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
Status of Claims
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and the fee set forth in 37 CFR 1.17 (e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/13/2026 has been entered.
Claims 1,6,11, 15 and 19 have been amended.
Claim 20 has been added.
Claims 1-20 are currently pending and have been examined.
Response to Applicant’s Arguments
Applicant’s amendments and arguments filed on 05/13/2026 have been fully considered and discussed in the next section. Applicant is reminded that the claims must be given its broadest, reasonable interpretation.
With regard to claims 1-19 rejection under 35 USC § 101:
Amended Claim 1 - Step 2A, Prong One: The Claim Is Directed to an Abstract Idea
Applicant argues that “The amended claim does not recite gathering and analyzing data in a generic manner. Rather, it recites a technically specific machine learning training pipeline with the following concrete computational operations that have no analog in human activity or mental processes (page 4/11)”.
Examiner disagrees. The applicant's argument that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 1 because the claim recites a technically specific machine learning training pipeline with the following concrete computational operations that have no analog in human activity or mental processes is not convincing. The only abstract idea bucket in which performance by a human is required is the "Mental Process" bucket which requires that the steps be capable of being performed in the human mind. The claims of the instant invention have not been identified as a "Mental Process". Instead the claims of the instant invention have been identified as "Certain Methods of Organizing Human Activities". The Subject Matter Eligibility Guidelines indicate that namely commercial or legal interaction that recite "advertising, marketing or sales related activities or behaviors" is a subcategory of "Certain Methods of Organizing Human Activities". There is no requirement that these "advertising, marketing, or sales related activities" be performed by a human being. Therefore, all steps involved in the performance of advertising, marketing or sales related activities are part of the abstract idea itself irrespective of whether they are performed by a computer or performed by a human being. Thus, the Applicant's arguments are moot. The claim rejection of claims 1-19 under 35 USC § 101 is maintained.
(a) A domain-specific numerical encoding scheme.
Applicant argues that “The claim recites that each training vector "encodes each course as a first numerical value and each prerequisite relationship between courses as a second numerical value derived from the first numerical values of the courses in the prerequisite relationship." This is not a generic data-gathering step. It is a specific encoding methodology in which prerequisite relationships are represented as values derived from the course values (e.g., if algebra I = 1 and algebra II = 2, the prerequisite relationship is encoded as 2-1). This derived encoding preserves the directional dependency structure between courses in a numerical format specifically designed for consumption by a machine learning model. No human organizing activity involves encoding academic dependency structures into derived numerical values (page 5/11)”.
Examiner disagrees. The applicant's argument that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 1 because the claim recites encodes each course as a first numerical value and each prerequisite relationship between courses as a second numerical value derived from the first numerical values of the courses in the prerequisite relationship and/ or This derived encoding preserves the directional dependency structure between courses in a numerical format specifically designed for consumption by a machine learning model. Thus, No human organizing activity involves encoding academic dependency structures into derived numerical values is not convincing. The only abstract idea bucket in which performance by a human is required is the "Mental Process" bucket which requires that the steps be capable of being performed in the human mind. The claims of the instant invention have not been identified as a "Mental Process". Instead the claims of the instant invention have been identified as "Certain Methods of Organizing Human Activities". The Subject Matter Eligibility Guidelines indicate that namely commercial or legal interaction that recite "advertising, marketing or sales related activities or behaviors" is a subcategory of "Certain Methods of Organizing Human Activities". There is no requirement that these "advertising, marketing, or sales related activities" be performed by a human being. Therefore, all steps involved in the performance of advertising, marketing or sales related activities are part of the abstract idea itself irrespective of whether they are performed by a computer or performed by a human being. Thus, the Applicant's arguments are moot. The claim rejection of claims 1-19 under 35 USC § 101 is maintained.
(b) NLP-based prerequisite extraction.
