Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This communication is a First Office Action Non-Final on Merits. Claims 1-20, as originally filed, are currently pending and have been considered below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. This application repeats a substantial portion of prior Application No. 18531466, and adds disclosure not presented in the prior application such as generate one or more recruitment scores for candidates….; generate one or more offer ranges for the one or more candidates; output system to provide information associated with the one or more offer ranges generated for candidates (Claims 1,10, 19). Any claim that only contains subject matter that is fully supported in compliance with the statutory requirements of pre-AIA 35 U.S.C. 112, first paragraph, by the parent application of a CIP will have the effective filing date of the parent application (12/6/23). On the other hand, any claim that contains a limitation that is only supported as required by pre-AIA 35 U.S.C. 112, first paragraph, by the disclosure of the CIP application will have the effective filing date of the CIP application (11/5/25). See, e.g., Santarus, Inc. v. Par Pharmaceutical, Inc., 694 F.3d 1344, 104 USPQ2d 1641 (Fed. Cir. 2012).
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. 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: a data obtaining subsystem configured to; a data analyzing subsystem configured to; a data processing subsystem configured to; a query generating subsystem; a score generating subsystem; a decision supporting subsystem; an output subsystem configured to in claims 1, 10 and 19.
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. Spec [0006] The memory is communicatively coupled to the one or more hardware processors. The memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors. Alos see Fig1 #112, 114, [0046] the “module” or “subsystem” may be implemented mechanically or electronically, so a module includes dedicated circuitry or logic that is permanently configured (within a special-purpose processor) to perform certain operations. In another embodiment, a “module” or s “subsystem” may also comprise programmable logic or circuitry (as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations.
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 § 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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1: Identifying Statutory Categories
In the instant case, claims 1-9 are directed to a system, Claims 10-18 are directed to a method and claims 19-20 are directed to a non-transitory medium. Thus, the claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong 1 Identifying a Judicial Exception
Under Step 2A, prong 1, Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 10 and 19 recite methods that, generate an AI-based interviewer simulating human-based interactions for conducting an ongoing interview with one or more candidates; obtain data associated with the one or more candidates through at least one of: one or more image and audio during the ongoing interview, wherein the data associated with the one or more candidates comprise at least one of: profile information, responses in form of audio and text, and non-verbal cues, associated with the one or more candidates; analyze the data associated with the one or more candidates obtained during the ongoing interview, wherein analyzing the data associated with the one or more candidates comprises processing of the responses of the one or more candidates, and interpreting the one or more non-verbal cues; process the analyzed responses of the one or more candidates to determine one or more contextual attributes associated with the responses, wherein the one or more contextual attributes comprise at least one of: one or more verbal attributes, one or more non-verbal attributes, one or more performance attributes, and one or more contextual interaction attributes; automatically generate one or more follow-up interview questions to be delivered to the one or more candidates during the ongoing interview based on the analyzed responses from the one or more candidates, contextual attributes associated with the responses; generate one or more recruitment scores for the one or more candidates based on at least one of: the analyzed responses, the one or more contextual attributes, and interpreted non-verbal cues, associated with the one or more candidates, generate one or more offer ranges for the one or more candidates based on the one or more recruitment scores, wherein the one or more offer ranges are configured to assist for one or more users in recruitment-related decision making; and provide information associated with at least one of: one or more selected candidates, and the one or more offer ranges generated for the one or more selected candidates, to the one or more users associated with the one or more users.
These limitations as drafted, are a process that, under its broadest reasonable interpretation, covers methods of organizing human activity (including commercial interactions such as business relations, managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) including interaction between person and computer) and mathematical calculations (generating one or more recruitment scores/offer range), but for the recitation of generic computer components. That is, other than reciting the structural elements (such as one or more hardware processors; and a memory communicatively coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors; wherein the plurality of subsystems comprises: an AI-based interviewer generating subsystem, a data obtaining subsystem, one or more image capturing devices and one or more audio devices, a data analyzing subsystem, using natural language processing (NLP) techniques, a data processing subsystem, using one or more machine learning (ML) models, query generating subsystem, applying an AI model to the one or more contextual attributes, a score generating subsystem; using the AI model; a decision supporting subsystem, an output subsystem, one or more user interfaces, one or more electronic devices), the claims are directed to generating one or more offer ranges for a candidate during an interviewing process by analyzing data associated with candidate and recruitment score of candidate. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of organizing human activity but for the recitation of generic computer components, the claim recites an abstract idea.
