates, thereby providing optimum candidate shortlisting, and mitigating bias”, (Para 0053) “Further, the system 102 may be configured to display the skills of the candidate on the online platform 104, thereby employing a visual representation of the score of the candidate to identify patterns or trends at a glance.”]
conducting, by a plurality of interview subsystems, a dynamic automated interview with the candidate; [see at least Kaushik: (Para 0061) “In an operation, the system 102 may employ the virtual interaction to assess the skills of the candidate from the list of shortlisted candidates. The virtual interaction may refer to virtual interview that takes place on the online platform 104”, (Para 0003) “During video interviews, the candidate may or may not interact with a live interviewer.”]
ingesting, during the dynamic automated interview, audio and video streams and processing the audio and video streams in parallel pipelines, [see at least Kaushik: (Para 0013) “According to additional system embodiments, the response data further comprises at least one of: user input data, video data, or audio data”, (Para 0062) “The user device 106 may have internal built in sensors, or communicatively coupled to one or more sensors (such as, but not limited to cameras and microphones) to receive the response data (such as the video data, and the audio data)”]
and using time-aligned outputs of the dynamic automated interview on a shared timeline to produce an anomaly score; [see at least Kaushik: (Para 0102) “Further, the system 102 may leverage the use of AI model 110 to provide real-time feedback coupled with emotion analysis and nuanced evaluation of both verbal and non-verbal cues, thereby optimizing the candidate assessment process. Additionally, the system 102 may provide dynamic scoring based on the AI analysis”]
fusing the unified candidate profile dataset, the structured candidate summary, interview transcripts, and the anomaly score to compute one or more evaluation scores relative to the competency weightings and decision thresholds; [see at least Kaushik: (Para 0010) “According to additional system embodiments, the processor is configured to retrieve a resume of the candidate, analyze, using the trained AI model, the resume of the candidate to generate an initial score; and shortlist the candidate for the virtual interaction based on the initial score”, (Para 0012) “According to additional system embodiments, the processor is further configured to obtain the user input based on the list of the shortlisted candidates and the resume of the candidate”, (Para 0015) “ The operation comprises retrieving assessment data, wherein the assessment data comprises at least one of: a plurality of assessment parameters, and a weightage score associated with each of the plurality of assessment parameters. The operation further comprises retrieving response data of a candidate associated with the assessment data. Further, the operation comprises generating, using a trained AI model, a score for the response data based on the assessment data”, (Para 0064) “ In an example, if the score of the candidate is above a threshold value, then the candidate may be considered suitable for hiring”]
However, Kaushik does not teach but Colter does teach:
a plurality of analysis agents operating in parallel; [see at least Colter: (Para 0055) “We can automate major recruiting workflows using AI agents that collaborate to accomplish tasks”, (Para 0125) “Also, while processes or blocks are at times shown as being performed in series, these processes or blocks can instead be performed or implemented in parallel, or can be performed at different times”]
generating a unique interview link comprising a time-limited authentication token to initiate a candidate interview session; [see at least Colter: (Para 0063) “Scheduler Agent—interacts with HM and candidate to find a mutually available time. Calendar Agent—checks availability in calendars and proposes meeting slots. Meeting Confirmation Agent—sends calendar invites once time is agreed upon.”]
invoking the plurality of analysis agents and the interview subsystems, wherein outputs of each subsystem are evaluated against configurable thresholds to determine next-stage activation or termination; [This limitation is only in claims 1 and 14; see at least Colter: (Para 0062) “Job Parser and Resume Parser Agents use LLMs to extract and classify key details from job posts and candidate resumes/profiles. Matcher Agent uses semantic search, keywords, and other signals to automatically suggest potential matches between candidates and open roles. Recommender Agent proactively identifies and suggests candidates that may be a good fit for a new job posting based on previous matches and peer candidates”, (Para 0063) “scheduler Agent—interacts with HM and candidate to find a mutually available time. Calendar Agent—checks availability in calendars and proposes meeting slots. Meeting Confirmation Agent—sends calendar invites once time is agreed upon”]
and generating a report based on the unified candidate profile dataset and recording a trace record, thereby enabling auditability and reproducibility. [see at least Colter: (Para 0090) “Data-driven analytics and reports provide insights for better hiring”, (Para 0027) “blockchain technology (or distributed ledger technology) generally refers to a growing list of digital records (e.g., blocks) that are connected by a cryptographic hash of the previous block. Each block may further include a timestamp and other data related to an action that has occurred”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multistage artificial intelligence recruiting work flow (Kaushik) with multiagent system for different tasks (Colter). One of ordinary skill would have recognized the benefits of using different agents in a multistage recruiting system to assist in different portions of the process. Combining these elements would have yielded predictable results to one of ordinary skill.
