DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
This communication is in response to communications received on 12/11/25. Claim(s) 1, 2, 6, 7, 10, 11, 15, 16, 17, and 18 is/are amended, claim(s) none is/are cancelled, claim(s) none is/are new, and applicant does not provide any information on where support for the amendments can be found in the instant specification. Therefore, Claims 1-20 is/are pending and have been addressed below.
Response to Arguments
Applicant’s arguments, see applicant’s remarks, filed 12/11/25, with respect to rejections under 35 USC 112 for claim(s) 2, 11, and 18 have been fully considered and are persuasive. The Examiner respectfully withdraws rejections under 35 USC 112 for claim(s) 2, 11, and 18.
Applicant’s arguments, see applicant’s remarks, filed 12/11/25, with respect to rejections under 35 USC 101 for claim(s) 1-20 have been fully considered but they are not persuasive as far as they apply to the amended 101 rejection(s) below.
Applicant respectfully traversed the rejection on pg. 9-12.
The Examiner respectfully disagrees because the items used for the argument NLP, computer vision, bias, question generation, and confidence intervals are not supported by the specification thus the examiner can’t determine the validity of the arguments.
Applicant is relying on 2106.05(d) “well understood, routine, and conventional” however Examiner is relying on 2106.05(f) “apply it.” Examiner relied on “apply it” because of item (2) Whether the claim invokes computers or other machinery merely as a tool to perform an existing process of 2106.05(f).
The use of multiple machine learning methods by itself will not overcome the apply designation above.
Thus, the argument(s) are unpersuasive.
Applicant’s arguments, see applicant’s remarks, filed 12/11/25, with respect to rejections under 35 USC 103 for claim(s) 1-20 have been fully considered but they are not persuasive as far as they apply to the amended 103 rejection(s) below.
Applicant respectfully traversed the rejection on pg. 13-17.
The Examiner respectfully disagrees because the items used for the argument NLP, computer vision, bias, question generation, and confidence intervals are not supported by the specification thus the examiner can’t determine the validity of the arguments. The claims in view of the specification are taught by the current prior art.
Thus, the argument(s) are unpersuasive.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action and those claims are 11-15. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: machine learning (ML) module in claim 1-2, 9, 10-11, and 17-18.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed. Ariad, 598 F.3d at 1350, 94 USPQ2d at 1171; Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002) (holding that generic claim language appearing in ipsis verbis in the original specification did not satisfy the written description requirement because it failed to support the scope of the genus claimed)”. Additionally, original claims may fail to satisfy the written description requirement when the invention is claimed and described in functional language but the specification does not sufficiently identify how the invention achieves the claimed function. Ariad, 598 F.3d at 1349, 94 USPQ2d at 1171.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. Representative claim(s) 1, 10, and 17 recite(s)
“parsing, by the RS, the data using a skill filter to derive a plurality of matching features comprising quantified skill metrics and experience vectors using natural language processing algorithms that extract semantic meaning from unstructured text data;
providing, by the RS, the plurality of the matching features to a machine learning (ML) module comprising a neural network trained on historical recruitment data with weighted feature vectors optimized for real-time processing constraints;
receiving, by the RS, a recommendation from the ML module pertaining to interviewing the applicant based on predictive scoring algorithms that dynamically adjust prediction confidence thresholds based on position-specific success metrics;
responsive to a receipt of a positive recommendation, accessing, by the RS, real-time live interview data during an active interview session, the live interview data comprising one or more of an audio stream, a video feed, or real-time response metrics processed through computer vision algorithms that analyze facial expressions, speech patterns, and response timing to generate behavioral assessment vectors that remove subjective human bias in candidate assessment and reduce interview evaluation time;
deriving, by the RS, a feature vector from the live interview data in real-time during the interview using natural language processing and sentiment analysis algorithms that transform audio signals into structured data representations for machine learning processing;
passing, by the RS, the feature vector to the ML module for generating a predictive model that processes real-time behavioral and linguistic patterns to output employment compatibility scores with confidence intervals; and
receiving, by the RS, predictive outputs from the ML module indicating a quantitative degree score degree to which the applicant fits the position based on real-time analysis of interview performance metrics wherein the system automatically adjusts interview question difficulty based on real-time performance assessment to optimize data collection for predictive accuracy.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Examiner notes the bolded portion of the representative claims above is new matter. Initially, Examiner notes the bolded portion recites
“parsing, by the RS, the data using a skill filter to derive a plurality of matching features comprising quantified skill metrics and experience vectors using natural language processing algorithms that extract semantic meaning from unstructured text data”,
“machine learning (ML) module optimized for real-time processing constraints”,
“receiving a recommendation based on predictive scoring algorithms that dynamically adjust prediction confidence thresholds based on position-specific success metrics”,
