DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
The office action is being examined in response to the application filed by the applicant on November 5, 2025.
Claims 1-20 are pending and have been examined.
Claims 1, 3, 6-8, 10, 13-15, 17, and 20 are amended.
This action is made FINAL.
Priority
Acknowledgment is made of applicant’s claim for priority under 35 U.S.C. 119(e) to Provisional Application No. 63/393,510, filed on July 29, 2022.
Response to Arguments
Applicant submitted amendments with respect to the Abstract on November 5, 2025, that modifies the wordcount such that it is under 150 words, therefore the Objection has been withdrawn.
35 U.S.C. § 101 Arguments
Applicant's arguments filed November 5, 2025 with regards to 35 U.S.C. § 101 have been fully considered but they are not persuasive.
In response to applicant’s arguments on pages 11-13, the assertions that the claims recite an ordered combination of element that are indicative of integration into a practical application and include significantly more than the exception itself, are not persuasive. The additional elements in the claims are the model, trained and calibrated into 2 trained versions of the same model characterized by data, a third model, and the general-purpose computing structures, as depicted in the full analysis, below. The steps of the claim that are sending, receiving, or displaying data, are not abstract ideas. The remaining elements are abstract ideas. The additional elements are applied as tools to perform the abstract ideas. The applicant’s assertion, on pages 11, 12, and 13, that the processing pipelines are implemented by a computing system, the defined, trained, and calibrated model is applied, and the weighting is implemented by applying the third model, support the findings that the claims are adding the words “apply it” with the abstract ideas, i.e. using the general-purpose computing structures and the models as tools implemented to perform the abstract ideas.
The claims are focused on the nature of the data being manipulated via the performance of generic business functions recited in the abstract ideas in the claims. The claims recite implementations of the abstract ideas via the additional, non-abstract elements, that attempt to cover any solution to the problem with no restriction on how the results are accomplished and no description of the mechanisms for accomplishing the results.
The models and functions are generated, trained, calibrated, and applied, without detailing the scope of the models or functions. Therefore, the claims recite the intended use of the models/functions to perform characterized tasks like generating by training and calibrating the models and applying the models and function, recited in terms of the intended results of applying the models to return characterized data. The claims are generally linking these abstract ideas to computing structures and to the model in the technical fields of mathematical/statistical models, academic/research models, and/or machine learning models. The specification does not reveal that the core of the invention is related to advances in any of these characterized models or to the functioning of the general-purpose computing structures. Since these abstract ideas are not performed in a meaningful way beyond the general linkage to the additional elements, the claims, as a whole, are a drafting effort designed to monopolize the exception.
The assertions by the applicant, on pages 12, that amended claim 1 includes unconventional steps and ordered combinations that are not well-understood, routine, or conventional, are not persuasive. The assertion is not rooted in the scope of the 35 U.S.C. § 101 rejection made by the Examiner. Since the claims are not comprised of elements that are considered either extra solution activity or “well-understood, routine, or conventional,” the Examiner did not assert and the claims do not trigger any analyses under MPEP 2106.05(d) or (g).
The assertion by the applicant on pages 12-13, that the combination of elements in the claims are directed to an inventive concept, such that the additional elements meaningfully limit the abstract ideas to integrate them into a practical application, and amount to significantly more than the abstract ideas, are not persuasive. The analyses must show that the additional elements create the indication of integration of the abstract ideas into a practical application and/or that they amount to significantly more than the recited abstract ideas. The Applicant does not identify the additional elements. The arguments do not overturn the previous findings because the assertions are focused on the abstract ideas themselves. While the claims may offer administrative utility, the application of models and computing structures to characterize data amounts to an abstract idea implemented using the computing structures and models, which does not provide for an inventive concept.
Please find the updated 35 U.S.C. § 101 analysis below reflecting the amendments. The arguments are not persuasive and the 35 U.S.C. § 101 is Maintained.
35 U.S.C. § 102(a)(1) Arguments
Applicant's arguments filed November 5, 2025 with regards to 35 U.S.C. § 102(a)(1) have been fully considered but they are not persuasive.
