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 .
This communication is in response to application 17/886,294 filed on 08/11/2022. Claim 1 has been amended and new claims 2-18 are hereby entered. No claims are allowed.
Response to Arguments
Applicant’s arguments, see page 8 of Applicant remarks, filed 6/30/2025, with respect to claim 1 rejection have been fully considered and are persuasive. The objection of claim 1 has been withdrawn.
Applicant's arguments filed 6/30/2025 with respect to 35 USC 101 and 103 are fully considered but they are not persuasive.
Regarding 35 USC 101: The applicants amendments do not overcome the 35 USC 101 rejection. The amendments necessitate a new 101 rejection, and the new claims still recite an abstract idea without practical application or significantly more
Regarding 35 USC 103: The applicants amendments have necessitated new grounds of rejection rendering the arguments moot. Further, the applicant argues Pathria does not teach training and test data being different; however, the claims describe data with nonfunctional descriptive features, see rejection of new dependent claims 4 and 5.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The claim elements “computing circuit”, “generator circuit”, and “one transmitter” in claims 7-18 are not present in the specification. For the purposes of compact prosecution the claim elements are interpreted as components of a general purpose computer.
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 7-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claims do not fall within at least one of the four categories of patent eligible subject matter because they are directed to signals per se, mere information in the form of data. This could include transitory forms of signal transmission such as carrier waves or propagating electrical signals. The claims recite a tangible computer readable medium. The examiner suggests instead reciting a “non-transitory computer readable medium” to overcome this rejection of claims 7-12.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) with no practical application and without significantly more.
The claimed invention is directed to an abstract idea in that the instant application is directed to a mental process (See MPEP 2106.04(a)(2)(III)). The independent claims (1, 7, and 13) recite a method and systems to collect and analyze data to predict matriculation at an institution. These claim elements are being interpreted as concepts performed in the human mind (including observation, evaluation, judgement, and opinion). Evaluating past acceptance data to predict future acceptances can be performed by the human mind. The claims recite an abstract idea consistent with the “mental process” grouping set forth in the MPEP 2106.04(a)(2)(III).
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites an “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea. The instant application is directed towards a method and systems to implement the identified abstract idea of receiving information, processing information, and displaying the result of the analysis (i.e. collecting historical applicant data to analyze and predict applicant matriculation and the like) in a general computer environment. The claims do not include additional elements that amount to significantly more than the judicial exception. The independent claims recite the additional elements “a computer”, “multiple artificial intelligence models”, “a tangible computer-readable medium”, “a computer circuit”, “generator circuit”, and “transmitter”. These claim 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 general computer environment. The machines merely act as a modality to implement the abstract idea and are not indicative of integration into a practical application (i.e., the additional elements are simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A Prong Two analysis, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
In regards to the dependent claims:
Claims 2-6, 8-12, and 14-28 recite no new abstract ideas or new additional elements and do not impact analysis under 35 USC 101
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 (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.
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.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Pathria (US 6728695 B1) in view of Harbaugh (US 20020174123 A1).
Regarding claims 1, 7, and 13, Pathria teaches:
A computer-implemented method, comprising: processing, with multiple artificial-intelligence models, training data; [see at least Pathria: (Column 1, Lines 26-27) “The methods and systems illustrated in FIGS. 2, 4, 5, 6 and 7 are utilized during production in the computer system”, (Column 5, Lines 22-25) “a development dataset of documents is standardized at 30, unraveled at 50, and canonicalized at 60. Variables are derived at 70 and used to train predictive models at 80”]
generating, with the multiple artificial-intelligence models in response to the training data, corresponding training results; [see at least Pathria: (Column 6, Lines 6-9) “Predictive models must be trained on a development dataset prior to being deployed for production use. This training requires a dataset wherein the output for each input object is known”]
identifying, in response to the corresponding training results, one of the artificial-intelligence-model architectures; [see at least Pathria: (Column 13, Lines 47-51) “Typically, a predictive model learns this mapping through a training process carried out on a dataset of known input/output pairs. Examples of predictive models include linear regression and neural networks. The preferred form of the invention uses a back-propagating neural network.”]
testing the identified one of the artificial-intelligence-model architectures with test data to obtain predicted versions of known results; [see at least Pathria: (Column 13, Lines 54-59) “Based on the true output for each corresponding input, the weights within the network are automatically adjusted to more accurately predict the outputs. The process is repeated, with variations, until the weights for the inputs correctly predict the known outcomes in the training dataset”]
if a number of the predicted versions that agree with the known results equals or exceeds a threshold, then selecting the identified one of the artificial-intelligence-model architectures [see at least Pathria: (Figure 4) “run predictive models”, (Column 13, Lines 54-59) “Based on the true output for each corresponding input, the weights within the network are automatically adjusted to more accurately predict the outputs. The process is repeated, with variations, until the weights for the inputs correctly predict the known outcomes in the training dataset”, (Column 17, Lines 13-18) “Each resume runs through a simple preliminary model 94, such as comparing the vectors produced by semantic analysis on each document 95, and only those with sufficiently high scores from the simple model are fully assessed by the final predictive model at 96.”]
processing, with the selected one of the artificial-intelligence-model architectures, natural language in written applications prepared by applicants to an institution; [see at least Pathria: (Column 12, Lines 16-17) “During canonicalization, semantic analysis translates a text document to a numerical vector”, (Columns 14, Line 65 – Column 15, Line 2) “During development, several tools are created as follows: standardization tools; canonicalization tools; knowledge base; derived variables; and predictive models. During production, these tools are deployed.”]
predicting, with the selected one of the artificial-intelligence-model architectures, for each of the applicants in response to the processing of the natural language in the corresponding one of the written applications, a respective likelihood that the applicant will accept an offer of admission to the institution; [see at least Pathria: (Column 5, Lines 55-62) “For example, from analyzing high-school student and college records, colleges which will accept a particular high-school student can be predicted. Also, predictions can be made as to which ones of the colleges, the student would be successful. By comparing original applications of students against their college records, a college can predict the college success of new applicants.”]