Applicant argues that “The claim recites that "prerequisite concepts for each course are extracted using natural language processing and included as input in each training vector." This is a computationally intensive operation - applying NLP algorithms to course content to automatically extract prerequisite concepts - that cannot be performed as a mental process and is not a method of organizing human activity (page 5/11).
Examiner disagrees. The recitation of "prerequisite concepts for each course that are extracted using natural language processing and included as input in each training vector”, is directed to analyzing data and determining results based on the analysis. Since analyzing data is part of the abstract idea itself, any improvement obtained by automating the analyzing of the data in an improvement to the abstract idea which is an improvement in ineligible subject matters (see SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract. As such, the claims as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising, marketing and sales activities or behaviors because they merely gather data, analyze the data, determine results based on the analysis, generate tailored content based on the results, and transmit the tailored content.
The additional element of using an NPL does no more than apply or link the use of the recited judicial exception to a particular technological environment/field of use. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
Additionally, Applicant's argument that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 1 because the claim recites "prerequisite concepts for each course that are extracted using natural language processing and included as input in each training vector cannot be performed as a mental process is not convincing. The only abstract idea bucket in which performance by a human is required is the "Mental Process" bucket which requires that the steps be capable of being performed in the human mind. The claims of the instant invention have not been identified as a "Mental Process". Instead the claims of the instant invention have been identified as "Certain Methods of Organizing Human Activities". The Subject Matter Eligibility Guidelines indicate that namely commercial or legal interaction that recite "advertising, marketing or sales related activities or behaviors" is a subcategory of "Certain Methods of Organizing Human Activities". There is no requirement that these "advertising, marketing, or sales related activities" be performed by a human being. Therefore, all steps involved in the performance of advertising, marketing or sales related activities are part of the abstract idea itself irrespective of whether they are performed by a computer or performed by a human being. Thus, the Applicant's arguments are moot. The claim rejection of claims 1-19 under 35 USC § 101 is maintained.
(c) A specific training objective.
Applicant argues that “The claim recites that the model "is configured to learn relationships between courses and the prerequisite concepts based on the first numerical values and the second numerical values by adjusting parameters during training to minimize prediction errors between output sequences generated by the second machine learning model and each target course sequence." This recites the concrete optimization objective - minimizing prediction errors between the model's generated sequences and pre-labeled target course sequences - not merely that the model "learns" or "is trained." The iterative adjustment of model parameters across a dataset of training vectors to converge on target sequences is a fundamentally computational operation that cannot be replicated mentally or characterized as organizing human activity (page 5/11)”.
Examiner disagrees. The recitation of "the model "is configured to learn relationships between courses and the prerequisite concepts based on the first numerical values and the second numerical values by adjusting parameters during training to minimize prediction errors between output sequences generated by the second machine learning model and each target course sequence”, and / or optimization objective - minimizing prediction errors between the model's generated sequences and pre-labeled target course sequences and / or The iterative adjustment of model parameters across a dataset of training vectors to converge on target sequences is directed to analyzing data and determining results based on the analysis. Since analyzing data is part of the abstract idea itself, any improvement obtained by automating the analyzing of the data in an improvement to the abstract idea which is an improvement in ineligible subject matters (see SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract. As such, the claims as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising, marketing and sales activities or behaviors because they merely gather data, analyze the data, determine results based on the analysis, generate tailored content based on the results, and transmit the tailored content.
The additional element of using an ML (e.g. a model) does no more than apply or link the use of the recited judicial exception to a particular technological environment/field of use. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
Additionally, Applicant's argument that the claims overcome the 35 USC 101 rejection under Step 2a, Prong 1 because the claim recites " The iterative adjustment of model parameters across a dataset of training vectors to converge on target sequences is a fundamentally computational operation that cannot be replicated mentally is not convincing. The only abstract idea bucket in which performance by a human is required is the "Mental Process" bucket which requires that the steps be capable of being performed in the human mind. The claims of the instant invention have not been identified as a "Mental Process". Instead the claims of the instant invention have been identified as "Certain Methods of Organizing Human Activities". The Subject Matter Eligibility Guidelines indicate that namely commercial or legal interaction that recite "advertising, marketing or sales related activities or behaviors" is a subcategory of "Certain Methods of Organizing Human Activities". There is no requirement that these "advertising, marketing, or sales related activities" be performed by a human being. Therefore, all steps involved in the performance of advertising, marketing or sales related activities are part of the abstract idea itself irrespective of whether they are performed by a computer or performed by a human being. Thus, the Applicant's arguments are moot. The claim rejection of claims 1-19 under 35 USC § 101 is maintained.
