DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is a Final Action in response to the communications filed on 09/29/2025.
Claims 1 – 2, 4 – 5, 7 – 8, 10 – 11, 13 – 14, 16, and 18 – 20 have been amended.
Claims 21 – 22 are new claims.
Claims 1 – 8, 10 – 16, and 18 – 22 are pending in this application.
Response To Remarks
Examiner’s Response to Remarks.
Examiner’s Response to Claim Rejections – 35 U.S.C. § 101.
Examiner’s Response to Rejections of Claims 1 – 20 under 35 U.S.C. § 103.
Examiner’s Response to Claim Rejections – 35 U.S.C. § 101.
Applicant’s arguments are persuasive. The claim as a whole recites eligible subject matter via wherein a window with information is surfaced via a graphical user interface (GUI) when a user proximately interacts with an individual visual augmentation of the plurality of visual augmentations; generate a plurality of visual augmentations for a plurality of goal-related items of a plurality of items within the personalized data feed; apply the plurality of visual augmentations to the plurality of goal-related items; based on the new interaction with the item, and generating 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 selected user goal; and determine real-time interaction data of the user, wherein the real-time interaction data includes time spent reading or reviewing the item; and 35 U.S.C. § 101 rejection is removed.
Examiner’s Response to Rejections of Claims 1-20 under 35 U.S.C. 103
Applicant argues claims 1 – 20, under 35 U.S.C. § 103 are patentable over Le Chevalier, U.S. Publication No. 2019/0385470 in view of Beaty et al., U.S. Publication No. 2020/0302818.
Examiner respectfully disagrees. Applicant has amended claims 1 – 2, 4 – 5, 7 – 8, 10 – 11, 13 – 14, 16, and 18 – 20 and claims 1 – 20, are rejected under 35 U.S.C. § 103, now over Le Chevalier, in view of Beaty, in view of Gradin in view of Venkiteswaran in light of the current amendments as set forth below.
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 – 22, 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” in view of Gradin et al. (U.S. Publication 2011/0137940) henceforth as “Gradin” in view of Venkiteswaran, Kevin et al. (US 2018/0060371) henceforth as “Venkiteswaran”.
Claims 1, 10, and 18:
A system for providing a personalized data feed, the system comprising: a processor; Le Chevalier teaches in ¶ 0100, The system typically includes one or more processors (e.g., CPUs and/or GPUs), one or more network interfaces (wired and/or wireless), memory, and one or more communication buses interconnecting these components. Le Chevalier teaches in ¶ 0101, memory includes volatile and/or non-volatile memory. Memory (e.g., the non-volatile memory within memory) includes a non-transitory computer-readable storage medium.
to the plurality of goal-related items, wherein a window with information is surfaced via a graphical user interface (GUI): calculate a goal completion metric value indicating current overall progress of a selected user goal of the plurality of user goals using interaction data of the user; Le Chevalier teaches in ¶ 0052, for two users (one of them being user), learning profile module generates one or more metrics for completion of one or more sets of learning activities. Examples of metrics include: time taken to complete each learning activity individually; time taken to complete a particular set of learning activities in aggregate; completion velocity referring to whether the user started slow but then got faster (i.e., accelerated), or started fast but got slow (i.e., decelerated), or stayed the same (i.e., no change); outcome of recall activities in the set of learning activities; outcome of the set of learning activities in aggregate (e.g., student grade); Le Chevalier teaches in ¶ 0082, an interface for presenting an ordered playlist to a user according to some embodiments. In some embodiments, the ordered playlist contains content files (and/or links to content files) of different types. Accordingly, the content files may include at least two of: recall activities, active activities, and passive activities. However, in one particular case, the ordered playlist contains content files and/or links to content files of only recall activities. In this case, the educational objective is an assessment of user, such as an exam, mid-term, etc., and may be provided by a user other than user, such as his or her professor, teacher, tutor, parent, etc. Le Chevalier teaches in ¶ 0095 and above, calculating an expected outcome and progress indicator for completion of a content item.
