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 is a final rejection responsive to claims filed on 11/17/2025. Claims 1, 2, 13, and 31-32 are amended. Claims 1-5, 7-8, 10, 13-17, 20-22, 27, 29 and 31-32 remain pending and are examined herein.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. PCT/CA2022/050399, filed on 03/16/2022. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. This PCT filing also claims priority to provisional application #63/162,239 filed on 03/17/2021. Priority is acknowledged and the earliest effective priority date of 03/17/2021 is granted to the instant application.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7, 8, 10, 13-17, 20-22, 27, 29, 31 and 32 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Is the claim to a Process, Machine, Manufacture, or Composition of Matter?
Claims 1-5, 7, 8, 10, 13-17, 20-22, 27, 29: A method comprising:
Claim 31: A computing device comprising at least one processor and a non-transitory storage device storing computer readable instructions, for execution by the at least one processor to cause the computing device to:
Claim 32: One or more non-transitory computer readable media storing instructions executable by at least one processor to, cause a computing device to perform a method comprising:
Claim 1 and its dependent claims are directed to a method which falls under “process.” Claim 31 recites a computing device with a processor and non-transitory storage device, which falls under apparatus or “machine.” Claim 32 recites a non-transitory computer readable medium which falls under “machine” or “manufacture.” Therefore the claims recite at least one of the four eligible subject matter categories and are to be further analyzed under step 2.
Step 2a Prong 1: Is the claim reciting an Judicial Exception(A Law of Nature, a Natural Phenomenon (Product of Nature), or An Abstract Idea?)
The claims under the broadest reasonable interpretation in light of the specification are analyzed herein. Representative claims 1, 31, and 32 are marked up, isolating the abstract idea from additional elements, wherein the abstract idea is in bold and the additional elements have been italicized as follows:
Claims 1: A method comprising:
Claim 31: A computing device comprising at least one processor and a non-transitory storage device storing computer readable instructions, for execution by the at least one processor to cause the computing device to:
Claim 32: One or more non-transitory computer readable media storing instructions executable by at least one processor to, cause a computing device to perform a method comprising:
Claim 1 Body (also representative of claims 31 and 32) : a) analyzing content for presentation by one or more computing devices to a user, the content comprising a plurality of sub-content items, the analyzing determining, for each sub- content item, an item minimal duration for presentation based on data type of the sub-content item, and wherein, for each of the sub-content items, the analyzing determines a text complexity measure and a text length for determining the item minimal duration for the sub-content item;
b) determining from tracking data, for each of the sub-content items, an actual presentation duration and a focus measure of attention, in aggregate across all computing devices that presented the content to the user, wherein the tracking data is sequentially generated in response to user interactivity with the one or more computing devices during presentation of the content and the tracking data comprises sub-content items positional data and interactions with the sub-content items from inputs received, wherein the focus measure is determined by combining sequential behavioral indicators selected from at least one of computing device inputs, scroll rate, or viewport dwell time, with device context data;
c) computing a rating determined at least in part by the focus measure, the actual presentation duration, and the item minimal duration for each sub-content item and aggregating results across the plurality of sub-content items and across the one or more computing devices, wherein the rating represents an imputed knowledge level of the content; and
d) providing the rating for display.
When evaluating the bolded limitations of the claims under the broadest reasonable interpretation in light of the specification, it is clear that representative claims 1, 31, and 32 recite an abstract idea within the category of “certain methods of organizing human activity.” More specifically, the present invention falls under the sub-grouping “managing personal behavior or relationships or interactions between people” include social activities, teaching, and following rules or instructions as outlined in MPEP 2106.04(a)(2)(II)(C). The claims at hand recite managing personal behavior or interactions in the form of “determining from tracking data, for each of the sub-content items, an actual presentation duration and a focus measure of attention, wherein the tracking data is generated in response to user interactivity, computing a rating determined at least in part by the focus measure, the actual presentation duration, and the item minimal duration; and d) providing the rating for display.” The claims as a whole and every step recites data collection, processing, and display steps in order to manage personal behavior or interactions. This is made even more clear in the specification, “[0005] Users of the content, content providers and others are interested in tracking and assessing interaction activity with sub-content items, for example, to accurately, reliably and granularly (e.g. on a subject basis) measure and share user engagement and content comprehension.”