“responsive to a receipt of a positive recommendation, accessing, by the RS, real-time response metrics processed through computer vision algorithms that analyze facial expressions, speech patterns, and response timing to generate behavioral assessment vectors that remove subjective human bias in candidate assessment and reduce interview evaluation time”,
“deriving, by the RS, a feature vector from the live interview data in real-time during the interview using natural language processing and sentiment analysis algorithms that transform audio signals into structured data representations for machine learning processing”,
“passing, by the RS, the feature vector to the ML module for generating a predictive model that processes real-time behavioral and linguistic patterns to output employment compatibility scores with confidence intervals”, and
“receiving, by the RS, predictive outputs from the ML module indicating a quantitative degree score degree to which the applicant fits the position based on real-time analysis of interview performance metrics wherein the system automatically adjusts interview question difficulty based on real-time performance assessment to optimize data collection for predictive accuracy”
which does not appear to be supported by the originally filed disclosure. Examiner notes the closest portions of the original disclosure include [0075, 0080, 0086, 0072, 0050-0057, 0038, 0032, 0075] which only states
parsing limitation: [0075, 0080] “At block 314, the processor 204 may parse the data by a skill filter to derive a plurality of matching features”;
providing limitation: [0086, 0072, 0050-0057] ([0086]) “a neural network may be used in the AI/ML”, ([0072]) “The AI/ML module 107 may have access to a ledger 109 of the blockchain 110 for retrieval or storage of historical employment data that may be used as training data sets”, and ([0050-0057]) real time for interview data;
receiving limitation: [0038] “As data is continuously collected from more and more candidate interviews, it will be used to train a machine learning model to determine the weightage for each factor that predicts the outcome of candidate job offer most accurately. The ML model may also segment and create unique weights for the factors by location, job position, wage rate, etc. attributes. This trained model may be used to provide more predictive scores to interviewers before the interview. These scores may also be used to determine priority interview slots to higher scoring candidates.”;
responsive and deriving limitation: [0032] “In one embodiment of the present disclosure, segment of the video of the interview is recorded or a live video feed data is provided to the AI/ML module. The system may parse the video data to produce a feature vector that may be processed by the AI/ML module. Once the interview is completed, the system may generate a summary which may include both the charted answers, any free text responses noted by the interviewer, as well as video snippets of “bookmarked” answers. … In one embodiment of the present disclosure, segment of the video of the interview is recorded or a live video feed data is provided to the AI/ML module. The system may parse the video data to produce a feature vector that may be processed by the AI/ML module.”, and
passing and receiving limitation: [0075] “The processor 204 may fetch, decode, and execute the machine-readable instructions 222 to, responsive to a receipt of a positive recommendation, access live interview data. The processor 204 may fetch, decode, and execute the machine-readable instructions 224 to derive a feature vector from the live interview data. The processor 204 may fetch, decode, and execute the machine-readable instructions 226 to pass the feature vector to the ML module for generating a predictive model. The processor 204 may fetch, decode, and execute the machine-readable instructions 228 to receive predictive outputs from the ML module indicating a degree to which the applicant fits the position.”.
None of these portions however disclose the above bolded claim language. Appropriate correction/clarification is required. Claim(s) 2-9, 11-16, and 18-20 is/are rejected because they depend on claim(s) 1, 10, and 17.
Claim(s) 2, 11, & 18, 6 & 15, and 7 & 16 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. Representative claim(s) 2, 11, & 18, 6 & 15, and 7 and 16 recite(s)
“generating a skill filter based on a set of skills associated with the position for which the ML module provides candidate recommendations,”
“providing the employment verdict to at least one HR node for HR-specific employment an approval over a blockchain consensus configured for recruitment decision validation,” and
“executing a smart contract specifically configured for employment record management to record the data related to the applicant for the position along with the employment verdict, a timestamp and a location identifier on a ledger of a blockchain as an immutable employment record”.
The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Examiner notes the bolded portion of the representative claims above is new matter. Initially, Examiner notes the bolded portion recites
“generating a skill filter for which the ML module provides candidate recommendations”,
“providing the employment verdict to at least one HR node for HR-specific employment an approval over a blockchain consensus configured for recruitment decision validation”, and
“executing a smart contract specifically configured for employment record management to record the data related to the applicant for the position”
which does not appear to be supported by the originally filed disclosure. Examiner notes the closest portions of the original disclosure include [0029-0030, 0066, 0076, 0087, 0090] which only states
generating: [0029-0030] “The AI/ML-based automated system may use a customized Skill Filter for each job type/position based on an employment criterion provided by a potential employer. The Skill Filter may be generated by an ML module based on position and/or job requirements.
