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
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 13, 2026 has been entered.
Status of Claims
Claims 21, 30 and 36 have been amended.
Claims 21-40 are currently pending and have been examined.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 21-29 are drawn to methods while claim(s) 30-40 is/are drawn to an apparatus. As such, claims 21-40 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
Claim 21 (representative of independent claim(s) 30 and 36) recites the following steps:
A computer-implemented method for determining a match between an educational institution and a prospective student, the method comprising:
causing present[ation] to the prospective student,
inflexible institution criteria associated with the prospective student, the inflexible institution criteria comprising a tuition cost threshold or a desired geographic location;
objective student data associated with the prospective student, the objective student data comprising a grade point average, a test score, a degree program, and demographic information; and subjective student data associated with the prospective student, the subjective student data comprising sentiments toward values and experiences;
generating [information] associated with the prospective student based on the objective student data;
generating [information]associated with the prospective student based on the subjective student data;
causing present[ation] to a former student associated with the educational institution
objective former student data associated with the former student, the objective former student data comprising employment information and demographic information; and
subjective former student data associated with the former student, the subjective former student data comprising sentiments toward values and experiences during enrollment at the educational institution;
generating [information] associated with the former student based on the objective former student data;
generating [information] associated with the former student based on the subjective former student data;
determining a subset of educational institutions that meet the inflexible institution criteria associated with the prospective student;
determine an objective matching index;
determine a subjective matching index;
determining the match between the educational institution and the prospective student based on the objective matching index, the subjective matching index, and inclusion of the educational institution in the subset of educational institutions; and
present an indication of the determined match to the prospective student
These steps, under its broadest reasonable interpretation, describe or set-forth determining matches between an institution and a prospective student, which amounts to a [“managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)”. These limitations therefore fall within the "certain methods of organizing human activity" subject matter grouping of abstract ideas.
Alternatively, these steps, under its broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) determining matches between an institution and a prospective student (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the "mental processes" subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 21 recites an abstract idea (Step 2A - Prong One: YES).
Independent claim(s) 30 and 36 is determined to recite an abstract idea under the same analysis.
Step 2A - Prong Two:
This judicial exception is not integrated into a practical application. The claim(s) recite the additional elements/limitations of:
a first user interface
via a first computing device
the first user interface employing a first artificial intelligence interview bot
a first input vector
a second input vector
a second user interface to be
via a second computing device, the second user interface employing a second artificial intelligence interview bot
a first training vector
a second training vector
training a first neural network using the first training vector;
training a second neural network using the second training vector;
applying the first input vector associated with the prospective student as input to the first neural network
applying the second input vector associated with the prospective student as input to the second neural network
wherein, the first and second neural networks are decomposed into one or more sub-models, each of which corresponds to a specific set of shared demographic characteristics of a predefined group of former students and wherein, the objective and subjective matching indices are determined using sub-models for demographic characteristics that match those of the prospective student.
A computing system for determining a match between an educational institution and a prospective student, the system comprising: memory comprising computer readable instructions; and processing circuitry (Claim 30)
A non-transitory computer-readable storage medium having instructions stored thereon that, when executed by processing circuitry (Claim 36)
The requirement to execute the claimed steps/functions listed above is equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. This/these limitation(s) do/does not impose any meaningful limits on producing the abstract idea and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A -Prong Two: NO).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above in "Step 2A - Prong 2", the requirement to execute the claimed steps/functions listed above is equivalent to adding the words "apply it" on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as "significantly more" (see MPEP 2106.05 (f)).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Regarding Dependent Claims:
Dependent claims 22, 24-26, 28, 31, 33-35, 37, and 39, fail to include any additional elements and are further part of the abstract idea as identified by the Examiner.
Dependent claims 23, 27, 29, 32, and 38 include additional limitations that are part of the abstract idea except for:
wherein the first neural network comprises a first supervised neural network and the second neural network comprises a second supervised neural network. (Claim 23 and 32 and 38)
the first training vector (Claim 27)
third user interface (Claim 29)
via a third computing device, the third user interface employing the artificial intelligence interview bot (Claim 29)
a third training vector (Claim 29)
a fourth training vector (Claim 29)
training the first neural network using the third training vector; and training the second neural network using the fourth training vector (Claim 29)
The additional elements of the dependent claims are equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Patent Publication: 11,301,945 Recruiting And Admission System teaches “ the system may evaluate prospects which have applied for admission to the institution to generate recommendations for acceptance by an admission officer. The system may recommend a prospect based on the prospect's potential for success in the institution. Success may correspond to metrics such as a likelihood of graduating from the educational institution or a likelihood of obtaining employment subsequent to graduating from the institution. Prospects sharing attributes with previously successful students may be selected for recommendation.” But does not explicitly teach “determining the match between the educational institution and the prospective student based on the objective matching index, the subjective matching index, and inclusion of the educational institution in the subset of educational institutions…”
Non-Patent Literature: S. Fong, Y. -W. Si and R. P. Biuk-Aghai, "Applying a hybrid model of neural network and decision tree classifier for predicting university admission," 2009 7th International Conference on Information, Communications and Signal Processing (ICICS), Macau, China, 2009, pp. 1-5, doi: 10.1109/ICICS.2009.5397665.