Regarding Claims 2 and 16, the combination of Kaushik and Colter teach the limitations of claim 1, Kaushik further teaches:
wherein the analysis agents operated in parallel comprise one or more of: a skill analysis agent, a job stability analysis agent, a certification analysis agent, a gap analysis agent, an experience analysis agent, and a relevance analysis agent. [see at least Kaushik: (Para 0064) “At 312, a score may be generated. In an embodiment, the system 102 may be configured to generate, using the AI model 110, the score to assess the skills”]
Regarding Claims 4 and 18, the combination of Kaushik and Colter teach the limitations of claim 1, Kaushik further teaches:
wherein the parallel pipelines comprise at least two of a speaker diarization pipeline, a lip-audio synchronization analysis pipeline, a gaze and face tracking pipeline, and an object detection pipeline. [see at least Kaushik: (Para 0084) “In an embodiment the system 102 may be further configured to analyze, using the trained AI model 110, each of the one or more response segments based on the corresponding response features. Such an analysis may include, but is not limited to facial analysis, audio analysis, and text analysis based on the type of the response data”, (Para 0085) “Further, the system 102 may generate, using AI model 110, a transcript for the audio portion corresponding to each video frame using speech to text techniques.”]
Regarding Claims 5 and 19, the combination of Kaushik and Colter teach the limitations of claim 1, Kaushik further teaches:
autonomously determining whether to advance, reject, or reassign the candidate to an alternate role based on the one or more evaluation scores and the decision thresholds; [see at least Kaushik: (Para 0064) “In an example, if the score of the candidate is above a threshold value, then the candidate may be considered suitable for hiring. On the contrary, if the score of the candidate is below the threshold value, then the candidate may not be considered suitable for hiring”]
and storing, in a data store, the autonomous determination together with evidentiary artifacts comprising time-coded media references, transcripts, and agent outputs [(Para 0040) “The memory 204 may be further configured to store the assessment data, response data, and resume data”, (Para 0053) “Further, the system 102 may be configured to display the skills of the candidate on the online platform 104, thereby employing a visual representation of the score of the candidate to identify patterns or trends at a glance”]
Regarding Claim 7, the combination of Kaushik and Colter teach the limitations of claim 1, Kaushik further teaches:
further comprising: coordinating a plurality of human resources workflow stages comprising job-description generation, resume screening, interview scheduling, automated interview execution, and evaluation reporting, [see at least Kaushik: (Figures 3A-3B and Figure 4), (Para 0059) “the system 102 may employ the AI model 110 to match the skills and keywords mentioned in the job description with those found in the candidate's profile or resume”, (Para 0062) “ At 308, one or more questions may be displayed. Once the virtual interaction is initiated for the shortlisted candidates, the system 102 may be configured to display the one or more questions on the web page to assess the skills of the candidate”, (Para 0064) “At 312, a score may be generated. In an embodiment, the system 102 may be configured to generate, using the AI model 110, the score to assess the skills of the candidate based on the response data, as described in FIG. 4”]
each stage being implemented as an independently deployable service communicating through standardized orchestration messages; and [see at least Kaushik: (Figures 3A-3B and Figure 4), (Para 0072) “The exemplary operations illustrated in block diagram 400 may start at 402 and may be performed by any computing system, apparatus, or device, such as by the system 102 of FIG. 1 or the processor 202 of FIG. 2A. Although illustrated with discrete blocks, the exemplary operations associated with one or more blocks of the block diagram 300b may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the particular implementation”]
dynamically adapting an execution order, retry logic, or data-flow routing of said stages based on prior stage outcomes, feedback signals, or processing latency thresholds. [see at least Kaushik: (Figures 3A-3B and Figure 4), (Para 0061) “In an example, if the initial score for the resume of the candidate is above a threshold value, then the resume may be shortlisted. Thereafter, the system 102 may be configured to add the resume of the candidate to the list of shortlisted candidates and initiate the virtual interaction”]
