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 .
Status of Claims
This communication is a First Office Action Non-Final on Merits. Claims 1-20 are currently pending and have been considered below.
Priority
The present application, filed on 09/05/2024, claims priority to Provisional Application 63/580,469, filed on 09/05/2023.
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 without a practical application and significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1-10 are directed to a method (i.e. a process). Claims 11-20 are directed to a system (i.e. a machine). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1, analogous to independent claim 11 recites: A method of facilitating assessment of a suitability of a candidate for a role, the method comprising: receiving, a reference cognitive data associated with an entity, wherein the reference cognitive data is associated with a desired candidate associated with the role corresponding to the entity; receiving, a candidate cognitive data associated with the candidate from at least one of the entity, wherein the candidate cognitive data corresponds to a cognitive skill of the candidate; analyzing, the candidate cognitive data and the reference cognitive data; determining, a cognitive parameter based on the analyzing; and transmitting at least one of the cognitive parameter and a selection data based on the cognitive parameter to the entity. The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under the abstract groupings of:
Certain methods of organizing human activity (commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations; (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). As the claims discuss assessment of a suitability of a candidate for a role, which is a clear business relations, and one of certain methods of organizing human activity. Mental Processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion (independent claim 1 recites: ”assessment of a suitability of a candidate for a role”; “: receiving a reference cognitive data associated with an entity”; “receiving a candidate cognitive data associated with the candidate from at least one of the entity”; “candidate cognitive data corresponds to a cognitive skill of the candidate”; “analyzing, the candidate cognitive data and the reference cognitive data”; “determining a cognitive parameter based on the analyzing”; “transmitting at least one of the cognitive parameter and a selection data based on the cognitive parameter”.) Concepts performed in the human mind as mental processes because the steps of assessing, receiving, corresponding, determining, and analyzing data mimic human thought processes of observation, evaluation, judgement and opinion, perhaps with paper and pencil, where data interpretation is perceptible in the human mind. See In re TLI Commc’ns LLCPatentLitig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)). Dependent claims 2-10 and 12-20 add additional limitations, for example: (claims 2 and 12) wherein each of the candidate cognitive data and the reference cognitive data corresponds to a plurality of cognitive skills comprising an attention span, a perceptive skill, a memory skill, a logical reasoning skill, a problem solving skill, and a processing skill; (claims 3 and 13) transmitting a candidate assessment data to at one of the candidate and the entity, present the candidate assessment data to the candidate, wherein the candidate cognitive data is based on a response corresponding to the candidate cognitive assessment, wherein the response is generated by the candidate; (claims 4 and 14) receiving from at least one of a candidate and the entity, generate the cognitive data associated with the cognitive skill of the candidate; (claims 5 and 15) wherein the reference cognitive data comprises a standard cognitive data associated with a desired cognitive skills data associated with the role; (claims 6 and 16) generating, a recommendation plan associated with development of the cognitive skills of the candidate, wherein the generating of the recommendation plan is based on the candidate cognitive data and the reference cognitive data; and transmitting the recommendation plan to at least one of the candidate and the entity; (claims 7 and 17) transmitting, a self-assessment data to at least one of the candidate and the entity, present a self-assessment data to the candidate, wherein at least one of the candidate and the entity receive a response corresponding to the self-assessment data from the candidate; receiving, the response; generating, a recommended role data associated with the entity, wherein generation of the recommended role data is based on the response and the cognitive parameter; and transmitting, the recommended role data to at least one of the candidate and the entity; (claims 8 and 18) wherein the analysis of the reference cognitive data and the candidate cognitive data is based on determine a pattern associated with the reference cognitive data and the candidate cognitive data; (claims 9 and 19) wherein the reference cognitive data is based on a reference response data corresponding to a reference assessment data, wherein the reference response data is provided by a plurality of desired candidates associated with the role; (claims 10 and 20) generate a scaled score corresponding to reference response data corresponding to each of the plurality of desired candidates, wherein a reference cognitive data is generated using the scaled score based on a criterion and a mean of the scaled score, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of methods of certain methods of organizing human activity and mental processes but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – (claims 1 and 11) communication device, entity device; candidate device, processing device; (claims 3 and 13) end processing device, end communication device, end presentation device; (claims 4 and 14) communication device, sensor data; (claims 7 and 17) end processing device, end input device, end presentation device; (claims 8 and 18) machine learning model. These 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 generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it 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). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing or machine learning, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, claims 1-20 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations fail to 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. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. 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.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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 non-obviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Polli et al. (US 2021/0264371 A1), hereinafter “Polli”, over Orbach (US 2021/0027867 A1), hereinafter “Orbach”.
