DETAILED OFFICE ACTION
Status of the Application
This Office Action is in response to Application Serial 19/221,178. Claims 1-20 are examined below.
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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statement
Applicant did not submit an information disclosure statement (IDS) for consideration by the examiner.
Drawings
The drawings are objected to because Figures 3A, 3B, and 3C gray scale. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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: “… utilizing an enhanced survey module, wherein the analysis comprises a combination of qualitative data analysis and quantitative data analysis” in claim 1, and “ the enhanced survey module via a trained machine learning module,” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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 1-9 are process.
Claims 10-17 are process.
Claims 18-20 machine.
Claims 1- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims (claim 10 and similarly claim 1 and claim 18) recite, “ … identifying, … a plurality of data types associated with input data; determining, … a plurality of parameters corresponding to each data type of the plurality of data types; analyzing, … the plurality of parameters utilizing …, wherein the analysis comprises a combination of qualitative data analysis and quantitative data analysis; calibrating … the enhanced … to generate a notification associated with the analysis of the plurality of parameters; dynamically updating, … the notification based on an output of the calibration … and the analysis, the notification comprising at least one recommendation for subsequent action based on the analysis; and automatically executing, …, the at least one recommendation.” The claims 1- 20 recite the abstract concept of identifying different kinds of data within it, and assigning parameters to each data type, and generating notification that includes one or more recommendations.
(Examiner notes the claim 1 and 18 limitations are broader than claim 10; however, the abstract idea in the independent claims 1 and independent claim 18 are similar to the abstract idea in claim 10.)
The associating data types with input data, determining corresponding data, and analyzing data to make a recommendation are observations and evaluations that can be completed in the human mind and using pen and paper. The observation and evaluation of data and calibration of data are mental concepts. The claims recite mental concepts, which is/are an abstract concept(s). The limitations recite abstract concepts that are grouped as mental concepts; thus, the claims are directed to a judicial exception under the first prong of Step 2A.
The judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of, “A computer-implemented method,” “by a processor,” “an enhanced survey module,” “survey module via a trained machine learning module” in claim 10 (and similarly at claim 18); however, when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05 (f).
The dependent claims do not recite additional elements beyond the additional elements that are recited in the independent claims.
Regarding the Applicant’s limitations that recite, “the enhanced survey module via a trained machine learning module”, the claim is using the machine learning module, which is apply it – MPEP 2106.05(f). Machine learning when broadly recited can be a mathematical calculation. As recited the claims are using a computer to perform the data analysis and calibration of data. Applicant is encouraged to clarify machine learning and calibration as supported by the instant specification. Additionally, Applicant is Subject Matter Eligibility Guidance Example 47.
The claims 1-20 fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses 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.
At Step 2A prong two, the claims do not integrate the judicial exception into a practical application. Furthermore, the claims do not recite an improvement that is rooted in technology. The claims are using a computer to conduct the abstract concept. The abstract concept is improved using a computer. The abstract concept is not integrated into a practical application, so the judicial exception is not integrated into a practical application.
At Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, because the additional elements when considered both individually and as an ordered combination do not amount to significantly more. (See MPEP 2106.05 (f) Mere Instruction to Apply an Exception – Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct at 235.)
At Step 2B, it is MPEP 2106.05 (d) – Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function (s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified exception (the abstract idea). Looking at the limitation as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent claims 2-9 further narrow the abstract idea of independent claim 1. Dependent claims 11-17 further narrow the abstract idea of independent claim 10. Dependent claims 19 and 20 further narrow the abstract idea of independent claim 18. The claims 1-20 are not patent eligible and do not amount to significantly more.
Claims 1- 20 are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1- 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen (2023, Ethics and discrimination in artificial intelligence-enabled recruitment practices) in view of Harris (2023, Mitigating Age Biases in Resume Screening AI Models).
Regarding Claim 1,
A computer-implemented method comprising: identifying, by a processor, a plurality of data types associated with input data; determining, by the processor, a plurality of parameters corresponding to each data type of the plurality of data types; analyzing, by the processor, the plurality of parameters utilizing an enhanced survey module, wherein the analysis comprises a combination of qualitative data analysis and quantitative data analysis; dynamically generating, by the processor, a notification based on the analysis, the notification comprising at least one recommendation for subsequent action based on the analysis; and automatically executing, by the processor, the at least one recommendation via the enhanced survey module.
Similar to claim 10. Chen in view of Harris.