Regarding Claims 10 and 22, the combination of Kaushik and Colter teach the limitations of claim 1
While Kaushik teaches a multistage artificial intelligence recruiting workflow, it does not explicitly teach but Colter does teach:
further comprising maintaining, for each report, a record comprising model versions, configuration snapshots, and processing steps used to generate the report, thereby enabling auditability and reproducibility. [The limitations recite maintain a report and record. The data within the report is nonfunctional descriptive material that does not carry patentable weight in the claims; see at least Colter: (Para 0065) “Metrics Collector Agent aggregates data on job posts, candidates, hiring performance, user engagement and more. Stores data in Postgres. Natural Language Query Agent allows users to ask questions in plain English (e.g. “How many developer jobs were posted this month?”) and converts to SQL query to generate reports visually”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multistage artificial intelligence recruiting work flow (Kaushik) with a report record (Colter). The invention is merely a combination of old elements, and one of ordinary skill would have recognized keeping a reportable record of data would allow for insights into decision making. One of ordinary skill would have recognized the results of the combination predictable.
Regarding Claim 11, the combination of Kaushik and Colter teach the limitations of claim 7
While Kaushik teaches a multistage artificial intelligence recruiting workflow, it does not explicitly teach but Colter does teach:
further comprising enforcing dynamic security and compliance policies based on context, such that access permissions, encryption keys, or data-retention durations are automatically varied according to workflow stage, data sensitivity, or jurisdictional regulations. [see at least Colter: (Para 0021) “The mutable data may be stored as metadata associated with the NFT, such as qualifications, certifications, current employment information, past employment information, a user's contact information, or other user information. In some implementations, the metadata of the NFT may include a link to a uniform resource identifier (URI) or uniform resource locator (URL) pointing to the mutable data, enabling the mutable data to be stored off-chain in a permissioned database (e.g., where only certain, permissioned actors have access to such information) to preserve data security of Personally Identifiable Information (PII).”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multistage artificial intelligence recruiting work flow (Kaushik) with security measures (Colter). The invention is merely a combination of old elements, and one of ordinary skill would have recognized including security measures would help protect sensitive data in a recruiting system. One of ordinary skill would have recognized the results of the combination predictable.
Regarding Claims 12 and 23, the combination of Kaushik and Colter teach the limitations of claim 1, Kaushik further teaches:
further comprising: normalizing outputs from the audio and video pipelines, resume analytics, and JD parsing into a queryable timeline; [see at least Kaushik: (Para 0069) “At 322, output may be displayed. In an embodiment, the system 102 may be configured to display the list of shortlisted candidates and the initial score, as described in FIG. 2C. In an example, the system 102 may be configured to display score of the shortlisted candidates and resume of the candidates on the user interface for easy access. Such visual representation may be helpful for the hiring manager or professional to determine whether or not a deserving candidate is assessed incorrectly due to the assigned initial score”]
However, Kaushik does not each but Colter does teach:
and associating timestamped evidence links to each evaluation score and decision. [See at least Colter: (Para 0027) “blockchain technology (or distributed ledger technology) generally refers to a growing list of digital records (e.g., blocks) that are connected by a cryptographic hash of the previous block. Each block may further include a timestamp and other data related to an action that has occurred”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multistage artificial intelligence recruiting work flow (Kaushik) timestamped links (Colter). The invention is merely a combination of old elements, and one of ordinary skill would have recognized including timestamped records would allow for traceability in the system. One of ordinary skill would have recognized the results of the combination predictable.