Step 2A Prong 2 - This judicial exception is not integrated into a practical application because the claim merely describes how to generally “apply” the concept of receiving interview data, analyzing it, and generating offer range. In particular, the claims only recite the additional elements – one or more hardware processors; and a memory communicatively coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors; wherein the plurality of subsystems comprises: an AI-based interviewer generating subsystem, a data obtaining subsystem, one or more image capturing devices and one or more audio devices, a data analyzing subsystem, using natural language processing (NLP) techniques, a data processing subsystem, using one or more machine learning (ML) models, query generating subsystem, applying an AI model to the one or more contextual attributes, a score generating subsystem; using the AI model; a decision supporting subsystem, an output subsystem, one or more user interfaces, one or more electronic devices. The additional 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 generic computer component or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Furthermore, limiting the limitation of “automatically generating one or more follow up questions” is merely an indication that it is to be using a computer, which does not meaningfully limit the claims. The additional elements of using a user interface to receive and display data associated with the abstract idea, is merely an example of generally linking the abstract idea to a particular technological environment or field of use as outlined in MPEP 2106.05(h). User interfaces are recited so generally that they do not meaningfully limit the abstract idea. The limitations of “using natural language processing (NLP) techniques; using one or more machine learning (ML) models; applying an AI model to the one or more contextual attributes; using the AI model”, are recited at high level of generality. Further, such training and applying of a machine learning/AI model is no more than putting data into a black box machine learning operation, devoid of technological implementation and application details. Each step requires a generic computer to perform generic computer functions. The claim does not provide any details about how the ML/AI models are applied to generate recruitment score or generating offer range. The claims are directed to an abstract idea. Simply implementing the abstract idea on generic components is not a practical application of the abstract idea. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims are directed to an abstract idea. When considered in combination, the claims do not amount to improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a), applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b), effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c), or 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, as discussed in MPEP 2106.05(e). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
Step 2B: Considering Additional Elements
The claimed invention is directed to an abstract idea without significantly more. The claim does not include additional elements that are sufficient to amount significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claims describe how to generally “apply” to; generating one or more offer ranges for a candidate during an interviewing process by analyzing data associated with candidate and recruitment score of candidate. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception because mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claims are not patent eligible. The dependent claim(s) when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail to establish that the claim(s) is/are not directed to an abstract idea. The dependent claims are not significantly more because they are part of the identified judicial exception. See MPEP 2106.05(g). The claims are not patent eligible. With respect to one or more hardware processors; and a memory communicatively coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors; wherein the plurality of subsystems comprises: an AI-based interviewer generating subsystem, a data obtaining subsystem, one or more image capturing devices and one or more audio devices, a data analyzing subsystem, using natural language processing (NLP) techniques, a data processing subsystem, using one or more machine learning (ML) models, query generating subsystem, applying an AI model to the one or more contextual attributes, a score generating subsystem; using the AI model; a decision supporting subsystem, an output subsystem, one or more user interfaces, one or more electronic devices, these limitations are described in Applicant’s own specification as generic and conventional elements. See Applicants specification, Paragraph [0046] details “ the “module” or “subsystem” may be implemented mechanically or electronically, so a module includes dedicated circuitry or logic that is permanently configured (within a special-purpose processor) to perform certain operations. In another embodiment, a “module” or s “subsystem” may also comprise programmable logic or circuitry (as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations., [0050] The one or more electronic devices 102 and the candidate system 104 may be, but is not limited to, a laptop computer, a desktop computer, a tablet computer, a phablet computer, a smartphone, a wearable device, a smart watch, a personal digital assistant (PDA), a Virtual/Augmented Reality (AR/VR) device, an image capturing device, a depth-based image capturing device, and the like. [0060] The computer implemented system 112 comprises one or more hardware processors 202, a memory 204, and a storage unit 206. The memory 204 comprises the plurality of subsystems 114 in form of programmable instructions executable by the one or more hardware processors 202. Further, the plurality of subsystems 114 includes a data obtaining subsystem 210, a data extraction subsystem 212, a key segment identification subsystem 214, a data determination subsystem 216, an insight generation subsystem 218, a score generating subsystem 220, an output subsystem 222, and a training subsystem 224. [0113] Different ML techniques may be employed based on the nature of the questions being asked. For instance, classification models might be used to categorize candidates based on their responses (e.g., identifying strengths or weaknesses), while regression models could assess the qualitative impact of those responses on overall candidate scoring. NLP algorithms are integral to interpreting candidate responses accurately.” These are basic computer elements applied merely to carry out data processing such as, discussed above, receiving, analyzing, transmitting and displaying data, which fall under well-understood, routine and conventional functions of generic computers. Furthermore, the use of such generic computers to receive or transmit data over a network has been identified as a well understood, routine and conventional activity by the courts. See Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AVAuto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result-a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)); Also see MPEP 2106.05(d) discussing elements that the courts have recognized as well-understood, routine and conventional activities in particular fields. Lastly, the additional elements provides only a result-oriented solution which lacks details as to how the computer performs the claimed abstract idea. Therefore, the additional elements amount to mere instructions to apply the exception. See MPEP 2106.05(f).