The AI/ML-based automated system may apply the Skill Filter to a matching criteria-pre-configured by the client (i.e., an employer).”
providing: [0066, 0075] ([0066]) “Then, the verdict may be provided to the hiring authority via HR node(s) 113 over the blockchain 110 for a final approval and generation of a job offer in case of a positive employment verdict. The HR nodes may provide a blockchain consensus for the employment verdict.” and ([0076]) “The blockchain 110 may be configured to use one or more smart contracts that manage transactions for multiple participating nodes (e.g., 101, 102, etc.).”, and
executing: [0066, 0076, 0087, 0090] ([0066, 0076]) same as above, [0087, 0090] “In another embodiment, the AI/ML module 107 may use a decentralized storage such as a blockchain 110 (see FIG. 1B) that is a distributed storage system, which includes multiple nodes that communicate with each other. The decentralized storage includes an append-only immutable data structure resembling a distributed ledger capable of maintaining records between mutually untrusted parties.”.
None of these portions however disclose the above bolded claim language. Appropriate correction/clarification is required.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 1, 10, and 17 that, under its broadest reasonable interpretation, is directed to matching job candidates to jobs.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 10-16 recites a series of steps) and system (claim(s) 1-9 and 17-20 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) (emphasis added):
Claim 10: receiving, by a recruitment server (RS) node, data related to an applicant for a position from a data provider node associated with the applicant;
parsing, by the RS node, the data using a skill filter to derive a plurality of matching features comprising quantified skill metrics and experience vectors using natural language processing algorithms that extract semantic meaning from unstructured text data;
providing, by the RS node, the plurality of the matching features to a machine learning (ML) module comprising a neural network trained on historical recruitment data with weighted feature vectors optimized for real-time processing constraints;
receiving, by the RS node, a recommendation from the ML module pertaining to interviewing the applicant based on predictive scoring algorithms that dynamically adjust prediction confidence thresholds based on position-specific success metrics;
responsive to a receipt of a positive recommendation, accessing, by the RS node, real-time live interview data during an active interview session, the live interview data comprising one or more of an audio stream, a video feed, or real-time response metrics processed through computer vision algorithms that analyze facial expressions, speech patterns, and response timing to generate behavioral assessment vectors that remove subjective human bias in candidate assessment and reduce interview evaluation time;
deriving, by the RS node, a feature vector from the live interview data in real-time during the interview using natural language processing and sentiment analysis algorithms that transform audio signals into structured data representations for machine learning processing;
passing, by the RS node, the feature vector to the ML module for generating a predictive model that processes real-time behavioral and linguistic patterns to output employment compatibility scores with confidence intervals; and
receiving, by the RS node, predictive outputs from the ML module indicating a quantitative degree score degree to which the applicant fits the position based on real-time analysis of interview performance metrics wherein the system automatically adjusts interview question difficulty based on real-time performance assessment to optimize data collection for predictive accuracy.
Claim(s) 1 and 17: same analysis as claim(s) 10.
Dependent claims 2-9, 11-16, and 18-20 recite the same or similar abstract idea(s) as independent claim(s) 1, 10, and 17 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with matching candidates to jobs.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea include natural language processing, neural network, sentiment analysis, machine learning processing, system (claim(s) 1, 10, 17), processor, server, machine learning (ML) module, predictive model (claim(s) 1), server, network, memory, machine learning (ML) module, predictive model (claim(s) 10), non-transitory computer readable medium, processing component, machine learning (ML) module, predictive model (claim(s) 17), ML module (claim 2, 11, 18, 9), processor (claim 2-9, 18-20), blockchain (claim(s) 6-7, 15-16).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0107]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0107]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Claim(s) 1-5, 8-9, 10-14, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Champaneria (US 2019/0251516 A1) in view of Westerheide et al. (US 2022/0067665 A1).