TV. Amended Claim 1 - Step 2B: The Claim does not Recite Significantly More.
Applicant argues that “First, the domain-specific numerical encoding scheme is not a generic preprocessing step like tokenization or normalization. Encoding courses as first numerical values and encoding prerequisite relationships as second numerical values derived from the first numerical values is a specific, unconventional data representation designed to preserve the directional dependency structure between courses in a format optimized for machine learning sequence prediction. This is not how data is conventionally encoded for machine learning consumption. The Examiner has not identified - and Applicant submits cannot identify - evidence that this particular derived encoding scheme is conventional, routine, or well-understood in the art (page 6/11)”.
Examiner disagrees. Since Encoding courses as first numerical values and encoding prerequisite relationships as second numerical values derived from the first numerical values is a specific, unconventional data representation designed to preserve the directional dependency structure between courses in a format optimized for machine learning sequence prediction is part of the abstract part of the abstract idea itself, they are not capable of transforming the abstract idea into a practical application under Step 2a, Prong 2 and not capable of being considered "significantly more" under Step 2b.
Only technological improvements rooted in the "additional elements" of a claim are capable of transforming an abstract idea into a practical application under Step 2a, Prong 2, and only "additional elements" are capable of being considered "significantly more" under Step 2b.
Additional elements are those elements outside of the identified abstract idea itself. In the instant case the only additional elements are “ML model, display and user interface”, as evidenced by Applicant’s specification [30, 75, 81] and fig 8 with the associated Fig are a general-purpose computer system. Applicant’s specification paragraph 30 teaches ( FIG. 8 presents an example of a general-purpose computer system on which aspects of the present disclosure can be implemented). Paragraph 75, teaches (machine learning module 110 may comprise one or more neural networks, which are a class of machine learning models inspired by the structure and functioning of the human brain. They consist of interconnected nodes, called neurons or artificial neurons, organized into layers. Neural networks are capable of learning complex patterns and representations from data. The neural network executed by machine learning module 110 may be one of the following: a feedforward neural network (FNN), convolution neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) network, autoencoder, generative adversarial network (GAN). and 81] teaches the recited ML within the claims are generic off the shelf ML). Paragraph 81, teaches, ( In autoencoder is a type of neural network used for unsupervised learning and dimensionality reduction, and consists of an encoder that compresses input data into a lower-dimensional representation (encoding) and a decoder that reconstructs the original input from the encoding), which are just general-purpose computers with generic computing components upon which the abstract idea is applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2 or be considered significantly more under Step 2b.
Also, the claims are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept. The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. Thus, any purported technological improvement obtained by practicing the claimed invention is rooted solely in the abstract idea itself which is merely applied using the general-purpose computer, and not rooting in the additional elements upon which the abstract idea is applied.
Improvements of this nature are improvement to an abstract idea which are improvements in ineligible subject matter (SAP v. Investpic decision: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.). As such Applicant's claimed solution is NOT technological and does not addresses a technological problem. Accordingly, the claim rejection of claims 1-19 under 35 USC § 101 is maintained.
Applicant argues that “Second, the combination of (a) NLP-based prerequisite extraction, (b) the domain-specific derived encoding, (c) structured training vectors with target course sequences, and (d) parameter optimization to minimize prediction errors between model output and target sequences constitutes an ordered combination that is not merely the sum of its parts. Each step feeds into the next: NLP extracts prerequisite concepts, those concepts are encoded using the derived numerical scheme into training vectors, and the model adjusts its parameters to minimize errors against the target sequences contained in those vectors. This pipeline produces a technically specific result - a trained model that can generate valid course sequences based on learned prerequisite relationships - that conventional systems cannot produce (page 6/11)”.
