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 .
This Office Action is in response to Application 19/218,320 filed on 05/25/2025.
Claim 18 does not recite any limitations.
Claims 1-19 are currently pending and examined below.
Claim Objections
Claim 18 is objected to because of the following informalities: Claim 18 does not recite any limitations. This appears to be due to a typographical error. 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 10, 14, and 17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2 and 14 recite the limitation “wherein the plurality of input parameters is aligned with the Common Core Standard and college board students.” However, it is unclear how the plurality of input parameters are aligned with the “college board students.” The specification is completely silent with regard to how the plurality of input parameters are aligned with “college board students.” Paragraph 25 of the specification does state that the plurality of input parameters are aligned with “college board standards”. The Examiner suggests changing the word “student” to “standard” to overcome the rejection.
Claim 10 recites the limitation "the common standard” in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 17 recites the limitations "the child standards” and “the parent standard” in line 1. There is insufficient antecedent basis for this limitation in the 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 1-17 and 19 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a nature phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-17 and 19 is/are directed towards a statutory category (i.e., a process, machine, manufacture, or composition of matter) (Step 1, Yes).
Step 2A Prong One:
Claim 1 recites (additional elements underlined):
A method for dynamically evaluating mastery status of a user based on one or more academic standards, the method comprising:
executing code using one or more processors of a computer system to cause the computer system to perform operations comprising:
receiving a plurality of input parameters from a database, wherein the plurality of input parameters includes one or more academic standards related to one or more curriculum, wherein each curriculum includes one or more courses such that each course includes one or more units, and each unit includes one or more topics and associated standards;
generating a hierarchical table based on the plurality of input parameters, wherein the hierarchical table shows relationship of the one or more academic standards within the curriculum and relates the one or more academic standards to one or more curriculums;
receiving user performance data including data related to one or more learning resources accessed by the user and associated mastery level of the user on the accessed learning resources;
utilizing an algorithm to compare the user performance data to the hierarchical table for calculating a mastery status against the one or more academic standards;
updating the mastery status of the user corresponding to the one or more academic standards.
Under the broadest reasonable interpretation, the limitations outlined above that describe or set forth the abstract idea, cover performance of the limitations in the mind but for the recitation of generic computer(s) and/or generic computer component(s). That is, other than reciting the additional elements identified below, nothing in the claim precludes the limitations from practically being performed in the mind. These limitations are considered a mental process because the limitations include an observation, evaluation, judgment, and/or opinion. These limitations are also similar to “collecting information, analyzing it, and displaying certain results of the collection and analysis” and/or “collecting and comparing known information” which were determined to be mental processes in MPEP 2106.04(a)(2)(III)(A). The Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer” (see MPEP 2106.04(a)(2)(III)(C)). The mere nominal recitation of the additional elements identified above do not take the claims out of the mental process grouping. Therefore, the claim recite a mental process (Step 2A Prong One, Yes).
The limitations outlined above also describe or set forth evaluation of mastery status of a user based on one or more academic standards which is considered as managing personal behavior or relationships or interactions between people. Therefore, the claim recites a certain method of organizing human activity (Step 2A Prong One, Yes).
The limitations outlined above that describe or set forth the abstract idea are also considered mathematical concepts at least because the above limitations utilize an algorithm to compare the user performance data to the hierarchical table for calculating a mastery status against the one or more academic standards. These limitations are similar to “organizing information and manipulating information through mathematical correlations” which was determined to be a mathematical concept in MPEP 2106.04(a)(2)(II). Therefore, the claim recites a mathematical concept (Step 2A Prong One, Yes).
Step 2A Prong Two:
In Step 2A Prong Two, the additional element(s) outlined above are recited at a high level of generality, and under the broadest reasonable interpretation, are generic computer(s) and/or generic computer component(s) that perform generic computer functions. The additional element(s) are merely used as tools, in their ordinary capacity, to perform the abstract idea. The additional element(s) amount adding the words “apply it” with the judicial exception. Merely implementing an abstract idea on generic computer(s) and/or generic computer component(s) does not integrate the judicial exception similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. The Examiner notes that “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent eligible subject matter" (see pp 10-11 of FairWarning IP, LLC. v. Iatric Systems, Inc. (Fed. Cir. 2016)). The additional elements also amount to generally linking the use of the abstract idea to a particular technological environment or field of use (e.g., in a computer environment). The courts have found that simply limiting the use of the abstract idea to a particular environment does not integrate the judicial exception into a practical application. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. There is no indication that the combination of elements improves the functioning of a computer, improves any other technology or technical field, applies or uses the judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, applies the judicial exception with, or by use of a particular machine, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claims as a whole is more than a drafting effort designed to monopolize the exception. Their collective functions merely provide generic computer implementation (Step 2A Prong Two, No).
Step 2B:
In Step 2B, the additional elements also do not amount to significantly more for the same reasons set forth with respect to Step 2A Prong Two. The Examiner notes that revised Step 2A Prong Two overlaps with Step 2B, and thus, many of the considerations need not be reevaluated in Step 2B because the answer will be the same. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Their collective functions merely provide generic computer implementation (Step 2B, No).
Claims 2-7 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 1 (i.e., mathematical concepts, certain methods of organizing human activities and/or mental processes).
Claims 2-4 and 7 do not recite any other additional elements. Therefore, for the same reasons explained above with respect to claim 1, claims 2-4 and 7 also do not integrate the judicial exception into a practical application or amount to significantly more.
Claim 5 recites the additional element “in the database”. Claim 6 recites the additional element “via different learning platforms”. However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 8 recites (additional elements underlined):
A method for dynamically recommending learning resources to a user based on mastery on one or more academic standards, the method comprising:
executing code using one or more processors of a computer system to cause the computer system to perform operations comprising:
receiving a plurality of input parameters from a database, includes one or more academic standards related to one or more curriculum, wherein each curriculum includes one or more courses such that each course includes one or more units, and each unit includes one or more topics and associated standards;
generating a hierarchical table based on the plurality of input parameters, wherein the hierarchical table shows relationship of the one or more academic standards within the curriculum and relates the one or more academic standards to one or more curriculums;
receiving one or more learning resources from a database;
utilizing an algorithm to map the one or more learning resources to the hierarchical table for mapping the learning resources to the one or more academic standards;
receiving user performance data on the one or more learning resources indicating mastery level of the user on the one or more learning resources; and
recommending at least one learning resource to the user based on the mastery level of the user on the one or more academic standards.