Regarding claim 1, 10, and 17 (currently amended), Champaneria teaches a method, comprising
{a processor of a recruitment;
and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: - claim 1}
{a non-transitory computer readable medium comprising instructions, that when read by a processing component, cause the processing component to perform – claim 17} [see at least [0007] “Such a method may operate to match résumés and job descriptions, and may initiate communications between a candidate and a recruiter once an appropriate match has been found.”;
[0051] “Additionally, these sequence of actions described herein can be considered to be embodied entirely within any form of computer readable storage medium having stored therein a corresponding set of computer instructions that upon execution would cause an associated processor to perform the functionality described herein.”]:
receiving, by a recruitment, data related to an applicant for a position from the applicant [see at least [0008-0009] “The method may next include generating, on the automated recruitment system, a first data point matrix, the data point matrix based on the one or more job requirements. …
Next, the method may include, for a plurality of candidates, performing a step of receiving, on the automated system, a sourced résumé of a candidate in the plurality of candidates (which may be, for example, a formal résumé, or may be another source of candidate information such as a candidate social media profile)”];
parsing, by the RS, the data using a skill filter to derive a plurality of matching features comprising quantified skill metrics and experience vectors using natural language processing algorithms that extract semantic meaning from unstructured text data [examiner note: as noted by the 112 rejection there is not support for this limitation and it is being interpreted based on instant specification [0075, 0080] “At block 314, the processor 204 may parse the data by a skill filter to derive a plurality of matching features”,
see at least [0093] “In a pre-processing step 31, the system may perform initial filtering of candidates based on some criteria. This criteria may include, for example, any or all types of available structured data, such as proximity, salary range, years of experience (such as, for example, years of experience in general or years of experience with a specific skill), or any other available data.”];
providing, by the RS, the plurality of the matching features to a machine learning (ML) module comprising a neural network;
receiving, by the RS, a recommendation from the ML module pertaining to interviewing the applicant based on predictive scoring algorithms that dynamically adjust prediction confidence thresholds based on position-specific success metrics [for the limitations above,
Examiner notes the receiving limitation is interpreted based on instant specification [0038] as receiving, by the RS, a recommendation from the ML module pertaining to interviewing,
then see at least [0089] “Turning now to exemplary FIG. 4, FIG. 4 displays an exemplary embodiment of a matching step 3. In a matching step 3, the system may attempt to match job candidates to jobs. This step may include, for example, a pre-processing step 31, a core matching step 32, a post-processing step 33, and a confirmation step 34.”;
[0061] “For example, according to some exemplary embodiments, a system may make use of, as a method of matching information in the job description to information of one or more potential candidates for the position: machine learning; one or more neural networks;”];
[0160] matching to determine suitability for interview “if past candidates that were selected for an interview had certain skills or experience listed in their résumés”
[0264] combination of parts “The foregoing description and accompanying figures illustrate the principles, preferred embodiments and modes of operation of the invention. However, the invention should not be construed as being limited to the particular embodiments discussed above. Additional variations of the embodiments discussed above will be appreciated by those skilled in the art (for example, features associated with certain configurations of the invention may instead be associated with any other configurations of the invention, as desired).”;
[0264, 0061, 0160] ([0264]) combine ([0061]) machine learning to match a candidate to a job to focus not just on skill matching ([0160]) but also focus on skill matching for an interview];
responsive to a receipt of a positive recommendation, accessing, by the RS, real-time live interview data during an active interview session, the live interview data comprising one or more of an audio stream, a video feed, or real-time response metrics processed through computer vision algorithms that analyze facial expressions, speech patterns, and response timing to generate behavioral assessment vectors that remove subjective human bias in candidate assessment and reduce interview evaluation time [examiner note: applicant has not acted as his or her own lexicographer to specifically define or redefine live interview data in the instant specification [0031, 0075, 0080] thus the broadest reasonable interpretation is being used,
examiner note: responsive limitation is not supported by instant specification and the limitation is interpreted based on instant specification [0032] as responsive to a receipt of a positive recommendation, accessing, by the RS, live interview data,
then see at least [0089] “Turning now to exemplary FIG. 4, FIG. 4 displays an exemplary embodiment of a matching step 3. In a matching step 3, the system may attempt to match job candidates to jobs. This step may include, for example, a pre-processing step 31, a core matching step 32, a post-processing step 33, and a confirmation step 34.”;
[0102] “According to an exemplary embodiment, a post-processing step 33 may be performed in order to perform additional filtering of candidates that have been selected as part of a matching step 32. In some exemplary embodiments, a post-processing step 33 may perform filtering by the use of an AI bot, by the use of inference engine results, and so forth.”;
[0160] access interview data “the AI BOT may base such recommendations on the job description, the conversation that has been conducted with the candidate, previous interviews that have been conducted with other candidates or the résumés of previous candidates that have been considered or interviewed, company information, information about the hiring manager, industry news information, or any other information that may be accessible to the AI BOT.”;