Regarding Claim 13, the combination of Kaushik and Colter teach the limitations of claim 1, Kaushik further teaches:
further comprising ingesting recruiter feedback and hiring-outcome data as reinforcement signals to adjust rule weights, decision thresholds, or orchestration parameters over time without altering underlying model weights, thereby improving consistency and alignment with organizational hiring goals. [see at least Kaushik: (Para 0070) “In such an example, the hiring manager may provide a feedback using the user input to identify areas where the AI model 110 might have misjudged candidates. In an example, the system 102 may receive feedback on candidates who were shortlisted and rejected based on the generated initial score. The system 102 may be configured to retrain the AI model 110 based on the received feedback, thereby optimizing the assessment process to evaluate the skills of the candidates in an efficient manner”]
Regarding Claim 15, the combination of Kaushik and Colter teach the limitations of claim 14,
While Kaushik teaches a multistage artificial intelligence recruiting workflow, it does not explicitly teach but Colter does teach:
wherein the one or more hardware processors are configured to generate an interpretable audit trail linking each automated decision to its contributing data sources, timestamps, agent outputs, and confidence metrics, thereby enabling human reviewers to trace and validate end-to-end decision logic. [See at least Colter: (Para 0027) “blockchain technology (or distributed ledger technology) generally refers to a growing list of digital records (e.g., blocks) that are connected by a cryptographic hash of the previous block. Each block may further include a timestamp and other data related to an action that has occurred”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multistage artificial intelligence recruiting work flow (Kaushik) with an audit trail (Colter). The invention is merely a combination of old elements, and one of ordinary skill would have recognized keeping a linked trail of data would allow for traceable insights into decision making and actions. One of ordinary skill would have recognized the results of the combination predictable.
Claims 6, 8-9, 20-21, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Kaushik (US 20250252403 A1) in view of Colter (US 20250053928 A1) in further view of Thompson (US 20250371317 A1).
Regarding Claims 6, 20, and 25, the combination of Kaushik and Colter teach the limitations of claim 1
While the combination teaches a multistage multiagent artificial intelligence recruiting system, it does not explicitly teach but Thompson does teach:
further comprising: detecting failed sub-tasks while dispatching the structured fields of resume data to the plurality of analysis agents; [see at least Thompson: (Para 0314-0315) “The task completion sub-process 1010 can verify, or confirm that the task or the sub-tasks have been successfully performed, achieved, or satisfied by the automated agent or other agents using, e.g., a completion criterion, a verification criterion, or other criteria…. The error handling function can detect, identify, or classify the errors, the exceptions, or the failures”]
and repeating only the detected failed sub-tasks, thereby improving throughput and reducing latency of the analysis agents. [see at least Thompson: (Para 0315) “For example, if an output of the execution 1008 or result aggregation deviates from an expected output by more than a threshold amount, the error handling function can skip the response formulation or modify the response formulation to, e.g., request more input from the user and/or obtain more context data”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multistage multiagent artificial intelligence recruiting work flow (Kaushik and Colter) with the detection and execution of failed sub-tasks (Thompson). The invention is merely a combination of old elements, and one of ordinary skill would have recognized detecting and repeating failed sub-tasks would be essential to execute the workflow of a multistage multiagent system. One of ordinary skill would have recognized the results of the combination predictable.
Regarding Claims 8 and 21, the combination of Kaushik and Colter teach the limitations of claim 1,
While Kaushik teaches a multistage artificial intelligence recruiting workflow, it does not explicitly teach but Colter does teach:
enforcing role-based access control for data retrieval. [see at least Colter: (Para 0021) “enabling the mutable data to be stored off-chain in a permissioned database (e.g., where only certain, permissioned actors have access to such information) to preserve data security of Personally Identifiable Information (PII)”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multistage multiagent artificial intelligence recruiting work flow (Kaushik) with security measures (Colter). The invention is merely a combination of old elements, and one of ordinary skill would have recognized including role-based access would help protect sensitive data in a recruiting system. One of ordinary skill would have recognized the results of the combination predictable.