With regards to the Applicant’s arguments, on pages 15-16, the assertions that Gal differs from the instant application because Gal only focuses on a single model in a single educational environment vs multiple product specific models trained and calibrated on their associated product specific and disparately sourced data, are not persuasive. The specification from the instant application discloses in ¶ [0024] “configured to apply product-specific rules, statistical models, and machine learning validation tools to create product-specific model pipelines.” Similarly, Gal discloses ¶ [0016] “creating the pedagogic Bayesian network is included within an algorithm which creates one or more statistically evolving models based on relational concept mapping.” Further, Gal ¶ [0017] discloses a dynamic pedagogic Bayesian network; a plurality of copies of the dynamic pedagogic Bayesian network [i.e. multiple copies of the base model] represent a model of said student at a plurality of interconnected time points,” and [0082] “a plurality of models of cognitive processes, levels of learning activities, complexity of gained competencies, general and subject-specific topics.” In these particular excerpts from Gal, the network of models that are created may be implemented for students or subject specific topics that may represent different products and product specific versions of the models, which is further disclosed in [0126] of Gal. The applicant’s assertions, also on page 15, that Gal does not comprise calibrating by utilizing each specific product’s own representative data sets, independently, are also not persuasive. Gal discloses ¶ [0016] “creating the pedagogic Bayesian network is included within an algorithm which creates one or more statistically evolving models based on relational concept mapping,” (i.e. a main model that is trained and calibrated into product specific versions of the model). The applicant’s further assertions that Gal does not disclose nor teach assigning weights to data from different products, independent from each other, based on time-based observations to form a combined estimate, are also not persuasive. Gal discloses at least ¶ [0029] “for example, the computer-aided assessment module is to determine a weighted pedagogic score corresponding to said set of one or more conditional distribution functions, based on the sum of weights of scores corresponding to said possible values,” which discloses both individual weight in and a combined score based on the weights. The instant application discloses in ¶ [0038] of the specification “Weights w representing the strength of the signals from the set of products,” such that the weights are not specifically separated by individual products, but may be based across data from the set of products. Both Gal and the instant application disclose that the weights are based upon at least one function, i.e. where each function is based on an individual product. Gal further supports the use of multiple independent sources of information in ¶ [0102].
The applicant’s further assertions are based on the amended claim language and no further arguments were presented. Therefore, the comments necessitate an updated 35 U.S.C. § 102(a)(1) below.
The assertions regarding the single educational environment of Gal appears to match the educational environment of the instant application because they both represent assessments of students through a learning environment, implementing more than one program of learning, i.e. product, where the data is sourced internally and outsourced from external third parties.
Please find the updated 35 U.S.C. § 102(a)(1) analysis below reflecting the amendments. The arguments are not persuasive and the 35 U.S.C. § 102(a)(1) is Maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims:
Regarding Claims 1, 8 and 15, the claims recite generating a first and second function, each by training and calibrating a base function with data, applying the first and second function to generate proficiency data, generating a combined proficiency data, applying weighting via a function, and generating a recommendation which are abstract ideas in the category of mathematical concepts, more specifically, mathematical calculations and relationships because the claims present relationships between different data to return the final data at each step and the functions perform calculations to return the solution.
Regarding Claims 1, 8 and 15 recite the abstract idea of “certain methods of organizing human activity,” more specifically, “managing personal behavior or relationships or interactions between people" include social activities, teaching, and following rules or instructions.” These claims perform six generate data functions, and a generate profile function. The functions are recited to map student data from two different academic products to a set of skills in an academic, grade-level-based ontology, to estimate skill proficiency for students for these ontological skills based on the data, and to combine the skill proficiency estimates from each product to form a combined student profile. Therefore, these claims manage teaching and learning behavior for students and the assessors that evaluates each students’ educational performance data, where the data may drive what students are perceived to understand from the curriculum at a grade, which drives learning strategies chosen by educators and student’s placement within the educational structure (MPEP 2105.04(a)(2)(II)).
These same functions recited above, The two generating a function limitations by training and calibrating each function, applying the two functions, applying the weighting function, the six generating data functions, a generate profile function, and generate a display interface function, are additionally abstract ideas in the category of “Mental Processes,” or processes that can be performed in the human mind, including observations, evaluations, judgments, and opinions. These limitations are merely sorting and filtering data in to skill groups, estimating skill proficiency, grouping and combining the proficiency data into one single skill groups from data across two products, and generating a display showing the results (mental processes could comprise drawing a graph on paper), all of which can be performed in the human mind or with the aid of pen and paper and comprise observations, evaluations, judgements, and opinions about student data (MPEP 2105.04(a)(2)(III)).
