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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/05/2025 has been entered.
Status of Claims
This action is a Non-Final Action in response to the communications filed on 05/05/2025.
Claims 1, 10, and 18 are amended.
Claims 9 and 17 are canceled.
Claims 1 – 8, 10 – 16, and 18 – 20 are pending in this application.
Reply To The Remarks
Response to 35 U.S.C. § 101
Response to 35 U.S.C. § 103
Examiner’s Response to II. Claim Rejections – 35 U.S.C. § 101
Examiner’s Response to Claimed Invention Does Not Recite a Judicial Exception (2A Prong One)
Applicant argues Claim 1, as amended, does not recite a judicial exception.
Examiner respectfully disagrees. Claims 1 – 8, 10 – 16, and 18 – 20, are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without reciting significantly more. Pursuant to step 1 in MPEP 2107.03, claim is directed to method which is a statutory category. As explained in MPEP 2106.04(II) a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim (i.e., mathematical concepts, certain methods of organizing human activities such as a fundamental economic practice, or mental processes). Applicant’s claim 1 in summary, collects data, analyzes the data, displays the results, and then updates the machine learning component; and merely recites a mental process (which includes observation, evaluation, judgement, or opinion) pursuant to MPEP 2106.04(a)(2)(III)(A). Claim 1 also falls under mathematical concepts, where the claim limitations perform mathematical calculations and form mathematical relationships. Claim 1 also recites certain methods of organizing human activity, where the claim is managing interactions between people that includes social activities. Claims 10 and 18 are similar and recite the same abstract ideas identified above. Accordingly, claims 1, 10, and 18 are directed towards judicial exceptions.
Examiner’s Response to The Claim as a Whole Integrates the Recited Judicial Exception into a Practical Application
Applicant argues the present claims as a whole integrate the abstract idea into a practical application.
Examiner respectfully disagrees. Claim 1 as a whole does not integrate the judicial exception into a practical application, and there are no additional elements recited in the claim beyond the judicial exception. Applicant’s claim 1 recites generic computer components ¶¶ 0024 and 0025, used as a tool to perform a mental process, certain methods of organizing human activity, and mathematical concepts. The limitations of the dependent claims, 2 – 8, and 11 – 16, and 19 – 20, are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation.
Examiner’s Response to The Claims Have Additional Elements That Provide an Inventive Concept
Applicant argues the present claims have additional elements, individually and in combination, that provide an inventive concept.
Examiner respectfully disagrees. There is no inventive concept, as Applicant is merely resolving a business problem. Claims 10 and 18, are similar to claim 1 and recite the same ideas. Applicant’s claim recites “calculate a goal completion metric value indicating current overall progress of the user goal using interaction data of a user;” “determine whether the user interacts with an item of the plurality of items within the personalized data feed;” “based on determining that the user interacts with the item, update the interaction data of the user;” re-calculate the priority for each remaining item of the plurality of items within the personalized data feed based on the updated interaction data; automatically re-organize the plurality of items within the personalized data feed in accordance with the re-calculated priority, wherein an item with a highest priority, moves to a top of the personalized data feed on the GUI; update a machine learning component using the interaction data and user feedback associated with goal-related items for a given user-selected goal, wherein the machine learning component improves selection of goal-related items from the plurality of items to assist the user in achieving the goal while increasing user interaction with items in the personalized data feed; generate a progress indicator within the personalized data feed, the progress indicator representing the calculated goal completion metric value indicating the current overall progress of the user goal; and calculate a priority for each item of the plurality of items within the personalized data feed. Claim 1 is merely using a model to make a determination, and this is data processing and analysis that is evaluating and displaying for observation. There is no inventive concept.
For the reasons above claims 1 – 8, 10 – 16, and 18 – 20 are rejected under 35 U.S.C. § 101.