Examiner disagrees. the combination of (a) NLP-based prerequisite extraction, (b) the domain-specific derived encoding, (c) structured training vectors with target course sequences, and (d) parameter optimization to minimize prediction errors between model output and target sequences constitutes an ordered combination that is not merely the sum of its parts. Each step feeds into the next: NLP extracts prerequisite concepts, those concepts are encoded using the derived numerical scheme into training vectors, and the model adjusts its parameters to minimize errors against the target sequences contained in those vectors that produces a technically specific result - a trained model that can generate valid course sequences based on learned prerequisite relationships is directed to analyzing data and determining results based on the analysis.
Since analyzing data is part of the abstract idea itself, any improvement obtained by automating the analyzing of the data in an improvement to the abstract idea which is an improvement in ineligible subject matters (see SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
Here, the use of NLP and/ or a model transactions fails to (a) improve another technology or technical field and (b) improve the functioning of the computer itself and (c) applies the abstract idea with or by use of, a particular machine, which is a generic computer performing generic computer functions and are not seen to recite an improvement to another technology or technical field, an improvement to the functioning of the computer itself.
Indeed, the identified improvements recited by Applicant are really, at best improvements to the performance of the abstract idea (e.g., improvements made in the underlying business method ( the combination of (a) NLP-based prerequisite extraction, (b) the domain-specific derived encoding, (c) structured training vectors with target course sequences, and (d) parameter optimization to minimize prediction errors between model output and target sequences constitutes an ordered combination that is not merely the sum of its parts. Each step feeds into the next: NLP extracts prerequisite concepts, those concepts are encoded using the derived numerical scheme into training vectors, and the model adjusts its parameters to minimize errors against the target sequences contained in those vectors where a trained model that can generate valid course sequences based on learned prerequisite relationships) and not in the operations of any additional elements or technology.
As such, the examiner finds that any improvement obtained by practicing the claimed invention is an improvement to a business process. Second, under Step 2a, Prong 2, the improvement to a technology or technological field must be rooted in the additional element. Additional elements are those elements outside of the identified abstract idea itself. In the instant case the only additional elements are “ML model, display and user interface”, as evidenced by Applicant’s specification [30, 75, 81] and fig 8 with the associated Fig are a general-purpose computer system. Applicant’s specification paragraph 30 teaches ( FIG. 8 presents an example of a general-purpose computer system on which aspects of the present disclosure can be implemented). Paragraph 75, teaches (machine learning module 110 may comprise one or more neural networks, which are a class of machine learning models inspired by the structure and functioning of the human brain. They consist of interconnected nodes, called neurons or artificial neurons, organized into layers. Neural networks are capable of learning complex patterns and representations from data. The neural network executed by machine learning module 110 may be one of the following: a feedforward neural network (FNN), convolution neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) network, autoencoder, generative adversarial network (GAN). and 81] teaches the recited ML within the claims are generic off the shelf ML), which are just general-purpose computers with generic computing components upon which the abstract idea is applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2 or be considered significantly more under Step 2b.
Also, the claims are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept. The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. Thus, any purported technological improvement obtained by practicing the claimed invention is rooted solely in the abstract idea itself which is merely applied using the general-purpose computer, and not rooting in the additional elements upon which the abstract idea is applied. As such, the applicant's arguments are not convincing and the claim rejection of claims 1-19 under 35 USC § 101 is maintained.
Applicant argues that “Third, the improvement is rooted in the additional elements, not in the abstract idea. The Examiner stated that improvements must be "rooted in the 'additional elements"' to overcome § 101. Here, the improvement is in the additional elements: the specific encoding scheme, the NLP extraction, the structured training vectors, and the optimization objective are all improvements to how the machine learning model is built and trained. These are not improvements to the "underlying business method" of curriculum management - they are improvements to the computational model itself (page 7/11)”.