Furthermore, these steps/components are not explicitly recited and therefore must be construed at the highest level of generality and amount to mere instructions to implement the abstract idea on a computer. Therefore, the claimed invention does not demonstrate a technologically rooted solution to a computer-centric problem or recite an improvement to another technology or technical field, an improvement to the function of any computer itself, applying the exception with, or by use of, a particular machine, effect a transformation or reduction of a particular article to a different state or thing, add a specific limitation other than what is well-understood, routine and conventional in the field, add unconventional steps that confine the claim to a particular useful application, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment such as computing. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Taking the additional claimed elements individually and in combination, the computer components at each step of the process perform purely generic computer functions. Viewed as a whole, the claims do not purport to improve the functioning of the computer itself, or to improve any other technology or technical field. Use of an unspecified, generic computer does not transform an abstract idea into a patent-eligible invention. Thus, the claims do not amount to significantly more than the abstract idea itself.
Dependent claims 2-9, 11-18, and 20 add additional limitations, but these only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as Independent claims.
Claims 2, 11 and 20 further limit the abstract idea by further defining generating AT based interviewer including identify one or more objectives of the AI-based interviewer by creating one or more interactive visual representations conducting the ongoing interview effectively; generate a lifelike AI-based interviewer based on user personas relevant to one or more targeted interview domains comprising at least one of: one or more job roles and industries; generate a visually appealing AI-based interviewer reflecting an identity and desired competencies of a human interviewer using a three dimensional modelling application; perform at least one of: analyzing one or more inputs, processing the one or more inputs, and responding to the one or more inputs of the one or more candidates in real-time, by integrating one or more NLP capabilities within the AI-based interviewer; train the AI-based interviewer with a set of competency-based questions and the one or more follow-up interview questions, relevant to a career path of the one or more candidates using the one or more ML models; utilize one or more behavioral models to guide one or more interactions of the AI-based interviewer to emulate human-like behaviors comprising at least one of: changing tone, expressing empathy, providing facial expressions, and adjusting emotional responses, based on the responses from the one or more candidates; implement a conversation engine for the AI-based interviewer to adapt for follow-up questions based on the responses from the one or more candidates, using the AI model; synchronize at least one of: lip movements, gestures, and the facial expressions, of the AI-based interviewer with verbal communication of the AI-based interviewer during the ongoing interview,; and generate a natural sounding voice matching a visual persona of the AI-based interviewer. The concept of generating the AI based interviewer is abstract idea of organizing of human activity because it is merely using a computer as a tool to generate an AI interviewer. The additional elements including using a speech synthesis technology, a conversation engine, using a visual and audio synchronization technique are recited at high level of generality. Whether analyzed individually, or in an ordered combination with the previous additional elements, the claims are still collectively equivalent to “apply it,” because it is merely limiting the abstract idea to be performed on a generic computer, and still using machine learning/AI as the black box to carry out the abstract idea without meaningfully limiting how machine learning is used to arrive at the claimed outcome. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 3, 12 further limit the abstract idea by further defining analyzing data obtained during interview including process the responses of the one or more candidates using the natural language processing (NLP) techniques, by: obtaining the data associated with the one or more candidates which is simply data gathering. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 4, 13 recites obtain the analyzed responses comprising at least one of: verbal response and the non-verbal responses, from the one or more candidates; identify the one or more contextual attributes from the analyzed responses; utilize the one or more ML models being trained on one or more datasets of interview transcripts and the one or more follow-up interview questions, to analyze context and intention behind the responses of the one or more candidates; interpret the nuances in a language capturing subtleties around meaning and intent guiding question formulation using the AI model with the NLP techniques; and generate contextually appropriate one or more follow-up interview questions based on the identified one or more contextual attributes, using one or more neural network architectures. Whether analyzed individually, or in an ordered combination with the previous additional elements, the claims are still collectively equivalent to “apply it,” because it is merely limiting the abstract idea to be performed on a generic computer, and still using machine learning/AI as the black box to carry out the abstract idea without meaningfully limiting how machine learning is used to arrive at the claimed outcome. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 5, 14 recites filter the one or more follow-up interview questions based on relevance of the specific context provided by one or more previous responses of the one or more candidates; generate multiple variation of the one or more follow-up interview questions for at least one of: natural conversation flow, avoiding rigid scripts, and enabling dynamic interactions; structure the one or more follow-up interview questions to assess competencies related to the one or more job roles; and interact with the one or more candidates with one or more follow-up questions, adapting to a flow of conversation with the one or more candidates. These steps are directed to abstract idea of organizing of human activity. Whether analyzed individually, or in an ordered combination with the previous additional elements, the claims are still collectively equivalent to “apply it,” because it is merely limiting the abstract idea to be performed on a generic computer. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 6-7, 15-16 further recite steps for generating recruitment score and offer range including obtain information associated with at least one of: the analyzed responses, the one or more contextual attributes, and interpreted non-verbal cues, of each candidate of the one or more candidates; generate one or more weights to at least one of: the analyzed responses, the one or more contextual attributes, and interpreted non-verbal cues, of the one or more candidates, using a scoring model; and compute the one or more recruitment scores for each candidate based on the one or more weights generated for at least one of: the analyzed responses, the one or more contextual attributes, and interpreted non-verbal cues, of each candidate of the one or more candidates; obtain information associated with the one or more recruitment scores generated for the one or more candidates; analyze one or more target parameters and benchmarks based on at least one of: one or more industry standards, historical data, one or more organizational compensation structures, and competitor analyses; and generate the one or more offer ranges for each candidate of the one or more candidates by correlating the one or more offer ranges with the one or more recruitment scores, based on at least one of: the one or more target parameters and benchmarks and one or more factors, using the AI model, wherein the one or more factors comprise at least one of: experience level, skill set, and overall fit for the job role, of the one or more candidates within an organization. These steps are directed to abstract idea of organizing of human activity and mathematical calculations. Whether analyzed individually, or in an ordered combination with the previous additional elements, the claims are still collectively equivalent to “apply it,” because it is merely limiting the abstract idea to be performed on a generic computer. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 8, 17 further limits analyzing data wherein the AI-based interviewer is configured to analyze at least one of: emotion recognition, gaze tracking, and head movement through a real-time webcam, for performing at least one of: adjusting tone, pacing, and questioning in style using a multi-modal fusion, by adapting at least one of: the data obtaining subsystem to continuously capture visual data with high-resolution video streams associated with the one or more candidates from webcam inputs, processing frame-by-frame visual data at rates for real-time emotion detection and behavioral analysis; the data analyzing subsystem configured to: analyze the visual data using computer vision techniques to identify emotions through facial expressions of the one or more candidates; categorize the facial expressions of the one or more candidates as at least one of: happiness, sadness, confusion, anxiety, and confidence based on real-time analysis of facial movements and micro-expressions using a convolutional neural network model; identify key facial features comprising eyebrow position, mouth curvature, eye openness, and cheek muscle tension, to analyze the emotion recognition; track eye movements, fixation points, and gaze direction, of the one or more candidates, to assess candidate engagement and attention levels, using a computer vision system; and track head position, tilt angles, and movement patterns, of the one or more candidates, to assess candidate comfort, agreement and disagreement signals, and overall engagement levels; the data processing subsystem configured to: process the analyzed responses to determine the one or more contextual attributes using machine learning models, wherein the visual data obtained from the real-time webcam input is processed to dynamically modify the one or more contextual attributes that are provided as input to a transformer-based large language model (LLM); generate unified embeddings indicating verbal content and real-time visual behavioral data; and correlate verbal responses with simultaneous visual cues to detect incongruence between spoken words and body language, using the transformer-based LLM; and the query generating subsystem configured to: utilize one or more behavioral models to guide interactions and emulate human-like behaviors comprising changing tone based on detected emotional states from the real-time webcam; monitor visual indicators of cognitive processing comprising prolonged gaze aversion and facial expressions indicating concentration, to adjust the pacing; and generate contextually appropriate follow-up questions influenced by real-time visual feedback. These steps are directed to abstract idea of organizing of human activity and mathematical calculations. Whether analyzed individually, or in an ordered combination with the previous additional elements, the claims are still collectively equivalent to “apply it,” because it is merely limiting the abstract idea to be performed on a generic computer. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
Claims 9, 18 recite the AI-based interviewer generating subsystem is configured to emulate at least one of: the lip movements, the gestures, the facial expressions, and speech tone through a text-to-speech engine with phoneme sync, using a LLM, by: synchronizing the lip movements with verbal communication during the ongoing interview using visual and audio synchronization techniques; analyzing generated speech content at a phoneme level, mapping each speech sound to corresponding viseme representations indicating lip and mouth movements; synchronizing gestures of the AI-based interviewer with verbal communication during the ongoing interview; analyzing LLM-generated content for contextual cues triggering corresponding hand and arm movements, comprising counting gestures for enumerated points and descriptive gestures for spatial concepts; utilizing the one or more behavioral models to guide interactions of the AI-based interviewer to emulate human-like behaviors comprising the gestures based on the one or more responses from the one or more candidates; utilizing the one or more behavioral models to guide the interactions and emulate human-like behaviors comprising providing facial expressions based on the one or more responses from the one or more candidates; processing emotional context from LLM outputs and candidate analysis to select corresponding facial expressions indicating empathy, interest, concern, and encouragement; utilizing the one or more behavioral models to guide the interactions comprising changing tone based on the one or more responses from the one or more candidates; and modifying vocal parameters comprising pitch, pace, volume, and intonation to match emotional context determined by the LLM and candidate analysis systems. The limitations of analyzing LLM generated content merely adds the words apply it (or an equivalent) with the judicial exception , or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea as discussed in MPEP 2106.05(f). These steps are directed to abstract idea of organizing of human activity and mathematical calculations. Whether analyzed individually, or in an ordered combination with the previous additional elements, the claims are still collectively equivalent to “apply it,” because it is merely limiting the abstract idea to be performed on a generic computer. The claims do not provide any new additional elements beyond abstract idea. Therefore, whether analyzed individually or as an ordered combination, they fail to integrate the abstract idea into a practical application or provide significantly more than the abstract idea.