Examiner’s Response to II. Claim Rejections – 35 U.S.C. § 103
Applicant has amended claims 1, 10, and 18 and argues the rejection of Claims 1-20 under 35 U.S.C. § 103 as being unpatentable over Le Chevalier, U.S. Publication No. 2019/0385470 (hereinafter "Le Chevalier") in view of Beaty et al., U.S. Publication No. 2020/0302818 (hereafter "Beaty") is respectfully traversed.
Examiner respectfully disagrees. Applicant has amended claim 1 and further search and consideration is required to respond to Applicant’s amendments. Rejection under 35 U.S.C. § 103 remains.
Claim Rejections – 35 U.S.C. § 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 – 8, 10 – 16, and 18 – 20 are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
Claims 1, 10, and 18:
providing a personalized data feed;
calculate a goal completion metric value indicating current overall progress of the user goal using interaction data of a user;
prioritize placement of the plurality of items within the personalized data feed in accordance with the priority of each item of the plurality of items;
present the organized personalized data feed via a graphical user interface (GUI);
determine whether the user interacts with an item of the plurality of items within the personalized data feed;
based on determining that the user interacts with the item, update the interaction data of the user;
re-calculate the priority for each remaining item of the plurality of items within the personalized data feed based on the updated interaction data;
automatically re-organize the plurality of items within the personalized data feed in accordance with the re-calculated priority, wherein an item with a highest priority, moves to a top of the personalized data feed on the GUI;
update a machine learning component using the interaction data and user feedback associated with goal-related items for a given user-selected goal, wherein the machine learning component improves selection of goal-related items from the plurality of items to assist the user in achieving the goal while increasing user interaction with items in the personalized data feed;
generate a first visual augmentation for a first goal-related item of a plurality of items within the personalized data feed, the first goal-related item is associated with a first user goal;
generate a progress indicator within the personalized data feed, the progress indicator representing the calculated goal completion metric value indicating the current overall progress of the user goal;
calculate a priority for each item of the plurality of items within the personalized data feed;
generate a second visual augmentation for a second goal-related item of a plurality of items within the personalized data feed, the second goal-related item is associated with a second user goal, wherein the first visual augmentation and the second visual augmentation are different, and wherein the first visual augmentation and the second visual augmentation are selected from a list comprising of overlay of color, underscoring, offset, or font change;
The limitations of Claim 1, under its broadest reasonable interpretation, recites mental processes, related to observation and evaluation of data, but for the recitation of a generic computer component (e.g., system, a processor, memory, and machine learning component), and uses a computer as a tool to perform a mental process. For example, evaluate a first visual augmentation for a first goal-related item… associated with a first user goal; evaluate a second visual augmentation for a second goal related-item… a list comprising of overlay of color, underscoring, offset, or font change; evaluate a goal completion metric value indicating current overall progress of the user goal using interaction data of a user; evaluate a priority for each item of the plurality of items within the personalized data feed; evaluate priority placement of the plurality of items… which are unrelated to the user goal; observe the personalized data feed via a graphical user interface (GUI); evaluate whether the user interacts with an item of the plurality of items within the personalized data feed; based on determining that the user interacts with the item, evaluate the interaction data of the user; evaluate the priority for each remaining item of the plurality of items within the personalized data feed based on the updated interaction data; and evaluate a machine learning component using the interaction data and user feedback… items in the personalized data feed all involve observing and evaluating data. Accordingly, the claim recites an abstract idea of mental processes.
Claim 1 recites the abstract idea, certain methods of organizing human activity. For example, generate a first visual augmentation for a first goal-related item… associated with a first user goal; generate a second visual augmentation for a second goal-related item of a plurality of items… selected from a list comprising if overlay or color, underscoring, offset, or font change; calculate a foal completion metric value indicating current overall progress of the user using data of a user; generate a progress indicator within the personalized data feed… overall progress of the user goal; prioritize placement of the plurality of items within the personalized data feed… which are unrelated to the user goal; determine whether the user interacts with an item of the item of the plurality of items within the personalized data feed; based on determining that the user interacts with the item, update the interaction data of the user; and update a machine learning component using interaction data and user feedback associated… with items in the personalized data feed. As recited, claim 1 falls within the abstract grouping certain methods of organizing human activity, managing interactions between people that includes social activities. Accordingly claim 1 recites certain methods of organizing human activity.