Examiner disagrees. the specific encoding scheme, the NLP extraction, the structured training vectors, and the optimization objective do no more than claim the application of generic machine learning to new data environments without disclosing improvements to the machine learning models to be applied , are patent ineligible under 35 USC § 101.
Even if the machine learning is actually trained to perform an action (s). “using such a trained machine learning provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
As thus, the claims are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept. The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement. Thus, any purported technological improvement obtained by practicing the claimed invention is rooted solely in the abstract idea itself which is merely applied using the general-purpose computer, and not rooting in the additional elements upon which the abstract idea is applied. As such, the applicant's arguments are not convincing and the claim rejection of claims 1-19 under 35 USC § 101 is maintained.
V. Amended Claim 6 - Hardware Resource Matching does not Provides Additional Technical Grounding
Applicant argues that “ Amended claim 6 further narrows the claim by reciting that the resources utilized for each course comprise hardware resources, and that the third machine learning model matches each hardware resource requirement with a projected availability of the hardware resources for a particular time period. This adds a concrete, real-world constraint - the availability of physical hardware such as computers and lab equipment - to the model's output generation. The third model does not merely reorder courses in the abstract; it evaluates the projected availability of physical hardware resources at a particular institution for a particular time period and matches course requirements against those projections. This is a specific, technically grounded operation that ties the model's output to real-world hardware constraints that fluctuate over time (e.g., "hardware may be unavailable due to IT issues"). Claim 6 thus provides an additional layer of technical specificity beyond claim l's training methodology, further confirming that the claims are directed to a concrete technological solution rather than an abstract idea (page 7/11)”.
Examiner disagrees. The recitation of using a trained machine learning model in the limitations merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained machine learning model” limits the identified judicial exceptions “evaluates the projected availability of physical hardware resources at a particular institution for a particular time period and matches course requirements against those projections” using the trained machine learning model to generate such data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). The claimed model do no more than claim the application of generic machine learning to new data environments without disclosing improvements to the machine learning models to be applied , are patent ineligible under 35 USC § 101. As such Applicant's claimed solution is NOT technological and does not addresses a technological problem. Accordingly, the claim rejection of claims 1-19 under 35 USC § 101 is maintained.
VI. New Claim 20 - Quantified Hardware Resource Importance Levels
Applicant argues that “ New claim 20 further narrows claim 6 by reciting that the third machine learning model evaluates an importance level assigned to each hardware resource, and that the importance level affects the matching of each hardware resource requirement with the projected availability. This introduces a quantified weighting scheme (e.g., a laptop may have an importance level of 7/10) that the model uses to prioritize among hardware resource constraints when generating its output sequence. This is a concrete, domain-specific computational feature that further distinguishes the claimed invention from generic data analysis (page 7/11)”.
Examiner disagrees. The recitation of using a trained machine learning model in the limitations merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a trained machine learning model” limits the identified judicial exceptions “evaluates an importance level assigned to each hardware resource, and that the importance level affects the matching of each hardware resource requirement with the projected availability” using the trained machine learning model to generate such data,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). The claimed model do no more than claim the application of generic machine learning to new data environments without disclosing improvements to the machine learning models to be applied , are patent ineligible under 35 USC § 101.
As such Applicant's claimed solution is NOT technological and does not addresses a technological problem. Accordingly, the claim rejection of claims 1-20 under 35 USC § 101 is maintained.
VII. Policy Reasons Support the Allowance of the Claims
Applicant argues that “ The amended claims squarely align with the policy goals set forth in Desjardins because, as explained above, they recite a specific technological implementation that improves model training and deployment. Pending legislation further underscores the policy basis for allowing the pending claims. The bipartisan Patent Eligibility Restoration Act of 2025 (S. 1546) was introduced to "modify and clarify" § 101 jurisprudence. Although the Patent Eligibility Restoration Act has not been enacted and does not control examination, its findings capture a broad, bipartisan policy consensus: eligibility should not exclude bona fide software innovations that improve computer technology (page 8/11)”.
