DETAILED ACTION
This communication is in response to Application No. 18/103,606 filed on January 31, 2023 in which claims 1-20 are presented for examination
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 .
Specification
The abstract of the disclosure is objected to because “A” in “Based on the classifications of the unstructured data, A plurality of friction points . . .” (Ln. 5-6) should be lower case. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 2, 13, 18, and 20 are objected to because of the following informalities:
“included a journey map” should be “included in a journey map” (Claim 2, ln. 1-2; Claim 13, ln. 1-2; Claim 20, ln. 1-2).
“is configured generate” should be “is configured to generate” (Claim 18, ln. 2)
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly
point out and distinctly claim the subject matter which the inventor or a joint inventor regards
as the invention.
Regarding Claim 1, the claim recites “normalizing the unstructured data to classify the unstructured data consistent with a machine learning classification model to classify the unstructured data into a plurality of classifications; based on the classifications . . . ” (ln. 5 -8). When discussed in regard to the step of “normalizing”, “classify” is not positively recited as a claimed element of the method. Instead, it is discussed as the motivation for the positively recited element of “normalizing”. As a result, there is insufficient antecedent basis for the recitation of “the classifications” limitation in the claim. Therefore, the claim is rejected. The claim should be amended to positively recite the element of classifying or to otherwise modify the recitation of “the classifications”.
Additionally, the claim recites the terms “friction points” (ln. 9 and 12), “achievement points” (ln. 9 and 12), and “educational journey” (ln. 1 and 13), which are relative term that render the claim indefinite. These terms are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degrees of “friction”, “achievement”, or “journey” that is required, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As a result, it is not clear what “points” qualify as “friction points” or “achievement points” and it is not clear what “predictions” qualify as “a predictive mapping of an educational journey” (ln. 1). Therefore, the claim is rejected. The claim should be amended to clarify the scope of these relative terms.
Regarding Claim 2, the claim recites “friction points” (ln. 2-3), “achievement points” (ln. 2-4), and “journey” (ln. 1), which are indefinite for substantially the same reasons as those articulated in regard to the rejection of Claim 1. As a result, the claim is similarly rejected and should be amended in a similar manner. Additionally, the claim is rejected because it is dependent on a rejected claim.
Regarding Claim 3, the claim is rejected because it is dependent on a rejected claim.
Regarding Claim 4, the claim recites “journey” (ln. 5), which is indefinite for substantially the same reasons as those articulated in regard to the rejection of Claim 1. As a result, the claim is similarly rejected and should be amended in a similar manner. Additionally, the claim is rejected because it is dependent on a rejected claim.
Regarding Claim 5, the claim is rejected because it is dependent on a rejected claim.
Regarding Claim 6, the claim recites “friction points” (ln. 1-2) and “achievement points” (ln. 2), which are indefinite for substantially the same reasons as those articulated in regard to the rejection of Claim 1. As a result, the claim is similarly rejected and should be amended in a similar manner. Additionally, the claim is rejected because it is dependent on a rejected claim.
Regarding Claims 7-11, the claims are rejected because they are dependent on a rejected claim.
Regarding Claim 12, the claim recites “configured to normalize the unstructured data to classify the unstructured data consistent with the machine learning classification model to classify the unstructured data into a plurality of classifications and based on the classifications” (ln. 9-12), “friction points” (ln. 13 and 18), “achievement points” (ln. 13 and 18), and “journey” (ln. 14-15, and 19), which are indefinite for substantially the same reasons as those articulated in regard to the rejection of Claim 1. As a result, the claim is similarly rejected and should be amended in a similar manner.
Additionally, the claim recites “the educational journey” (ln. 14-15, and 19). However, there is insufficient antecedent basis for this limitation in the claim. Therefore, the claim is rejected. The claim should be amended to remedy this issue, such as by replacing the initial recitation of “the educational journey” with “an educational journey”.
Regarding Claim 13, the claim recites “friction points” (ln. 2-3), “achievement points” (ln. 3-4), and “journey” (ln. 2), which are indefinite for substantially the same reasons as those articulated in regard to the rejection of Claim 1. As a result, the claim is similarly rejected and should be amended in a similar manner. Additionally, the claim is rejected because it is dependent on a rejected claim.
Regarding Claims 14-18, the claims are rejected because they are dependent on a rejected claim.
Regarding Claim 19, the claim recites ““normalizing the unstructured data to classify the unstructured data consistent with a machine learning classification model to classify the unstructured data into a plurality of classifications; based on the classifications . . . ”” (ln. 9-12), “friction points” (ln. 13 and 17), “achievement points” (ln. 13 and 17), and “journey” (ln. 5, 14-15, and 17), which are indefinite for substantially the same reasons as those articulated in regard to the rejection of Claim 1. As a result, the claim is similarly rejected and should be amended in a similar manner.
Regarding Claim 20, the claim recites “friction points” (ln. 2-3), “achievement points” (ln. 3-4), and “journey” (ln. 2), which are indefinite for substantially the same reasons as those articulated in regard to the rejection of Claim 1. As a result, the claim is similarly rejected and should be amended in a similar manner. Additionally, the claim is rejected because it is dependent on a rejected claim.