For the same reasons explained above with respect to claim 1, claim 8 also recites an abstract idea in Step 2A Prong One (i.e., mental process, certain method of organizing human activity, and mathematical concepts). The Examiner notes that claim 8 also recites advertising/marketing activities because learning resources are being recommended to the user. For the same reasons explained above with respect to claim 1, claim 8 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claims 9-12 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 8 (i.e., mathematical concepts, certain methods of organizing human activities and/or mental processes).
Claims 9-11 do not recite any other additional elements. Therefore, for the same reasons explained above with respect to claim 8, claims 9-11 also do not integrate the judicial exception into a practical application or amount to significantly more.
Claim 12 recites the additional element “on a user interface.” However, this additional element also does not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 13 recites (additional elements underlined):
A system for dynamically evaluating mastery status of a user based on one or more academic standards, the system comprising:
one or more processors of a computer system; and
a memory, coupled to the one or more processors, storing code that when executed by the computer system causes the computer system to perform operations comprising:
receiving a plurality of input parameters from a database, via a mastery evaluation and learning resource recommendation system, wherein the plurality of input parameters includes one or more academic standards related to one or more curriculum, wherein each curriculum includes one or more courses such that each course includes one or more units, and each unit includes one or more topics and associated standards;
generating a hierarchical table, via a hierarchical table generation module, integrated within the mastery evaluation and learning resource recommendation system, based on the plurality of input parameters, wherein the hierarchical table shows relationship of the one or more academic standards within the curriculum and relates the one or more academic standards to one or more curriculums;
receiving, a user performance data from a user performance database, via a mastery status detection module, integrated within the mastery evaluation and learning resource recommendation system, including data related to one or more learning resources accessed by the user and associated mastery level of the user on the accessed learning resources;
utilizing an algorithm via the mastery status detection module to compare the user performance data to the hierarchical table for calculating a mastery status against the one or more academic standards;
updating the mastery status of the user corresponding to the one or more academic standards.
For the same reasons explained above with respect to claim 1, claim 13 also recites an abstract idea (i.e., mental process, certain method of organizing human activity, and mathematical concepts). For the same reasons explained above with respect to claim 1, claim 13 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claims 14-17 and 19 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 13 (i.e., mathematical concepts, certain methods of organizing human activities and/or mental processes).
Claims 14-15 and 17 do not recite any other additional elements. Therefore, for the same reasons explained above with respect to claim 13, claims 14-15 and 17 also do not integrate the judicial exception into a practical application or amount to significantly more.
Claim 16 recites the additional element “in the database.” Claim 19 recites the additional element “application.” However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-4, 6-8, 10-13, 15, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berk et al. (US 2019/0035294 A1, hereinafter “Berk”) in view of Moore et al. (US 2004/0024776 A1, hereinafter “Moore”), in further view of Cappellucci et al. (US 2003/0039949 A1, hereinafter “Cappellucci”).
As per Claim 1, Berk discloses A method for dynamically evaluating mastery status of a user based on one or more academic standards, the method comprising (¶ 5 “Systems and methods in accordance with various embodiments allow for aggregating, normalizing, and interpreting competency-based assessment data from the various sources to give a comprehensive mastery score for a given standard.”):
executing code using one or more processors of a computer system to cause the computer system to perform operations comprising (Figures 1 and 3. Also see at least ¶¶ 69-71, 73-74, 83, and 121):
receiving user performance data including data related to one or more learning resources accessed by the user and associated mastery level of the user on the accessed learning resources (¶ 4 “the assessment computing device is connected to a network to receive scores and other information from various sources, such as from the image capturing device, the computing device, the user computing device, and third party assessment source computing devices.” ¶ 47 “Systems and methods in accordance with various embodiments aid in the analysis of learning progression by aggregating, normalizing, and interpreting data from a variety of sources to provide comprehensive mastery scores for given standards. Various embodiments allows for students, teachers, administrators, parents, and guardians to easily track the mastery of various standards and subjects by each student. Various embodiments enable educators to understand and track student exposure, competence, and confidence scores for various standards and also enable parents and guardians to understand student progress against those standards.” ¶ 61 “ In some embodiments, assessment data is collected from the one or more third party assessment source computing devices 44. In various embodiments, the one or more third party assessment source computing devices 44 are any education resource computing devices that provide standards and/or competency-based assessment data, such as computing devices for Khan Academy®, TenMarks®, Lexia®, Dreambox®, Newsela®, or the like. Many students use one or more online sources, such as the above-mentioned third party sources for additional learning. The use of third party assessment sources allows for a student to utilize a variety of learning environments and strategies. In various embodiments, a variety of learning sources are employed to provide a better overall assessment of the student's progression and mastery of a subject. In various embodiments, the assessment computing device 30 is configured to collect assessment data from the one or more third party assessment source computing devices 44 over the network 40 at a variety of intervals. For example, the assessment data may be collected daily, weekly, monthly, or at another interval. In some embodiments, the assessment data is automatically pushed by the one or more third party assessment source computing devices 44 to the assessment computing device 30 over the network 40 when available or at some interval of time. In some embodiments, the assessment computing device 30 is configured to request the assessment data from the one or more third party assessment source computing devices 44 over the network 40. In some embodiments, the assessment computing device 30 is configured to collect the assessment data from each of the one or more third party assessment source computing devices 44 using an application programming interface (API) provided by the corresponding third party assessment source for communicating with the corresponding third party assessment source computing device 44.” );