However, Pathria does not teach but Harbaugh does teach:
and extending a written offer of admission to one or more of the applicants having likelihoods of acceptance greater than or equal to a threshold likelihood. [see at least Harbaugh: (Para 0009) “For instance, those candidates having standardized test scores and a GPA which far exceed a preferred criteria can be extended an offer of admission without further consideration”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the machine learning method to make predictions (Pathria) with the extension of an offer (Harbaugh). One of ordinary skill would have recognized that extending an offer after predicting matriculation would yield predictable results. The invention is merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, yielding predictable results
Regarding claims 2, 8, and 14, Pathria in view of Harbaugh teach the limitations set forth above. Pathria further teaches:
further comprising generating the training data by: identifying at least one part of applications of applicants who previously accepted offers of admission to the institution; [see at least Pathria: (Column 6, Lines 36-41) “In the example of resumes, jobs listed in the experience segment are considered as job descriptions for which the previous jobs comprise the job history of a person who obtained that job. These unraveled pseudo-documents can be used as known input/output pairs for training predictive models at 80”, (Column 5, Lines 50-62) “it will become apparent to those skilled in the art that many different types and kinds of documents containing text can be subjected to information analysis and predictive modeling in accordance with the method and apparatus of the present invention. For example, from analyzing high-school student and college records, colleges which will accept a particular high-school student can be predicted. Also, predictions can be made as to which ones of the colleges, the student would be successful. By comparing original applications of students against their college records, a college can predict the college success of new applicants”]
and formatting the respective at least one part of each of the applications into the training data. [see at least Pathria: (Column 6, Lines 42-43) “Thereafter, canonicalizing a document is creating a canonical, numerical representation of the document.”]
Regarding claims 3, 9, and 15, Pathria in view of Harbaugh teach the limitations set forth above. Pathria further teaches:
wherein generating the corresponding training results comprises: processing the training data with model architectures to generate model versions; [see at least Pathria: (Column 5, Lines 22-25) “a development dataset of documents is standardized at 30, unraveled at 50, and canonicalized at 60. Variables are derived at 70 and used to train predictive models at 80”, (Column 17, Lines 13-18) “Each resume runs through a simple preliminary model 94, such as comparing the vectors produced by semantic analysis on each document 95, and only those with sufficiently high scores from the simple model are fully assessed by the final predictive model at 96.”]
processing the training data with the model versions; [see at least Pathria: (Column 5, Lines 22-25) “a development dataset of documents is standardized at 30, unraveled at 50, and canonicalized at 60. Variables are derived at 70 and used to train predictive models at 80”, (Column 6, Lines 6-9) “Predictive models must be trained on a development dataset prior to being deployed for production use. This training requires a dataset wherein the output for each input object is known.”]
and generating the corresponding training results in response to processing the training data with the model versions. [see at least Pathria: (Column 13, Lines 18-21) “Predictions are made by running documents through predictive models, which can be considered as formulas that map input variables to an estimation of the output.”]
Regarding claims 4, 10, and 16, Pathria in view of Harbaugh teach the limitations set forth above. Pathria further teaches:
wherein the test data is different than the training data. [The claims describe nonfunctional descriptive material that do not carry patentable weight in the claims. The steps the model performs remain the same irrespective to whether the nature of test data set is the same or different.]
Regarding claims 5, 11, and 17, Pathria in view of Harbaugh teach the limitations set forth above. Pathria further teaches:
wherein the test data is a portion of the training data not processed by the multiple artificial-intelligence models. [The claims describe nonfunctional descriptive material that do not carry patentable weight in the claims. The steps the model performs remain the same irrespective to whether the nature of test data set is the same or different.]
Regarding claims 6, 12, and 18, Pathria in view of Harbaugh teach the limitations set forth above. Pathria further teaches:
collecting retraining data related to the natural language in the written applications; [see at least Pathria: (Column 6, Lines 15-16) “Such training data can be obtained from a variety of sources, including recruiters, employers, and job boards”, (Column 10, Lines 53-56) “Semantic analysis techniques include latent semantic analysis (LSA) and probabilistic latent semantic analysis (PLSA). In the preferred embodiment, in the example of resumes and job postings, PLSA is used”]
and retraining the selected one of the artificial-intelligence-model architectures with the retraining data. [see at least Pathria: (Column 13, Lines 53, 59) “Observations from the training dataset are fed to the model. Based on the true output for each corresponding input, the weights within the network are automatically adjusted to more accurately predict the outputs. The process is repeated, with variations, until the weights for the inputs correctly predict the known outcomes in the training dataset.”]
Conclusion
Relevant art not relied upon:
Scarborough (US 20020042786 A1): Scarborough further teaches building learning models with training datasets and different testing datasets to adjust models, see at least: (Para 0083) “One possible way of building a neural network is to divide the input data into three sets: a training set, a test set, and a hold-out set. The training set is used to train the model, and the test set is used to test the model and possibly further adjust it.”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Benjamin Truong, whose telephone number is 703-756-5883. The examiner can normally be reached on Monday-Friday from 9 am to 5 pm (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, Nathan Uber SPE can be reached on 571-270-3923. 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.
/B.L.T. /Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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