[0212, 0216-0217, 0219] live interview data “In some exemplary embodiments, it may also be desired to have the AI BOT complete all or part of an interview procedure, in addition to performing a review step 7 before an interview and evaluating an interview result in an offer step such as is shown in FIG. 8. For example, in an exemplary embodiment, an AI BOT may complete a first part of an interview procedure or a first interview, which may include, for example, basic question-and-answer portions for the candidate (for example, the candidate may be asked about a time they demonstrated a particular skill)”;
[0223] other live interview data “According to an exemplary embodiment, once the interview has been completed, a transcript of the interview may be prepared.”];
deriving, by the RS, a feature vector from the live interview data in real-time during the interview using natural language processing and sentiment analysis algorithms that transform audio signals into structured data representations for machine learning processing [examiner note: deriving limitation is not supported by instant specification and the limitation is interpreted based on instant specification [0032] as deriving, by the RS, a feature vector from the live interview data,
then see at least [0223] analyze interview data “According to an exemplary embodiment, once the interview has been completed, a transcript of the interview may be prepared. … In some exemplary embodiments, the transcript of the interview may be fed into an AI engine for analysis … In some exemplary embodiments, this comparison may use, for example, text analytics, sentiment analysis, or a comparison of the data point matrix of the interview transcript with the data point matrix of candidates that have been hired for a similar role. In such exemplary embodiments, or in other exemplary embodiments, a data point matrix may be created for all interview transcripts.”;
[0059, 0092] use any data to create a data vector “Various other representations of data may be constructed using the general framework of a data point matrix. For example, a data vector may be constructed within the data point matrix, which may be represented by a conceptual line that can be drawn through the locales of the greatest concentrations of data points in a data point matrix. …
In an exemplary embodiment, the conversion of résumé data and job description data into data point matrices, or data sets (such as data vectors) on a data point matrix, may be used to store both all of the data entities contained by the system and all of the relationships between the data entities contained by the system.”];
passing, by the RS, the feature vector to the ML module for generating a predictive model that processes real-time behavioral and linguistic patterns to output employment compatibility scores with confidence intervals [examiner note: passing limitation is not supported by instant specification and the limitation is interpreted based on instant specification [0075] as passing, by the RS, the feature vector to the ML module for generating a predictive model,
then see at least [0223] analyze interview data “According to an exemplary embodiment, once the interview has been completed, a transcript of the interview may be prepared. … In some exemplary embodiments, the transcript of the interview may be fed into an AI engine for analysis … In some exemplary embodiments, the AI Engine may then determine whether the candidate is to be brought in for a second round of interviews. For example, according to an exemplary embodiment, the AI Engine may look at other candidates that have been hired or interviewed for a similar role at this company, and/or may look at other candidates that have been hired or have been interviewed for similar roles at other companies, and may compare the transcript to the performance of those other candidates. In some exemplary embodiments, this comparison may use, for example, text analytics, sentiment analysis, or a comparison of the data point matrix of the interview transcript with the data point matrix of candidates that have been hired for a similar role. In such exemplary embodiments, or in other exemplary embodiments, a data point matrix may be created for all interview transcripts.”;
[0061] “In some exemplary embodiments, a system may make use of one or more alternative methods of matching information in the job description to information of one or more potential candidates for the position, in addition to the use of a data point matrix or instead of the use of a data point matrix. For example, according to some exemplary embodiments, a system may make use of, as a method of matching information in the job description to information of one or more potential candidates for the position: machine learning; one or more neural networks; multi-layer perceptrons; support vector machines; … predictive learning via rule ensembles; … . Other methods of matching information in the job description to information of one or more potential candidates for the position may be understood and may be used, as may be desired.”;
[0264] combination of parts “The foregoing description and accompanying figures illustrate the principles, preferred embodiments and modes of operation of the invention. However, the invention should not be construed as being limited to the particular embodiments discussed above. Additional variations of the embodiments discussed above will be appreciated by those skilled in the art (for example, features associated with certain configurations of the invention may instead be associated with any other configurations of the invention, as desired).”;
[0264, 0223, 0061] ([0264]) combine ([0223]) comparison of data point matrix of interview ([0061]) with one of many machine learning and/or predictive models]; and
receiving, by the RS, predictive outputs from the ML module indicating a quantitative degree score degree to which the applicant fits the position based on real-time analysis of interview performance metrics wherein the system automatically adjusts interview question difficulty based on real-time performance assessment to optimize data collection for predictive accuracy [examiner note: passing limitation is not supported by instant specification and the limitation is interpreted based on instant specification [0075] as receiving, by the RS, predictive outputs from the ML module indicating a degree to which the applicant fits the position,
then see at least [0223] “According to an exemplary embodiment, once the interview has been completed, a transcript of the interview may be prepared. … In some exemplary embodiments, the transcript of the interview may be fed into an AI engine for analysis … In some exemplary embodiments, the AI Engine may then determine whether the candidate is to be brought in for a second round of interviews.”].