Where this references teaches “This paper proposes a hybrid model of neural network and decision tree classifier that predicts the likelihood of which university a student may enter, by analyzing his academic merits, background and the university admission criteria from that of historical records,” but does not teach “generating a first input vector associated with the prospective student based on the objective student data; generating a second input vector associated with the prospective student based on the subjective student data…”
Response to Arguments
Applicant's arguments filed with respect to the rejection under 35 USC 101 have been fully considered but they are not persuasive.
Applicant Argues: Applicant submits that with the amendment, claims 21, 30 and 36 represent a specific technical improvement over the prior art.
Examiner maintains the previous response. The Examiner notes that this claim of significantly more is not representative of an "actual" improvement to the technology itself, but at best is an improvement to the business method or abstract idea itself. In fact, Applicant can provide no tangible findings that there was actually anything different and/or improved in the instant system compared to prior "conventional systems", other than a mere allegation and unsubstantiated, conclusory statement that the instant invention improves existing systems and is significantly more than using mere instructions to implement an abstract idea on a computer. The Examiner respectfully notes that the features of the claimed invention do not represent an improvement, it is merely performing operations using an interface/ device. The amended language addressing the sub-models is only described by its function and does not represent a technical improvement. Moreover, the Examiner respectfully notes that the needed "improvement" in terms of patent eligibility is not one resulting from programming a generic processor to perform a different (or even improved) function, but rather a specific and actual improvement to the machine itself is needed. Based on these findings of fact, the Examiner contends the claims are indeed directed towards an abstract idea and Applicant's arguments to the contrary are considered to be non-persuasive.
Applicant Argues: Applicant's independent claims 21, 30, and 36 recite a particular solution that includes (i) using a first artificial intelligence interview bot to elicit the specific data from the prospective student that is then used to assemble the two input vectors, (ii) using a second artificial intelligence interview bot to elicit the specific data from the former student that is then used to assemble the two training vectors, (iii) explicitly training the first neural network and the second neural network using the two training vectors, respectively.
Examiner respectfully disagrees. The Specification fails to clearly evidence how the use of a neural network being trained with prospective and former student vector data is an actual technological improvement over, or differs from, the expected general concept of training using neural networks. It is unclear how the neural network is being integrated in any specialized manner that serves any specialized technical purpose/solution. The amended claims directed to a first and second neural network does not enhance the claims to patent eligible subject matter.
The claims are not rooted in machine learning technology, and the claims do not solve a technical problem that only arises in AI or machine learning. MPEP § 2106.05(a).
The amended limitations being referred to simply apply data analysis to train the generic first and second neural network and do nothing more than use computational instructions to be implemented in a computer processing environment, simply to "apply it" without any improvement to the computer functionality or technology itself.
Applicant Argues: Applicant's independent claims 21, 30, and 36 provide an improvement in the technical field of educational software systems, and therefore recite significantly more than any alleged abstract idea.
Examiner respectfully disagrees. Referring to the Recentive Analytics v. Fox Corp decision, the U.S. Court of Appeals for the Federal Circuit affirmed the district court’s dismissal of a patent infringement lawsuit brought by Recentive Analytics against Fox Corporation, where it was determined that the machine learning models employed were conventional. The Federal Circuit reaffirmed that iteratively training a machine learning model on data does not transform an abstract idea into a patent-eligible invention. Similarly, confining the trained machine learning model to a particular technological field is insufficient unless the implementation introduces a specific, non-generic improvement to computing technology and describes how this improvement is accomplished. It is important to note that most machine learning models are inherently trained on large, often complex datasets to generate a match or pair within the large data set. It is not apparent that such a non-generic improvement is reflective in the instant claims as the claims do not provide any detail that addresses any improvement to the broadly claimed training step. As such the rejection is maintained.
Furthermore, Examiner maintains the previous response. Applicant’s alleged improvement is not directed to an improvement to computer functionality/capabilities, an improvement to a computer-related technology or technological environment, and do not amount to a technology-based solution to a technology-based problem. A showing that a claim is directed to any improvement does not automatically mean a claim is patent eligible (e.g., an improved business function or an improved idea itself is not patent eligible). In this case, determining a match between an educational institution and a prospective student, is an abstract idea, and an “improved” way of determining a match between an educational institution and a prospective student using conventionally cited neural networks, if anything, an improvement to the idea itself.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RASHIDA R SHORTER whose telephone number is (571)272-9345. The examiner can normally be reached Monday- Friday from 9am- 530pm.
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/RASHIDA R SHORTER/Primary Examiner, Art Unit 3626