Step 2A Prong 2: The claims also recite data or groups of data, all characterizations of data or characterized groups of data that amount to non-functional descriptive information limitations. They are not abstract ideas, nor limitations that can be relied on to integrate the abstract idea into a practical application because they do not positively recite any additional functions that limit the claims or the structures of the claims.
Insofar as the claims 1, 8, and 15 recite in the preambles and claims, for claim 8, A system comprising: a memory to store instructions; and a processing device, operatively coupled to the memory, to execute the instructions to perform operations comprising, and for claim 15, A non-transitory computer-readable storage medium having instructions that, when executed by a processing device, cause the processing device to perform operations. The claims and the applicant’s specification at paragraphs [0061]-[0067] are simply reciting/disclosing general-purpose computing structures at a high level of generality. The specification does not reveal that the core of the invention relates to advances or improvements to the computing structures themselves. The claims amount to “apply it,” mere instructions to apply the Judicial Exceptions using generally linked, general-purpose computing structures.
Claims 1, 8, and 15 recite a base model, trained, and calibrated to become the first and second models, and third model for performing a weighting function. Models are disclosed in the specification ¶ [0024] as any statistical models or machine learning tools to create model pipelines, ¶ [0028] as a psychometrics model like Diagnostic Classification Models (DCM), Cognitive Diagnosis Models (CDM), or a Deterministic Inputs, Noisy "And" gate model (DINA) model, and ¶ [0031] as a Bayesian framework. Since the base model is trained and calibrated to become the first and second functions, the model is being applied as a tool to produce the generated models by training and calibrating the base model with data. The first and second models are implemented as tools to generate data. The third model is implemented as a tool to perform the weighting function. Therefore, the models are tools user to perform the abstract ideas, i.e. adding the words “apply it” with the abstract ideas.
The claims recite automation of generic business processes that were historically performed by hand like manipulating, combining, generating, and displaying data that include a final universal profile, recommendations, proficiencies, skill/ontology mappings, probabilities of mastery, various weightings, and combined estimates of skill proficiencies. The claims recite general purpose model frameworks that attempt to cover any solution to the problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the results. The specification does not reveal that the core of the invention relates to advances in statistical methods, mathematical functions, academia or research-based modeling frameworks, i.e. psychometric modeling, or machine learning models. The claims generally link the use of the abstract ideas to the technological environments of statistical methods, mathematical functions, models, data models, or machine learning models.
Insofar as the use of abstract ideas are generally linked to the technological fields of statistical methods, mathematical functions, modeling, machine learning methods, data models, training, calibration, application, and weighting through the use of models, graphic user interfaces, and general-purpose computing structures. The claims do not add meaningful functions or limitations, or reveal improvements upon the technologies or technical fields. The claims are focused on the nature of the data being manipulated – i.e., the descriptive nature of the data without detailing an inventive concept beyond the abstract ideas., the claims do not apply or use the abstract ideas in some other meaningful way beyond the general linkage to the technological environments. Therefore, the claims, as a whole, while looking at the additional elements, are no more than a drafting effort designed to monopolize the recited judicial exceptions.
The claims recite: receive first and second data, train the models (receiving, at the model, data sent to the model), calibrate the models (receiving, at the model, collected data sent to the model), display data, and generate a graphical user interface. The specification does not reveal that the core of the invention relates to advances to the technologies of sending and receiving data, training models (feeding, i.e. sending data), calibrating models (feeding, i.e. sending data), displaying data, or generating or using graphical user interfaces.
The claims as a whole, while looking at additional elements individually and in combination, do not integrate the judicial exceptions into a practical application.
Step 2B: The analysis above for Step 2A is commensurate with the analysis for this Step 2B, such that the claims, as a whole, do not include additional elements that are sufficient to amount to significantly more than the judicial exception when taken individually and in combination (MPEP 2106.05).
Dependent Claims:
Claims 2, 9, and 16 further limit the display of the profile, adding a description of a proficiency. This limitation is not an abstract idea as it is merely further limiting the non-functional descriptive information presented, or merely displaying further data characterizations. The specification does not disclose that the invention makes advances to display graphical user interface (GUI) architecture or methods, where displaying data and the GUI are disclosed at a high level of generality, focusing on the descriptive nature of the data being displayed. These limitations cannot be relied upon to integrate the claims as a whole, while looking at additional elements individually and in combination, into a practical application or significantly more.