Champaneria doesn’t/don’t explicitly teach but, in the field pertinent to the particular problem with which the applicant was concerned such as job to applicant matching, Westerheide discloses
{a processor of a recruitment server connected to at least one applicant data provider node over a network - claim 1} [see at least Fig. 1 and [0084, 0082] server and network “computing system 116 may include one or more servers 128” and “in one example, the computing devices 12, 13, 15 and the cloud-based computing system 116 may communicate with a network 112”]
receiving, by a recruitment server (RS) node, data from a data provider node associated with the applicant;
the RS node;
providing, by the RS, data to a machine learning (ML) module comprising a neural network trained on historical recruitment data with weighted feature vectors;
receiving, by the RS node, data;
the RS node;
the RS node;
passing, by the RS node, data to the ML; and
receiving, by the RS node, data from the ML module [for the limitations above,
Examiner note: there is not support for the providing limitation and the limitation is interpreted based on instant specification [0086, 0072, 0050-0057] as providing, by the RS, the plurality of the matching features data to a machine learning (ML) module comprising a neural network trained on historical recruitment data with weighted feature vectors,
then see at least Fig. 1 and [0082] “The system architecture 10 may include a computing device 12 of a user (e.g., referrer), a computing device 13 of a candidate, and/or a computing device 15 of a hiring entity (e.g., employer, job poster, etc.) communicatively coupled to a cloud-based computing system 116.”;
Fig. 1 and [0084, 0082] server and network “computing system 116 may include one or more servers 128” and “in one example, the computing devices 12, 13, 15 and the cloud-based computing system 116 may communicate with a network 112”;
Fig. 1 and [0084] “The servers 128 may execute an artificial intelligence (AI) engine that uses one or more machine learning models 154 to perform at least one of the embodiments disclosed herein.”;
[0089] “As described in more detail below, the one or more machine learning models 154 may comprise, e.g., a single level of linear or non-linear operations (e.g., a support vector machine [SVM]) or the machine learning models 154 may be a deep network, i.e., a machine learning model comprising multiple levels of non-linear operations. Examples of deep networks are neural networks, including generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks”;
[0097] “Based on experience and history of similar referral processes over time, machine learning models may modify elements of the algorithm to improve the effectiveness of the prediction.”;
[0059] “The machine learning models may be continuously trained to determine matches and/or referrer's scores based on newly received data pertaining to candidates, referrers, and/or jobs. The disclosed techniques may update various weights based on which candidates hiring entities select.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Champaneria with Westerheide to include the limitation(s) above as disclosed by Westerheide. Doing so would improve Champaneria’s (Champaneria) candidate recruitment via additional candidate inputs [see at least Westerheide [0003] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Champaneria and b) Westerheide and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 2, 11, and 18 (currently amended), modified Champaneria teaches the method of claim 10
and Champaneria teaches further comprising, generating a skill filter based on a set of skills associated with the position for which the ML module provides candidate recommendations [Examiner note: there is not support for the generating limitation and the limitation is interpreted based on instant specification [0029-0030] as further comprising, generating a skill filter based on a set of skills associated with the position,
then see at least [0093] “In a pre-processing step 31, the system may perform initial filtering of candidates based on some criteria. … For example, it may be desired to, as part of a pre-processing step, sort out all candidates who are not within a 50-mile radius (or within an estimated 1-hour drive) of a job site. In another example, it may be desired to filter out all candidates having a salary known to be in excess or significantly in excess than a salary of a position being offered (or who can be estimated to have a salary in excess of the position being offered based on their job title).”;
[0008] examples of some criteria (from [0093]) “one or more job requirements of the job description (such as, for example, the job title, the skills and experience required and preferred, the education requirements, the day-to-day duties and expectations of the position, and so forth)”].