wherein the new interaction comprises reading or reviewing the item; Le Chevalier teaches in ¶ 0093, in Fig. 7, the content items include content items of differing formats. As an example, content item is a video file, content item may be a word document, and so on. Further, content items include content items that reflect differing types of user interactions. As an example, content item is a passive activity involving the user watching a video, content item is a recall activity involving the user doing a quiz, content item is a recreational activity designed to help the student relax.
calculate a priority for each item of the plurality of items within the personalized data feed; Le Chevalier teaches in ¶ 0095 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. Le Chevalier teaches in ¶ 0016, an education platform 110 that provides personalized education activities to a plurality of users, including users 101 and 102.
re-calculate the priority for each remaining item of the plurality of items within the personalized data feed based on the real-time 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;
and update a machine learning component using the real-time interaction data and user feedback associated with the plurality of goal-related items for the selected user goal, wherein the machine learning component improves selection of goal-related items from the plurality of items to assist the user in achieving the plurality of user goals while increasing user interaction with items in the personalized data feed. Le Chevalier teaches in ¶ 0033, the learned model updates itself, concepts stored in the content records are also updated. Le Chevalier teaches in ¶ 0042, activities are defined as “active” when a user creates new own user generated content, such as, personal notes, highlights, and other comments, asking questions when help is needed, solving problems, and connecting and exchanging feedback with peers, among others. Le Chevalier teaches in ¶ 0060, an input including at least an educational objective for a first user and a time constraint of a finite duration of time for completion of the educational objective is received. The input can be received from the first user, or from another user, such as, the user's professor, in natural language and may indicate an upcoming milestone, such as an exam, mid-term, assignment due date, or simply a user's desire to learn a topic or achieve a goal. Le Chevalier teaches in claim 1, selecting, from an educational content repository, a plurality of educational content files that meet the educational objective, selecting a subset of educational content files from the plurality of educational content files based on a learning profile associated with the user and that is estimated to be rendered by the user within the effective time, determining an order for the educational content files in the subset, wherein the order is optimized to achieve the educational objective for the user;
based on the new interaction with the item, determine real-time interaction data of the user, wherein the real-time interaction data includes time spent reading or reviewing the item; Le Chevalier teaches in ¶ 0041, passive activities include a user's passive interactions with content in content repository 120, such as, when a user reads a textbook. As another example, when content 322 is a video file, attributes of interaction 438 may indicate that the user 101 watched the video, skipped portions of the video, favorited the video, gave the video a favorable or an unfavorable rating, and so on. Le Chevalier teaches in ¶ 0044, user record 400 may further include a learning profile 440 for user. The determination of learning profile 440 by education platform 110 is described further with reference to Fig. 5. Learning profile 440 may indicate one or more preferred modes of learning for user 101 and may indicate preferences for: type of activity preferred (e.g., active, passive, or recall), type of content (e.g., video, lecture, book, etc.), duration of activity (short vs. long), and so on. For example, one user may learn better by watching videos, while another may learn better by reading text. Le Chevalier teaches in ¶ 0094, as user renders a content item (say content item 711), the interface is updated so as to show an updated ordered list 710 containing items 712-715, an updated total estimated rendering time 722 for rendering items 712-715, and an updated progress tracker 730 (e.g., 10% of 100% complete). Further, even the content items in the ordered playlist 710 may be updated based on a computed velocity of content rendered.
While Le Chevalier teaches machine learning and iterative learning, optimizing, estimating a second subset of content records, content items, updating the learning profile, personalized learning, assignments, assessments, an updated progress tracker, an effective time available to the user for rendering a playlist of educational content files is calculated, an expected outcome, as may be calculated, one or more metrics for completion of learning activities, educational objective, and achieve a goal, and Le Chevalier is related to Beaty through providing personalized learning activities and tracking progress, Le Chevalier does not explicitly teach applying weights, 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 selected user goal; Beaty teaches in ¶ 0011, selecting flashcards from the flashcards database that match the learning standard sections that are associated with the questions defined in the first digital assessment form; and creating entries in the flashcards database that assign the selected flashcards to the particular student. Beaty teaches in ¶ 0066, define a weight for each learning standard section to thereby prioritize some sections over others Beaty teaches in ¶ 0074, the configuration of API server 202 and the structure of flashcards database 215 facilitates the presentation of flashcards that provide questions that are personalized to an individual student's performance. Beaty teaches in ¶ 0080, the teacher can also specify the learning standard section applicable to the goal (e.g., section 8.G.A.1 of the Common Core), the specific tasks to be completed to reach the goal, a point value assigned to each task, a due date for competing the goal and a learning curve. Beaty teaches in ¶ 0082, When the teacher selects the Create Goal button, the content populated into the user interface can be submitted to API server as shown in Fig. 6A. API server can then generate appropriate queries to create a goal within goals database 212. For example, API server can create an entry in goals table 212a that links a GoalID to the appropriate LearningStandardID, task list, user list, due date, learning curve, etc. API server can also create an entry for each assigned student in the user/goals table 212b; 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; Beaty teaches in ¶ 0118, a unique arrangement of server-side components and functionality that facilitate the process of identifying and presenting learning content that is personalized for each student. With the platform, a teacher or administrator can better identify where each student may be falling short and can easily present learning content to address the specific learning standard sections where the student needs to improve.