Claim 1 recites the abstract idea, mathematical concepts. For example, generate a first visual augmentation for a first goal-related item… associated with a first user goal; generate a second visual augmentation for a second goal-related item… selected from a list comprising of overlay of color, underscoring, offset, or font change; calculate a goal completion metric value… using interaction data of a user; generate a progress indicator within the personalized data feed… indicating the current overall progress of the user goal; calculate a priority for each item if the plurality of items within the personalized fata feed; prioritize placement of the plurality of items… which are unrelated to the user goal; re-calculate the priority for each remaining item of the plurality of items within the personalized data feed based on the updated interaction data; automatically re-organize the plurality of items… moves to a top of the personalized data feed on the GUI; and update a machine learning component using the interaction data and user feedback… with items in the personalized data feed all perform mathematical calculations and form mathematical relationships. According, claim 1 recites mathematical concepts.
Independent claims 10 and 18 substantially recite the same subject matter of claim 1 and also include the abstract ideas identified above with additional elements such as a processor, computer-readable storage device, a memory, and a system which are generic computer components as per (see at least Spec. ¶¶ 0024 and 0025), any device as per Applicant’s Specifications shown here:
“[0024] In the example of FIG. 1, the computing device 102 represents any device executing computer-executable instructions 104 (e.g., as application programs, operating system functionality, or both) to implement the operations and functionality associated with the computing device 102. The computing device 102 in some examples includes a mobile computing device or any other portable device. A mobile computing device includes, for example but without limitation, a mobile telephone, laptop, tablet, computing pad, netbook, gaming device, and/or portable media player. The computing device 102 can also include less- portable devices such as servers, desktop personal computers, kiosks, or tabletop devices. Additionally, the computing device 102 can represent a group of processing units or other computing devices. [0025] In some examples, the computing device 102 has a processor 106 and a memory 108. The computing device 102 in other examples includes a user interface device 110.”
and thus are not practically integrated nor significantly more.
The dependent claims encompass the same abstract ideas as well. For instance, claim 2 is directed towards observing a user-selected metric and evaluate the goal completion metric and per-item predicted completion metrics; claim 3 is directed towards observing a set of goal-related items and evaluate a unique goal-related progress indicator; claim 4 is directed towards evaluating a per-item predicted completion metric value and a per-item progress indicator; claim 5 is directed towards observing a plurality of visual augmentations and evaluating a per-item completion metric value and a per-item progress indicator; claim 6 is directed towards observing track user data feed interaction and per-item predicted completion metric values; claim 7 is directed towards observing new completed goal-related item and evaluating the goal completion metric; claim 8 is directed towards observing the user feedback report of false positives and indication from the user whether an item is useful for a specific goal; claim 11 is directed towards observing a user-selected metric and evaluating the goal completion metric; claim 12 is directed towards observing a set of goal-related items and evaluate a unique goal-related progress indicator; claim 13 is directed towards evaluating a per-item predicted completion metric value and a per-item progress indicator; claim 14 is directed towards evaluating a per-item completion metric value and evaluating a plurality of per-item progress indicators; claim 15 is directed towards observing user data feed interaction; claim 16 is directed towards observing the user feedback report of false positives and indication from the user whether an item is useful for a specific goal; claim 19 is directed towards evaluating a per-item predicted completion metric value and a per-item progress indicator; and claim 20 is directed towards observing a plurality of visual augmentations and evaluating a per-item completion metric value and a per-item progress indicator. Thus the dependent claims further limit the abstract concepts found in the independent claims.
The judicial exceptions are not integrated into a practical application. Claim 18 recites the additional elements “a computer readable storage device” and “a processor”. Claim 1 recites the additional elements “a system,” “a processor,” and “a memory”. The additional elements of “a computer readable storage device,” “a processor,” “a system,” and “a memory” are considered generic computer components (see at least Spec. ¶¶ 0024 and 0025).