Regarding Claim 2,
The method of claim 1, wherein the plurality of parameters comprises outputs of calculated predictions corresponding to each data type of the input data.
[similar to claim 11] – See Chen and Harris.
Regarding Claim 3,
The method of claim 1, wherein the enhanced survey module further comprises a natural language processing module configured to analyze the plurality of parameters.
[similar to claim 12] – See Chen.
Regarding Claim 4,
The method of claim 1, wherein the dynamically generated notification further comprises a recommendation for subsequent action based on comparing the plurality of parameters to predetermined thresholds.
[Similar to claim 13] - See claim 10 Chen teaches AI and biases related to gender and race. Harris teaches parametric tests and statistical significance. Harris p.3
Regarding Claim 5,
The method of claim 1, wherein the input data comprises historical data associated with outputs of action-specific focus group discussions.
[Similar to claim 14] – See Claim 10 Chen.
Regarding Claim 6,
The method of claim 1, wherein automatically executing the at least one recommendation further comprises invoking an executable action to optimize system performance based on the analysis.
[similar to claim 15] – See Chen and Harris.
Regarding Claim 7,
The method of claim 1, wherein analyzing the plurality of parameters further comprises normalizing the plurality of parameters to account for variances in the input data.
[similar to claim 16] – See Chen and Harris.
Regarding Claim 8,
The method of claim 1, further comprising calibrating the enhanced survey module using a trained machine learning algorithm on the plurality of parameters prior to dynamically generating the notification.
See Claim 10 – Chen and Harris.
Regarding Claim 9,
The method of claim 1, wherein the dynamically generated notification is formatted to include assessments of equity, diversity, and inclusion metrics derived from the analysis.
[similar to claim 16] – See Chen
Regarding Claim 10,
A computer-implemented method comprising: identifying, by a processor, a plurality of data types associated with input data; determining, by the processor, a plurality of parameters corresponding to each data type of the plurality of data types; analyzing, by the processor, the plurality of parameters utilizing an enhanced survey module, wherein the analysis comprises a combination of qualitative data analysis and quantitative data analysis; calibrating, by the processor, the enhanced survey module via a trained machine learning module to generate a notification associated with the analysis of the plurality of parameters; dynamically updating, by the processor, the notification based on an output of the calibration of the enhanced survey module and the analysis, the notification comprising at least one recommendation for subsequent action based on the analysis; and automatically executing, by the processor, the at least one recommendation via the enhanced survey module.
Chen teaches algorithmic discrimination caused by AI enabled recruitment. Chen [abstract].; Chen Figure 1 illustrates screen of data, calibrations (removal), and notes reasons for exclusion.
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Chen teaches AI recruitment efficiency. Chen discloses artificial intelligence can accel erate the hiring procedure, produce an outstanding candidate experience, and reduce costs (Johansson and Herranen, 2019). It can bring job information to applicants faster, allowing them to make informed decisions about their interests early in the hiring process. Artificial intelligence can also screen out many uninterested applicants and remove them from the applicant pool, thus reducing the number of applicants recruiters need to select later. It is even possible to source reticent candidates with the help of artificial intelligence and have more time to concentrate on the best match.[ Chen p.3 column 2]
Chen teaches datasets serve as the foundation of machine learning (ML). If an algorithm’s data collection lacks quantity and quality, it will fail to represent reality objectively, leading to inevitable bias in algorithmic decisions. Chen [p.5];
Chen teaches the hiring process, insufficient data may exclude historically underrepresented groups (Jackson, 2021). Assessing the success of potential employees based on existing employees perpetuates a bias toward candidates who resemble those already employed (Raghavan et al., 2020). Chen discloses as AI improves the algorithm, the model accommodates the lack of representation, reducing sensitivity to the underrepresented groups. The algorithm favors the represented group, operating less effectively for other groups (Njoto, 2020). Chen [p.6].
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Harris teaches parametric tests and statistical significance. Harris [p.3]; Harris teaches a fairness metric, which is a calibration of bias. Harris [abstract].
Chen teaches AI and discrimination theory. Harris examines discrimination against older job seekers seeking new employment. It would have been obvious to combine before the effective filing date, examining hiring algorithms, as taught by Chen, with training an AI model and applying bias correction techniques, as taught by Harris, to ad correct for biases based on race, gender, and age. Harris [abstract].
Regarding Claim 11,
The method of claim 10, wherein the plurality of parameters comprises outputs of calculated predictions corresponding to each data type of the input data.