utilizing an algorithm to compare the user performance data … for calculating a mastery status against the one or more academic standards (¶ 5 “Systems and methods in accordance with various embodiments allow for aggregating, normalizing, and interpreting competency-based assessment data from the various sources to give a comprehensive mastery score for a given standard. Various embodiments provide for a learning progression analysis that takes competency-based assessment data from any standards-based source including existing education tools and educator assessments, normalizes those scores to a particular mastery scale, such as a five point mastery scale, and applies an algorithm to determine if a student has mastered a specific standard. Various embodiments allow educators to see a map of all standards for a student showing what they have been exposed to and what they have mastered, even if the student is working across multiple digital tools and with multiple educators inputting assessment data over many years.” ¶ 6 “Systems and methods in accordance with various embodiments aggregate data from any standards or competency-based assessment source. Sources include, for example, third party tools such as Khan Academy®, TenMarks®, and/or the like, as well as educator input assessments via a playlist, directly into a learning progression user interface, via an image capture mobile application that captures learning moments as they happen in the classroom, via printed Quick Response (QR) codes attached to paper-based work, and/or the like. Various embodiments normalizes the received data to a standard master scale, such as a five point scale or the like, and apply an algorithm to determine if a student has mastered a given standard. Various embodiments display an exposure and mastery standards map with the mastery data to demonstrate a student's learning frontier, gaps, and strengths. In various embodiments, systems and methods for learning progression analysis can be adapted to work with any standard set, such as Common Core Standards, Next Generation Science Standards, state standard sets, International Baccalaureate standards, custom standard sets, and/or the like. Also, in various embodiments the learning progression data is used by the system to suggest personalized work, activities, and/or goals for a given student as well as to do smart groupings of students and otherwise make personalized recommendations.” ¶ 64 “In various embodiments, the assessment computing device 30 is configured to analyze aggregate scores from various sources, such as photos of projects or documents, scanned codes or labels, directly created online documents, third party source assessment data, and/or the like, and to compare the scores against various educational standards. In various embodiments, the assessment computing device 30 is configured to determine a proficiency of a student in various subjects based on the student's performance according to the various educational standards. In various embodiments, the assessment computing device 30 is configured to track a learning progression of the student by analyzing the proficiency of the student in various subjects over time based on the collected assessment data. Also, in various embodiments, the assessment computing device 30 is configured to provide tools of varying scopes for a user, such as a teacher, parent, student, administrator, or other individual, to view the proficiency and progression of one or more students.” );
updating the mastery status of the user corresponding to the one or more academic standards (¶ 5 “aggregating, normalizing, and interpreting competency-based assessment data from the various sources to give a comprehensive mastery score for a given standard. Various embodiments provide for a learning progression analysis that takes competency-based assessment data from any standards-based source including existing education tools and educator assessments, normalizes those scores to a particular mastery scale, such as a five point mastery scale, and applies an algorithm to determine if a student has mastered a specific standard [i.e., updating the master status of the user corresponding to the one or more academic standards].” ¶ 77 “In various embodiments, the assessment computing device 30 is configured to execute the score normalization module 58 to convert disparate assessment scales from different sources to a single, common assessment scale to allow for the assessment computing de vice 30 to aggregate and compare assessments from different sources and to determine if a student has mastered a given standard. In some embodiments, the assessment computing device 30 is configured to execute the score normalization module 58 to convert all assessment scores received from all sources to a 5-point scale by matching the rubrics for the different levels in the 5-point scale. For example, the first level may correspond to the student being exposed to the subject matter, the second level may correspond to the student demonstrating an emerging understanding of the subject matter, the third level may correspond to the student practicing the subject matter, the fourth level may correspond to the student meeting requirements for mastery of the subject matter, and the fifth level may correspond to the student exceeding requirements for mastery of the subject matter. In some embodiments, the first level represents a 20% competency, the second level represents a 40% competency, the third level represents a 60% competency, the fourth level represents 80% competency, and the fifth level represents 100% competency. In various embodiments, the assessment computing device 30 is configured to convert scores from one scale into another scale. For example, in some embodiments the assessment computing device 30 is configured to convert scores from a 1-7 scale, a 1-50 scale, or an A-F scale, to a 1-5 scale. By converting assessments from different assessment sources with different assessment scales to a common scale, the assessments are able to be compared and/or combined to create an overall assessment for a student [i.e., updating the mastery status of the user corresponding to one or more academic standards].”).
While Berk discloses all of the above limitations, including the steps of aggregating scores from various sources against various educational standards, Berks does not appear to explicitly disclose wherein the plurality of input parameters includes one or more academic standards related to one or more curriculum, wherein each curriculum includes one or more courses such that each course includes one or more units, and each unit includes one or more topics and associated standards; generating a hierarchical table based on the plurality of input parameters, wherein the hierarchical table shows relationship of the one or more academic standards within the curriculum and relates the one or more academic standards to one or more curriculums; [compare user performance data] to the hierarchical table.
However, Moore teaches wherein the plurality of input parameters includes one or more academic standards related to one or more curriculum, wherein each curriculum includes one or more courses such that each course includes one or more units, and each unit includes one or more topics and associated standards (¶ 52 “In one embodiment, the RDBMS defines a set of data tables, which contain the bulk of the educational content. In the case of the exemplary embodiment, as illustrated in FIG. 11, there are five such data tables: "Courses", "Units", "Activities/Resources", "Objectives" and "Standards." Each table, in sequential order, is related to the next by the well-known technique of M:M ("many to many") database relations. Thus any course may be seen as containing a series of units, which in turn contain any number of activities, which contain any number of objectives, which finally are correlated to any number of standards. Since the relation is M:M and not 1:M ("one to many"), each unit may be shared between more than one course, each activity in more than one unit, and so on and so forth. For example, at the bottom of FIG. 11, the "Intro Lab" unit is shared by a Publisher company's "Biology" and "Chemistry" course, as well as by a teacher-authored course, "Intro Medicine". Each of the M:M links in the database contains an additional text field in which the user may record notes pertaining to the alignment.” ¶ 54 “The RDBMS also defines a set of data tables that contain the organizational information necessary to categorize the content and allow it to be easily located. The number and names of organizational data tables used may vary with each application. In the case of the exemplary embodiment, there are several such tables: "Curricula", "Frameworks", "Subjects", "Grades", "Types", "Sources", "Categories", and "Topics." As illustrated in FIG. 11, the "Curriculum" and "Frameworks" tables are the top-level data types, and all other organizational fields fall in a hierarchy under these types. At the bottom of the hierarchy are located the plurality of data tables mentioned above. "Courses", "Units", "Activities/Resources", and "Objectives" fall under a hierarchy headed by the "Curricula" type, while "Standards" fall under a "Frameworks" headed hierarchy. Members of the hierarchy are related moving downward by 1:M database relations. "Subjects", "Grades", and "Types" are subsumed by either one record in the "Curricula" table or the "Frameworks", and thus constitute the second-from-top level of the hierarchy. Under each record from "Subjects" fall "Categories", and under one record each from "Grades" and "Categories" is the most-specific type, "Topics." Also see at least Figures 11 and 19.);