The dependent claims do not integrate into a practical application. As such, the additional elements individually or in combination do not integrate the exception into a practical application, but rather, the recitation of any additional element amounts to merely reciting the words “apply it” (or equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (See MPEP 2106.05(f)). The dependent claims also do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are merely used to apply the abstract idea to a technological environment. These limitations do not include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. See MPEP 2106.05d. Thus, the claims do not add significantly more to an abstract idea. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter. See (Alice Corporation Pty. Ltd. v. CLS Bank International, et al.).
Subject Matter Distinguished over prior art
Regarding Claims 1, 10 and 19. Preuss et al. (US 11,216,784 B2) discloses the computer implemented system for automatically generating one or more offer ranges for one or more candidates during an interviewing process,
Preuss discloses the computer implemented system comprising: one or more hardware processors; and a memory communicatively coupled to the one or more hardware processors (Col 26 lines 12-20 methods, systems, and computer program products according to implementations of this disclosure. Aspects thereof are implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus) wherein the memory comprises a plurality of subsystems in form of programmable instructions (Col 6 lines 22-28 the automated reaction assessment system 108 may include one or more engines or processing modules 130, 132, 134, 136, 138, 140, 142, 146 that perform processes associated with generating personality aspect mappings to questions for available positions and performing video assessments of submitted candidate interview videos based on the generated personality aspect mappings.) executable by the one or more hardware processors, wherein the plurality of subsystems comprises:
Preuss discloses an AI-based interviewer generating subsystem configured to generate an AI-based interviewer simulating human-based interactions for conducting an ongoing interview with one or more candidates (Fig 4 # 402 customized video avatar Col 8 lines 10-25, 34-36 an interview management engine (e.g., interview management engine 144 in FIG. 1) can generate a customized video avatar 402 presented within the UI screen 400 that functions as the “interviewer” of the candidate 102 by speaking the questions to the candidate 102. In some examples, the employer 104 and/or candidate 102 can design the features of the avatar (gender, facial features, voice). The avatar 402 begins the interview process by explaining the purpose and execution of the interview process the candidate is going through and guides the candidate through providing a variety of types of close-ended responses. For example, the avatar 402 can instruct the candidate 102 to provide “yes” or “no” responses slowly, quickly, or at a normal speed. );
Preuss discloses a data obtaining subsystem configured to obtain data associated with the one or more candidates through at least one of: one or more image capturing devices and one or more audio devices, during the ongoing interview (Col 8 lines 29-40 the interview management engine 144 can obtain baseline verbal (e.g., speed and latency of response) and nonverbal (e.g., facial expression and prosody) data from the candidate 102 via the UI screen 400. Col 16 lines 64-67 the user interface screen presented by the interview management engine 502 may prompt the candidate 102 to look at the camera and remain in a neutral state. In other examples, the user interface screen may not provide any direction to the candidate 102 regarding how to act when the mood/emotional state video data is captured. Col 7 lines 30-36 the data management engine 132 can also link captured nonverbal response features 126 (e.g., facial expression and prosodic features) and verbal response features 128 (e.g., speed/latency of the response along with the response) to the respective interview question data 112, question scores 120 and candidate profile data 118.),
Preuss discloses wherein the data associated with the one or more candidates comprise at least one of: profile information (Col 19 lines 40-44 the interview question data 112 includes multiple sets of interview questions associated with each of the candidate profiles stored as candidate profile data 118 in the data repository 110.), responses in form of audio and text, and non-verbal cues (Col 18 lines 15-35 the detected facial expression information can be extracted from the video data captured for the mood/emotional state (508). In some examples, the real-time calculation engine 504 can also detect changes in facial expression as the candidate 102 responds to each of the yes/no baseline prompts. the auditory facet and facial expression information captured by the real-time calculation engine 504 can be used by the backend calculation engine 506 to determine at least one baseline prosody score (514) and at least one baseline facial expression score (516). The at least one baseline prosody score can include a baseline tone or inflection in the candidate's voice as the candidate 102 responds to a yes/no baseline prompt.), associated with the one or more candidates (Col 18 lines 30-36The baseline prosody score, in some examples, can also include a baseline speed of verbal response for the candidate 102. In one example, the backend calculation engine 506 can calculate a baseline speed of verbal response for each of a “yes” and a “no” answer. Additionally, the baseline prosody scores can also include a strength of vocal tone (e.g., amplitude and frequency of the candidate's voice) for each of a “yes” and a “no” response. In some examples, the backend calculation engine 506 calculates the baseline prosody score by discarding outlier prosodic features extracted by the real-time calculation engine 504, averaging the extracted prosodic feature attributes for the remaining frames.);