For instance, the steps of generate a first visual augmentation for a first goal-related item… associated with a first user goal; generate a second visual augmentation for a second goal-related item… comprising of overlay of color, underscoring, offset, or font change; calculate a goal completion metric value indicating current overall progress of the user goal using interaction data of a user; prioritize placement of the plurality of items within the personalized data feed… which are unrelated to the user goal; present the organized personalized data feed via a graphical user interface (GUI); determine whether the user interacts with an item of the plurality of items within the personalized data feed; based on determining that the user interacts with the item, update the interaction data of the user; re-calculate the priority for each remaining item of the plurality of items within the personalized data feed based on the updated interaction data; automatically re-organize the plurality of items within the personalized data feed… moves to a top of the personalized data feed on the GUI; update a machine learning component using the interaction data and user feedback… while increasing user interaction with items in the personalized data feed; generate a progress indicator within the personalized data feed… calculated goal completion metric value indicating the current overall progress of the user goal; and evaluate a priority for each item of the plurality of items within the personalized data feed are considered extra-solution activity (e.g., data gathering). Each of the additional limitations are no more than mere instructions to apply the exception using a generic computer component (e.g., a processor). The combination of these additional elements are no more than mere instructions to apply the exception using a generic computer component (e.g., a processor). Therefore, the additional elements do not integrate the abstract ideas into a practical application because the additional elements do not impose meaningful limits on practicing the idea. Accordingly, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount significantly more than the judicial exception. As stated above, the additional elements of “a computer readable storage device,” “a processor,” “a system,” “a memory,” and a machine learning component are considered generic computer components performing generic computer functions and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
Dependent claims 2 – 8, and 11 – 16, and 19 – 20, when analyzed both individually and in combination are also held to be ineligible for the same reasons above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure, that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 8, and 10 – 20, are not patent eligible.
Claim Rejections – 35 U.S.C. § 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 8, 10 – 16, and 18 – 20, are rejected under 35 U.S.C. 103 as being unpatentable over Le Chevalier, Vincent (U.S. Publication No. 2019/038,5470) (henceforth as “Le Chevalier”) in view of Beaty, Robert M. et al. (U.S. Publication No. 2020/030,2818) (henceforth as “Beaty”),
Claims 1, 10, and 18:
Le Chevalier teaches the following:
a processor; and a memory comprising computer-readable instructions, the memory and the computer-readable instructions configured to cause the processor to: Le Chevalier teaches in ¶ 0023, computer-readable storage medium such as storage device, loaded into a memory, and executed by a processor;
A system for providing a personalized data feed, the system comprising; Le Chevalier teaches in ¶ 0003, personalized learning activities in an online learning platform.
calculate a goal completion metric value indicating current overall progress of the user goal using interaction data of a user; see at least Le Chevalier teaches in ¶ 0095 Also presented to the user is an expected outcome as may be calculated in Fig. 6B. Le Chevalier further teaches in Fig. 7, a progress tracker, where 0 of 100% is complete.
present the personalized data feed via a graphical user interface (GUI); Le Chevalier teaches in ¶ 0011 a user interface for presenting a personalized ordered playlist to a user;
determine whether the user interacts with an item of the plurality of items within the personalized data feed; Le Chevalier teaches in ¶ 0095, the teachers content in an ordered playlist and the teacher may interact with the content in the ordered playlist by asking questions or discussing concepts;
based on determining that the user interacts with the item, update the interaction data of the user; Le Chevalier teaches in ¶ 0057, the teacher’s learning profile is updated by the learning profile module as the teacher accesses information changes and the teacher’s response to the ordered playlist is also updated in the teacher’s learning profile.