Examiner disagrees. The instant claims bear no similarity to the Ex Parte Desjardins Holdings decision, because the instant claim merely filtering the inputs used to train a machine learning model and using bias values to account for outlier behavior while focusing learning on events associated with typical conditions, whereas Ex Parte Desjardins (claims to a method of training a machine learning model were directed to improvements in the machine learning technology itself and additionally included data structure elements reciting adjustments in values to plurality of performance parameters while preserving prior values). Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about 2 previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. In the instant claims, the claimed model do no more than claim the application of generic machine learning to new data environments without disclosing improvements to the machine learning models to be applied , are patent ineligible under 35 USC § 101. As such Applicant's claimed solution is NOT technological and does not addresses a technological problem. Accordingly, the claim rejection of claims 1-20 under 35 USC § 101 is maintained.
With regard to claims 1-19 rejection under 35 USC § 102/103 is considered and the claim rejection is withdrawn.
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 directed to a system and a method which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
However, 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 claim(s) recite(s) the following abstract idea:
receiving, content describing a topic and a plurality of sub-topics associated with the topic;
executing a first algorithm configured to determine compatibility scores between the content and a plurality of curricula, wherein each curriculum of the plurality of curricula is a sequence of courses and indicates resources utilized for each respective course in the sequence of courses;
identifying at least one curriculum with a compatibility score greater than a threshold compatibility score;
executing at least one other algorithm configured to generate a modified curriculum in which the content is inserted into an original sequence of courses associated with the at least one curriculum based on prerequisites of the content and available resources to provide access to the content, wherein the at least one other machine learning model comprises a second algorithm trained on a dataset comprising a plurality of training vectors, each training vector comprising a target course sequence and an input course sequence comprising a respective input course and a respective curriculum,
wherein each training vector encodes each course as a first numerical value and each prerequisite relationship between courses as a second numerical value derived from the first numerical values of the courses in the prerequisite relationship, wherein prerequisite concepts for each course are extracted using natural language processing and included as input in each training vector,
wherein the second algorithm is configured to learn relationships between courses and the prerequisite concepts based on the first numerical values and the second numerical values by adjusting parameters during training to minimize prediction errors between output sequences generated by the second algorithm and each target course sequence; and
transmitting (e.g. generating), for display , the modified curriculum;
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely commercial or legal interactions because they recite advertising, marketing and sales activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application because the claim only recites the additional elements of using a computer with one or more hardware processors coupled to a non-transitory memory and configured to execute one or more algorithms instructions (e.g, one or more Machine learning model(s), user interface and display, ( e.g. a general purpose computer with generic computer components).
The following limitations, if removed from the abstract idea and considered additional elements, merely perform generic computer function of processing, storing, communicating (e.g., transmitting and receiving), and displaying data and, as such, are insignificant extra-solution activities (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving, via a user interface (UI), content describing a topic and a plurality of sub-topics associated with the topic;
transmitting (e.g. generating), for display on the UI, the modified curriculum;
More The additional technical elements above are recited at a high-level of generality (i.e., as a generic processor and generic computer components performing a generic computers function of processing, communicating and displaying) such that it amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and generic computer components. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on one or more computers, or merely uses computers as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes).
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea. Specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a computer with one or more hardware processors coupled to a non-transitory memory and configured to execute one or more algorithms instructions (e.g., one or more Machine learning model(s), user interface and display, ( e.g. a general purpose computer with generic computer components).