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 abstract ideas without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a process claim. Therefore, Claims 1-11 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, steps of the claimed subject matter are mental processes. Specifically, the claim recites
“A method of generating a predictive mapping of an educational journey of a student at an education institution, the method comprising” (mental process – amounts to exercising judgment to form an opinion on a predictive mapping, based on observed or known student information, which may be aided by pen and paper);
“normalizing the unstructured data to classify the unstructured data” (mental process – amounts to exercising judgment to normalize known or observed data for classification, which may be aided by pen and paper);
“based on the classifications of the unstructured data, identifying at least one of a plurality of friction points that hinder a particular student's progress in the educational journey or a plurality of achievement points that promote the particular student's progress in the educational journey” (mental process – amounts to exercising judgment to evaluate information to form an opinion on parts of information that belong to a category, which may be aided by pen and paper); and
“using the friction points or achievement points, generating prediction information of the particular student's progress in the educational journey, the prediction information” (mental process – amounts to exercising judgment to evaluate information to form an opinion on a prediction, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“receiving, over a computer network, from a database of student records, unstructured data about a particular student of the educational institution . . . and transmitting the prediction information over the computer network to an administrator machine” (receiving and transmitting data amounts to insignificant extra-solution activity because the receiving and transmission of data is incidental to the claimed subject matter) and
“consistent with a machine learning classification model to classify the unstructured data into a plurality of classifications . . . being consistent with a machine learning prediction model” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“receiving, over a computer network, from a database of student records, unstructured data about a particular student of the educational institution . . . and transmitting the prediction information over the computer network to an administrator machine” (transmitting data over a network is well‐understood, routine, and conventional, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration) and
“consistent with a machine learning classification model to classify the unstructured data into a plurality of classifications . . . being consistent with a machine learning prediction model” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-11. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 2 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the prediction information is included a journey map comprising an indication of a plurality of the friction points or a plurality of the achievement points, including summary information for the plurality of the friction points or the plurality of the achievement points” (mental process – amounts to exercising judgment to form an opinion, where the opinion includes specific indication and summary information, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 2 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 3:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 3 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the unstructured data comprises at least one of mentor notes, email interactions, assessment responses, instructor notes, social media posts, course surveys, personality test responses, or aptitude test responses” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the unstructured data comprises at least one of mentor notes, email interactions, assessment responses, instructor notes, social media posts, course surveys, personality test responses, or aptitude test responses” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 3 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 4:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 4 depends on. Here, the claim recites additional elements that are mental processes. Specifically,
“generating the prediction information of the particular student's progress in the educational journey” (mental process – amounts to forming an opinion based on known or observed information, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“receiving over the computer network, structured data” (providing data amounts to insignificant extra-solution activity because the transmission of data is incidental to the claimed subject matter) and
“wherein the structured data is used in the machine learning prediction model in” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“receiving over the computer network, structured data” (transmitting data is well‐understood, routine, and conventional, see generally Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; see also buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration) and
“wherein the structured data is used in the machine learning prediction model in” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Accordingly, Claim 4 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 5:
Step 2A Prong 1: See the rejection of Claim 4 above, which Claim 5 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the structured data and unstructured data comprises at least one of mentor notes, email interactions, helpdesk tickets, program information, assigned grades, discipline write ups, assessment responses, financial aid status, transferred credits, academic resource interactions, instructor notes, marketing data, social media posts, governmental body reports, course survey responses, personality test responses, or aptitude test responses” (amounts to merely generally linking the use of the judicial exception to a particular technological environment or field of use, which do not impose any meaningful limits on practicing the abstract idea).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the structured data and unstructured data comprises at least one of mentor notes, email interactions, helpdesk tickets, program information, assigned grades, discipline write ups, assessment responses, financial aid status, transferred credits, academic resource interactions, instructor notes, marketing data, social media posts, governmental body reports, course survey responses, personality test responses, or aptitude test responses” (merely generally linking the use of the judicial exception to a particular technological environment or field of use does not provide an inventive concept).
Accordingly, Claim 5 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 6:
Step 2A Prong 1: See the rejection of Claim 4 above, which Claim 6 depends on. Here, the claim recites additional elements that are mental processes. Specifically, the claim recites
“wherein the structured data is used to generate friction points or achievement points” (mental process – amounts to exercising judgment to form an opinion, based on known or observed information, which may be aided by pen and paper).
Step 2A Prong 2 & Step 2B: There are no elements left for consideration of implementation within a practical application or for consideration of significantly more.
Accordingly, Claim 6 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 7:
Step 2A Prong 1: See the rejection of Claim 4 above, which Claim 7 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the structured data is used to refine the machine learning prediction model” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the structured data is used to refine the machine learning prediction model” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Accordingly, Claim 7 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 8:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 8 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the unstructured data, based on the classifications, is used to refine the machine learning prediction model” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the unstructured data, based on the classifications, is used to refine the machine learning prediction model” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Accordingly, Claim 8 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 9:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 9 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the machine learning classification model comprises a natural language processing model” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the machine learning classification model comprises a natural language processing model” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Accordingly, Claim 9 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 10:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 10 depends on.
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“wherein the machine learning prediction model comprises a root cause analysis model” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“wherein the machine learning prediction model comprises a root cause analysis model” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Accordingly, Claim 10 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 11:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 11 depends on. Here, the claim recites additional elements that are mental processes. Specifically,
“generate intervention output suggesting action to alter the generated prediction information” (mental process – amounts to forming an opinion based on known or observed information, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“transmitting the intervention output over the computer network to the administrator machine” (providing data amounts to insignificant extra-solution activity because the transmission of data is incidental to the claimed subject matter) and
“using the machine learning prediction model to” (mere instructions to apply the exception using generic computer components does not provide an inventive concept).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“transmitting the intervention output over the computer network to the administrator machine” (transmitting data is well‐understood, routine, and conventional, see generally Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; see also buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration) and
“using the machine learning prediction model to” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea).
Accordingly, Claim 11 is rejected as being directed to an abstract idea without significantly more.