re-calculate the priority for each remaining item of the plurality of items within the personalized data feed based on the updated interaction data; Le Chevalier teaches in ¶ 0071, the playlist generation module selects a second subset of content records from the first subset of content records, where the second subset is estimated;
automatically re-organize the plurality of items within the personalized data feed in accordance with the re-calculated priority, wherein an item with a highest priority, moves to a top of the personalized data feed on the GUI; Le Chevalier teaches in ¶ 0071, optimizing the content records of the second subset to achieve the user’s educational objective;
update a machine learning component using the interaction data and user feedback associated with goal-related items for a given user-selected goal, wherein the machine learning component improves selection of goal-related items from the plurality of items to assist the user in achieving the goal while increasing user interaction with items in the personalized data feed; Le Chevalier teaches in ¶ 0022, using machine learning and iterative learning techniques on the education data Le Chevalier teaches in ¶ 0089, optimizing to achieve the educational objective for the user.
and wherein the first visual augmentation and the second visual augmentation are selected from a list; Le Chevalier teaches in ¶ 0072, selecting content items from hundreds of items where the content items may be arranged in a list.
While Le Chevalier teaches machine learning and iterative learning, optimizing, estimating a second subset of content records, updating the learning profile, and calculating goal completion, Le Chevalier does not explicitly teach goals however, Beaty teaches the following:
generate a first visual augmentation for a first goal-related item of a plurality of items within the personalized data feed, the first goal-related item is associated with a first user goal; see at least Beaty Fig. 6C a user tracking goals, where a goal for the user may be to get approval for your idea, and a check mark in the box may be likened to a visual augmentation, and Charlotte Fields is a first user.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
generate a second visual augmentation for a second goal-related item of a plurality of items within the personalized data feed, the second goal-related item is associated with a second user goal, wherein the first visual augmentation and the second visual augmentation are different; Beaty teaches in Fig. 6D another platform user, Daniel May, has goals where a present project in class may be a goal and the goal is highlighted by a check mark that is different than the first user and a shaded five.
While Le Chevalier teaches machine learning and iterative learning, optimizing, estimating a second subset of content records, updating the learning profile, and calculating goal completion, Le Chevalier does not explicitly teach progress indicator however, Beaty teaches the following:
generate a progress indicator within the personalized data feed, the progress indicator representing the calculated goal completion metric value indicating the current overall progress of the user goal; see at least Beaty teaches in ¶ 0083, with a goal created, the teacher, students and administrator can view the students' progress on the goal. Figs. 6C and 6D illustrate example dashboards that the platform can provide to enable users to track a student's progress on a goal;
comprising of overlay of color, underscoring, offset, or font change; Beaty teaches in Fig. 6C font change or overlay of color.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
While Le Chevalier teaches machine learning and iterative learning, optimizing, estimating a second subset of content records, updating the learning profile, and calculating goal completion, Le Chevalier does not explicitly teach define the number of times there were no search results for a flashcard however, Beaty teaches the following:
calculate a priority for each item of the plurality of items within the personalized data feed; Beaty teaches in ¶ 0095 determining weights for the learning standard items where the school may prioritize the learning standard sections where learning standard items are on the personalized activities.; Beaty teaches in ¶ 0118, the learning content is personalized for each student.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
While Le Chevalier teaches machine learning and iterative learning, optimizing, estimating a second subset of content records, updating the learning profile, and calculating goal completion, Le Chevalier does not explicitly teach weights for calculating priority however, Beaty teaches the following:
prioritize placement of the plurality of items within the personalized data feed in accordance with the calculated priority of each item of the plurality of items, wherein the goal-related items have a higher priority than items in the personalized data feed which are unrelated to the user goal; Beaty teaches in ¶ 0094, using weights for calculating priority where the standards database can define a weight for each learning standard item which represents a school's priority for that learning standard section; Beaty teaches in ¶ 0066, standards database can define a weight for each learning standard section to thereby prioritize some sections over others. API server can be configured to present learning standard items that have insufficient flashcards in an order that is based both on the value of the MissCnt and the priority given to the corresponding section. Each time a new flashcard is created, the MissCnt for the corresponding section can be decremented, where MissCnt may improve the ranking of a goal-related item or decrease the ranking of a goal-related item.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