Preuss discloses a data analyzing subsystem configured to analyze the data associated with the one or more candidates obtained during the ongoing interview (Col 4 lines 37-48 Upon receiving the video data of the candidate response to the open-ended video question, the system can analyze the content of the open-ended question response. In some examples, the system can apply a trained speech-to-text algorithm and natural language classifier to determine how well the candidate fits one or more ideal personality characteristics for the available position.) , wherein analyzing the data associated with the one or more candidates comprises processing of the responses of the one or more candidates using natural language processing (NLP) techniques (Col 5 lines 40-48 When a candidate 102 submits video responses to the identified interview questions, the automated reaction assessment system 108, in some implementations, generates question response transcripts by performing speech-to-text conversion on an audio portion of each of the video files to create interview question transcripts. In some examples, a natural language classifier can be specifically trained to detect positive and negative polarizations of the personality aspects from the personality model within an interview question transcript. , and interpreting the one or more non-verbal cues (Col 7 lines 30-36 the data management engine 132 can also link captured nonverbal response features 126 (e.g., facial expression and prosodic features) and verbal response features 128 (e.g., speed/latency of the response along with the response) to the respective interview question data 112, question scores 120 and candidate profile data 118., Col 8 lines 29-32 the interview management engine 144 can obtain baseline verbal (e.g., speed and latency of response) and nonverbal (e.g., facial expression and prosody) data from the candidate 102 via the UI screen 400.);
Preuss discloses a data processing subsystem configured to process the analyzed responses of the one or more candidates to determine one or more contextual attributes associated with the responses using one or more machine learning (ML) models (Col 12 lines 21-28 The automated reaction assessment system 108, in some implementations, can also include a classification engine 138 that is configured to apply one or more trained machine learning algorithms to classify one or more detected facial expression features and/or prosodic features as being associated with a positive (yes), neutral, or negative (no) response to a close-ended question.) , wherein the one or more contextual attributes comprise at least one of: one or more verbal attributes, one or more non-verbal attributes (Col 12 lines 21-28 The automated reaction assessment system 108, in some implementations, can also include a classification engine 138 that is configured to apply one or more trained machine learning algorithms to classify one or more detected facial expression features (non-verbal) and/or prosodic features as being associated with a positive (yes), neutral, or negative (no) response to a close-ended question. Col 13 lines 26-32 the AI training engine 142 uses customized training data sets 124 to train each of the machine learning classifiers to detect nonverbal features (e.g., facial expression features and prosodic features) within video data capturing candidate responses to close-ended questions.) , one or more performance attributes, and one or more contextual interaction attributes;
Preuss discloses a query generating subsystem configured to automatically generate one or more follow-up interview questions to be delivered to the one or more candidates during the ongoing interview based on the analyzed responses from the one or more candidates (Col 21 lines 7-16 if the candidate's response to a question asking whether the candidate meets minimum education requirements is slow and/or weak relative to the benchmarked scores, then in some examples, the real-time calculation engine 504 may identify a follow-up interview question also associated with the candidate's educational experience.), by applying an AI model to the one or more contextual attributes associated with the responses ( Col 21 lines 18-28 if the candidate response scores for the interview question about educational experience meet and/or exceed the benchmarked values, then in some examples, the real-time calculation engine 504 may identify the next question to be one associated with a different subject, such as an amount of work experience. In some implementations, the real-time calculation engine 504 determines the next question to present to the candidate 102 based on just the response to the respective question. In other examples, the real time calculation engine 504 can determine the next question based on all of the candidate's previous scores, such as by using a cumulative score. Col 23 lines 19-29 the trustworthiness score can provide a measure of how well the spoken response and the nonverbal aspects of the response agree with one another based on the calculated trustworthiness score. For example, if a candidate 102 provides “yes” to a question asking whether the candidate meets minimum required education requirements, but the candidate's detected facial expression is “worried” and the prosodic features indicate uncertainty (wavering tone, upward intonation when responding), then the calculated trustworthiness score may be low, indicating that the candidate's response is not trustworthy. Col 24 lines 39-42 updating the profile estimation (534) may be performed before, after, or simultaneously with determining the next interview question (522).);