Claims 3, 10, and 17 further limit the recommendations generated in claim by characterizing the data comprised in the recommendations, which is non-functional descriptive information that carries no patentable weight. These claims recite no additional elements, such that they cannot be integrated into a practical application or amount to significantly more.
Claims 4, 11, and 18, add a new function, monitor a growth metric, associated with the first skill over a time period. The monitor function recites an abstract idea in the same categories as the independent claim, "Certain Methods of Organizing Human Activity" for “managing personal behavior or relationships or interactions between people" which “include social activities, teaching, and following rules or instructions.” The claim uses student skill determinations from student academic data to evaluate each students’ academic growth in the skill, further impacting behavioral management of a student according to their academic data. This function is also in the abstract idea category of “Mental Processes” for implementing observations, evaluations, judgments, and opinions” where a growth metric, skill, and time can be monitored in the mind or with pen and paper. The claimed functions are recited at a high level of generality. The claims are focused on the nature of the data being manipulated – i.e., the descriptive nature of the data without detailing an inventive concept beyond the abstract ideas. Generally linking the Judicial Exception to a technological fields of using student skill determinations from student academic data to evaluate each students’ academic growth in the skill over time, without exhibiting some other meaningful approach such that the claims as a whole are not more than a drafting effort designed to monopolize the exception is not indicative of integration into a practical application (MPEP 2016.05(e) and (h)). For the same reason the claims do not integrate the abstract ideas into a practical application above, the claims, as a whole, do not include additional elements sufficient to amount to significantly more than the judicial exception, when taken individually and in combination.
Claims 5, 12, and 19 add a new function, generate a first prediction, associated with performance in the first skill. The generate a prediction function recites an abstract idea in the same categories as the independent claim. This function is in the abstract idea category of "Certain Methods of Organizing Human Activity" for “managing personal behavior or relationships or interactions between people" which “include social activities, teaching, and following rules or instructions” because it generates a prediction that corresponds to the first skill associated with student performance, all based on student academic data, further impacting behavioral management of a student according to their academic data. This function is also in the abstract idea category of “Mental Processes” for implementing observations, evaluations, judgments, and opinions” where the prediction of student performance in a skill can be performed in the mind or with pen and paper. The claimed functions are recited at a high level of generality. The claims are focused on the nature of the data being manipulated – i.e., the descriptive nature of the data without detailing an inventive concept beyond the abstract ideas. They are simply applying the Judicial Exception to the technological field of the prediction of student performance in a skill based on student academic data, without adding meaningful functions or limitations. such that the claims as a whole are not more than a drafting effort designed to monopolize the exception is not indicative of integration into a practical application of the claims as a whole, while looking at additional elements individually and in combination (MPEP 2016.05(e) and (h)). For the same reason the claims do not integrate the abstract ideas into a practical application above, the claims, as a whole, do not include additional elements sufficient to amount to significantly more than the judicial exception, when taken individually and in combination.
Claims 6 and 13 add a new function, generate an update to the combined estimate of skill proficiency relating to the first skill based on updated first and second performance data over time. The generate an update function recites an abstract idea in the same categories as the independent claim. This function is in the abstract idea category of "Certain Methods of Organizing Human Activity" for “managing personal behavior or relationships or interactions between people" which “include social activities, teaching, and following rules or instructions” because it generates an updated combined estimate of skill proficiency in a skill based on updated data over time for a student, all based on student academic data, further impacting behavioral management of a student according to their academic data. This function is also in the abstract idea category of “Mental Processes” for implementing observations, evaluations, judgments, and opinions” where the update generation can be performed in the mind or with pen and paper. The claimed functions are recited at a high level of generality. The claims are focused on the nature of the data being manipulated – i.e., the descriptive nature of the data without detailing an inventive concept beyond the abstract ideas. They are simply applying the Judicial Exception to the technological field of the updating skill proficiency of student’s skill based on student academic data, without adding meaningful functions or limitations. such that the claims as a whole are not more than a drafting effort designed to monopolize the exception is not indicative of integration into a practical application of the claims as a whole, while looking at additional elements individually and in combination (MPEP 2016.05(e) and (h)). For the same reason the claims do not integrate the abstract ideas into a practical application above, the claims, as a whole, do not include additional elements sufficient to amount to significantly more than the judicial exception, when taken individually and in combination.