Chen teaches various organizations have issued principles promoting equity, ethics, and responsibility in AI (Zuiderveen Borgesius, 2020). The Organization for Economic Cooperation and Development (OECD) has provided recommendations on AI, while the European Commission has drafted proposals regarding the influence of algorithmic systems on human rights. In 2019, the European Commission established a high-level expert group on AI, which proposed ethical guidelines and self-regulatory measures regarding AI and ethics. Chen [p.8]
Regarding Claim 12,
The method of claim 10, wherein the enhanced survey module further comprises a natural language processing module configured to analyze the plurality of parameters.
Chen uses artificial intelligence in recruitment. Chen teaches Natural language processing. Chen teaches Gender. Gender stereotypes have infiltrated the “lexical embed ding framework” utilized in natural language processing (NLP) techniques and machine learning (ML). Chen [p.6].
Regarding Claim 13,
The method of claim 10, wherein the dynamically generated notification further comprises a recommendation for subsequent action based on comparing the plurality of parameters to predetermined thresholds.
Chen teaches model construction. Chen teaches data bias, beliefs, and preconception through personal biases. Chen teaches where engineers considered education, occupation, and gender when assigning labels to the algorithm. When gender is considered the crucial criterion, it influences how the algorithm responds to the data. Chen [p.6]
Regarding Claim 14,
The method of claim 10, wherein the input data comprises historical data associated with outputs of action-specific focus group discussions.
Chen teaches The machine corpus contains biases that closely resemble the implicit biases observed in the human brain. Artificial intelligence has the potential to perpetuate existing patterns of bias and discrimination because these systems are typically trained to replicate the outcomes achieved by human decision-makers (Raso et al. 2018). What is worse, the perception of objectivity surrounding high-tech systems obscures this fact Chen teaches if an algorithmic system is trained on biased and unrepresentative data, it runs the risk of replicating that bias. Chen [p.6]
Regarding Claim 15,
The method of claim 10, wherein automatically executing the at least one recommendation further comprises invoking an executable action to optimize system performance based on the analysis.
Similar to claim 11 and claim 12. See Chen [p.8]
Regarding Claim 16,
The method of claim 10, wherein analyzing the plurality of parameters further comprises normalizing the plurality of parameters to account for variances in the input data.
See claim 10 . Chen [p.6],[p.8] and Harris [p.3], [abstract].
Chen teaches AI and discrimination theory. Harris examines discrimination against older job seekers seeking new employment. It would have been obvious to combine before the effective filing date, examining hiring algorithms, as taught by Chen, with training an AI model and applying bias correction techniques, as taught by Harris, to adjust the internal state to predict correctly the next time. Mesquita [p.4].
Regarding Claim 17,
The method of claim 10, wherein the dynamically generated notification is formatted to include assessments of equity, diversity, and inclusion metrics derived from the analysis.
Chen teaches AI-driven hiring applications impact various aspects, such as reviewing applicant profiles online, analyzing applicant information, scoring assessments based on hiring criteria, and generating preliminary rankings automatically. Secondly, interviewers perceive benefits in AI-driven recruitment for job seekers. It eliminates subjective human bias, facilitates automated matchmaking between individuals and positions, and provides automated response services. Moreover, AI reduces the workload on humans and enhances efficiency. See Chen [p. 9].
Regarding Claim 18,
A system comprising: non-transient computer memory, storing software instructions;it least one or more components of at least one processor configured to execute the software instructions that cause the at least one processor to perform steps to:identify a plurality of data types associated with input data; determine a plurality of parameters corresponding to each data type of the plurality of data types; analyze the plurality of parameters utilizing an enhanced survey module, wherein the analysis comprises a combination of qualitative data analysis and quantitative data analysis; dynamically generate a notification based on the analysis, the notification comprising at least one recommendation for subsequent action based on the analysis; and automatically execute the at least one recommendation via the enhanced survey module.
See claim 10. Chen and Harris.
Regarding Claim 19,
The system of claim 18, wherein the enhanced survey module further comprises a natural language processing module configured to analyze the plurality of parameters.
[similar to claim 12] – See Chen.
Regarding Claim 20,
The system of claim 18, wherein the software instructions further comprise calibrating the enhanced survey module using a trained machine learning algorithm on the plurality of parameters prior to dynamically generating the notification.
See claim 10 – Chen and Harris.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mesquita (2021, Python AI: How to Build a Neural Network & Make Predictions) –illustrates machine learning at a high level.
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/THEA LABOGIN/Examiner, Art Unit 3624
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