While Le Chevalier teaches machine learning and iterative learning, optimizing, estimating a second subset of content records, content items, updating the learning profile, personalized learning, assignments, assessments, an updated progress tracker, an effective time available to the user for rendering a playlist of educational content files is calculated, an expected outcome, as may be calculated, one or more metrics for completion of learning activities, educational objective, and achieve a goal, and Le Chevalier is related to Beaty through providing personalized learning activities and tracking progress, and Beaty teaches personalized learning system, learning curve presenting a first task with a circle with a letter A, tracking students’ progress, and applying weights, and Le Chevalier and Beaty relate to Gardin through providing content to users. Neither Le Chevalier nor Beaty explicitly teach updates can include field changes in a data record, posts such as explicit text or characters submitted by a user, status updates, uploaded files, and links to other data or records. However Gardin teaches the following:
and a memory comprising computer-readable instructions, the memory and the computer-readable instructions configured to cause the processor to: generate a plurality of visual augmentations for a plurality of goal-related items of a plurality of items within the personalized data feed, the plurality of goal-related items associated with a plurality of user goals, wherein the plurality of visual augmentations are selected from a list comprising underscoring, offset, font size change, or font style change; Gradin teaches in ¶ 0005, using conventional database management techniques, it is difficult to know about the activity of other users of a database system in the cloud or other network. For example, the actions of a particular user, such as a salesperson, on a database resource may be important to the user's boss. The user can create a report about what the user has done and send it to the boss, but such reports may be inefficient, not timely, and incomplete. Also, it may be difficult to identify other users who might benefit from the information in the report. Gardin teaches in ¶ 0037, types of updates can include field changes in a data record, posts such as explicit text or characters submitted by a user, status updates, uploaded files, and links to other data or records. Gradin teaches in ¶ 0047, the terms "feed" and "information feed" generally include a combination (e.g. a list) of feed items or entries with various types of information and data. Gardin teaches in ¶ 0048, comments are organized as a list explicitly tied to a particular feed tracked update, post, or status update; Gradin teaches in ¶ 0110, the database system receives a request to update a first record. In one embodiment, the request is received from a first user; Gradin teaches in ¶ 0111, The request for the update of a field of a record is an example of an event associated with the first record for which a feed tracked update may be created; Gradin teaches in ¶ 0112, the database system writes new data to the first record. In one embodiment, the new data may include a new value that replaces old data; Gradin teaches in ¶ 0123, a follower can access his/her news feed to see the feed tracked update. In one embodiment, the user has just one news feed for all of the records that the user is following. In one aspect, a user can access his/her own feed by selecting a particular tab or other object on a page of an interface to the database system. Once selected the feed can be provided as a list, e.g., with an identifier (e.g. a time) or including some or all of the text of the feed tracked update. In another embodiment, the user can specify how the feed tracked updates are to be displayed and/or sent to the user. Gradin teaches in ¶ 0123, a user can specify a font for the text, a location of where the feed can be selected and displayed, amount of text to be displayed, and other text or symbols to be displayed (e.g. importance flags). Gradin teaches in ¶ 0132, a second user 430 can access the new feed tracked update 3 in various ways. In one embodiment, second user 430 can send a request 4 for the record feed. For example, second user 430 can access a home page (detail page) of the record 425 (e.g. with a query or by browsing), and the feed can be obtained through a tab, button, or other activation object on the page. The feed can be displayed on the screen or downloaded. Gradin teaches in ¶ 0154, an administrator (of the system or of a specific tenant) can define which events of which related objects are to have feed tracked updates written about them in a parent record. In another embodiment, a user can define which related object events to show. In one implementation, there are two types of related lists of related objects: first class lookup and second class lookup. Each of the records in the related lists can have a different rule for whether a feed tracked update is generated for a parent record. Each of these related lists can be composed as custom related lists. In various embodiments, a custom related