Claims 7 and 14 further limit the functions of updating the first and second performance data, incorporating a weighting assignment based on time passed or signal strength corresponding the associated first or second products. . The weighting assignment function recites an abstract idea in the same categories as the independent claim. This function is in the abstract idea category of "Certain Methods of Organizing Human Activity" for “managing personal behavior or relationships or interactions between people" which “include social activities, teaching, and following rules or instructions” because it adds a weight based on time or signal strength which effectively filters, sorts, and ranks particular student academic data, further impacting behavioral management of a student according to their academic data. This function is also in the abstract idea category of “Mental Processes” for implementing observations, evaluations, judgments, and opinions” where the weighting of student academic data according to time or signal strength (per application specification [0039] “based on the number and recency of student responses to questions in that product) can be performed in the mind or with pen and paper. The claimed functions are recited at a high level of generality. The claims are focused on the nature of the data being manipulated – i.e., the descriptive nature of the data without detailing an inventive concept beyond the abstract ideas. They are simply applying the Judicial Exception to the technological field of adding a weight based on time or signal strength which effectively filters, sorts, and ranks particular student academic data without adding meaningful functions or limitations. such that the claims as a whole are not more than a drafting effort designed to monopolize the exception is not indicative of integration into a practical application of the claims as a whole, while looking at additional elements individually and in combination (MPEP 2016.05(e) and (h)). For the same reason the claims do not integrate the abstract ideas into a practical application above, the claims, as a whole, do not include additional elements sufficient to amount to significantly more than the judicial exception, when taken individually and in combination.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-20 are rejected under U.S.C. § 102(a)(1) as being anticipated by Gal, US20100190142A1.
Regarding claims 1, 8, and 15: Angel discloses: Claim 1 Preamble: A method comprising; Claim 8 Preamble: A system comprising: a memory to store instructions; and Claim 15 Preamble: A non-transitory computer-readable storage medium having instructions that, when executed by a processing device, cause the processing device to perform operations comprising: a processing device, operatively coupled to the memory, to execute the instructions to perform operations comprising: (The prior art comprises methods and systems);
receiving, by a processing device, first data associated with a first instructional product and second data associated with a second instructional product [0028] (receive data), [0070] data is monitored, recorded, mined, and stored, i.e. received, for educational, pedagogic and administrative entities, learning activities and related content, and for conducting research and formative assessment for improvement of teaching methodologies, flow or sequence of learning activities, or the like, e.g. from one or more instructional products,” [0108] (data is collected including first and second data associated with at least two instructional products);
generating a first plurality of skill-mapped items based on a mapping of at least a portion of first data associated with a first instructional product to a first skill in an ontology;[0066] “allocate a first learning object,” [0076] (content allows third-party content, i.e. external products alongside the internal products) [0083] (activities are concept tagged to the pedagogy of the ontology), [0095] “the student's capability of solving numerical problems hidden variable 221) is dependent on the student's calculation capability (hidden variable 223)” (first skill-mapped product);
wherein the ontology comprises a set of skills corresponding to a grade level in a first academic subject; [0090] (ontology is used in the same manner, based on grade level, pedagogy, subject, and finer granularity of skills, where skill mapping is also referred to as concept-tagging, and outcome concepts are academic subjects)
generating a second plurality of skill-mapped items based on a mapping of at least a portion of second data associated with a second instructional product to the first skill in the ontology; [0076] (content allows third-party content, i.e. external products alongside the internal products) [0083] (activities are concept tagged to the pedagogy of the ontology), [0095] “the student's capability of solving verbal mathematical problems (hidden variable 222) is dependent on both the student's calculation capability (hidden variable 223) and the student's reading comprehension capability (hidden variable 224)” (second skill-mapped product);
generating a first product-specific model associated with the first instructional product, wherein the generating comprises: [0016] “creating the pedagogic Bayesian network is included within an algorithm which creates one or more statistically evolving models based on relational concept mapping,” (i.e. a main model that is trained and calibrated into product specific versions of the model), [0026] “the computer-aided assessment module is to create a set of one or more conditional distribution functions corresponding to an estimation of the probability of possible values,” [0126] (generation of a product specific model that utilizes data from the learning objects, i.e. instructional products);”