Even when considering the amendments to the claim, the claims still recite more of the same abstract idea because the claims analyze personal behavior and provide a rating for display, which still falls under “managing personal behavior, interactions, or relationships between people.” Even though the claims recite interactions between a user and a computer, as stated in MPEP 2106.04(a)(2), “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
When considering each limitation in depth, they still fall within at least one sub-category of certain methods of organizing human activity. For example, limiting the determining an item minimal duration for presentation to be “based on data type of the sub-content item, and wherein, for each of the sub-content items, the analyzing determines a text complexity measure and a text length for determining the item minimal duration for the sub-content item;” still recites the steps at such a high-level of generality that they are no more than instructions on how to manage personal behavior. The claims merely claim the idea of determining the item minimal duration, and the types of data(all of which are personal behavior) to analyze, but recites it broadly such that it is merely a “black box.” In other words, it recites the input data to use, and what the outcome should be, but not the mechanisms that arrive at the outcome.
Furthermore, aggregating the data from more than one source, is still part of the abstract idea because it is just merely analyzing personal behavior from more than one data source. Additionally, specifying that the focus measure is “determined by combining sequential behavioral indicators selected from at least one of inputs, scroll rate, or viewport dwell time, with context data,” also is falls within the “certain methods of organizing human activity,” because it also is a “black box” which provides the intended data to inputted (inputs, scroll rate, viewport dwell time, context data), and the intended output (focus measure), but does not recite enough particularity to how to arrive at the intended focus measure (combining sequential behavioral indicators is merely claiming the idea of the outcome).
Finally, amendment “for each sub-content item and aggregating results across the plurality of sub-content items and across the one or more computer devices, wherein the rating represents an imputed knowledge level of the content” still does not recite enough particularity to be more than “managing personal behavior,” because it is merely claiming what the rating represents but did not claim the focus score with enough particularity (merely a “black box”). Computing ratings for multiple sub-content items and multiple sources(such as a computer), does not affect the abstract idea category, as performing the abstract idea multiple times still falls within performing an abstract idea.
Therefore, the claims recite an abstract idea of “tracking and assessing interaction activity” and displaying a rating for display which falls within “certain methods of organizing human activity.”
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Claims 1, 31, and 32 recite the following additional elements:
-one or more computing devices in claims 1, 31, 32
-processor in claims 31, 32
-non-transitory storage device/computer readable medium in claims 31, 32
- in aggregate across all computing devices, and aggregating results...across the one or more computing devices, in claims 1, 31, 32
- computing device inputs in claims 1, 31, 32
- device context data
The additional elements listed above, when considered individually and in combination with the claim as a whole, no more than a recitation of the words “apply it” (or an equivalent) or mere instructions to implement an abstract idea or other exception on generic computing components as outlined in MPEP 2106.05(f). In this case, the abstract idea of “tracking and assessing interaction activity” is performed on generic computing devices such as computing device, processor, non-transitory storage device/computer readable medium. Even when considering the amendments that now recite one or more computing devices, or that the data is aggregated across various computing devices, it is still equivalent to “apply it,” because even instructing the use of multiple computers or devices still falls under “apply it,” especially when the computers or other devices are merely used as a tool to perform the abstract idea. Furthermore, aggregating information from multiple computers, as it is claimed, is not an improvement to computer functionality nor is it a particular solution to a technological problem. Furthermore, the fact that the inputs must be “computing device” inputs, or that the context data must be “device” context data, also is no more than “apply it,” because it is merely reciting the abstract idea in a manner such that the interaction is performed in a computer MPEP 2106.04(a)(2)(II) states that even interactions between a person and a computer can fall within “certain methods of organizing human activity,” when the certain activity itself falls within the sub-groupings. MPEP 2106.04(a)(2)(II), “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
It is clear in Figs. 5 and 6 and at least paragraphs [0139-142] that the computing device and its components are generic and don’t require a specific, improved computer infrastructure. Furthermore, no improvements to any technology or technological field have been purported, which is one of the factors in assessing integration into a practical application outlined in MPEP 2106.05(a). Therefore, the claims recite an abstract idea without integration into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claims 1, 31, and 32 recite the following additional elements:
-one or more computing devices in claims 1, 31, 32
-processor in claims 31, 32
-non-transitory storage device/computer readable medium in claims 31, 32
- in aggregate across all computing devices, and aggregating results...across the one or more computing devices, in claims 1, 31, 32
- computing device inputs in claims 1, 31, 32
- device context data