Regarding Claim 12:
Step 1: Claim 12 is a machine claim. Therefore, Claims 12-18 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, steps of the claimed subject matter are mental processes. Specifically, the claim recites
“normalize the unstructured data to classify the unstructured data” (mental process – amounts to exercising judgment to normalize known or observed data for classification, which may be aided by pen and paper);
“based on the classifications of the unstructured data, identify at least one of a plurality of friction points that hinder a particular student's progress in the educational journey or a plurality of achievement points that promote the particular student's progress in the educational journey” (mental process – amounts to exercising judgment to evaluate information to form an opinion on parts of information that belong to a category, which may be aided by pen and paper); and
“using the friction points or achievement points, generate prediction information of the particular student's progress in the educational journey, the prediction information” (mental process – amounts to exercising judgment to evaluate information to form an opinion on a prediction, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A computing system comprising: one or more processors; and one or more computer-readable media having stored thereon instructions that are executable by the one or more processors . . . network hardware configured to . . . consistent with the machine learning classification model to classify the unstructured data into a plurality of classifications and . . . a categorization engine comprising a machine learning classification model, implemented by the one or more processors and the instructions, configured to . . . a prediction engine comprising a machine learning prediction model, implemented by the one or more processors and the instructions, configured to . . . being consistent with a machine learning prediction model . . . and wherein the network hardware is configured to . . . ” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea) and
“receive, over a computer network, from a database of student records, unstructured data about a particular student of an educational institution . . . transmit the prediction information over the computer network to an administrator machine” (receiving and transmitting data amounts to insignificant extra-solution activity because the receiving and transmission of data is incidental to the claimed subject matter).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“A computing system comprising: one or more processors; and one or more computer-readable media having stored thereon instructions that are executable by the one or more processors . . . network hardware configured to . . . consistent with the machine learning classification model to classify the unstructured data into a plurality of classifications and . . . a categorization engine comprising a machine learning classification model, implemented by the one or more processors and the instructions, configured to . . . a prediction engine comprising a machine learning prediction model, implemented by the one or more processors and the instructions, configured to . . . being consistent with a machine learning prediction model . . . and wherein the network hardware is configured to . . . ” (mere instructions to apply the exception using generic computer components does not provide an inventive concept) and
“receive, over a computer network, from a database of student records, unstructured data about a particular student of an educational institution . . . transmit the prediction information over the computer network to an administrator machine” (transmitting data over a network is well‐understood, routine, and conventional, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration).
For the reasons above, Claim 12 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 13-18. The additional limitations of the dependent claims are addressed below.
Regarding Claim 13, the claim recites limitations that are all substantially the same as limitations of Claim 2, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 13 is rejected under the same rationale.
Regarding Claim 14, the claim recites limitations that are all substantially the same as limitations of Claim 3, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 14 is rejected under the same rationale.
Regarding Claim 15, the claim recites limitations that are all substantially the same as limitations of Claim 8, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 15 is rejected under the same rationale.
Regarding Claim 16, the claim recites limitations that are all substantially the same as limitations of Claim 9, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 16 is rejected under the same rationale.
Regarding Claim 17, the claim recites limitations that are all substantially the same as limitations of Claim 10, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 17 is rejected under the same rationale.
Regarding Claim 18, the claim recites limitations that are all substantially the same as limitations of Claim 11, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 18 is rejected under the same rationale.
Regarding Claim 19:
Step 1: Claim 19 is a machine claim. Therefore, Claims 19-20 are directed to a statutory category of eligible subject matter.
Step 2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Here, steps of the claimed subject matter are mental processes. Specifically, the claim recites
“perform predictive journey mapping” (mental process – amounts to exercising judgment to form an opinion on a predictive mapping, based on observed or known student information, which may be aided by pen and paper);
“normalizing the unstructured data to classify the unstructured data” (mental process – amounts to exercising judgment to normalize known or observed data for classification, which may be aided by pen and paper);
“based on the classifications of the unstructured data, identify at least one of a plurality of friction points that hinder a particular student's progress in the educational journey or a plurality of achievement points that promote the particular student's progress in the educational journey” (mental process – amounts to exercising judgment to evaluate information to form an opinion on parts of information that belong to a category, which may be aided by pen and paper); and
“using the friction points or achievement points, generate prediction information of the particular student's progress in the educational journey, the prediction information” (mental process – amounts to exercising judgment to evaluate information to form an opinion on a prediction, which may be aided by pen and paper).
Step 2A Prong 2: This judicial exception is not integrated into a practical application.
The claim recites the additional elements:
“A computing system comprising one or more processors; and one or more computer-readable media having stored thereon instructions that are executable by the one or more processors to configure the computer system to perform . . . including instructions that are executable to configure the computer system to perform at least the following . . . consistent with a machine learning classification model to classify the unstructured data into a plurality of classifications . . . being consistent with a machine learning prediction model” (amounts to mere instructions to apply the judicial exception on generic and unspecialized computer components, which do not impose any meaningful limits on practicing the abstract idea) and
“receive, over a computer network, from a database of student records, unstructured data about a particular student of an educational institution . . . and transmit the prediction information over the computer network to an administrator machine” (receiving and transmitting data amounts to insignificant extra-solution activity because the receiving and transmission of data is incidental to the claimed subject matter).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
The claim recites the additional elements:
“A computing system comprising one or more processors; and one or more computer-readable media having stored thereon instructions that are executable by the one or more processors to configure the computer system to perform . . . including instructions that are executable to configure the computer system to perform at least the following . . . consistent with a machine learning classification model to classify the unstructured data into a plurality of classifications . . . being consistent with a machine learning prediction model” (mere instructions to apply the exception using generic computer components does not provide an inventive concept) and
“receive, over a computer network, from a database of student records, unstructured data about a particular student of an educational institution . . . and transmit the prediction information over the computer network to an administrator machine” (transmitting data over a network is well‐understood, routine, and conventional, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014); therefore the limitation, which is recited with a high level of generality, remains insignificant extra-solution activity even upon reconsideration).
For the reasons above, Claim 19 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claim 20. The additional limitations of the dependent claim are addressed below.
Regarding Claim 20, the claim recites limitations that are all substantially the same as limitations of Claim 2, in the form of a machine. The claim is also directed to performing mental processes without integration into a practical component or significantly more.
Accordingly, Claim 20 is rejected under the same rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kil et al. (hereinafter Kil) (Patent Pub. No. US 2017/0256172 A1) in view of Kumar et al. (hereinafter Kumar) (Patent Pub. No. US 2020/0012930 A1).