generating a hierarchical table based on the plurality of input parameters, wherein the hierarchical table shows relationship of the one or more academic standards within the curriculum and relates the one or more academic standards to one or more curriculums (¶ 52 “In one embodiment, the RDBMS defines a set of data tables, which contain the bulk of the educational content. In the case of the exemplary embodiment, as illustrated in FIG. 11, there are five such data tables: "Courses", "Units", "Activities/Resources", "Objectives" and "Standards." Each table, in sequential order, is related to the next by the well-known technique of M:M ("many to many") database relations. Thus any course may be seen as containing a series of units, which in turn contain any number of activities, which contain any number of objectives, which finally are correlated to any number of standards. Since the relation is M:M and not 1:M ("one to many"), each unit may be shared between more than one course, each activity in more than one unit, and so on and so forth. For example, at the bottom of FIG. 11, the "Intro Lab" unit is shared by a Publisher company's "Biology" and "Chemistry" course, as well as by a teacher-authored course, "Intro Medicine". Each of the M:M links in the database contains an additional text field in which the user may record notes pertaining to the alignment.” ¶ 54 “The RDBMS also defines a set of data tables that contain the organizational information necessary to categorize the content and allow it to be easily located. The number and names of organizational data tables used may vary with each application. In the case of the exemplary embodiment, there are several such tables: "Curricula", "Frameworks", "Subjects", "Grades", "Types", "Sources", "Categories", and "Topics." As illustrated in FIG. 11, the "Curriculum" and "Frameworks" tables are the top-level data types, and all other organizational fields fall in a hierarchy under these types. At the bottom of the hierarchy are located the plurality of data tables mentioned above. "Courses", "Units", "Activities/Resources", and "Objectives" fall under a hierarchy headed by the "Curricula" type, while "Standards" fall under a "Frameworks" headed hierarchy. Members of the hierarchy are related moving downward by 1:M database relations. "Subjects", "Grades", and "Types" are subsumed by either one record in the "Curricula" table or the "Frameworks", and thus constitute the second-from-top level of the hierarchy. Under each record from "Subjects" fall "Categories", and under one record each from "Grades" and "Categories" is the most-specific type, "Topics." Also see at least Figures 11 and 19.);
[compare user performance data] to the hierarchical table (Abstract “A system and method for managing content utilizes a relational database management system, which organizes materials such as curriculum materials into, for example, an organizational hierarchy. The system and method allows a user to align materials to a variety of standards including, for example, local, school district, state, and national standards. The system and method also enables a user assess student performance to determine which areas of a curriculum are meeting standards and which require improvement and modification. The system and method further enables the user to assess the effectiveness of a curriculum and curriculum materials by integrating student performance data with such factors as curriculum materials used, assignment histories, and student profile data. Assessment of student performance may also be achieved by integrating data from a first plurality of different user-sites, for example, a first plurality of schools within a school district or a first plurality of school districts within a state.” Also see Figures 11 and 19).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the features of Moore with Berk. One of ordinary skill in the art would have been motivated to do so in order to quickly assess the progress and performance of a student (Moore, ¶ 60). One of ordinary skill in the would have also been motivated to do so in order to quickly view the entirely curriculum in an easy to read format. One of ordinary skill in the art would have been motivated to do so in order to easily identify the prerequisites for the curriculums/courses/units/topics. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
While the combination of Berk/Moore teach a plurality of input parameters from a database, they do not appear to explicitly teaches receiving the plurality of input parameters. Therefore, the combination of Berk/Moore do not appear to teach receiving a plurality of input parameters from a database.
However, Cappellucci teaches receiving a plurality of input parameters from a database (¶ 72 “FIG. 7 shows a flowchart of a simple process 70 for performing a correlation query. A correlation query is a process to find those information objects and elements that are correlated against a particular information object or element. For example, finding State Standards that are correlated to lesson Plan. The process includes the steps of entering the search parameters that form the basis of the search at 72. The parameters can include an identification of the lesson plan and the indication that the results should be from information resources containing state standards. In step 74, the system finds all MLOs correlated against the input information object (in our example, the lesson plan). In step 76, the system finds all information object or element correlated against all MLOs found in step 74 which are State Standards and in step 78 the system retrieves the information objects or elements, the State Standards we were searching for.” Also see at least Figures 1, 3, and 7-9.).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the step of receiving the plurality of input parameters from a database as taught by Cappellucci, into the combination of Berk/Moore. One of ordinary skill in the art would have been motivated to do so in order to obtain the most recent educational standards. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 3, Berk does not appear to explicitly teach wherein the hierarchical table includes one or more academic standards, clusters, and details relevant to each one or more academic standards.
However, Moore teaches wherein the hierarchical table includes one or more academic standards, clusters, and details relevant to each one or more academic standards (¶ 52 and 54. Also see at least Figures 11 and 19).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the features of Moore with Berk. One of ordinary skill in the art would have been motivated to do so in order to quickly assess the progress and performance of a student (Moore, ¶ 60). One of ordinary skill in the would have also been motivated to do so in order to quickly view the entirely curriculum in an easy to read format. One of ordinary skill in the art would have been motivated to do so in order to easily identify the prerequisites for the curriculums/courses/units/topics. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 4, Berk does not appear to explicitly teach wherein the hierarchical table is represented as a tree- like model wherein the one or more academic standards has parent-child relationship with the other one or more academic standards.
However, Moore teaches wherein the hierarchical table is represented as a tree-like model wherein the one or more academic standards has parent-child relationship with the other one or more academic standards (¶ 55 and Figures 11 and 19. Also see at least ¶¶ 52 and 54).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the features of Moore with Berk. One of ordinary skill in the art would have been motivated to do so in order to quickly assess the progress and performance of a student (Moore, ¶ 60). One of ordinary skill in the would have also been motivated to do so in order to quickly view the entirely curriculum in an easy to read format One of ordinary skill in the art would have been motivated to do so in order to easily identify the prerequisites for the curriculums/courses/units/topics. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 6, Berk, discloses wherein the mastery level of the user indicates the mastery of the user for the accessed learning resources received via different learning platforms (The Examiner notes that the above italicized and underlined limitation is not given patentable weight because it is nonfunctional descriptive material. Claim 6 merely describes the what mastery level indicates. However, for the sake of advancing prosecution, see at least ¶¶ 5-6, 9, 11, 51, 61-62, and 77).
As per Claim 7, Berk discloses wherein the mastery status indicates the proficiency and understanding the user has while accessing the one or more learning resources (The Examiner notes that the above italicized and underlined limitation is not given patentable weight because it is nonfunctional descriptive material. Claim 6 merely describes the what mastery status indicates. However, for the sake of advancing prosecution, see at least ¶¶ 5-6, 9, 11, 51, 61-62, and 77).