“training, based on a first set of characteristics of the first instructional product, a first model configured to output a first set of probabilities of mastery for the set of skills based on a first set of model parameters, and [0017] “discloses a dynamic pedagogic Bayesian network; a plurality of copies of the dynamic pedagogic Bayesian network [i.e. multiple copies of the base model] represent a model of said student at a plurality of interconnected time points and [0082] “a plurality of models of cognitive processes, levels of learning activities, complexity of gained competencies, general and subject-specific topics,” (where subject specific topics and specific students represent models for the products, models of the students, and models of students for each different product, i.e. product specific models), [0084] (a model is trained to become a knowledge map to estimate mastery for skills, i.e. the model is trained via inputs), [0091] “an algorithmic construct that allows estimation of and inference,” [0177] “limit the scope of each KN model to a number of variables which may provide meaningful assessment for a single domain,”
calibrating the first set of model parameters of the first trained model using a first set of representative student responses to questions mapped to the set of skills presented in the first instructional product; [0005] “determining a set of one or more observable pedagogic variables based on one or more observable task performance items reflected in the log of interactions,” [0034] “the computer-aided assessment module is to create a dynamic pedagogic Bayesian network; wherein a plurality of copies of the dynamic pedagogic Bayesian network represent a model of said student at a plurality of interconnected time points; and wherein the computer-aided assessment module is to estimate the pedagogic parameter based on said dynamic pedagogic Bayesian network,” [0084] (a trained model is calibrated using student responses to calibrate the knowledge map to better estimate mastery for skills), ([0138] “estimating a pedagogic parameter,”
generating a second product-specific model associated with the second instructional product, wherein the generating comprises: [0016] “creating the pedagogic Bayesian network is included within an algorithm which creates one or more statistically evolving models based on relational concept mapping,” (i.e. a main model that is trained and calibrated into product specific versions of the model), [0026] “the computer-aided assessment module is to create a set of one or more conditional distribution functions corresponding to an estimation of the probability of possible values,” [0126] (generation of a product specific model that utilizes data from the learning objects, i.e. instructional products);
training, based on a second set of characteristics of the second instructional product, a second model configured to output a second set of probabilities of mastery for the second set of skills based on a second set of model parameters, and [0017] “discloses a dynamic pedagogic Bayesian network; a plurality of copies of the dynamic pedagogic Bayesian network [i.e. multiple copies of the base model] represent a model of said student at a plurality of interconnected time points and [0082] “a plurality of models of cognitive processes, levels of learning activities, complexity of gained competencies, general and subject-specific topics,” (where subject specific topics and specific students represent models for the products, models of the students, and models of students for each different product, i.e. product specific models), [0084] (a model is trained to become a knowledge map to estimate mastery for skills, i.e. the model is trained via inputs), [0091] “an algorithmic construct that allows estimation of and inference,” [0177] “limit the scope of each KN model to a number of variables which may provide meaningful assessment for a single domain,”
calibrating the second set of model parameters of the second trained model using a second set of representative student responses to questions mapped to the second set of skills skill presented in the second instructional product; [0005] “determining a set of one or more observable pedagogic variables based on one or more observable task performance items reflected in the log of interactions,” [0034] “the computer-aided assessment module is to create a dynamic pedagogic Bayesian network; wherein a plurality of copies of the dynamic pedagogic Bayesian network represent a model of said student at a plurality of interconnected time points; and wherein the computer-aided assessment module is to estimate the pedagogic parameter based on said dynamic pedagogic Bayesian network,” [0084] (a trained model is calibrated using student responses to calibrate the knowledge map to better estimate mastery for skills), ([0138] “estimating a pedagogic parameter”
applying the first product-specific model to first performance data associated with the first instructional product, to generate a first estimate of skill proficiency corresponding to the first plurality of skill-mapped items and the first instructional product; [0026] “one or more conditional distribution functions corresponding to an estimation of the probability of possible values,” [0093] ”a student … producing a set of one or more observable results, namely, Task Performance (TP) items 241-244,” [0095 and Figure 2B] (shows mapping of individual products, 241-244 in product 1 correlate to estimates of proficiencies in 221 and 223, and 251-253 in product 2 correlate to estimates of proficiencies in 222, 223, and 224, all within one skill, 220; further described in the first and second estimates of proficiency and in [0096]), [0096] “In a demonstrative example, each one of the four hidden variables 221-224 may have three possible values: “weak”, “medium”, or “strong”, corresponding to the level of mastering by the student of this capability or knowledge item. …the state of each one of the hidden variables 221-224 may be described by a triple-valued probability distribution function, whose three values sum to 1. This is demonstrated in FIG. 2B, in which triple-valued probability distribution functions 261-264 are shown, corresponding to hidden variables 221-224. In each one of the probability distribution functions 261-264, the most likely value for each hidden variable is underlined,” [0104] “, the probability distributions 261-264 may correspond to the marginal prior probabilities of the four hidden variables 221-224, respectively.”