Regarding Claim 1, Kil teaches a method of generating a predictive mapping of an educational journey of a student at an education institution, the method comprising (Para. [0005], “A student data-to-insight-to-action-to-learning analytics method in accordance with an embodiment of the invention comprises computing student success predictions, student engagement predictions, and student impact predictions to interventions ”, where the “predictions” are incorporated into a predictive mapping, see Fig. 14, of an educational journey of a student at an educational institution; see also Para. [0039], “By virtue of having a different subset of top predictors during various stages of a student's academic journey, higher-education (HE) institutions can develop more timely and context-aware student outreach programs and policies”):
receiving, over a computer network, from a database of student records, unstructured data about a particular student of the educational institution (Para. [0166], “one implementation of the micro intervention delivery subsystem 106 . . . uses incoming event data streams from multiple student event data sources, such as, but not limited to, SIS, LMS, CRM, card swipe, smartphones of students, and surveys to delivery message nudges to students at opportune times”, where the “student event data sources” are databases of student records and the “incoming event data steams” include unstructured data, Para. [0169], “These multiple-data streams are converted into user-centric time series event data that adhere to defined entity-event taxonomies and stored as the user event logs 1502”, where “converted into” demonstrates the “streams” were not structured because they did not “adhere to defined entity-event taxonomies”; Fig. 3, where the “micro Intervention Delivery Subsystem 106” receives “Event Data”, which is provided over a computer network, see Fig. 15 and Para. [0168], “partner data 1508 encompassing various enterprise data from colleges and universities are ingested through an Application Programming Interface (API) 1510 that leverages third-party plugin tools 1512, especially for data sources managed through enterprise platform vendors' cloud services”, where and API “that leverages third-party plugin tools” and “cloud services” “to ingest data” from the smartphones “1520” of students at particular “colleges and universities” requires a computer network);
[converting] . . . the unstructured data [into structured data] . . . [by performing conversions] to classify the unstructured data . . . to classify the unstructured data into a plurality of classifications (Para. [0169], “These multiple-data streams are converted into user-centric time series event data that adhere to defined entity-event taxonomies and stored as the user event logs 1502 so that open-source tools that target such data schema can be leveraged”, where the “conver[sion]” organizes the “data streams” into structured “entity-event taxonomies”; see also Para. [0167], “A user event log 1502 contains student event data stream (timestamped records of student activity)” and Fig. 14, where the column “Student info” includes classifications of data from the “data steams”, such as “ACT = 31” and “HS GPA = 3.8”);
based on the classifications of the unstructured data, identifying at least one of a plurality of friction points that hinder a particular student's progress in the educational journey or a plurality of achievement points that promote the particular student's progress in the educational journey (Para. [0107], “the student impact prediction subsystem 104 builds models . . . using student information . . . as shown in FIG. 14”, where “the student impact prediction subsystem 104 builds models” based on the classifications of unstructured data, because the structured “entity-event taxonomies” are generated by classifying the unstructured “data streams”, see above; Para. [0036], “the student impact prediction subsystem 104 includes a multi-level linked-event feature extraction module 112”, where the “multi-level linked-event feature extraction module 112” “extract[s]” “linked-event feature[s]” to be “systematically analyzed”, Para. [0037], “The multi-level linked-event feature extraction module 112 provides the answer to the why question. For example, feature analysis shows new students with high ACT or SAT scores tend to persist at a lower rate when these students perform poorly on their mid-term exams. Furthermore, how they bounce back from such adversities can be a strong indicator of grit and future success. Such linked-event features can be systematically analyzed in terms of their predictive power, interpretability, engagement, and impact. FIG. 2 shows a table with examples of linked-event features divided into seven (7) categories in accordance with an embodiment of the invention”, where “features” like “high ACT” or “consistency” in “Academic performance” are achievement points that promote progress and “features” like “Unmet need” for “financial aid” are friction points that hinder progress);
using the friction points or achievement points, generating prediction information of the particular student's progress in the educational journey (Para. [0039], “Using such real-time linked-event features coupled with background information, the multi-modal student success prediction module 114 next predicts student success in multiple dimensions, such as, but not limited to, academic success, persistence, switching majors, time to and credits at graduation, and post-graduation success”, where the “multi-modal student success prediction model 114” uses the friction and achievement points, “linked-event features”, to generate a prediction on “student success in multiple dimensions”, which, in combination with the predictions from the “Student Engagement Prediction Module 116” and the ”Student Impact Prediction Module 118 ”, form the prediction information of the particular student's progress in the educational journey, see Fig. 1 and Para. [0041], “Engagement and impact predictions made by the student engagement prediction module 116 and the student impact prediction module 118 complete the hierarchical three-level prediction cycle that connects predictive insights to actions to results”; for more information see Para. [0039] – [0042]),
the prediction information being consistent with a machine learning prediction model (Para. [0109] – [0112], “The student-engagement model in the student engagement prediction module 116 y.sub.E=f(x) has the following attributes: 3. Learning algorithm”, where the prediction information is consistent with a machine learning prediction model because the “prediction” is produced using a “model” with a “learning algorithm”; see also Para. [0041], “the student engagement prediction module 116 and the student impact prediction module 118 complete the hierarchical three-level prediction cycle that connects predictive insights to actions to results. These predictions require the analysis results of the impact analysis subsystem 108”, where the “predictions” are also consistent with a machine learning prediction model because it uses “results of the impact analysis system 108” to generate its predictions, and the prediction model can therefore be considered as comprising both components of “104” and “108”, where “108” in turn use machine learning prediction models, see Fig. 12 and Para. [0019], “FIG. 12 shows different learning algorithms that can be used by the tier-3 impact analysis module”); and
transmitting the prediction information over the computer network to an administrator machine (Para. [0036], “In some embodiments, at least some of these components of the student impact prediction subsystem 104 are implemented as one or more software programs running in one or more computer systems using one or more processors and memories associated with the computer systems. These components may reside in a single computer system or distributed among multiple computer systems, which may support cloud computing”, where embodiments of “the student impact prediction subsystem 104”, which include “114”, “116”, and “118”, see Fig. 1, are “cloud” “distributed” as “multiple computer systems”, which is within the broadest reasonable interpretation of a computer network; Fig. 16, Fig. 18, and Para. [0176], “FIG. 16 depicts a homepage that illustrate how such connected, predictive, and action insights can be communicated to various stakeholders to create a virtuous circle in accordance with an embodiment of the invention”, where “predictive” information is provided to homepages and dashboards of administrative “stakeholders” which must have associated machines connected to the “cloud” “distributed” “computer systems”; see generally Para. [0046], “A student in a pilot program can receive treatment or micro intervention defined as contact between a student and an institutional entity encompassing, but not limited to, faculty, advisors, administrators, student mentors/mentees, and personal digital Sherpas or guides”).