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using computing devices, processor, non-transitory storage device/computer readable medium to perform the steps associated with “tracking and assessing interaction activity”, amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, especially when the claim fails to provide enough specificity and particularity to be considered as an improvement to computer functionality, or an improvement to technology. While it may allegedly claim an improvement to analyzing interactions, improving an abstract idea, is still an abstract idea. MPEP 2106.05(a) states, “Notably, the court did not distinguish between the types of technology when determining the invention improved technology. However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” Accordingly, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. Thus claims 1, 31 and 32 are not patent eligible because the claims are directed to an abstract without significantly more.
Dependent Claims 2-5, 7, 8, 10, 13-17, 20-22, 27, 29 have also been given the full two part analysis, considered both individually or in combination, in the following analysis:
Claims 2-5, 7, 22 and 27 further define the abstract idea by including additional steps, however, the additional steps includes are more of the same abstract idea because they describe managing human behavior and interactions between individuals, or are mere data sourcing, processing, and display steps towards the same abstract idea. For example, in claim 2, the item minimal duration is based on an amount and nature of information in the sub-content item, the nature including text complexity and text length. However, this is still more of the same abstract idea because it is merely instructions to manage personal behavior, and it merely recites the intended inputs to determine a score, but does not recite enough specificity or particularity on how to achieve such score. Therefore, it is more of the same abstract idea as it merely claims the idea of the solution, without claiming the mechanisms on achieving the solution. The only elements that could arguably be considered not part of the abstract idea are “social media” in claim 5 and “interface” in claims 5, 22 and 27. Even when considered these to be additional elements, they are merely general links to a particular technological environment or field of use, in this case generally linking the abstract idea of tracking and assessing interaction activity to “social media” and “user interface technology”(Please see MPEP 2106.05(h)). The implementation of social media and user interface technology does not meaningfully limit the implementation of the abstract idea, and no improvements have been made to the technology. (Please see MPEP 2106.05(a)). Therefore the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claim 8 further limits the abstract idea by defining the data processing steps used towards performing the abstract idea, with each data type having particular data processing steps associated with it. This is more of the same abstract idea and there are no additional elements to consider, therefore the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claims 10 and 14 further limit the abstracts by defining the source or format for the data being used towards performing the abstract idea. This is more of the same abstract idea and there are no additional elements to consider, therefore the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claim 13 further defines the abstract idea by repeating steps from representative claim for at least two different computing devices. As stated in regards to the amendments of claim 1, performing the abstract idea more than once is still more of the same abstract idea. The computing device of the separate session is still an additional element that is merely “applying it” or merely instructing the abstract idea to be performed on a generic computer as outlined in MPEP 2106.05(f). Therefore the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claim 15 further limits the abstract idea by performing verification of the content before performing the steps. This is more of the same abstract idea and there are no additional elements to consider, therefore the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claims 16 and 17 further define the abstract idea by adding the evaluation of “content contributions” which are recommending, reacting, commenting, and other interactions performed by the user on a post of another user. Therefore, the claims are more of the same abstract idea since the monitoring of these interactions are still “managing personal behavior or relationships or interactions between individuals.” There are no additional elements to consider claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claim 20 further limits the abstract idea by defining the “content” limitation to comprise a “web page” or “electronic document.” This is more of the same abstract with additional elements that are merely indicating that the document is in an electronic form/generic computing device, or a general link to the particular technological environment(web). Therefore the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claim 21 further limits the abstract idea by adding determining a “credit value” and the rating is further determined in associating a total credit value. This is more of the same abstract idea of “certain methods of organizing human activity,” because it is further adding more rules or instructions to manage personal behavior as it is merely defining how user behavior is scored. This is more of the same abstract idea and there are no additional elements to consider, therefore the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claim 29 further defines the abstract idea by adding more data display steps towards performing the abstract idea. This is more of the same abstract idea and there are no additional elements to consider, therefore the claims are still directed to an abstract idea without integration into a practical application or significantly more.