As per Claim 8, Berk discloses A method for dynamically recommending learning resources to a user based on mastery on one or more academic standards, the method comprising (¶ 5 “Systems and methods in accordance with various embodiments allow for aggregating, normalizing, and interpreting competency-based assessment data from the various sources to give a comprehensive mastery score for a given standard.” ¶ 6 “Also, in various embodiments the learning progression data is used by the system to suggest personalized work, activities, and/or goals for a given student as well as to do smart groupings of students and otherwise make personalized recommendations.):
executing code using one or more processors of a computer system to cause the computer system to perform operations comprising (Figures 1 and 3. Also see at least ¶¶ 69-71, 73-74, 83, and 121):
receiving user performance data on the one or more learning resources indicating mastery level of the user on the one or more learning resources (¶ 4 “the assessment computing device is connected to a network to receive scores and other information from various sources, such as from the image capturing device, the computing device, the user computing device, and third party assessment source computing devices.” ¶ 47 “Systems and methods in accordance with various embodiments aid in the analysis of learning progression by aggregating, normalizing, and interpreting data from a variety of sources to provide comprehensive mastery scores for given standards. Various embodiments allows for students, teachers, administrators, parents, and guardians to easily track the mastery of various standards and subjects by each student. Various embodiments enable educators to understand and track student exposure, competence, and confidence scores for various standards and also enable parents and guardians to understand student progress against those standards.” ¶ 61 “ In some embodiments, assessment data is collected from the one or more third party assessment source computing devices 44. In various embodiments, the one or more third party assessment source computing devices 44 are any education resource computing devices that provide standards and/or competency-based assessment data, such as computing devices for Khan Academy®, TenMarks®, Lexia®, Dreambox®, Newsela®, or the like. Many students use one or more online sources, such as the above-mentioned third party sources for additional learning. The use of third party assessment sources allows for a student to utilize a variety of learning environments and strategies. In various embodiments, a variety of learning sources are employed to provide a better overall assessment of the student's progression and mastery of a subject. In various embodiments, the assessment computing device 30 is configured to collect assessment data from the one or more third party assessment source computing devices 44 over the network 40 at a variety of intervals. For example, the assessment data may be collected daily, weekly, monthly, or at another interval. In some embodiments, the assessment data is automatically pushed by the one or more third party assessment source computing devices 44 to the assessment computing device 30 over the network 40 when available or at some interval of time. In some embodiments, the assessment computing device 30 is configured to request the assessment data from the one or more third party assessment source computing devices 44 over the network 40. In some embodiments, the assessment computing device 30 is configured to collect the assessment data from each of the one or more third party assessment source computing devices 44 using an application programming interface (API) provided by the corresponding third party assessment source for communicating with the corresponding third party assessment source computing device 44.” ); and
recommending at least one learning resource to the user based on the mastery level of the user on the one or more academic standards (¶ 6 “Systems and methods in accordance with various embodiments aggregate data from any standards or competency-based assessment source. Sources include, for example, third party tools such as Khan Academy®, TenMarks®, and/or the like, as well as educator input assessments via a playlist, directly into a learning progression user interface, via an image capture mobile application that captures learning moments as they happen in the classroom, via printed Quick Response (QR) codes attached to paper-based work, and/or the like. Various embodiments normalizes the received data to a standard master scale, such as a five point scale or the like, and apply an algorithm to determine if a student has mastered a given standard. Various embodiments display an exposure and mastery standards map with the mastery data to demonstrate a student's learning frontier, gaps, and strengths. In various embodiments, systems and methods for learning progression analysis can be adapted to work with any standard set, such as Common Core Standards, Next Generation Science Standards, state standard sets, International Baccalaureate standards, custom standard sets, and/or the like. Also, in various embodiments the learning progression data is used by the system to suggest personalized work, activities, and/or goals for a given student as well as to do smart groupings of students and otherwise make personalized recommendations [i.e., recommending at least one learning resource to the user based on the mastery level of the user on the one or more academic standards].” ¶ 82 “In various embodiments, the assessment computing device 30 is configured to execute the group management module 66 to suggest personalized work, activities, and/or goals for a given student as well as to do smart groupings of students and otherwise make personalized recommendations. In some embodiments, the assessment computing device 30 is configured to execute the group management module 66 to create a group of students based on the progression of each student as measured by the assessment scores, and based on a grouping strategy. For example, in some embodiments, the assessment computing device 30 is configured to generate an output to identify a group that includes students with a similar proficiency in a particular standard. In another example embodiment, the assessment computing device 30 is configured to generate an output to identify a group including one or more students with advanced proficiency in a particular standard and good peer leadership skills as well as one or more students with a lower proficiency that could benefit from tutoring by the one or more stronger students. The identified group may then be put together for studying one or more subjects for the particular standard [i.e., recommending at least one learning resource to the user based on the mastery level of the user on the one or more academic standards].”).
While Berk discloses all of the above limitations, including the steps of aggregating scores from various sources against various educational standards, Berks does not appear to explicitly disclose includes one or more academic standards related to one or more curriculum, wherein each curriculum includes one or more courses such that each course includes one or more units, and each unit includes one or more topics and associated standards; generating a hierarchical table based on the plurality of input parameters, wherein the hierarchical table shows relationship of the one or more academic standards within the curriculum and relates the one or more academic standards to one or more curriculums; receiving one or more learning resources from a database; utilize an algorithm to map the one or more learning resources to the hierarchical table for mapping the learning resource to the one or more academic standards.