Kil does not explicitly disclose . . . normalizing . . . consistent with a machine learning classification model . . . .
However, Kumar teaches . . . normalizing [data] . . . consistent with a machine learning classification model [to classify the data] . . . (Para. [0087], “each feature in some or all of the features (e.g., as illustrated in TABLE 1 above) may be normalized to a range of values between zero (0) and one (1) and sent/provided to a keyword classifier implemented by (or operating in conjunction with) natural language processor 208”; Para. [0088], “natural language processor 208 builds or implements a classification model (e.g., a random forest classification model, etc.) with the keyword classifier. The classification model may be used by the keyword classifier to predict the probability (“p”) of the filtered token being a keyword for the knowledge domain”).
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the converting of unstructured data by performing conversions to classify the instructed data into a plurality of classifications of Kil with the normalization of data for use by a machine learning classification model to classify the data of Kumar in order to represent features on a comparable scale, which will produce more dependable results (Kumar, Para. [0087], “each feature in some or all of the features (e.g., as illustrated in TABLE 1 above) may be normalized to a range of values between zero (0) and one (1) and sent/provided to a keyword classifier implemented by (or operating in conjunction with) natural language processor 208”; Kumar, Pg. 6, Col. 2, Table 1, where use of cross “Category” “Features”, such as features across categories of “Frequency”, “Structure Type”, and “Relationship” may produce skewed results if not first “normalized to a range of values”), and to utilize machine learning techniques to complete a wide variety of complex classification tasks (Kumar, Para. [0004], “Machine learning may be implemented based on a set of training data to train potentially complex models and algorithms for making predictions and further based on a set of test data to measure accuracies and robustness in the predictions made with the complex models and algorithms as trained with the set of training data”; see also Kumar, Para. [0088], “natural language processor 208 builds or implements a classification model (e.g., a random forest classification model, etc.) with the keyword classifier. The classification model may be used by the keyword classifier to predict the probability (“p”) of the filtered token being a keyword for the knowledge domain”, where the NLP model can effectively filter token based on learned domain knowledge), where both the normalization and use of an NLP ML classifier contribute to a system with maximal use of available data (see Kumar, Para. [0028], “Techniques as described herein can be used to enable AI models/algorithms operating with other applications and other use cases to make maximal/optimal/efficient use of the historic data available”).
Regarding Claim 2, Kil in view of Kumar teach the method of claim 1, wherein the prediction information is included a journey map comprising an indication of a plurality of the friction points or a plurality of the achievement points, including summary information for the plurality of the friction points or the plurality of the achievement points (Kil, Fig. 14, where the “Student Information” column includes an indication of a plurality of friction or achievement points, such as “student loan” and “proprietary school”, and the “Engagement rule” includes summary information for the points, such as “high loan amount”; see generally Kil, Para. [0039] - [0042], “Using such real-time linked-event features coupled with background information, the multi-modal student success prediction module 114 next predicts student success . . . Engagement rules are expressed in terms of linked-event features and prediction scores to isolate opportune moments for reaching out to students”).
Regarding Claim 3, Kil in view of Kumar teach the method of claim 1, wherein the unstructured data comprises at least one of mentor notes, email interactions, assessment responses, instructor notes, social media posts, course surveys, personality test responses, or aptitude test responses (Kil, Para. [0167], “The incoming event data stream consists of passive sensing data with student opt-in and institutional data consisting of, but not limited to, SIS, LMS, CRM, card swipe data, and location beacon data”, where, as discussed above, the “event data stream” includes the unstructured data, see Kil, Para. [0005], “A student data-to-insight-to-action-to-learning analytics method in accordance with an embodiment of the invention comprises . . . using at least linked-event features from multiple student event data sources”, which must include at least include personality test responses in order for “Survey: . . . RNL CSI” to be “from multiple student event data sources”, see Kil, Fig. 2 and Kil, Para. [0009], “FIG. 2 shows a table with examples of linked-event features divided into seven (7) categories in accordance with an embodiment of the invention”).