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (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 the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on one or more computers, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires one or more general-purpose computer and generic computer components (as evidenced from paragraphs 30, 75, 81 and fig 8 with the associated Fig are a general-purpose computer system, and the affinity v Direct TV decision which states that a User Interface is a generic computer component); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
Finally, the following limitations, if removed from the abstract idea and considered additional elements, would be considered insignificant extra solution activity as they are directed to merely receiving, displaying, storing, and/or transmitting data (see MPEP 2016.05(d)(II) and MPEP 2106.05(g)):
receiving, via a user interface (UI), content describing a topic and a plurality of sub-topics associated with the topic;
transmitting (e.g. generating), for display on the UI, the modified curriculum;
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e., “PEG” Step 2B=No). For the same reason these elements are not sufficient to provide an inventive concept. For these reasons, there is no inventive concept in the claim, and thus the claim is not patent eligible. Same Judicial analysis is applied here to independent claims 11 and 19.
The dependent claims 2-10, 12-18 and 20 appears to merely further limit the abstract idea of Certain methods of organizing Human Activity” as it relates to commercial interactions of advertising, marketing, or sales activities or behaviors; business relations); which is considered part of the abstract idea and therefore only further limit the abstract idea (i.e. MPEP Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. MPEP Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. MPEP Step 2B=No). Thus, based on the detailed analysis above, claims 1-20 are not patent eligible.
Possible Allowable Subject Matter
Claims 1-20 would be allowable over the prior art if the applicant were to be able to overcome the claim rejection under 35 USC 101 identified above.
The following is a statement of reasons for the indication of allowable subject matter: The most relevant prior the examiner has found is:
Sharma , US Pub No: 2020/0357296 A1 teaches Embodiments provide integrated education management methods and systems to provide a personalized learning platform to users. An integrated education management system (IEMS) that includes an intelligent SaaS platform provides user a single interface for accessing different departments, functionalities or services of an organization. The Intelligent SaaS Platform provides a personalized learning platform and a subscriber network to learners. The personalized learning platform provide individual learning plans that empower students to learn at their own choice of time, place, and pace. The subscriber network connects a plurality of users, such as learners, personal mentors of the learners, resource creators, tutorial agents, or the like to share/sell and buy personalized and customized learning plan for the learners. This platform also includes analytical tools and engines that provide learning recommendations for the learners. The analytical tools and engines use all indicators from the software that are used in providing the learning recommendations.
Cerpuran et al, US Pub No:2022/0327947 A1, teaches Systems and methods for automatically revising feedback in an electronic learning system are provided. The method involves operating at least one processor to: receive feedback text data submitted by a user to evaluate another user; identify a plurality of sentiment groups in the feedback text data, each sentiment group consisting of a portion of the feedback text data associated with a common sentiment; select at least one feedback processing module to process each sentiment group, the at least one feedback processing module comprising at least one machine-learned model; for each sentiment group, process the corresponding portion of the feedback text data using the at least one machine-learned model to determine at least one suggested revision for the portion of the feedback text data; and generate a revised version of the feedback text data indicating each suggested revision for the feedback text data.
However, the examiner has been unable to find prior art that discloses performing the claimed steps in the manner claimed. As such, claims 1-20 would be allowable over the prior art if the applicant were to be able to overcome the claim rejection of claims 1-20 under 35 USC 101 identified above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Sundararajan et al, US Pub No: 2022/0319346 A1, teaches A learning model is created using a computer which includes receiving input from a user. The learning model includes comparing the input to the knowledge area in a knowledge database to assess a level of proficiency on topics within the knowledge area. The learning model includes determining topics of knowledge within the knowledge area where a user currently meets a proficiency threshold for one or more topics. A work topic is identified within the knowledge area where the user does not meet the proficiency threshold. Study material is presented to the user for the work topic of the knowledge area using an interactive mechanism. Feedback is received regarding the study material for the work topic from the interactive mechanism from the user. The learning model includes evaluating the feedback from the user to determine a score which indicates when the user meets a proficiency threshold for the work topic.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Affaf Ahmed whose telephone number is 571-270-1835. The examiner can normally be reached on [M- R 8-6 pm ].
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AFAF OSMAN BILAL AHMED/Primary Examiner, Art Unit 3622
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