Regarding claim 3, 12, and 19, modified Champaneria teaches the method of claim 10
and Champaneria teaches further comprising, deriving the feature vector from recorded interview data comprising answers to interview questions [see at least [0080] “in some exemplary embodiments, the real-time transcription and storage of the conversation and real-time text-to-speech generation may allow the same logic as would be used for a text-based conversation to be used with the added steps of the conversation being recorded in textual form and then output in text-to-speech form.”;
[0215, 0218] “For example, in an exemplary embodiment, a candidate may be asked general questions in an interview and may have unlimited time (or a significant amount of time) to answer those general questions, but may also be asked to solve one or more problems and may have a limited amount of time to provide their solution, in order to ensure that the candidate can solve problems quickly.” and “In some exemplary embodiments, interview questions may be asked one at a time until a satisfactory answer is provided by the candidate.”;
[0223] analyze interview data “According to an exemplary embodiment, once the interview has been completed, a transcript of the interview may be prepared. … In some exemplary embodiments, the transcript of the interview may be fed into an AI engine for analysis … In some exemplary embodiments, this comparison may use, for example, text analytics, sentiment analysis, or a comparison of the data point matrix of the interview transcript with the data point matrix of candidates that have been hired for a similar role. In such exemplary embodiments, or in other exemplary embodiments, a data point matrix may be created for all interview transcripts.”;
[0059, 0092] use any data to create a data vector “Various other representations of data may be constructed using the general framework of a data point matrix. For example, a data vector may be constructed within the data point matrix, which may be represented by a conceptual line that can be drawn through the locales of the greatest concentrations of data points in a data point matrix. …
In an exemplary embodiment, the conversion of résumé data and job description data into data point matrices, or data sets (such as data vectors) on a data point matrix, may be used to store both all of the data entities contained by the system and all of the relationships between the data entities contained by the system.”].
Regarding claim 4, 13, and 20, modified Champaneria teaches the method of claim 10
and Champaneria teaches further comprising, generating an employment verdict based on the predictive outputs [see at least [0223] “According to an exemplary embodiment, once the interview has been completed, a transcript of the interview may be prepared. … In some exemplary embodiments, the transcript of the interview may be fed into an AI engine for analysis … In some exemplary embodiments, the AI Engine may then determine whether the candidate is to be brought in for a second round of interviews.”;
[0182-0183] “an AI BOT may be configured to submit one or more candidates to the hiring manager … The hiring manager, or any other appropriate party, may then undertake a review step 7. In a review step, a hiring manager may review the completed and vetted applications and make any necessary changes or take any other necessary steps (such as, for example, requesting more information from the candidate) and may then undertake to schedule an interview with the candidate. In an exemplary embodiment, the hiring manager may confirm or deny that the candidate will receive an interview”;
[0202] “If the candidate is considered to have passed the job interview, this information may be provided to the system, and the system may be configured to automatically generate and send an offer to the candidate via one or more of the preferred communication media 81.”].
Regarding claim 5 and 14, modified Champaneria teaches the method of claim 13
and Champaneria teaches further comprising, providing a job offer to the applicant's data provider node based on a positive employment verdict [see at least [0202] “If the candidate is considered to have passed the job interview, this information may be provided to the system, and the system may be configured to automatically generate and send an offer to the candidate via one or more of the preferred communication media 81.”].
Regarding claim 8, modified Champaneria teaches the system of claim 1,
and Champaneria teaches wherein the instructions further cause the processor to monitor the applicant's data provider node to detect any of applicant interactions comprising:
emails from an HR node opened by the applicant, wherein the emails comprising additional information about the position [see at least [0081] “In some exemplary embodiments, the AI BOT may also be proactive about placing outbound calls to candidates, for example if an email or SMS-based inquiry is not responded to within a certain period of time; for example, an AI BOT may be configured to place a call to the candidate and say “Hi Bill, this is Brian. I was calling about a job opportunity that you might be interested in. Is this a good time to talk?””]; and
tech checks by the applicant [examiner note: applicant has not acted as his or her own lexicographer to specifically define or redefine tech checks in the instant specification [0034-0037] thus the broadest reasonable interpretation is being used “In other words, the candidate showing additional interest in preparation for the interview”,
then see at least [0176] “For example, it may be envisioned that a candidate may provide an email response in which they express interest in an opportunity, but ask a number of questions about the company in preparation for an interview. The system may be configured to both finalize the candidate 51 and execute one or more question processing steps 52.”].