While the combination of Kaushik and Colter teach enforcing role-based access, they do not explicitly teach encryption. However, Thompson does teach:
further comprising encrypting personally identifiable information and [see at least Thompson: (Para 0069) “ In the example of FIG. 1, the components of the computing system 100 are implemented using at least one application server or server cluster, which can include a secure environment (e.g., secure enclave, encryption system, etc.) for the processing of data”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multistage artificial intelligence recruiting work flow (Kaushik and Colter) with encryption (Thompson). The invention is merely a combination of old elements, and one of ordinary skill would have recognized utilizing encryption would provide a strong security measure to help protect sensitive data in a recruiting system. One of ordinary skill would have recognized the results of the combination predictable.
Regarding Claim 9, the combination of Kaushik and Colter teach the limitations of claim 7.
While the combination teaches a multistage multiagent artificial intelligence recruiting system, it does not explicitly teach but Thompson does teach:
further comprising monitoring workload metrics across the plurality of workflow stages and [see at least Thompson: (Para 0131) “At block 310, the adaptive machine learning-based orchestrator 216 can monitor the status of the plan generated or updated at block 308”]
automatically provisioning, scaling, or re-routing processing nodes through container orchestration policies to maintain throughput and latency targets. [see at least Thompson: (Para 0199) “Embodiments of the architecture 400 are data-driven. For example, the architecture 400 can include a small service that is deployed and acts as a factory for agent instances. Since the agents maintain their state in memory storage, any agent instance can be spawned to take over, which helps for horizontal scaling and fault tolerance”, (Para 0231) “The orchestration observer agent 508 can be an agent that can monitor, evaluate, or validate the orchestration of the actions performed by the orchestration agent 506. The orchestration observer agent 508 can use one or more AI services or tools to query policies or otherwise perform the observation functions. The orchestration observer agent 508 can communicate with the orchestration agent 506 via, e.g., the asynchronous distributed coordination 460”, (Para 0506) “AI model service 1590 can include a monitoring service that periodically generates, publishes, or broadcasts latency and/or other performance metrics associated with the models. For example, AI model service 1590 can provide a set of APIs that can be used by an agent or agent system to obtain performance metrics for large language models and/or other machine learning models”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multistage multiagent artificial intelligence recruiting work flow (Kaushik and Colter) with monitoring workloads and making adjustments (Thompson). The invention is merely a combination of old elements, and one of ordinary skill would have recognized the benefits of monitoring metrics to promote a more robust system. One of ordinary skill would have recognized the results of the combination predictable.
Claims 3 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kaushik (US 20250252403 A1) in view of Colter (US 20250053928 A1) in further view of Wong (US 11238411 B1).
Regarding Claims 3 and 17, the combination of Kaushik and Colter teach the limitations of claim 1. Kaushik further teaches:
further comprising conducting the dynamic automated interview with the candidate by: establishing a producer-consumer queue that stores question-response entries; [see at least Kaushik: (Para 0061) “At 306, a virtual interaction may be initiated. In an embodiment, the system 102 may be configured to initiate the virtual interaction for the candidate”, (Para 0063) “At 310, response data may be received. In an embodiment, the system 102 may be configured to retrieve the response data of a candidate associated with the one or more questions”]
While the combination of Kaushik and Colter teach a multistage multiagent artificial intelligence recruiting workflow, it does not explicitly teach but Wong does teach:
producing acknowledgements responsive to candidate inputs; and generating, when the queue is empty, a next interview question conditioned on prior candidate responses and the JD schema. [see at least Wong: (Column 8, lines 23-29) “At step 332, an adaptive interview is conducted. In one embodiment, the adaptive interview selects each question from the question bank adaptively. The candidate's answer is evaluated in real time and the results is used to select the next question. With the adaptive selection of each question, the interview is more efficient and accurate”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the multistage multiagent artificial intelligence recruiting work flow (Kaushik and Colter) with an adaptive interview process (Wong). The invention is merely a combination of old elements, and one of ordinary skill would have recognized the benefits using adaptive interview questions to better assess candidate skills. One of ordinary skill would have recognized the results of the combination predictable.
Conclusion
Pertinent art not relied upon
NPL Wallaroo.AI: Working Smarter with Machine Learning. The reference discusses orchestrating multiple machine learning models together
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Benjamin Truong, whose telephone number is 703-756-5883. The examiner can normally be reached on Monday-Friday from 9 am to 5 pm (EST)
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber SPE can be reached on 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300 Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/B.L.T./
Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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