Regarding Claim 4, Kil in view of Kumar teach the method of claim 1, further comprising: receiving over the computer network, structured data (Kil, Para. [0036], “In some embodiments, at least some of these components of the student impact prediction subsystem 104 are implemented as one or more software programs running in one or more computer systems using one or more processors and memories associated with the computer systems. These components may reside in a single computer system or distributed among multiple computer systems, which may support cloud computing”, where embodiments of “the student impact prediction subsystem 104”, which include “114”, “116”, and “118”, see Kil, Fig. 1, are “cloud” “distributed” as “multiple computer systems”, which is within the broadest reasonable interpretation of a computer network, and therefore the transmission from the “Evidence-Based Action Knowledge Database 102” to the components of the “Student Impact Prediction Subsystem 104” is a receiving over the computer network, and the transmission is of the structured data, see Kill, Fig. 14, where the column “Student info” includes classifications of data from the “data steams”, such as “ACT = 31” and “HS GPA = 3.8”; Kil, Para. [0107], “the student impact prediction subsystem 104 builds models . . . using student information . . . as shown in FIG. 14”, where “the student impact prediction subsystem 104 builds models” uses the structured “entity-event taxonomies” from “102”, see Kil, Fig. 1 and Kil, Para. [0091], “the evidence-based action knowledge database (EAKD) 102 can be used to build and deploy the student engagement and impact prediction models”);
and wherein the structured data is used in the machine learning prediction model in generating the prediction information of the particular student's progress in the educational journey (Kill, Para. [0036], “the student impact prediction subsystem 104 includes a multi-level linked-event feature extraction module 112”, where the “multi-level linked-event feature extraction module 112” “extract[s]” “linked-event feature[s]” from the structured data to be “systematically analyzed”, Kil, Para. [0037], “Such linked-event features can be systematically analyzed in terms of their predictive power, interpretability, engagement, and impact. FIG. 2 shows a table with examples of linked-event features divided into seven (7) categories in accordance with an embodiment of the invention”; Kil, Para. [0039], “Using such real-time linked-event features coupled with background information, the multi-modal student success prediction module 114 next predicts student success in multiple dimensions, such as, but not limited to, academic success, persistence, switching majors, time to and credits at graduation, and post-graduation success”, where the “multi-modal student success prediction model 114” uses the friction and achievement points, “linked-event features”, to generate a prediction on “student success in multiple dimensions”, which, in combination with the predictions from the “Student Engagement Prediction Module 116” and the ”Student Impact Prediction Module 118 ”, form the prediction information of the particular student's progress in the educational journey, see Kil, Fig. 1 and Kil, Para. [0041], “Engagement and impact predictions made by the student engagement prediction module 116 and the student impact prediction module 118 complete the hierarchical three-level prediction cycle that connects predictive insights to actions to results”; for more information see Kil, Para. [0039] – [0042]).
Regarding Claim 5, Kil in view of Kumar teach the method of claim 4, wherein the structured data and unstructured data comprises at least one of mentor notes, email interactions, helpdesk tickets, program information, assigned grades, discipline write ups, assessment responses, financial aid status, transferred credits, academic resource interactions, instructor notes, marketing data, social media posts, governmental body reports, course survey responses, personality test responses, or aptitude test responses (Kil, Fig. 14, where the structured data includes relational information such as at least financial aid status, “student loan = $500/credit” and “Pell grant recipient”, and, as a result, the unstructured data used to generate the structured data, see Kil, Para. [0169], “These multiple-data streams are converted into user-centric time series event data that adhere to defined entity-event taxonomies and stored as the user event logs 1502 so that open-source tools that target such data schema can be leveraged”, must have also comprised this information).
Regarding Claim 6, Kill in view of Kumar teach the method of claim 4, wherein the structured data is used to generate friction points or achievement points (Kill, Fig. 14, where the column “Student info” includes classifications of data from the “data steams”, such as “ACT = 31” and “HS GPA = 3.8”; Kil, Para. [0107], “the student impact prediction subsystem 104 builds models . . . using student information . . . as shown in FIG. 14”, where “the student impact prediction subsystem 104 builds models” uses the structured “entity-event taxonomies” generated by classifying the unstructured “data streams”; Kil, Para. [0036], “the student impact prediction subsystem 104 includes a multi-level linked-event feature extraction module 112”, where the “multi-level linked-event feature extraction module 112” “extract[s]” “linked-event feature[s]” to be “systematically analyzed”, Kil, Para. [0037], “The multi-level linked-event feature extraction module 112 provides the answer to the why question. For example, feature analysis shows new students with high ACT or SAT scores tend to persist at a lower rate when these students perform poorly on their mid-term exams. Furthermore, how they bounce back from such adversities can be a strong indicator of grit and future success. Such linked-event features can be systematically analyzed in terms of their predictive power, interpretability, engagement, and impact. FIG. 2 shows a table with examples of linked-event features divided into seven (7) categories in accordance with an embodiment of the invention”, where “features” like “high ACT” or “consistency” in “Academic performance” are achievement points that promote progress and “features” like “Unmet need” for “financial aid” are friction points that hinder progress).
Regarding Claim 7, Kill in view of Kumar teach the method of claim 4, wherein the structured data is used to refine the machine learning prediction model (Kil, Para. [0114], “The evidence-based action knowledge database (EAKD) 102 stores tier-1, tier-2, and tier-3 impact results to promote the development and retraining of student-engagement and student-impact prediction models”, where the “impact results”, which are the basis for “development and retraining” are in turn based on the outputs of “The micro intervention delivery subsystem 106”, see Kil, Para. [0043] – [0044], “the micro intervention delivery subsystem 106 operates to deliver micro interventions . . . the three-tier impact analysis subsystem 108 operates to look for results of delivered micro interventions in several time scales using three-tier analyses”, which, uses the structured data to determine the output from “The micro intervention delivery subsystem 106”, see Kil, Para. [0166] – [0169], “one implementation of the micro intervention delivery subsystem 106 in accordance with an embodiment of the invention is shown as a nudge delivery subsystem 1500. The nudge delivery subsystem 1500 uses incoming event data streams from multiple student event data sources . . . A nudge log 1504 contains triggered nudges or messages to be delivered to particular students at specific times based on engagement rules being fired . . . These multiple-data streams are converted into user-centric time series event data that adhere to defined entity-event taxonomies and stored as the user event logs 1502 so that open-source tools that target such data schema can be leveraged”, therefore, the refining, at least indirectly, uses the structured data based on the classifications; see also Kil, Para. [0073] – [0074] and [0077] – [0078] for additional information on improving the impact analysis system).