Preuss discloses a score generating subsystem configured to generate one or more scores for the one or more candidates based on at least one of: the analyzed responses, the one or more contextual attributes, and interpreted non-verbal cues, associated with the one or more candidates, (Col 18 lines 27-38 calculation engine 506 to determine at least one baseline prosody score (514) and at least one baseline facial expression score (516). The at least one baseline prosody score can include a baseline tone or inflection in the candidate's voice as the candidate 102 responds to a yes/no baseline prompt. The baseline prosody score, in some examples, can also include a baseline speed of verbal response for the candidate 102. Col 19 lines 65-67, Col 20 lines 1-10 the real-time calculation engine 504 calculates a response score from the captured response data based on benchmarked latency for the respective question (532). For example, each question in the interview question data 112 can have benchmarked scores associated with a candidate profile that reflects language and cultural dependencies. In some implementations, the benchmarked scores can include average or typical response speeds and/or latency for the respective question, and the calculated response score can represent an amount of difference between the captured response and the benchmarked data. (based on analyzed response);
Preuss does not specifically teach automatically generating one or more offer ranges for one or more candidates during an interviewing process; generate one or more recruitment scores using the AI model, a decision supporting subsystem configured to generate one or more offer ranges for the one or more candidates based on the one or more recruitment scores using the AI model, wherein the one or more offer ranges are configured to assist for one or more users in recruitment-related decision making; and an output subsystem configured to provide information associated with at least one of: one or more selected candidates, and the one or more offer ranges generated for the one or more selected candidates, to the one or more users through one or more user interfaces associated with one or more electronic devices of the one or more users.
Olshansky (US 11,144,882 B1) teaches at Col 6 lines 42-52 The system 100 can include a kiosk or booth 110. The booth 110 can include one or more cameras, microphones, and depth sensors, as will be discussed below in reference to FIG. 2. A candidate 104 can be located within the booth 110. The candidate can identify and authenticate themselves to the server 106 as a login entity. The candidate 104 can participate in a video interview while in the booth. If the candidate 104 is determined to be a good fit for the job opening, the candidate 104 can be connected for live communication with the employer 102 while still in the booth 110. (Col 6 lines 57-67) The booth 110 can provide prompts or questions to the candidate 104, through a user interface, for the candidate 104 to respond to. The candidate's response to each of the prompts can be recorded with the video cameras and microphones. The candidate's behavioral data can be recorded with a depth sensor. The system server 106 can analyze, evaluate, and update the candidate's known information while the candidate 104 is participating in the video interview (FIG. 4). Col 10 lines 60-67 FIG. 7, after the real-time connection between the employer 102 and the candidate 104 has ended, the system 100 can conduct certain activities to gather data about the live communications conducted during the real-time connection and based on the analysis conducted leading up to and during the real-time connection. An inquiry 758 is sent to the employer 102, asking whether the employer 102 would like to offer or has offered the job to the candidate 104 as shown in FIG. 7. Col 11 lines 1-12 If the employer 102 intends to send a job offer to the candidate 104, the system 100 can send a job offer 760 to the candidate 104. If the employer 102 does not intend to send a job offer to the candidate 104, the system 100 can still send information that is helpful to the candidate 104, such as sending an expected salary range 760 to the candidate 104. The expected salary range can be an output from a salary analysis module. Inputs to the salary analysis module include the candidate data and market data.)
Ashjian et al. (US 12,591,855) discloses managing an entire recruitment process, including the full review of candidate resumes, and generating an Resume Score, conducting interviews via an AI agent implemented by the Offerday system, and generating an interview score based on such interview, generating an ApplicantIQ score, filtering and recommending and selecting candidates, and making offers of employment to selected candidates. The interview generation sub-module may generate data and analytical scores related to those activities, and filtering, recommending, selecting candidates, and/or making offers of employment to selected candidates (e.g., based on such data and/or analytical scores),
Lifferth (US 2023/0360000 A1) discloses an Artificial Intelligence (AI) based recruitment management, the method comprising. An AI-based tracker module having a graphical user interface is configured for receiving a plurality of job applications from a plurality of candidates via respective user devices. The tracker is configured to predict the hiring probability of a candidate based on: a compensation viability score, a speed score, a skill sets of the candidate. [0010] calculate a compensation viability score (CVS score) for each of the received job applications to determine a compensation range for a particular job position [0014] the compensation viability score (CVS) is calculated based on the candidate's indicated salary.