Claim Rejections – 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8, 10, 13, 14, 20, 22, 31, and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Brinton et al. (US 20210049923 A1) hereinafter Brinton in view of Qiu et al. (US 20200153776 A1) hereinafter Qiu, further in view of Stucker et al. (US 20180246866 A1) hereinafter Stucker.
Regarding Claims 1, 31, 32:
Brinton discloses teaches:
Claim 1: A method comprising (Brinton [0005] In particular, the present invention is directed to methods to, at least educationally, customize a collection of content comprising a course and to individualize or adapt its sequence of delivery for a particular student, even as the course is being delivered, in a way that does not necessitate upfront burdensome input from an instructor/author or any other human.)
Claim 31: A computing device comprising at least one processor and a non- transitory storage device storing computer readable instructions, which when executed for execution by the at least one processor to cause the computing device to; (Brinton [0043] The present invention is necessitated by use of a computer, which is needed because the course is delivered via the internet and all user interactions are via the internet. [0021] Finally, the methods include determining an at-that-time optimal next sequence from the set of potential sequences by the processor of the present invention, in a modeling sense, traversing each of the potential paths, and choosing the one with the highest predicted value or utility relative to that student. [0044] From a hardware system perspective, the present invention includes a server architecture that contains one or more databases for storage of user and content information, which may or may not be updated over time, as well as storage of behavioral data)
Claim 32: One or more non-transitory computer readable media storing instructions executable by at least one processor cause a computing device to perform a method comprising(Brinton [0044] From a hardware system perspective, the present invention includes a server architecture that contains one or more databases for storage of user and content information, which may or may not be updated over time, as well as storage of behavioral data. A content store contains the content items such as videos and/or references to external (stored external to the system of the present invention but accessible by the present invention) content that is available from third party content providers. The server architecture may include several processing stages (backend) that are responsible for the collection of measurements of user learning behavior, analytics such as content analytics, user learning behavior analytics and decision making. The software may be installed on multiple server instances that will allow for the scalability to millions of users and utilizes technologies typical for ‘Big Data’ processing, such as distributed processing, in-memory databases, high throughput message brokers and parallelization.)
-a) analyzing content for presentation by one or more computing devices to a user, (Brinton [0012] A general sequence of content Files in the course is established based on the syllabus (or equivalent) in the first place, or some other determinable logical flow. In a preferred embodiment, the course syllabus is provided by way of the course author and the syllabus is used by the present invention to define the sequence of Modules, the sequence of Files within each Module, and the sequence of Segments within each File. In cases where this structure is not defined, course content may be provided which is then analyzed using NLP and sequenced based upon the content topic breakdown. In this way, a course may provide as much or as little structure as is needed, with reliance on the topic analysis to determine an appropriate sequencing where structure is not defined. [0010] In the present invention, content is delivered to the student electronically, such as over an internet connection. The student uses an internet-friendly device, such as a personal computer, tablet, or smart phone, which may have an app and/or a graphical user interface (GUI) on which the student views and interacts with the content.) Since the claims allow for one computer device, the limitation is satisfied.
-the content comprising a plurality of sub-content items, (Brinton [0009] For the purpose of this application, one can think of a course divided into Modules, each Module serving to include all learning material for at least one portion of the course such as a syllabus topic or sub-topic. A Module is formed of one or more Files, each File being of one or more types of content medium, such as video, images, or text, and including at least some portion of a representation of requisite content for a Module.) In view of the present specification at least in [0019], sub-content refers to text, image, video, or audio.