list can be composed of custom objects, the lists can contain a variety of records or items (e.g. not restricted to a particular type of record or item), and can be displayed in a customized manner. Gradin teaches in ¶ 0213, if a user does not want a feed item to be generated upon every change on a given field, but only if the change exceeds a certain threshold or range, then such custom feeds can be conditionally generated with the customized triggers. Gardin teaches in 0340, a user can enter search criteria so that the feed items currently displayed are searched and a new list of matching feed items is displayed. A search box can be used to enter keywords. Picklists, menus, or other mechanisms can be used to select search criteria. In yet another embodiment, feed comments are text-indexed and searchable. Feed comments accessibility and visibility can apply on the search operation too. Gradin teaches in ¶ 0389, The presentation in the feed can be configured so a collapsed view of the clumps is initially displayed. For example, in Fig. 22, Christian Dennehy has published a post to the AM account, stating, "Had a fantastic monthly call with this team. They're impressive . . . " Updates to associated child records are collapsed into a link displayed in proximity to Christian's published posting, with the text, "View highlights of recent updates related to this account . . . " When the user clicks the link, a hanging clump of updates to child records of the AM account is presented, with similar structure as clump 2104 described above.
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 plurality of goal-related items have a higher priority than items in the personalized data feed which are unrelated to the plurality of user goals; Gradin teaches in ¶ 0038, The disclosed implementations provide for noteworthy changes that occur on records related to a common record, such child records of a parent record, to appear both on the common record feed and in the individually tailored news feeds of users following the parent record. Implementations of the disclosed systems, apparatus, and methods are also configured to control how many and which child record updates ultimately appear on a parent record feed. For example, some of the disclosed implementations are configured to differentiate more important/higher value updates from others by applying one or more criteria in the form of rules. In this way, a richer view of relevant updates, with more detail, can be provided in the feed without a user having to distinguish among numerous updates and click through or otherwise drill down into the feed to view updates of interest. Such information can be presented on a graphical user interface of a display device in the context of and in close proximity to parent record updates displayed in the user's feed. Gradin teaches in ¶ 0042, the particular settings of which child record updates to publish, or "chatter," in a parent record feed can be controlled and customized by a system administrator, depending on the desired implementation. Gradin teaches in ¶ 0154, lists can be composed as custom related lists. In various embodiments, a custom related list can be composed of custom objects, the lists can contain a variety of records or items (e.g. not restricted to a particular type of record or item), and can be displayed in a customized manner. Gradin teaches in ¶ 0175, users can rate feed tracked updates or messages (including comments). A user can choose to prioritize a display of a feed so that higher rated feed items show up higher on a display. Gradin teaches in ¶ 0298, examples provided above can be done periodically to create the feeds ahead of time or done dynamically at the time the display of a feed is requested. Such a dynamic calculation can be computationally intensive for a news feed, particularly if many users and records are being followed, although there can be a low demand for storage. Accordingly, one embodiment performs some calculations ahead of time and stores the results in order to create a news feed.
present the personalized data feed via the GUI receive new interaction with an item of the plurality of items within the personalized data feed; Gradin teaches in Fig. 23, GUI 2300 shows a control screen in which a user, generally a system administrator, is able to select fields to generate updates for publication to an information feed when those fields are changed on records that the user follows. Gradin teaches in ¶ 0255, a feed includes feed items, which include feed tracked updates and messages, as defined herein. Various feeds can be generated. For example, a feed can be generated about a record or about a user. Then, users can view these feeds. A user can separately view a feed of a record or user, e.g., by going to a home page for the user or the record. As described above, a user can also subscribe (follow) to user or record and receive the feed items of those feeds through a separate feed application (e.g. in a page or window), which is termed "chatter" in certain examples. The feed application can provide each of the feeds that a user is following in a single news feed.