Regarding claim 9, modified Champaneria teaches the system of claim 1,
and Champaneria teaches wherein the instructions further cause the processor to assign weights to interactions by the applicant and to provide the weights to the ML module for predictive ranking of the applicant prior to an interview [see at least [0089] weights can be applied at any stage “Further, in an exemplary embodiment, the criteria used by each of the pre-processing step 31, core matching step 32, post-processing step 33, and confirmation step 34 may be weighed in order to ensure the best possible matches, and may be dynamically adjusted (in terms of weight and in terms of which criteria are actually applied) in order to loosen or tighten the focus of a search.”;
[0098] scoring can be based on any criteria “The semantic engine may then determine which candidates have the highest scores, and may select candidates accordingly. For example, in an exemplary embodiment, a semantic engine may rank candidates based on the data point matrix scores of the candidates in each of the above areas (or in each of the above areas that are actually considered) and based on any other criteria, as desired.”;
[0091] further define criteria for matching (of [0089] ) to include user interactions “According to an exemplary embodiment, a system may be configured to automatically receive notifications of a user having added their résumé or having updated their résumé, or a job description being created or edited; for example, such a system may be tied into the operations software of a jobs board or source location. In an exemplary embodiment, a system may be configured to automatically perform searches for users having added a résumé or edited a résumé, or employers having added or edited a job description. In an exemplary embodiment, a system may exhibit different behaviors for users who are part of active campaigns (i.e. users who are considered to be candidates for at least one job) and users who are not; for example, in an exemplary embodiment, users who have been identified as being candidates may be monitored more closely, if desired.”].
Claim(s) 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Champaneria in view of Westerheide as applied to claim(s) 4 and 13 above and further in view of Thompson (US 2021/0279690 A1).
Regarding claim 6 and 15 (currently amended), modified Champaneria teaches the method of claim 13 .
Modified Champaneria doesn’t/don’t explicitly teach but, in the field pertinent to the particular problem with which the applicant was concerned such as job to applicant matching, Thompson discloses
further comprising, providing the employment verdict to at least one HR node for HR-specific employment an approval over a blockchain consensus configured for recruitment decision validation [Examiner note: there is not support for the providing limitation and the limitation is interpreted based on instant specification [0066, 0076] as further comprising, providing the employment verdict to at least one HR node for an approval over a blockchain consensus,
then see at least [0078] “Once the job-seeker is interviewed (in-person or virtual) and selected as potential candidate for hire, the hiring manager transmits the PATHFINDER-generated ‘NEW HIRE” datasheet to Human Resources (HR) to onboard the new hire.”;
[0054] “User confirms data, triggering system data accrued here consisting essentially of generated datasheet of selection(s) to transmit to hiring managers blockchain eFolder.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Champaneria with Thompson to include the limitation(s) above as disclosed by Thompson. Doing so would improve modified Champaneria’s (Champaneria) candidate recruitment via additional candidate inputs [see at least Thompson [0002-0021] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Champaneria and b) Thompson and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Champaneria in view of Westerheide as applied to claim(s) 4 and 13 above and further in view of Escobar (US 2020/0098072 A1).
Regarding claim 7 and 16 (currently amended), modified Champaneria teaches the method of claim 13 .
Modified Champaneria doesn’t/don’t explicitly teach but, in the field pertinent to the particular problem with which the applicant was concerned such as job to applicant matching, Escobar discloses
further comprising, executing a smart contract specifically configured for employment record management to record the data related to the applicant for the position along with the employment verdict, a timestamp and a location identifier on a ledger of a blockchain as an immutable employment record [Examiner note: there is not support for the executing limitation and the limitation is interpreted based on instant specification [0066, 0076, 0087, 0090] as further comprising, executing a smart contract to record the data related to the applicant for the position along with the employment verdict, a timestamp and a location identifier on a ledger of a blockchain,
then see at least [0007, 0055] employment verdict on blockchain “receive from a first user device first user attributes and transaction attributes; receive from a second user device second user attributes and a bid corresponding to the transaction attributes; receive from the first user device an acceptance of the bid; create a contract including components of the first user attributes, second user attributes and the transaction attributes; and store some of the components on a blockchain-based ledger.”;
[0030-0033] “As used herein, the term “block” generally refers to a record that is kept in a blockchain. … blockchain-based ledger 100 includes several blocks 110 … Each block 110 includes a previous hash 102 (a second block ID for a previous block), a transaction root 104 (a first block ID for first block), a timestamp 106”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Champaneria with Escobar to include the limitation(s) above as disclosed by Escobar. Doing so would improve modified Champaneria’s (Champaneria) candidate recruitment via additional candidate information such as reputation scores [see at least Escobar [0003-0005] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Champaneria and b) Escobar and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WEBB whose telephone number is (313)446-6615. The examiner can normally be reached on M-F 10-3.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.W./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624