Regarding Claim 1, Polli teaches A method of facilitating assessment of a suitability of a candidate for a role, the method comprising: (Polli, para 0003, teaches systems and methods that can be used by companies and entities to: (1) identify talent that is tailored to a company's needs for a specific job position, and (2) identify top employees and recommend placement of those employees in positions that optimize their potential);
receiving, using a communication device, a reference cognitive data from an entity device associated with an entity, wherein the reference cognitive data is associated with a desired candidate associated with the role corresponding to the entity; (See at least Polli, Figures 4-5; See at least Polli para 0320 and Figure 36 teaches the computing environment including a plurality of computer systems and a plurality of cell phones; para 0148, An impulsive subject can be viewed favorably if the company desires a subject more willing to take risks, think creatively, and act quickly); receiving, using the communication device, a candidate cognitive data associated with the candidate from at least one of a candidate device and the entity device, wherein the candidate cognitive data corresponds to a cognitive skill of the candidate; (Polli, para 0159, the traits extraction engine can determine whether a user has correctly selected, placed, and/or used different objects to complete a required neuroscience-based task. The traits extraction engine can assess the user's learning and cognitive skills);
analyzing, using a processing device, the candidate cognitive data and the reference cognitive data; (Polli, para 0018, processors may be configured to analyze the input data to assess each participant's cognitive skills);
determining, using the processing device, a cognitive parameter based on the analyzing; and (Polli, para 0010, the one or more processors may be configured to generate a fit score for the candidate based on the comparison of the measurements of the candidate's emotional and cognitive traits; Polli, para 0089, teaches the fit score can range from 0-100% and predict the likelihood that a subject would be suitable for a specific position or career industry. Examiner interprets the fit score as the cognitive parameter);
transmitting, using the communication device, at least one of the cognitive parameter and ... based on the cognitive parameter to the entity device (Polli, para 0163, The reporting engine may generate an overview of each candidate's suitability for a specific job position, based on assessments of the candidates' traits and fit scores for each candidate; Polli, para 0169, identify candidates, and present those candidates to a company for its hiring needs; Polli, para 0321, transmit data through network interfaces).
Yet, Polli does not appear to explicitly teach and in the same field of endeavor Orbach teaches a selection data (See at least Orbach, para 0015, teaches dimensions which are selected from the exemplary list (i) motivation, (ii) belief, (iii) know-how, (iv) capability, (v) state-of-mind, (vi) activity, and (vii) expectation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Polli with a selection data as taught by Orbach with the motivation to efficiently assess, prioritize, and re-assess individuals respectively for ongoing programs/activities or training activities (Orbach, Abstract). The Polli invention now incorporating the Orbach invention, has all the limitations of claim 1.
Regarding Claim 2, Polli, now incorporating Orbach, teaches The method of claim 1, wherein each of the candidate cognitive data and the reference cognitive data corresponds to a plurality of cognitive skills comprising an attention span, a perceptive skill, a memory skill, a logical reasoning skill, a problem solving skill, and a processing skill (Polli, para 0144, cognitive traits can be assessed and used by a business entity to determine whether a subject is suitable for employment. The cognitive traits that can be extracted or measured by the screening system may include, for example, processing speed, pattern recognition, continuous attention, ability to avoid distraction, impulsivity, cognitive control, working memory, planning, memory span, sequencing, cognitive flexibility, or learning).
Regarding Claim 3, Polli, now incorporating Orbach, teaches The method of claim 1 further comprising transmitting, using the communication device, a candidate assessment data to at one of the candidate device and the entity device, wherein each of the candidate device and the entity device comprises an end processing device, an end communication device and an end presentation device, wherein the end presentation device is configured to present the candidate assessment data to the candidate, wherein the candidate cognitive data is based on a response corresponding to the candidate cognitive assessment, wherein the response is generated by the candidate (See at least Polli para 0320 and Figure 36 teaches the computing environment including a plurality of computer systems and a plurality of cell phones; Polli, para 0321, teaches transmit data through network interfaces; Polli, para 0207, individual neuroscience-based assessments of the employees; Polli, para 0219, providing a computerized task to a subject. The task can be a neuroscience-based assessment of emotion or cognition. Upon completion of the tasks, the system can measure a performance value of the subject based on the subject's performance on the task).