applying the second product-specific model to second performance data associated with the second instructional product, to generate a second estimate of skill proficiency corresponding to the second plurality of skill- mapped items and the second instructional product; [0026] “one or more conditional distribution functions corresponding to an estimation of the probability of possible values,” [0093] ”a student … producing a set of one or more observable results, namely, Task Performance (TP) items 241-244,” [0095 and Figure 2B] (shows mapping of individual products, 241-244 in product 1 correlate to estimates of proficiencies in 221 and 223, and 251-253 in product 2 correlate to estimates of proficiencies in 222, 223, and 224, all within one skill, 220; further described in the first and second estimates of proficiency and in [0096]), [0096] “In a demonstrative example, each one of the four hidden variables 221-224 may have three possible values: “weak”, “medium”, or “strong”, corresponding to the level of mastering by the student of this capability or knowledge item. …the state of each one of the hidden variables 221-224 may be described by a triple-valued probability distribution function, whose three values sum to 1. This is demonstrated in FIG. 2B, in which triple-valued probability distribution functions 261-264 are shown, corresponding to hidden variables 221-224. In each one of the probability distribution functions 261-264, the most likely value for each hidden variable is underlined,” [0104] “, the probability distributions 261-264 may correspond to the marginal prior probabilities of the four hidden variables 221-224, respectively;”
generating, by a third model, based on the first estimate of skill proficiency and the second estimate of skill proficiency, a combined estimate of skill proficiency relating to the first skill, wherein the third model applies a weighting function based on timepoint data associated with first observations of the first performance data and second observations of the second performance data; [0028] “modify at least one of the probabilities of the possible values of the set of one or more conditional distribution functions,” (a weighting function is implemented to modify the probabilities of the values), [0029] “for example, the computer-aided assessment module is to determine a weighted pedagogic score corresponding to said set of one or more conditional distribution functions, based on the sum of weights of scores corresponding to said possible values,” (where the computer aided assessment module is a model), [0096] (probability function that combines all estimates of proficiencies related to the first skill), [0104] “the probability distributions 261-264 may correspond to the marginal prior probabilities of the four hidden variables 221-224, respectively” “[0097] “the parent set 271 includes two entities, namely, the calculation capability 223 and the reading comprehension capability 224; whereas the offspring set 272 includes one entity, namely, solving verbal mathematical problems 222. As indicated by arrows 273 and 274, the offspring capability of solving verbal mathematical problems 222 is dependent on both parent capabilities, namely, the calculation capability 223 and the reading comprehension capability 224,” [0098] “Accordingly, in some embodiments, the state of a variable in the PBN 210 is the conditional probability distribution of this variable, given the states of its parent nodes;”
generating a universal profile comprising at least the combined estimate of skill proficiency relating to the first skill; [0084] “the mappings between the activities performed by the student and the knowledge elements that these activities contribute to; and a model (e.g., a “required knowledge map”) of the knowledge elements and capabilities that the student is expected to master within a given learning unit, including the possible relationships between such elements. The knowledge map engine 173 utilizes these inputs to establish an “acquired knowledge map” estimating, at any given point in time, the degree to which the student mastered each of the required knowledge elements or capabilities. The knowledge map engine 173 may use graphical models of belief propagation to build a model of the knowledge map of the student, and may update this model over time, as information about more activities performed by the student becomes available,” [0126] (generation of a universal profile, combining the product specific results for a student based on ontological objectives of mastery);
generating an interactive graphical interface comprising a display of at least a portion of the universal profile and one or more recommendations relating to the first instructional product and the second instructional product. [0072 and 0118] (interactive display interface for tracking data and results, including the universal profile as disclosed in [0084] above), [0058] “adaptive assignment of content or learning activities or learning objects to students (e.g., based on their past performance in one or more learning activities, past successes, past failures, identified strengths, identified weaknesses),” (assignment of particular learning activities or objects is synonymous with products), [0073] “The additional presentation (or the refraining from additional presentation) may be performed by system 100 automatically,” (automatic analysis of student competencies in skills used to adaptively assign content recommendations) to accommodate the identified strengths and weaknesses of Student A,” [0076] (“dynamic personalization” of content, i.e. adaptive assignments from the system and third party systems), [0117] (ongoing assessment to support dynamic adaptive activity assignments).