CN119359269 A1 discloses AI interview invitation can be used for displaying interview invitation received by job-seeking user; The AI interview feedback can be used for displaying the feedback result of the interview video delivery of the job-seeking user based on the online AI video interview. For example, when the job-seeking user selects the “AI interview invitation", the job-seeking terminal can display the received interview invitation card in the page, each interview invitation card at least can comprise the corresponding position name, salary, user head portrait of the user, name, enterprise name, receiving time and interview control and so on, if the job-seeking user thinks that the corresponding post is more in accordance with its own requirement, then the online AI video interview can be triggered through the "immediate interview" control, so as to effectively simplify the interview flow of the job-seeking user through the online video interview.
JP2018181257 discloses the interview management server 110, the interview results of job seekers can be easily grasped by the average score or the like based on the evaluation sheet 71, and the efficiency of the judgment of employment can be enhanced. The interview management server 110, when conducting an online interview, the job offer side screen 41 shown in FIG. 3 can be displayed on the job offer side PC 10, and evaluation is performed while watching the job seeker's online video 61 and resume data 63
KR2001008121 A1 discloses An annual salary calculations system and the method are provided to objectively assess a level of an annual salary of a person by considering his work performance capability based on an online QA interview, and use the assessment data in seeking a worker.
NPL, Monedero, “The datafication of the workplace”, 2019 discusses Workable, which incorporates a set of AI-powered tools for hiring management, including sourcing. It features careers pages, job advertising in general and professional networks, social recruiting, employee referrals, people search and resumé parsing. Work able performs filtering of people in the network13 but also adds external candidates to the pool, for instance while browsing source code repositories such as Github14. The ML tools perform a ranking of candidates based on their experience and skills, but also on other estimated characteristics such as performance or the likelihood of them staying in the job. For instance, one of the features of the chatbot Mya is to engage with applicants, ask for additional information, answer questions the candidate has about the role, but also to assess eligibility and to deliver a ranked selection of candidates28. (Fig 9) Some tools allow for the personalisation of offers and predicts if the applicant is likely to accept the offer (Bogen and Aaron 2018). As an example, Oracle’s Recruiting Cloud48 promises they can estimate, based on previous data (although the details of what kind of data is not specified), the likelihood of a candidate accepting a job and how different changes in the offer will increase or decrease this probability offer (Bogen and Aaron 2018).
IN202521088120A discusses artificial intelligence (AI) based system and method for conducting mock interviews to candidates The present invention is designed to enhance the recruitment process through intelligent interview simulations. This platform leverages advanced AI technology to generate interview questions and provide tailored feedback, enabling both companies and job seekers to benefit from a structured interview experience.
However, the prior art fails to teach or suggest at least “generate one or more recruitment scores using the AI model; a decision supporting subsystem configured to generate one or more offer ranges for the one or more candidates based on the one or more recruitment scores using the AI model, wherein the one or more offer ranges are configured to assist for one or more users in recruitment-related decision making; and an output subsystem configured to provide information associated with at least one of: one or more selected candidates, and the one or more offer ranges generated for the one or more selected candidates, to the one or more users through one or more user interfaces associated with one or more electronic devices of the one or more users.”. The prior art teachings as recited above fail to set forth any sufficient rationale for combining or otherwise modifying any of the relevant prior art to arrive at the claimed invention, as a whole. To arrive at the claimed invention with the precise combination of claimed features would not have been obvious to one of ordinary skill in the art without relying on improper hindsight to substantially reconstruct Applicant's claimed invention.
Thus, the aforementioned combination of features claimed, as a whole, are not anticipated nor rendered obvious for any sufficient rationale by any of the prior art teachings. Furthermore, the prior art of record does not anticipate nor render obvious the combination of limitations for the dependent claims due to their respective dependencies to the independent claims 1, 10 and 19.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Champaneria (US11,126,970) discloses 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. (Col31 lines 59-64)
Ma (CN115630933) discusses The recruitment parameters include at least job information, such as job name, job location, salary range, skill requirement, education experience requirement and so on. the recruiting user when sending the interview creating request, the configuration of the parameter content one and sending to the recruitment processing system, such as the recruitment processing system 2 in FIG. 1.
Lewis (US 20020169631) discusses virtual interview process
Ismail (US 2026/0212576) discloses the metahuman's facial expressions, speech, and movements are synchronized to create a lifelike experience, making the interview as realistic as possible. In embodiments, the session begins with the user interacting with the metahuman.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANGEETA BAHL whose telephone number is (571)270-7779. The examiner can normally be reached 7:30 - 4PM.
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/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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