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; Gradin teaches in ¶ 0310, the most recent feed items (e.g. 100 most recent) are determined first. The other feed items may then be determined in a batch process. Thus, the feed item that a user is most likely to view can come up first, and the user may not recognize that the other feed items are being done in batch. In one implementation, the most recent feed items can be gauged by the event identifiers. In another embodiment, the feed items with a highest importance level can be displayed first. The highest importance being determined by one or more criteria, such as, who posted the feed item, how recently, how related to other feed items, etc.
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 and 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 with systems, apparatus, methods, and computer readable media for selecting updates to associated records to publish on an information feed in an on-demand database service environment of Gradin to assist businesses with updating personalized data feeds that includes tracking changes to records (Gradin Spec. ¶ 0114).
While Le Chevalier teaches machine learning and iterative learning, optimizing, estimating a second subset of content records, content items, updating the learning profile, personalized learning, assignments, assessments, an updated progress tracker, an effective time available to the user for rendering a playlist of educational content files is calculated, an expected outcome, as may be calculated, one or more metrics for completion of learning activities, educational objective, and achieve a goal, and Le Chevalier is related to Beaty through providing personalized learning activities and tracking progress, and Beaty teaches personalized learning system, learning curve presenting a first task with a circle with a letter A, tracking students’ progress, and applying weights, and Le Chevalier and Beaty relate to Venkiteswaran through the systems and methods involving processing and analytical analysis through the integration of document management on platforms and highlighting important text. However, Neither Le Chevalier, Beaty, nor Gradin explicitly teach cursor hovers over and popup window display. However Venkiteswaran teaches the following:
apply the plurality of visual augmentations when a user proximately interacts with an individual visual augmentation of the plurality of visual augmentations; Venkiteswaran teaches in ¶ 0039 the component identified in block 208 is displayed in a user interface such as user interface 300 of Fig. 3. Similarly, in block 228 of Fig. 2, the component identified in block 212 is displayed in a user interface such as user interface 300 of Fig. 3. In the example of Fig. 3, the displayed components are details component 304 and highlights component 308. Display of the components can be done in response to the determination in block 220 of Fig. 2. In some implementations, shared record data of the shared record identified in block 216 can be displayed via data elements such as data elements 312a and 312b of Fig. 3, e.g., “Contact Coyote” as a value of “Next Step” data element 312a. In some implementations, the number of components displayed is a user interface is not limited to components displayed in block 224 and 228 of Fig. 2. Additional components might be displayed in the same presentation of a user interface. Requests to display additional components can be handled continuously by server 104 of Fig. 1. For example, as cursor 512 of Fig. 5A hovers over “Opportunity Name: Anvils,” popup window component 508 is displayed. The shared record data displayed in popup window component 508 can include some of the shared record data previously displayed in block 224 and block 228 of Fig. 2, e.g. “Acme Anvils.”
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 and 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 and systems, apparatus, methods, and computer readable media for selecting updates to associated records to publish on an information feed in an on-demand database service environment of Gradin with systems, apparatus, methods, and computer program products for accessing and displaying shared data of Venkiteswaran to assist businesses implementing visual augmentations on platforms that are displayed through a graphical user interface (Venkiteswaran Spec. ¶ 0027).
Claims 2 and 11:
Le Chevalier, Beaty, Gradin, and Venkiteswaran teaches claims 1, 10, and 18. Beaty further teaches the following:
wherein the memory and the computer- readable instructions are further configured to cause the processor to: identify a user-selected metric for quantifying goal completion progress; 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.
and calculate the goal completion metric value 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 and systems, apparatus, methods, and computer readable media for selecting updates to associated records to publish on an information feed in an on-demand database service environment of Gradin and systems, apparatus, methods, and computer program products for accessing and displaying shared data of Venkiteswaran 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, Beaty, Gradin, and Venkiteswaran teaches 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.