Regarding Claims 2, 9, and 16: The method of claim 1, wherein the display of the at least the portion of the universal profile comprises a description of a proficiency associated with the first skill. [Figures 2A, 2B, 2C, 2D, and 5] (descriptions of proficiencies for different skills in a subject), [0073] (the display interface displays the detailed descriptions of the proficiencies of the first skill in the profile), [0079] (display interface further details what may be comprised within the different areas of the display including “explanations related to topics,” where topics can include descriptions of proficiencies seen in [0073] and [0079]).
Regarding Claims 3, 10, and 17: Angel discloses: The method of claim 1, System of claim 10 or Non-transitory CRM of claim 15: further comprising wherein the one or more recommendations comprise at least one of a first skill-mapped assignment associated with the first instructional product or a second skill-mapped assignment associated with the second instructional product. [0058] “adaptive assignment of content or learning activities or learning objects to students (e.g., based on their past performance in one or more learning activities, past successes, past failures, identified strengths, identified weaknesses),” (assignment of particular learning activities or objects is synonymous with products), [0073] “The additional presentation (or the refraining from additional presentation) may be performed by system 100 automatically,” (automatic analysis of student competencies in skills used to adaptively assign content recommendations) to accommodate the identified strengths and weaknesses of Student A,” [0076] (“dynamic personalization” of content, i.e. adaptive assignments from the system and third party systems), [0117] (ongoing assessment to support dynamic adaptive activity assignments.)
Regarding Claims 4, 11, and 18: Angel discloses: The method of claim 1, System of claim 10 or Non-transitory CRM of claim 15, further comprising monitoring a growth metric associated with the first skill over a time period. [0113] “monitoring students' progress in their level of understanding of different assessed variables;” [0123] (student information is tracked and the “gained knowledge” is measured and mapped, where this paragraph discloses monitoring growth over time), [0127] “students gained knowledge over a certain time,” [0176] (dynamic measurement showing “consecutive time steps or time points”
Regarding Claims 5, 12, and 19: Angel discloses: The method of claim 1, System of claim 10 or Non-transitory CRM of claim 15, further comprising generating a first prediction associated with performance corresponding to the first skill. [0173] (predicted future performance, where the prior art discloses “test”, it would be obvious to a person having ordinary skill in the art to predict performance corresponding to the first skill as the system in the prior art evidences actions surrounding the skills themselves and a test could be easily replaced with skill to perform the same function as it does in the disclosure of the prior art.).
Regarding Claims 6 and 13: Angel discloses: The method of claim 1, System of claim 10 or Non-transitory CRM of claim 15, further comprising generating an update to the combined estimate of skill proficiency relating to the first skill based at least in part on updated performance first data and updated second performance data collected over a period of time. [0124] (at least, includes the ongoing assessment of data with regards to cycles, i.e. time, including estimates of proficiency for skills), [0145] “The PBN includes a probabilistic model for graphical representation of postulated dependence properties of a set of variables, and the analytical representation of the corresponding probabilities, in a way that facilitates their updating when partial data becomes gradually available. In some embodiments,” (gradual availability is equivalent to over time), [0150] (updates to variables that contribute each activity causes a system update cycle providing an updated combined skill proficiency based on performance data.)
Regarding Claims 7 and 14: Angel discloses: The method of claim 1, System of claim 13, wherein the updated first performance data and the updated second performance data are assigned a weighting based on one or more of an amount of time passed or a signal strength corresponding to the first instructional product or the second instructional product. “[0127] “the CAA is used in conjunction with adaptive learning, in which the system programs itself by adjusting content, weights or strengths for producing the appropriate output, based on automatic flows,” (the flows are inclusive of at least first and second performance data).
Conclusion
THIS ACTION IS MADE FINAL. 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.
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ANGELA HATCH
Examiner
Art Unit 3626
/ANGELA HATCH/ Examiner, Art Unit 3626
/NATHAN C UBER/ Supervisory Patent Examiner, Art Unit 3626