Regarding Claim 4, Polli, now incorporating Orbach, teaches The method of claim 1 further comprising receiving, using the communication device, a cognitive ... data from at least one of a candidate device and the entity device, wherein each of the candidate device and the entity device comprises a ... configured to generate the cognitive ... data associated with the cognitive skill of the candidate (See at least Polli, Figures 4-5; See at least Polli para 0320 and Figure 36 teaches the computing environment including a plurality of computer systems and a plurality of cell phones; para 0148, An impulsive subject can be viewed favorably if the company desires a subject (candidate) more willing to take risks, think creatively, and act quickly (Examiner notes cognitive skills)).
Yet, Polli does not appear to explicitly teach and in the same field of endeavor Orbach teaches sensor (See at least Orbach, para 0022, teaching sensors (camera, microphone, touch pad, and even physiology specific appurtenances) of smart-phones, personal computers, and the like—may be employed to provide respective characteristic (e.g. emotion, heart rate variability, breathing rate, voice analysis, face expression analysis, etc.)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Polli with sensor as taught by Orbach with the motivation to efficiently assess, prioritize, and re-assess individuals respectively for ongoing programs/activities or training activities (Orbach, Abstract).
Regarding Claim 5, Polli, now incorporating Orbach, teaches The method of claim 1, wherein the reference cognitive data comprises a standard cognitive data associated with a desired cognitive skills data associated with the role (Polli, para 0169, sourcing models can be used by companies and recruiters to ‘source’ for talent. A user may use the sourcing models to identify candidates who meet a cut-off threshold, and present those candidates to a company for its hiring needs. Examiner interprets the cut-off threshold as standard data associated with desired skills associated with role.)
Regarding Claim 6, Polli, now incorporating Orbach, teaches The method of claim 1 further comprising: generating, using the processing device, a recommendation plan associated with development of the cognitive skills of the candidate, wherein the generating of the recommendation plan is based on the candidate cognitive data and the reference cognitive data; and (See at least Polli, para 0004, assist individuals in career-planning; Polli, para 0132, the screening system can extract emotional and cognitive traits from a candidate or a subject's neuroscience-based data, to determine the candidate's likelihood of succeeding in a specific job position);
transmitting, using the communication device, the recommendation plan to at least one of the candidate device and the entity device (Polli, para 0086, assist individuals in career-planning and talent identification. By using tests that measure a wide array of emotional and cognitive traits, the system and methods can ascertain the strengths and weaknesses of a test subject and apply that information to recommend which field(s) are suitable; Polli, para 0321, teaches transmit data).
Regarding Claim 7, Polli, now incorporating Orbach, teaches The method of claim 1 further comprising: transmitting, using the communication device, a self-assessment data to at least one of the candidate device and the entity device, wherein each of the candidate device and entity device comprises an end processing device, an end input device and an end presentation device, wherein the end presentation device is configured to present a self-assessment data to the candidate, wherein at least one of the candidate device and the entity device is configured to receive a response corresponding to the self-assessment data from the candidate; (See at least Polli para 0320 and Figure 36 teaches the computing environment including a plurality of computer systems and a plurality of cell phones; Polli, para 0321, teaches transmit data through network interfaces; Polli, para 0226, self-report measures completed by the test subjects; See also Orbach, Abstract, self-assessment responses from the client, regarding the client's respective motivation, belief, know-how, state-of-mind, activity, etc.);
receiving, using the communication device, the response; generating, using the processing device, a recommended role data associated with the entity, wherein generation of the recommended role data is based on the response and the cognitive parameter; and transmitting, using the communication device, the recommended role data to at least one of the candidate device and the entity device (Polli, para 0130, the model analytics engine can identify a subject's career propensity ... The subject may be a job-seeker, someone seeking to switch to a different field, a recent college graduate, post-graduates, a student, or individuals seeking assistance regarding career planning. ... may use the results to recommend one or more suitable careers to the subject; Polli, para 0131, generate a fit score for the subject by comparing the subject's traits against a plurality of models. The screening system can use the fit score to determine the subject's career propensity and recommend suitable career fields to the subject; Further, Polli, para 0132, the screening system can extract emotional and cognitive traits from a candidate or a subject's neuroscience-based data, to determine the candidate's likelihood of succeeding in a specific job position).
Regarding Claim 8, Polli, now incorporating Orbach, teaches The method of claim 1, wherein the analysis of the reference cognitive data and the candidate cognitive data is based on a machine learning model configured to determine a pattern associated with the reference cognitive data and the candidate cognitive data (Polli, para 0119, the model analytics engine can methodically identify certain traits and establish their relationships by analyzing the employees' behavioral output and job performance ratings using machine learning algorithms. Examiner notes machine learning models are designed to determine patterns in data).