Regarding Claim 8, Kil in view of Kumar teach the method of claim 1, wherein the unstructured data, based on the classifications, is used to refine the machine learning prediction model (Kil, Para. [0114], “The evidence-based action knowledge database (EAKD) 102 stores tier-1, tier-2, and tier-3 impact results to promote the development and retraining of student-engagement and student-impact prediction models”, where the “impact results”, which are the basis for “development and retraining” are in turn based on the outputs of “The micro intervention delivery subsystem 106”, see Kil, Para. [0043] – [0044], “the micro intervention delivery subsystem 106 operates to deliver micro interventions . . . the three-tier impact analysis subsystem 108 operates to look for results of delivered micro interventions in several time scales using three-tier analyses”, which, based on the classifications, uses the unstructured data to create structured data, which is used to determine the output from “The micro intervention delivery subsystem 106”, see Kil, Para. [0166] – [0169], “one implementation of the micro intervention delivery subsystem 106 in accordance with an embodiment of the invention is shown as a nudge delivery subsystem 1500. The nudge delivery subsystem 1500 uses incoming event data streams from multiple student event data sources . . . A nudge log 1504 contains triggered nudges or messages to be delivered to particular students at specific times based on engagement rules being fired . . . These multiple-data streams are converted into user-centric time series event data that adhere to defined entity-event taxonomies and stored as the user event logs 1502 so that open-source tools that target such data schema can be leveraged”, therefore, the refining, at least indirectly, uses the unstructured data based on the classifications; see also Kil, Para. [0073] – [0074] and [0077] – [0078] for additional information on improving the impact analysis system).
Regarding Claim 9, Kil in view of Kumar teach the method of claim 1, wherein the machine learning classification model comprises a natural language processing model (Kumar, Para. [0087], “each feature in some or all of the features (e.g., as illustrated in TABLE 1 above) may be . . . sent/provided to a keyword classifier implemented by . . . natural language processor 208”; Kumar, Para. [0088], “natural language processor 208 builds or implements a classification model (e.g., a random forest classification model, etc.) with the keyword classifier. The classification model may be used by the keyword classifier to predict the probability (“p”) of the filtered token being a keyword for the knowledge domain”).
The reasons for obviousness were provided in regard to the rejection of claim 1 above, and remain applicable here.
Regarding Claim 10, Kil in view of Kumar teach the method of claim 1, wherein the machine learning prediction model comprises a root cause analysis model (Kil, Para. [0109] – [0112], “The student-engagement model in the student engagement prediction module 116 y.sub.E=f(x) has the following attributes: 3. Learning algorithm”; Kil, Para. [0041], “the student engagement prediction module 116 and the student impact prediction module 118 complete the hierarchical three-level prediction cycle that connects predictive insights to actions to results. These predictions require the analysis results of the impact analysis subsystem 108”, where the machine learning model of “116” can reasonably be considered a root cause analysis model because it analyzes the outputs of “108” to generate its outputs, “116 . . . connects predictive insights to actions to results”, which “require[s] the analysis results of the impact analysis subsystem 108”, which “108” outputs are information on root cause “intervention” for “results”, see Kil, Para. [0044], “The tier-3 impact analysis measures the results of students exposed to various micro interventions using term-level metrics, such as, but not limited to, semester grade point average (GPA), successful course completion, engagement, persistence, graduation, job placement, and salary” and Kil, Para. [0179], “The analytics system 100 also provides three-tier impact analysis that resolves results-attribution ambiguity through micro-pathway construction between actions and results, which serves as an engine to both engagement and impact predictions”, where “micro-pathway construction between actions and results” provides root cause information for 116 and 118 “engagement and impact predictions”).
Regarding Claim 11, Kil in view of Kumar teach the method of claim 1, further comprising: using the machine learning prediction model to generate intervention output suggesting action to alter the generated prediction information (Kil, Para. [0041] – [0042], “Engagement and impact predictions made by the student engagement prediction module 116 and the student impact prediction module 118 . . . connects predictive insights to actions to results . . . with a particular emphasis on parameterization of intervention, student, and prediction characteristics . . . Engagement rules are expressed in terms of linked-event features and prediction scores to isolate opportune moments for reaching out to students”, where “116”, which contains the machine learning prediction model, is used to develop “Engagement rules” based on “intervention, student, and [engagement and impact] prediction characteristics”, which demonstrates the rules are based on the level of impact at altering the student success component of the generated information; Kil, Para. [0056], “The micro intervention delivery sub-system 106 operates to systematically evaluate a number of engagement rules . . . The micro intervention delivery subsystem 106 then facilitates delivery of an appropriate micro intervention corresponding to the highest ranked engagement rule”, where the “engagement rule[s]” used to generate the “appropriate micro intervention”);
and transmitting the intervention output over the computer network to the administrator machine (Kil, Para. [0043], “The micro intervention delivery subsystem 106 operates to deliver micro interventions”, where “106” “deliver[s] micro interventions” that are accessible to “administrators”, see generally Kil, Para. [0046], “Treatment or micro intervention: A student in a pilot program can receive treatment or micro intervention defined as contact between a student and an institutional entity encompassing, but not limited to, faculty, advisors, administrators, student mentors/mentees, and personal digital Sherpas or guides”; Kil, Fig. 1 and Kil, Para. [0059], “the three-tier impact analysis sub-system 108 . . . components may reside in a single computer system or distributed among multiple computer systems, which may support cloud computing”, where “106” delivers transmitted interventions to “cloud” “distributed” “computer systems”, which are within the broadest reasonable interpretation of over a computer network; Fig. 16-18 and Para. [0023] – [0025], “[0023] FIG. 16 depicts a homepage that illustrate how connected, predictive, and action insights can be communicated to various stakeholders to create a virtuous circle in accordance with an embodiment of the invention. FIG. 17 depicts a drill-down initiative page in accordance with an embodiment of the invention. FIG. 18 shows an example of a real-time student success program impact dashboard that can be provided by the student data-to-insight-to-action-to-learning analytics system”, where, though Fig. 1 is focused on the network transmission to “108”, network transmission to administrative “stakeholder” machines is required to allow for “dashboard” “initiative” “page[s]” to be displayed on “stakeholder” machines).