-b) determining from tracking data, for each of the sub-content items, an actual presentation duration and a focus measure of attention, (Brinton [0073] a. Play, pause, stop, fast forward, rewind, playback rate change, exit, and any other video player events, as well as corresponding timestamps, durations, and any other information that specifies user interaction with the video player. [0074] b. Page, font size, exit, and other text viewer events, as well as corresponding timestamps and durations that specifies user interaction with the text viewer. [0075] c. Slide change, completion, button press, and other events triggered from viewing a set of slides, as well as corresponding timestamps and durations that specify user interaction with the presentation viewer. [0082] Described below are a series of tracked behaviors, ranging from clicks, durations between clicks, clicks in a series, duration at particular videos, clicks of varying types during video play, and so on. [0110] d. Recommendations to revisit specific portions of a learning mode where the level of focus, as dictated by the quantities in (a)-(c), is exceedingly high or low.) Brinton teaches “duration at particular videos” which falls in the scope of “actual presentation duration.” Brinton’s level of focus is mapped to “a focus measure of attention.”
-wherein the tracking data is sequentially generated in response to user interactivity with the one or more computing devices during presentation of the content and the tracking data comprises sub-content items positional data and interactions with the sub-content items from inputs received; (Brinton [0159] The components of the User Modeling 102 stage are depicted in FIG. 4. As a user interacts with content in the Player 105, several types of Measurements 400 may be collected and parsed so as to determine the student’s overall strengths and weaknesses and specific positives and negatives relative to the topic material. In a preferred embodiment, the set of Measurements 400 collected by the Player 105 in FIG. 1 about each user includes, but is not limited to, the following: [0160] a. Play, pause, stop, fast forward, rewind, playback rate change, exit, and any other video player events, as well as corresponding timestamps, durations, and any other information that specifies user interaction with a video player. [0161] b. Page, font size, exit, and other text viewer events, as well as corresponding timestamps and durations that specifies user interaction with a text viewer. [0162] c. Slide change, completion, button press, and other events triggered from viewing a set of slides, as well as corresponding timestamps and durations that specify user interaction with the presentation viewer. [0163] d. Position and length of highlights placed on video or text at specific locations, or on a particular slide, where the video length is measured in time of video and the text length in number of objects from the starting position. [0164] e. Position and content of bookmarks placed on video or text at specific locations, or on a particular slide. [0165] f. Position and content of notes taken on video or text at specific locations, or on a slide, as well as whether these notes were either shared publically, shared with a specific set of users, or not shared. [0166] g. Information on each post made in discussion forums, including its content, whether it was meant as a question, answer, or comment, and the number of up-votes it received from other users or the instructor. Discussion forum posts are analyzed using NLP techniques which are able to detect sentiment as well as if a post is a question or statement. [0167] h. Submission, time spent, selected confidence level, and number of attempts made for each assessment submitted, as well as the points rewarded if the assessment was machine gradable.) Brinton lists out the types of data used to model the users interaction with content, including positional data and interaction with contents from inputs received. The broadest reasonable interpretation (BRI) of “sequentially” is that the tracking data is generated one after another, therefore the sentence “as a user interacts with content” satisfies the limitation because the tracking data is generated more than once.