Claims 2 and 11:
Le Chevalier and Beaty disclose claims 1, 10, and 18. Beaty further discloses the following:
identify a user-selected metric for quantifying goal completion progress; see at least Beaty teaches ¶ 0048, user/goals table at 212(b) includes entries that associate a particular user to a particular goal and that can be used to track the user's performance and the points for a goal or a complete task can be defined.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
and calculate the goal completion metric and per-item predicted completion metrics using the user-selected metric, wherein the user-selected metric comprises a percentage value representing completed goal-related items, a ratio of completed goal-related items to all goal- related items, and an estimated amount of time spent interacting with each goal-related item; see at least Beaty teaches in claim 18, wherein the predictive score for a particular assessment is generated based on the particular student's results on flashcards associated with learning standard sections that match the learning standard sections that are associated with the questions defined in the corresponding digital assessment form, where the learning standard is the goal set by the teacher or school administrator. Beaty further teaches in ¶ 0114, the student’s results as a percentage. For example, given that this is the first question, three of the four students have a score of 100% and the fourth student has a score of 0%, where three of the four students represent a ratio of completed goal-related items to all goal-related items. The display of the flashcard within user interface updates to reflect the class's percentage (75) during this flashcard session so that it can be compared to the class average and the school average. Beaty teaches in ¶ 0074, when the student answers a question incorrectly, API server can move the corresponding flashcard back to slot 1 to ensure that the flashcard that the student answered incorrectly will be placed in the slot with the highest probability of selection and repeated by the student. Beaty teaches in ¶ 0083, the tracking of time spent on the goal where the completion of each task can be represented on the learning curve using an icon that is positioned based on the point value of the task and the time when the task was completed.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
Claims 3 and 12:
Le Chevalier and Beaty disclose claims 1, 10, and 18. Beaty further discloses the following:
identify a set of goal-related items for each user-selected goal of a plurality of user-selected goals; Beaty teaches in ¶ 0005, principals, administrators and teachers can greatly benefit from the ability to determine an estimated likelihood of how every student will score on any given test at any point in time, where the goal may be the teachers’, administrators’, and principals’ having the ability to estimate likelihood of student test performance; Beaty teaches in ¶ 0007, a teacher’s goal may be able to determine the effectiveness of specific practice questions, content, videos and other materials.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
and generate a unique goal-related progress indicator representing a current goal completion metric value calculated for each user-selected goal using the interaction data associated with the set of goal-related items for each user-selected goal, the interaction data comprising a number of completed goal-related items of a plurality of goal-related items completed and a number of incomplete goal-related items in the plurality of goal-related items; Beaty teaches in ¶ 0040, reviewing a student's progress towards a goal. Beaty further teaches in ¶ 0081, Fig. 6B the learning curve provides a graphical representation of actual progress vs. expected progress. Beaty further shows in Fig 6B the tasks completed and tasks not completed, where the tasks completed and not completed are likened to goal-related items completed or not completed.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
Claims 4, 13, and 19:
Le Chevalier and Beaty disclose claims 1, 10, and 18. Beaty further discloses the following:
calculate a per-item predicted completion metric value indicating a predicted completion contribution associated with user completion of the goal-related item; Beaty teaches in ¶ 0087, For example, Fig. 7 illustrates an aggregate of scores that have been created representing the performance of all students in the school on flashcards related to science and on flashcards related to math where the per-item is likened to the flashcards related to science and the flashcards related to math and the aggregate score of each is likened to the metric value; likewise each individual student score may be shown as well. Beaty further teaches in ¶ 0088, the platform can enable a teacher or administrator to achieve the goal of comparing their student’s performance and the other teachers’ class performance relative to other students and teachers’ performance in addition to a school-wide performance by viewing side-by-side comparisons of students or teachers within this dashboard.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