Regarding Claim 12, Kil in view of Kumar teach a computing system comprising: one or more processors; and one or more computer-readable media having stored thereon instructions that are executable by the one or more processors (Kil, Para. [0004] - [0005], “Student data-to-insight-to-action-to-learning analytics system and method use an evidence-based action knowledge database to compute student success predictions, student engagement predictions, and student impact predictions to interventions . . . In some embodiments, the steps of this method are performed when program instructions contained in a computer-readable storage medium are executed by one or more processors”);
network hardware configured to . . . (Kil, Para. [0166], “one implementation of the micro intervention delivery subsystem 106 . . . uses incoming event data streams from multiple student event data sources, such as, but not limited to, SIS, LMS, CRM, card swipe, smartphones of students, and surveys to delivery message nudges to students at opportune times” and Kil, Para. [0168], “partner data 1508 encompassing various enterprise data from colleges and universities are ingested through an Application Programming Interface (API) 1510 that leverages third-party plugin tools 1512, especially for data sources managed through enterprise platform vendors' cloud services”, where the network for “data stream” “delivery” requires hardware to be functional, such as “smartphones”)
a categorization engine comprising a machine learning classification model, implemented by the one or more processors and the instructions, configured to . . . (Kil, Fig. 1, where, as discussed in detail above, the “Micro Intervention Delivery Subsystem 106” performs classification of unstructured data, which is a categorization engine implemented by one or more processors, see Kil, Para. [0004] - [0005], “Student data-to-insight-to-action-to-learning analytics system and method use an evidence-based action knowledge database to compute student success predictions, student engagement predictions, and student impact predictions to interventions . . . In some embodiments, the steps of this method are performed when program instructions contained in a computer-readable storage medium are executed by one or more processors”, and which, in view of Kumar, comprises an ML classification model, see Kumar, Para. [0088], “natural language processor 208 builds or implements a classification model (e.g., a random forest classification model, etc.) with the keyword classifier. The classification model may be used by the keyword classifier to predict the probability (“p”) of the filtered token being a keyword for the knowledge domain”)
a prediction engine comprising a machine learning prediction model, implemented by the one or more processors and the instructions, configured to (Kil, Fig. 1, where, as discussed in detail above, the “Student Impact Prediction Subsystem 104” and “Three-tier Impact Analysis Subsystem 108” function to generate predictions using a machine learning prediction model, and are thus, collectively a prediction engine, which can be implemented by one or more processors, see Kil, Para. [0004] - [0005], “Student data-to-insight-to-action-to-learning analytics system and method use an evidence-based action knowledge database to compute student success predictions, student engagement predictions, and student impact predictions to interventions . . . In some embodiments, the steps of this method are performed when program instructions contained in a computer-readable storage medium are executed by one or more processors”; see also Kil, Para. [0109] – [0112], “The student-engagement model in the student engagement prediction module 116 y.sub.E=f(x) has the following attributes: 3. Learning algorithm”) . . .
and wherein the network hardware is configured to . . . (Kil, Para. [0036], “In some embodiments, at least some of these components of the student impact prediction subsystem 104 are implemented as one or more software programs running in one or more computer systems using one or more processors and memories associated with the computer systems. These components may reside in a single computer system or distributed among multiple computer systems, which may support cloud computing”; Kil, Fig. 16 and Kil, Fig. 18, where the “computing systems” include hardware to provide the “predictions” to “stakeholder” “dashboards”, see Kil, Para. [0176], “FIG. 16 depicts a homepage that illustrate how such connected, predictive, and action insights can be communicated to various stakeholders to create a virtuous circle in accordance with an embodiment of the invention”)
The reasons for obviousness were provided in regard to the rejection of claim 1 above, and remain applicable here. Also, the remaining limitations are substantially the same as limitations of Claim 1, therefore it is rejected under the same rationale.
Regarding Claim 13, the additional elements of the dependent claim are substantially the same as limitations of Claim 2, therefore it is rejected under the same rationale.
Regarding Claim 14, the additional elements of the dependent claim are substantially the same as limitations of Claim 3, therefore it is rejected under the same rationale.
Regarding Claim 15, the additional elements of the dependent claim are substantially the same as limitations of Claim 8, therefore it is rejected under the same rationale.
Regarding Claim 16, the additional elements of the dependent claim are substantially the same as limitations of Claim 9, therefore it is rejected under the same rationale.
Regarding Claim 17, the additional elements of the dependent claim are substantially the same as limitations of Claim 10, therefore it is rejected under the same rationale.
Regarding Claim 18, the additional elements of the dependent claim are substantially the same as limitations of Claim 11, therefore it is rejected under the same rationale.
Regarding Claim 19, Kil teaches a computing system comprising one or more processors (Kil, Abstract, “Student data-to-insight-to-action-to-learning analytics system”; Kil, Para. [0006], “A student data-to-insight-to-action-to-learning analytics system in accordance with an embodiment of the invention comprises memory and a processor”);
and one or more computer-readable media having stored thereon instructions that are executable by the one or more processors to configure the computer system to perform predictive journey mapping, including instructions that are executable to configure the computer system to perform at least the following: . . . (Kil, Para. [0005], “In some embodiments, the steps of this method are performed when program instructions contained in a computer-readable storage medium are executed by one or more processors”; Kil, Abstract, “Student data-to-insight-to-action-to-learning analytics system and method use an evidence-based action knowledge database to compute student success predictions, student engagement predictions, and student impact predictions to interventions”).
The remaining limitations are substantially the same as limitations of Claim 1, therefore it is rejected under the same rationale.
Regarding Claim 20, the additional elements of the dependent claim are substantially the same as limitations of Claim 2, therefore it is rejected under the same rationale.
Conclusion
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/MATTHEW BRYCE GOLAN/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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