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 and systems, apparatus, methods, and computer readable media for selecting updates to associated records to publish on an information feed in an on-demand database service environment of Gradin and systems, apparatus, methods, and computer program products for accessing and displaying shared data of Venkiteswaran 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, Beaty, Gradin, and Venkiteswaran teaches claims 1, 10, and 18. Beaty further teaches the following:
wherein the memory and the computer- readable instructions are further configured to cause the processor to: calculate a per-item predicted completion metric value indicating a predicted completion contribution associated with user completion of a goal-related item of the plurality of goal-related items; 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.
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 selected user goal achievable by user completion of the goal-related item; Beaty teaches in ¶ 0011, selecting flashcards from the flashcards database that match the learning standard sections that are associated with the questions defined in the first digital assessment form; and creating entries in the flashcards database that assign the selected flashcards to the particular student. 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; Beaty teaches in ¶ 0048, user/goals table 212b facilitates identifying each goal that has been assigned to a student and the student's performance on such goals. Beaty teaches in ¶ 0083, the learning curve represents the desired progression on the tasks, the position of the current progress indicator relative to the curve defines whether the student is on track to complete the goal. In particular, if the current progress indicator falls below the learning curve, it will define that the student is falling behind. The appearance of the current progress indicator can be updated based on whether it is above or below the curve.
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 and systems, apparatus, methods, and computer readable media for selecting updates to associated records to publish on an information feed in an on-demand database service environment of Gradin and systems, apparatus, methods, and computer program products for accessing and displaying shared data of Venkiteswaran 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, Beaty, Gradin, and Venkiteswaran teaches claims 1, 10, and 18. Beaty further teaches the following:
wherein the memory and the computer- readable instructions are further configured to cause the processor to: calculate a per-item completion metric value for each goal-related item in the plurality of goal-related items; 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. Beaty teaches in ¶ 0006, Principals and administrators also need a near effortless means to intervene at a teacher level or student level based on granular performance data collected for each student or teacher; Beaty teaches in ¶ 0096, Backend server 204 can employ these outcomes to generate a predictive score for each of the assignments. For example, each assignment can identify a form which, as described above, may include a form composition that identifies the percentage of questions that relate to each learning standard section. Using the form composition and the student's outcome related to each learning standard section, backend server 204 can predict how the student will perform on the assignment. For example, if the student has only answered 15% of flashcards related to section 8.G.A.1 correctly and half of the questions in the assignment are related to section 8.G.A.1. backend server 204 can predict that the student is likely to perform poorly. As described above, the calculation of a predictive score can decay the student's older results.
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; Beaty teaches in Fig. 6D represents the dashboard at a subsequent time. At this point, Daniel May has completed all four tasks and therefore has been awarded the full 18 points for these tasks. His current progress indicator has therefore reached the top of the curve. In contrast, Charlotte Fields has only completed the first two tasks and has been award only 3 points. Her current progress indicator is therefore positioned below the learning curve and therefore has been changed in appearance to notify the viewer that she has fallen behind. Beaty teaches in ¶ 0086, the structure of goals database 212 and the configuration of API server 202 facilitate tracking and displaying a student's 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 and systems, apparatus, methods, and computer readable media for selecting updates to associated records to publish on an information feed in an on-demand database service environment of Gradin and systems, apparatus, methods, and computer program products for accessing and displaying shared data of Venkiteswaran 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, Beaty, Gradin, and Venkiteswaran teaches 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 and systems, apparatus, methods, and computer readable media for selecting updates to associated records to publish on an information feed in an on-demand database service environment of Gradin and systems, apparatus, methods, and computer program products for accessing and displaying shared data of Venkiteswaran 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, Beaty, Gradin, and Venkiteswaran teaches claims 1, 10, and 18. Le Chevalier further teaches the following:
wherein the memory and the computer- readable instructions are further configured to cause the processor to: identify a new completed goal-related item in the plurality of goal-related items; 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.
update the goal completion metric value to reflect the new completed goal-related item; 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. Le Chevalier teaches in ¶ 0068, playlist generation module 170 optionally computes an indicator of a relative strength of association between the concepts and educational objective. For example, for a first concept that is very strongly associated with the playlist generation module 170, playlist generation module 170 may assign, say, a score of 0.99, while for a second concept that is only mildly associated with the particular content item, playlist generation module 170 may assign a score of 0.4.