However, Moore teaches includes one or more academic standards related to one or more curriculum, wherein each curriculum includes one or more courses such that each course includes one or more units, and each unit includes one or more topics and associated standards (¶ 52 “In one embodiment, the RDBMS defines a set of data tables, which contain the bulk of the educational content. In the case of the exemplary embodiment, as illustrated in FIG. 11, there are five such data tables: "Courses", "Units", "Activities/Resources", "Objectives" and "Standards." Each table, in sequential order, is related to the next by the well-known technique of M:M ("many to many") database relations. Thus any course may be seen as containing a series of units, which in turn contain any number of activities, which contain any number of objectives, which finally are correlated to any number of standards. Since the relation is M:M and not 1:M ("one to many"), each unit may be shared between more than one course, each activity in more than one unit, and so on and so forth. For example, at the bottom of FIG. 11, the "Intro Lab" unit is shared by a Publisher company's "Biology" and "Chemistry" course, as well as by a teacher-authored course, "Intro Medicine". Each of the M:M links in the database contains an additional text field in which the user may record notes pertaining to the alignment.” ¶ 54 “The RDBMS also defines a set of data tables that contain the organizational information necessary to categorize the content and allow it to be easily located. The number and names of organizational data tables used may vary with each application. In the case of the exemplary embodiment, there are several such tables: "Curricula", "Frameworks", "Subjects", "Grades", "Types", "Sources", "Categories", and "Topics." As illustrated in FIG. 11, the "Curriculum" and "Frameworks" tables are the top-level data types, and all other organizational fields fall in a hierarchy under these types. At the bottom of the hierarchy are located the plurality of data tables mentioned above. "Courses", "Units", "Activities/Resources", and "Objectives" fall under a hierarchy headed by the "Curricula" type, while "Standards" fall under a "Frameworks" headed hierarchy. Members of the hierarchy are related moving downward by 1:M database relations. "Subjects", "Grades", and "Types" are subsumed by either one record in the "Curricula" table or the "Frameworks", and thus constitute the second-from-top level of the hierarchy. Under each record from "Subjects" fall "Categories", and under one record each from "Grades" and "Categories" is the most-specific type, "Topics." Also see at least Figures 11 and 19.);
generating a hierarchical table based on the plurality of input parameters, wherein the hierarchical table shows relationship of the one or more academic standards within the curriculum and relates the one or more academic standards to one or more curriculums (¶ 52 “In one embodiment, the RDBMS defines a set of data tables, which contain the bulk of the educational content. In the case of the exemplary embodiment, as illustrated in FIG. 11, there are five such data tables: "Courses", "Units", "Activities/Resources", "Objectives" and "Standards." Each table, in sequential order, is related to the next by the well-known technique of M:M ("many to many") database relations. Thus any course may be seen as containing a series of units, which in turn contain any number of activities, which contain any number of objectives, which finally are correlated to any number of standards. Since the relation is M:M and not 1:M ("one to many"), each unit may be shared between more than one course, each activity in more than one unit, and so on and so forth. For example, at the bottom of FIG. 11, the "Intro Lab" unit is shared by a Publisher company's "Biology" and "Chemistry" course, as well as by a teacher-authored course, "Intro Medicine". Each of the M:M links in the database contains an additional text field in which the user may record notes pertaining to the alignment.” ¶ 54 “The RDBMS also defines a set of data tables that contain the organizational information necessary to categorize the content and allow it to be easily located. The number and names of organizational data tables used may vary with each application. In the case of the exemplary embodiment, there are several such tables: "Curricula", "Frameworks", "Subjects", "Grades", "Types", "Sources", "Categories", and "Topics." As illustrated in FIG. 11, the "Curriculum" and "Frameworks" tables are the top-level data types, and all other organizational fields fall in a hierarchy under these types. At the bottom of the hierarchy are located the plurality of data tables mentioned above. "Courses", "Units", "Activities/Resources", and "Objectives" fall under a hierarchy headed by the "Curricula" type, while "Standards" fall under a "Frameworks" headed hierarchy. Members of the hierarchy are related moving downward by 1:M database relations. "Subjects", "Grades", and "Types" are subsumed by either one record in the "Curricula" table or the "Frameworks", and thus constitute the second-from-top level of the hierarchy. Under each record from "Subjects" fall "Categories", and under one record each from "Grades" and "Categories" is the most-specific type, "Topics." Also see at least Figures 11 and 19.);
receiving one or more learning resources from a database (¶ 42 “Referring now to the drawings, and initially to FIG. 3, there is illustrated a user displaying school district grade 9-10 mathematics objectives for the category, "Data Analysis, Statistics, and Probability". User can click on any of the listed objectives and get all curriculum items in the school's data base that address this specific objective; e.g., teacher-authored and purchased classroom activities, teaching resources, test questions, scoring rubrics, and state and national test questions and resources [i.e., receiving one or more learning resources from a database].”;
utilize an algorithm to map the one or more learning resources to the hierarchical table for mapping the learning resource to the one or more academic standards (Abstract “A system and method for managing content utilizes a relational database management system, which organizes materials such as curriculum materials into, for example, an organizational hierarchy. The system and method allows a user to align materials to a variety of standards including, for example, local, school district, state, and national standards.” ¶ 10 “Additionally, the present invention provides a computer-readable medium having a set of computer-executable instructions for managing content. The instructions include receiving a request from at least one user to align the content to at least one a plurality of standards using a alignment system wherein the alignment system either stores the content in its entirety or stores some of the content in addition to uniform resource identifier ("URI") link(s) to additional portions of the content that is stores on separate computer(s) and/or server(s). The instructions also include performing the alignment using the alignment system” ¶ 76 “The preferred embodiment may perform simple auto-alignment using a direct alignment methodology. The system may simply take each record to be aligned, and follow a set of rules [i.e., algorithm] to create a best guess alignment. One such rule may be to simply create alignments to all records that receive at least a certain minimal score when considered by the find-similar tool. Other variations may include but are not limited to: a) taking the N best matches regardless of score, b) only considering records in a certain portion of the hierarchy (e.g., only consider records in "Science" for alignment to records in "Biology"), c) dynamically limiting the hierarchy using the document routing tool (e.g. only consider records in the category determined to be most relevant by the document routing tool) or d) any other application logic which limits the records to be considered and/or the minimal criteria for alignment [i.e., utilizing an algorithm to map the one or more learning resources to the hierarchical table for mapping the learning resources to the one or more academic standards]. Generally, the GUI may allow the user to configure the analysis application to use any of these rules for alignment by toggling options and entering criteria. It may be desirable to allow the user to browse live data to determine the proper options. In the case where the rules have been fixed, a GUI may not be needed.” Claim 4 “ wherein the content comprises at least one of instructional data, planning data, implementation data, assessment data, school district instructional data, school district planning data, and school district assessment data.” Also see Figures 11 and 19).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the features of Moore with Berk. One of ordinary skill in the art would have been motivated to do so in order to quickly assess the progress and performance of a student (Moore, ¶ 60). One of ordinary skill in the would have also been motivated to do so in order to quickly view the entirely curriculum in an easy to read format. One of ordinary skill in the art would have been motivated to do so in order to easily identify the prerequisites for the curriculums/courses/units/topics. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
While the combination of Berk/Moore teach a plurality of input parameters from a database, they do not appear to explicitly teaches receiving the plurality of input parameters. Therefore, the combination of Berk/Moore do not appear to teach receiving a plurality of input parameters from a database.