and generate a per-item progress indicator associated with the goal-related item within the personalized data feed representing the per-item predicted completion metric value, wherein the per-item progress indicator provides a predicted level of additional user progress towards completion of the user goal achievable by user completion of the goal-related item; see at least Beaty teaches in ¶ 0047, Fig. 3B represents data structures that may be stored in goals database that has a goals table 212a and a user/goals table 212b. Goals may be assigned and tracked to show what tasks must be completed to achieve the goal in addition the deadline for completion of goal has a field of entry and finally a learning curve to compare the progress to other assigned users’ progress.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
Claims 5, 14, and 20:
Le Chevalier and Beaty disclose claims 1, 10, and 18. Beaty further discloses the following:
generate a plurality of visual augmentations associated with a plurality of goal-related items from the plurality of items displayed within the personalized data feed; Beaty teaches in ¶ 0113, in Fig. 11B, the question associated with the goal may be highlighted and currently being presented, where the highlighting is likened to visual augmentation; Beaty further teaches in ¶ 0083 in Fig. 6C, a learning curve that includes a current progress indicator in the form of a circle that is positioned based on the current time and the current point total earned by the student, where the metric values are represented A and B with a circle around them.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
calculate a per-item completion metric value for each goal-related item in the plurality of goal-related items; see at least Beaty teaches in Fig. 6B, calculated metrics for “Daniel May” task completion, where the completion metric values are represented in Fig. 6B by the letters A, B, C, and D.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
and generate a per-item progress indicator for each of the plurality of goal-related items within the personalized data feed, wherein each per-item progress indicator provides a representation of the calculated per-item completion metric value for a corresponding goal- related item in the personalized data feed; see at least Beaty teaches in ¶ 0083 in Fig. 6C, a learning curve that includes a current progress indicator in the form of a circle that is positioned based on the current time and the current point total earned by the student, where the metric values are represented A and B with a circle around them for Daniel May demonstrates a completed task relative to the progress curve indicator…
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
Claims 6 and 15:
Le Chevalier and Beaty disclose claims 1, 10, and 18. Beaty further discloses the following:
track user data feed interaction using the goal completion metric value and per-item predicted completion metric values associated with completed goal-related items and incomplete goal-related items; see at least Beaty teaches in ¶ 0026 Figs. 6B-6D illustrate example user interfaces that the platform can create to track the student’s completion of tasks defined within a goal;
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
Claim 7:
Le Chevalier and Beaty disclose claims 1, 10, and 18. Le Chevalier further discloses the following:
identify a new completed goal-related item in the plurality of goal-related items; see at least Le Chevalier teaches in ¶ 0043, users complete recall activities to study information learned from their passive activities, for example by using flashcards, solving problems provided in a textbook or other course materials, or accessing textbook solutions, where the completion of recall activities for purposes of studying is a new goal-related item.
and update the progress indicator to reflect additional user progress towards completing the user goal; see at least Le Chevalier teaches in ¶ 0033, as the learned model updates itself, concepts stored in the content records are also updated, where content records include user goals; Le Chevalier further teaches in ¶ 0074, content records may be non-educational relation activities.
update the goal completion metric to reflect the new completed goal-related item; see at least Le Chevalier teaches in ¶ 0054, the learning profile module compares the metrics generated and adjusts a score for user accordingly for completion of learning activities.
Claims 8 and 16:
Le Chevalier and Beaty disclose claims 1, 10, and 18. Beaty further discloses the following:
wherein the user feedback comprises report of false positives and indication from the user whether an item is useful for a specific goal; Beaty teaches in ¶ 0050, an incorrect answer by a student; the student is issued a flashcard; Beaty teaches in ¶ 0088 student’s performance using the flashcards.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine a method of automatically providing personalized learning activities to users of an online learning platform that has an educational objective and a finite duration of time for completion of the educational objective of Le Chevalier with a platform employed to implement a personalized learning system that can be implemented in a client-server environment in which a server or servers maintain a number of data structures of Beaty to assist businesses and schools with displaying a goals table to show completed task and perform (Beaty Spec. ¶ 0048).
Conclusion
Any inquiry concerning this communic