-wherein the focus measure is determined by combining sequential behavioral indicators selected from at least one of computing device inputs, scroll rate, or viewport dwell time, with device context data;(Brinton [0085] The events that form a motif can consist of any combination of behavioral action collected from a learner as he/she interacts with the course application, such as, but not limited to, play, pause, skip backwards, skip forward, rate change faster, rate change slower on a video or interactive slide presentation, scrolling up or down in an article or resizing the view, creating or sharing a note, and mouse movements. [0022] These (among other) various interactions are captured by the system of the present invention and processed into “behaviors” so as to determine the student's overall strengths and weaknesses and specific positives and negatives relative to the topic material. In another example, the approach used by the student may be considered, such as recognizing when the student may be reflecting on video content (e.g., pauses in playing video), reviewing content, skimming content, or speeding through content (such as at a faster than default rate). [0107] a. Depictions of video-watching quantities, such as percent completion (i.e., percent played), time spent, and frequency of different events for each user, both in aggregate across the video and for individual intervals. [0108] b. Depictions of text-viewing quantities, such as percent completion, time spent, and frequency of different events for each user, both in aggregate across the text document and for individual segments of the text. [0109] c. Visualizations of similar quantities of behavior collected on other forms of media, such as audio and presentations. [0110] d. Recommendations to revisit specific portions of a learning mode where the level of focus, as dictated by the quantities in (a)-(c), is exceedingly high or low. [0111] e. Depictions of learning style preferences, including the percentage of focus placed on each of the different modes (video, text, audio, and/or social learning), clusters of users based on these preferences. [0118] l. Depiction of the identified motifs (e.g., reflecting, revising, speeding, skimming), which users/content modes have exhibited these motifs, and how often they occur.) [0070] In an embodiment, each end user device has an interaction recorder (IR) loaded into memory, to monitor user interaction with the various learning modalities. In at least one embodiment, this IR is embedded in a GUI. For example, in a video, the time interval between two successive click actions (e.g., play, pause, jump, end of video, switching away from the video view, or closing the course application) is measured by the IR, as well as the UNIX Epoch time, starting position, and interval duration for each case.) The BRI of the limitation is that the focus measure is determined by using any inputs, scroll rate or viewport dwell time with any contextual information from the device. “Device context” data is broad enough to encapsulate any contextual data from the device which is satisfied in Brinton [0070]. Since the list only requires at least one of the computing device inputs, and scroll rate, even without teaching ‘viewport dwell time,’ Brinton still satisfies the limitation.
-c) computing a rating determined at least in part by the focus measure, the actual presentation duration for each sub-content item; (Brinton [0180] With Content Tagging 101 and User Modeling 102 established, the final stage shown in FIG. 1 is Path Switching 103. At a high level, this selects a sequence of segments that have a high likelihood of making the learning process more efficient based on the User Model. These segments are selected in part based on the topical breakdown of segments available from the Content Tagging 101 stage. [0182] As another example, suppose a student has moved through a content File rapidly. The User Modeling 102 may characterize this behavior as a motif of low engagement, again triggering the Path Switching 103. To obtain the topic distributions in this case, the invention would determine the Segments that elicited low engagement, and would query the Content Tagging database 104 for the topic distributions of these specific Segments. [0184] To do this, the system of the present invention ascertains the utility of showing the user a given sequence of Segments. Utility scores are created and updated over time for different possible sequences of Segments (“candidate sequences”). In a preferred embodiment, this utility score is based on at least three component scores (but could include more) that, in part, determine the effectiveness of this content at improving the specific user’s outcomes at this specific point in time: a similarity score, a distance score, and a historical score, which may be adjusted with new observations via reinforcement learning techniques and machine learning.) Brinton’s motif of low engagement is rating determined at least by the focus measure and actual presentation duration. Utility scores are another example of ratings determined at least in part by the focus measure, and the actual presentation duration.
-aggregating results across the plurality of sub-content items and(Brinton [0176] In a preferred embodiment, this would be a detailed mathematical composition based on the Segments at which points the motifs were detected. Since the topic distributions of the Segments have already been determined in the Content Tagging 101 stage, this can be accomplished by combining the distributions of those Segments together into a single distribution, for example, by finding the average of them.)