However, Cappellucci teaches receiving a plurality of input parameters from a database (¶ 72 “FIG. 7 shows a flowchart of a simple process 70 for performing a correlation query. A correlation query is a process to find those information objects and elements that are correlated against a particular information object or element. For example, finding State Standards that are correlated to lesson Plan. The process includes the steps of entering the search parameters that form the basis of the search at 72. The parameters can include an identification of the lesson plan and the indication that the results should be from information resources containing state standards. In step 74, the system finds all MLOs correlated against the input information object (in our example, the lesson plan). In step 76, the system finds all information object or element correlated against all MLOs found in step 74 which are State Standards and in step 78 the system retrieves the information objects or elements, the State Standards we were searching for.” Also see at least Figurers 1, 3, and 7-9.).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the step of receiving the plurality of input parameters from a database as taught by Cappellucci, into the combination of Berk/Moore. One of ordinary skill in the art would have been motivated to do so in order to obtain the most recent educational standards. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 10, Berk discloses wherein evaluating the mastery status of the user across multiple courses for the common standards further comprises (¶¶ 47-48, 64, and 82. Also see citations above.):
evaluating the user performance data across the common standards in multiple courses, and updating the mastery status across multiple courses sharing common academic standards (¶¶ 47-48, 64, and 82. Also see citations above.).
While the Berk evaluates the mastery status of the user across multiple courses for the common standards, Berk does not appear to explicitly teach utilizing the hierarchical table to link the common standards in multiple courses.
However, Moore teaches utilizing the hierarchical table to link the common standards in multiple courses (Abstract “A system and method for managing content utilizes a relational database management system, which organizes materials such as curriculum materials into, for example, an organizational hierarchy. The system and method allows a user to align materials to a variety of standards including, for example, local, school district, state, and national standards.” ¶ 10 “Additionally, the present invention provides a computer-readable medium having a set of computer-executable instructions for managing content. The instructions include receiving a request from at least one user to align the content to at least one a plurality of standards using a alignment system wherein the alignment system either stores the content in its entirety or stores some of the content in addition to uniform resource identifier ("URI") link(s) to additional portions of the content that is stores on separate computer(s) and/or server(s). The instructions also include performing the alignment using the alignment system” ¶ 76 “The preferred embodiment may perform simple auto-alignment using a direct alignment methodology. The system may simply take each record to be aligned, and follow a set of rules [i.e., algorithm] to create a best guess alignment. One such rule may be to simply create alignments to all records that receive at least a certain minimal score when considered by the find-similar tool. Other variations may include but are not limited to: a) taking the N best matches regardless of score, b) only considering records in a certain portion of the hierarchy (e.g., only consider records in "Science" for alignment to records in "Biology"), c) dynamically limiting the hierarchy using the document routing tool (e.g. only consider records in the category determined to be most relevant by the document routing tool) or d) any other application logic which limits the records to be considered and/or the minimal criteria for alignment. Generally, the GUI may allow the user to configure the analysis application to use any of these rules for alignment by toggling options and entering criteria. It may be desirable to allow the user to browse live data to determine the proper options. In the case where the rules have been fixed, a GUI may not be needed.” Claim 4 “ wherein the content comprises at least one of instructional data, planning data, implementation data, assessment data, school district instructional data, school district planning data, and school district assessment data.” Also see Figures 11 and 19).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the features of Moore with Berk. One of ordinary skill in the art would have been motivated to do so in order to quickly assess the progress and performance of a student (Moore, ¶ 60). One of ordinary skill in the would have also been motivated to do so in order to quickly view the entirely curriculum in an easy to read format. One of ordinary skill in the art would have been motivated to do so in order to easily identify the prerequisites for the curriculums/courses/units/topics. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 11, Berk discloses wherein tracking the mastery status of common standards across multiple courses requires a minimum score to reflect mastery status across the academic standards (¶ 77. Also see at least ¶¶ 47-48, 64, 75-76, 82, and citations above.).
As per Claim 12, Berk discloses wherein the mastery status and recommended learning resources are further displayed to the user on a user interface (¶¶ 6, 10-11, 16, 48, 82, 95-96, 119, and Figures 10 and 13).
As per Claim 13, it recites substantially similar to claim 1. Therefore, claim 13 is rejected using the same rationale.
As per Claim 15, it recites substantially similar limitations s claim 3. Therefore, claim 15 is rejected using the same rationale.
As per Claim 17, Berk does not appear to explicitly disclose wherein the child standards reference the parent standards, allowing a scalable representation of educational standards.
However, Moore teaches wherein the child standards reference the parent standards, allowing a scalable representation of educational standards (The Examiner notes that the above italicized and underlined limitation is not given patentable weight because it is merely describing an intended use of using the parent and child standards. However, for the sake of advancing prosecution, Moore teaches this limitation in at least ¶¶ 19 and 55. Also see Abstract and citations above.).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the Moore into Berk. One of ordinary skill in the art would have been motivated to do so for the advantage of having varying level of specificity preserved yet content pieces are not unnecessarily separated (Moore, ¶ 55). One of ordinary skill in the art would have been motivated to do so in order for the advantages of improving data organization, reducing data duplication, and faster information retrieval. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 19, Berk discloses wherein the user performance data, including mastery level of the user and learning resources accessed by the user is received from different learning applications (¶¶ 5-6 and 61. Also see citations above.).
Claim(s) 2 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berk in view of Moore, in view of Cappellucci, in further view of Cormode et al. (US 2010/0153064 A1, hereinafter “Cormode”).
As per Claim 2, Berk discloses wherein the plurality of input parameters is aligned with the Common Core Standard … (¶ 80 “the assessment computing device 30 is configured to execute the proficiency analysis module 62 to determine a student's mastery of one or more of the particular standards 75, such as a Common Core Standard, Next Generation Science Standard, state standard sets, International Baccalaureate standards, custom standard sets, or the like, milestone, or sub-milestone. In some embodiments, the assessment computing device 30 is configured to execute the proficiency analysis module 62 to receive normalized scores from the database 36, and to determine an aggregated score for a standard by aggregating scores for all of the one or more digital cards 68 tagged with the standard, and to use the aggregated score to determine a level of proficiency of the student for the standard. In some embodiments, the one or more standards 75 are grouped together in stones and sub-milestones, as discussed in more detail below.”).
While the combination of Berk/Moore/Cappellucci teach a plurality of input parameters is aligned with the Common Core Standard, they do not appear to teach the plurality of input parameters also aligned with college board students. Therefore, Berk/Moore do not teach the limitation and college board students.