Regarding Claim 9, Polli, now incorporating Orbach, teaches The method of claim 1, wherein the reference cognitive data is based on a reference response data corresponding to a reference assessment data, wherein the reference response data is provided by a plurality of desired candidates associated with the role (Candidate response data is taught throughout Polli, see at least para 0247-0249, teaches for example the Flanker Test - a set of response inhibition tests used to assess the ability to suppress responses that are inappropriate in a particular context. The Flanker Task can be used to assess selective attention and information processing capabilities. A target can be flanked by non-target stimuli, which correspond either to the same directional response as the target, to the opposite response).
Regarding Claim 10, Polli, now incorporating Orbach, teaches The method of claim 9, wherein the processing device is further configured to generate a scaled score corresponding to reference response data corresponding to each of the plurality of desired candidates, wherein a reference cognitive data is generated using the scaled score based on a criterion and a mean of the scaled score (Polli, para 0089, score can range from 0-100% and predict the likelihood that a subject would be suitable for a specific position or career industry. Prior to performing prediction analyses, the system can quantify the relationships in existing data, and the quantification can identify the main features of the data and provide a summary of the data. For example, before the system can predict whether a particular candidate can succeed at a specific company as a management consultant, the system can build a descriptive model of the relationship... The system's analytics engine can implement various data mining and clustering algorithms for unsupervised classification to generate these descriptive models. Examiner notes different variations of scores are certainly within the ability of those having ordinary skill in the art.)
Regarding Claim 11 , the claim is an obvious variant to claim 1 above, and is therefore rejected on the same premise. Polli teaches a system (See at least Polli, Abstract, teaches systems and methods used to assist in recruitment processes for employees).
Regarding claim 12, the claim recites analogous limitations to claim 2 above, and is therefore rejected on the same premise.
Regarding claim 13, the claim recites analogous limitations to claim 3 above, and is therefore rejected on the same premise.
Regarding claim 14, the claim recites analogous limitations to claim 4 above, and is therefore rejected on the same premise.
Regarding claim 15, the claim recites analogous limitations to claim 5 above, and is therefore rejected on the same premise.
Regarding claim 16, the claim recites analogous limitations to claim 6 above, and is therefore rejected on the same premise.
Regarding claim 17, the claim recites analogous limitations to claim 7 above, and is therefore rejected on the same premise.
Regarding claim 18, the claim recites analogous limitations to claim 8 above, and is therefore rejected on the same premise.
Regarding claim 19, the claim recites analogous limitations to claim 9 above, and is therefore rejected on the same premise.
Regarding claim 20, the claim recites analogous limitations to claim 10 above, and is therefore rejected on the same premise.
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Nitta US 2018/0158025 – Exploration based cognitive career guidance system - An exploration-based career guidance system is disclosed. The career guidance system receives an assessment regarding a candidate and identifies a first set of roles for the candidate based on the assessment. The system receives a selection of a role from among the first set of roles and provides a simulated experience of the selected role and receives a set of interaction data from the simulated experience. The system adjusts the assessment regarding the candidate based on the set of interaction data and identifies a second, different set of roles for the candidate based on the adjusted assessment.
NPL - Itzhak Aviv; Artem Barger; Slava Pyatigorsky, “Novel Machine Learning Approach for Automatic Employees’ Soft Skills Assessment: Group Collaboration Analysis Case Study”, Publisher IEEE, Published 2021, https://ieeexplore.ieee.org/document/9626760 - Machine Learning (ML) based assessment of soft skills is a challenging domain in social computing. In today’s enterprises, soft skills are regarded as one of the most crucial components for success. Based on data obtained from internal organizational sources, we evaluated the viability of utilizing ML to assess Group Communication Analysis (GSA) skills. We leveraged Latent Semantic Analysis (LSA) to understand and learn cognitive phenomena extracted from the communication extracted from the issue tracker of several popular open-source projects, with a specific input representation that allows the model to look for contextual cues in consecutive utterances. We looked at the data of 1520 engineers from three open-source software development projects that worked on an internal line of business operational systems. The ML models revealed five interpersonal and intrapersonal socio-cognitive GSA metrics in 10583 utterances and procedures, which were integrated into five clusters that assign socially engaged employee roles. Because there has not been any research on assessing employees’ Soft Skills utilizing ML, our proposed method is a first in this field, relying on internal organizational datasets to obtain reliable Soft Skills assessment evaluation.
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA R NOVAK whose telephone number is (571)272-2524. The examiner can normally be reached Monday - Friday 8:30am - 5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/R.R.N./Examiner, Art Unit 3629/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629