and update the progress indicator to reflect additional user progress towards completing the selected user goal; Le Chevalier teaches in ¶ 0054, At 534, learning profile module 170 compares the metrics generated at 532 and adjusts a score for user 101 accordingly. The score may be incremented when the metric comparison indicates that user 101 performed better than the other user (or average user), decremented when user 101 performed worse, and not adjusted when the performances were equivalent. The score represents a difference between the learning profile for user 101 and one other user (or average user). Learning profile module 170 may iteratively perform steps 532 and 534 until it determines n differential scores for user 101 representing the difference between the learning profile for user 101 and each other user (n−1) who has completed the or more sets of learning activities at 532, such as each other user in Bio 101, where 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, Beaty, Gradin, and Venkiteswaran teaches claims 1, 10, and 18. Gradin further teaches the following:
wherein a first visual augmentation of the plurality of visual augmentations is applied to a first set of goal-related items associated with a first goal of the plurality of user goals, and wherein a second visual augmentation of the plurality of visual augmentations is applied to a second set of goal related items associated with a second goal of the plurality of user goals; a user may be accessing a page associated with the first record, and may change a displayed field and hit save. In another embodiment, the database system can automatically create the request. For instance, the database system can create the request in response to another event, e.g., a request to change a field could be sent periodically at a particular date and/or time of day, or a change to another field or object. The database system can obtain a new value based on other fields of a record and/or based on parameters in the system. Gradin teaches in ¶ 0048, a feed item can be a message, such as a user-generated post of text data, and a feed tracked update to a record or profile, such as a change to a field of the record. A feed can be a combination of messages and feed tracked updates. Messages include text created by a user, and may include other data as well. Gradin teaches in ¶ 0123, and above the user can specify font for the text; examples of messages include posts, user status updates, and comments. Gradin teaches in ¶ 0128, the same list or a second list (which can be stored in a same location or a different location) can also include the fields and/or events that are tracked for the record types in the first list. Gradin teaches in claim 6 the one or more child records being of a first type, the parent record being of a second type. Gradin teaches in claim 7 the second type being an item selected from the group consisting of: an opportunity, a case, a contact, a task, and an event. Gradin teaches in claim 8 the one or more child records having a hierarchical relationship with the parent record. Gradin teaches in claim 9 the hierarchical relationship including one or more further records situated between the one or more child records and the parent record.
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 and 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 and systems, apparatus, methods, and computer program products for accessing and displaying shared data of Venkiteswaran with systems, apparatus, methods, and computer readable media for selecting updates to associated records to publish on an information feed in an on-demand database service environment of Gradin to assist businesses with updating personalized data feeds that includes tracking changes to records (Gradin Spec. ¶ 0114).
Claim 21:
Le Chevalier, Beaty, Gradin, and Venkiteswaran teaches claims 1, 10, and 18. Beaty further teaches the following:
wherein the window with information comprises the per-item progress indicator associated with a corresponding item of the plurality of goal-related items; Beaty teaches in ¶ 0086, the platform enables the creation of goals and the display of the students' progress towards these goals, and the structure of goals database and the configuration of API server facilitate tracking and displaying a student's 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 and systems, apparatus, methods, and computer readable media for selecting updates to associated records to publish on an information feed in an on-demand database service environment of Gradin and systems, apparatus, methods, and computer program products for accessing and displaying shared data of Venkiteswaran 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 22:
Le Chevalier, Beaty, Gradin, and Venkiteswaran teaches claims 1, 10, and 18. Le Chevalier further teaches the following:
wherein the window with information comprises a key performance indicator (KPI) associated with the user; Le Chevalier teaches in claim 7, tracking rendering of the educational content files in the ordered playlist in real-time; and presenting a progress tracker to the user.
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Strebinger, David Robert et al. (U.S. Publication No. 2011/0087534) discloses systems and methods associated with search queries and advertising platforms utilizing at least one social graph and related technologies.
- Reference Barton, Scott et al. (U.S. Publication No. 2011/0107383) discloses system displays web feed content on television, and a web feed content aggregation system retrieves syndicated web feed content on a web subscription basis.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
12/15/2025
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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