However, Cormode teaches and college board student (¶ 53 “A first example data presentation 600 that could be generated by the example statistical dominance processor 105 for an input data set corresponding to examination scores is illustrated in FIG. 6. The American College Board measures academic performance of students using a standardized test called the Standard Aptitude Test, or SAT. The SAT historically included two sections: a math section and a verbal section. A test participant receives scores and percentiles for each section. The participant may choose to have these SAT results sent to one or more colleges, universities, etc., for consideration during the admission process. The example presentation 600 of FIG. 6 corresponds to a two-dimensional data set of average SAT scores reported by 2244 colleges and available at http://www.ivywest.com/satscore.htm.”).
Berk suggests the use of one or more standards to determine a student mastery (see ¶ 80).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the college board standard of Cormode, into the combination of Berk/Moore/Cappellucci. One of ordinary skill in the art would have been motivated to do so in order to ensure college readiness, to determine which course(s) to place students based on their knowledge, and to increase the number of users who will use the claimed invention by incorporating college students. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 14, it recites substantially similar limitations as claim 2. Therefore, claim 14 is rejected using the same rationale.
Claim(s) 5 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berk in view of Moore, in view of Cappellucci, in further view of Agarwal et al. (US 2003/0065659 A1, hereinafter “Agarwal”).
As per Claim 5, Berk does not appear to explicitly disclose wherein the hierarchical table further includes ….
However, Moore teaches wherein the hierarchical table further includes … (¶ 52 and 54. Also see at least Figures 11 and 19).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the features of Moore with Berk. One of ordinary skill in the art would have been motivated to do so in order to quickly assess the progress and performance of a student (Moore, ¶ 60). One of ordinary skill in the would have also been motivated to do so in order to quickly view the entirely curriculum in an easy to read format One of ordinary skill in the art would have been motivated to do so in order to easily identify the prerequisites for the curriculums/courses/units/topics. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
While the combination of Berk/Moore/Cappellucci teach the hierarchical table, they do not appear to explicitly teach updating the hierarchical table by creating, updating and deleting records in the database.
However, Agarwal teaches updating the hierarchical table by creating, updating and deleting records in the database (Abstract, Figures 2-6, ¶¶ 88-90, claims 12-13).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the step of updating the hierarchical table by creating, updating and deleting records in the database as taught by Agarwal, into the combination of Berk/Moore/Cappellucci. One of ordinary skill in the art would have been motivated to do so that changes made are instantly reflected in the hierarchy structures in order to maintain consistency (Agarwal: Abstract and ¶¶ 39 and 87-89). One of ordinary skill in the art would have been motivated to do so that the most recent educational standard is used. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
As per Claim 16, it recites substantially similar limitations as claim 5. Therefore, claim 16 is rejected using the same rationale.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Berk in view of Moore, in view of Cappellucci, in further view of Heikkila et al (US 2015/0243177 A1, hereinafter “Heikkila”).
As per Claim 9, Berk discloses wherein utilizing the algorithm further comprises: utilizing algorithms to evaluate the learning resources accessed by the user … (¶ 5 “applies an algorithm to determine if a student has mastered a specific standard.” Also see at least ¶¶ 5 and 48.).
While Berk utilizes and algorithm to evaluate the learning resources accessed by the user, Berk does not appear to explicitly disclose mapping the learning resources corresponding to the standards within the hierarchical table.
However, Moore teaches mapping the learning resources corresponding to the standards within the hierarchical table (Abstract “A system and method for managing content utilizes a relational database management system, which organizes materials such as curriculum materials into, for example, an organizational hierarchy. The system and method allows a user to align materials to a variety of standards including, for example, local, school district, state, and national standards.” ¶ 10 “Additionally, the present invention provides a computer-readable medium having a set of computer-executable instructions for managing content. The instructions include receiving a request from at least one user to align the content to at least one a plurality of standards using a alignment system wherein the alignment system either stores the content in its entirety or stores some of the content in addition to uniform resource identifier ("URI") link(s) to additional portions of the content that is stores on separate computer(s) and/or server(s). The instructions also include performing the alignment using the alignment system” ¶ 76 “The preferred embodiment may perform simple auto-alignment using a direct alignment methodology. The system may simply take each record to be aligned, and follow a set of rules [i.e., algorithm] to create a best guess alignment. One such rule may be to simply create alignments to all records that receive at least a certain minimal score when considered by the find-similar tool. Other variations may include but are not limited to: a) taking the N best matches regardless of score, b) only considering records in a certain portion of the hierarchy (e.g., only consider records in "Science" for alignment to records in "Biology"), c) dynamically limiting the hierarchy using the document routing tool (e.g. only consider records in the category determined to be most relevant by the document routing tool) or d) any other application logic which limits the records to be considered and/or the minimal criteria for alignment [i.e., mapping the learning resources corresponding to the standards within the hierarchical table]. Generally, the GUI may allow the user to configure the analysis application to use any of these rules for alignment by toggling options and entering criteria. It may be desirable to allow the user to browse live data to determine the proper options. In the case where the rules have been fixed, a GUI may not be needed.” Claim 4 “ wherein the content comprises at least one of instructional data, planning data, implementation data, assessment data, school district instructional data, school district planning data, and school district assessment data.” Also see Figures 11 and 19).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the features of Moore with Berk. One of ordinary skill in the art would have been motivated to do so in order to quickly assess the progress and performance of a student (Moore, ¶ 60). One of ordinary skill in the would have also been motivated to do so in order to quickly view the entirely curriculum in an easy to read format. One of ordinary skill in the art would have been motivated to do so in order to easily identify the prerequisites for the curriculums/courses/units/topics. The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
While the combination of Berk/Moore/Cappellucci utilize the algorithm to map the learning resources corresponding to the standards within the hierarchical table and evaluate the learning resources accessed by the user, they do not appear to explicitly evaluate the learning resources as essential learning resources and non-essential learning resources.
However, in the same field of endeavor, Heikkila evaluates the learning resources as essential learning resources and non-essential learning resources (¶ 50. Also see at least ¶ 49 and Claim 17).
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the evaluation of the learning resources as essential learning resources and non-essential learning resources as taught by Heikkila, into the combination of Berk/Moore/Cappellucci. One of ordinary skill in the art would have been motivated to do so in order arrange learning materials according to their importance, and for the advantage preventing non-essential materials from blurring the understandability of the material (Heikkila, ¶ 50). The claimed invention is also merely a combination of old elements, and in the combination each element would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable (KSR Rationale A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hilton (US 2008/0261194 A1) discloses a process for generating and deploying a real-time automatic independent learning plan (ILP) for a person based on academic curriculum standards.
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/SAM REFAI/Primary Examiner, Art Unit 3621