-wherein the rating represents an imputed knowledge level of the content (Brinton [0117] k. Depictions of user learning paths, the level of mastery and/or learning style preference required for each path, the specific users traversing each path, and aggregate information about behavior and performance of users on respective paths. [0203] This provides the system with a final score measuring how effective each Segment is at helping remediate the behavior at hand, and with it, the system now has the information necessary to select candidate sequences to show. [0143] With the ability to break down a course to the Segment-level and reorder how that Segment-level content is presented, only Segments that a specific learner needs to learn from are shown to a particular student. This process thus saves students time and allows them to focus on topics that need their attention, such as those which need to be reinforced individually. [0185] For example, getting a single question wrong might queue up a three paragraph PDF for one user but a five paragraph PDF for another, whereas skimming over content in a Module might queue up a one paragraph of PDF and two minutes of video for one user, but three minutes of video for another, depending on, for example, a priori knowledge of the student's skills, learning style, etc.) Since the final score measures how effective the content is, the broadest reasonable interpretation of “the rating represents an imputed knowledge level of the content” is satisfied because the rating is determined based on the learner’s needs and the level that they are at, and what the content is teaching.
Brinton fails to teach:
- the analyzing determining, for each sub- content item, an item minimal duration for presentation based on data type of the sub-content item;
-and wherein, for each of the sub-content items, the analyzing determines a text complexity measure and a text length for determining the item minimal duration for the sub-content item;
-In step b, the determining from tracking data is in aggregate across all computing devices that presented the content to the user,
The step c), includes computing a rating determined at least in part by the item minimal duration for each sub-content item;(Brinton computes the rating using the focus measure and actual presentation duration but not item minimal duration)
- In step c), and aggregating results across the one or more computing devices,
-that the computing of a rating is for each sub-content item
-and d) providing the rating for display.
Alternatively, Qiu discloses a messaging system that trains a prediction model to estimate the time duration of reading and responding to incoming content. Qiu teaches:
- the analyzing determining, for each sub- content item, an item minimal duration for presentation based on data type of the sub-content item; (Qiu [0030] In example embodiments, the training engine 206 uses one or more of the monitored actual action times (e.g., signals) determined by the analysis engine 204 for each message. This gives the training engine 206 a ground truth label that is compared with the predicted action time. In one embodiment, a loss function is determined using an L2 norm of a total distance between the predicted action time and the ground truth label (e.g., actual read plus reply time if any) for all message for all users in a particular category. The training engine 206 attempts to reduce this loss function. [0050] predicts a total action time needed for each message for the users in each category. [0027] The analysis engine 204 processes the raw data and signals for model training. In example embodiments, the analysis engine 204 computes features (e.g., computing bucketed feature values such as message body length, whether user is mentioned in message, whether sender is in report line, whether sender is frequent correspondent, whether message is marked as high importance, user triage pattern (e.g., historical action distribution), historical respond speed, predicted focused ratio of the mail) and labels (reading and reply time for each message for each user) that may be needed as inputs for model training.) The broadest reasonable interpretation (BRI) of “item minimal duration” in light of at least paragraph [0123] of the present specification reads, “item minimal duration comprises an amount of time required by an ordinary person to consume (e.g. visually and/or aurally) the respective sub-content item using a focused attention, based on an amount and nature of information in the sub-content item.” The predicted action time in Qiu is mapped to the item minimal duration for presentation since it predicts an amount of time required to consume the text messages based on at least a “message body length,” and “historical reading and reply times.” Furthermore, “data type of the sub-content item” is a broad term that encapsulates any categorization of the sub-content item, therefore, “whether message is marked as high importance,” and “labels” satisfy this limitation.
-and wherein, for each of the sub-content items, the analyzing determines a text length for determining the item minimal duration for the sub-content item;(Qiu [0050] For each category, the training engine 206 trains the prediction model using input features (e.g., user identifier, message identifier, message body length, people relationship score, “at mentioned” or not, whether messages are mark as high importance or not, whether message was sent to a distribution list or not, in focused inbox or not) and predicts a total action time needed for each message for the users in each category.)
-In step b, the determining from tracking data is in aggregate across all computing devices that presented the content to the user(Qiu [0022] Moreover, any two or more of the systems or machines illustrated in FIG. 1 may be combined into a single system or machine, and the functions described herein for any single system or machine may be subdivided among multiple systems or machines. Additionally, any number and types of client devices 110 may be embodied within the environment 100. [0023] FIG. 2 is a block diagram illustrating an example embodiment of components within the messaging system 102. In example embodiments, the messaging system 102 performs operations to generated and train a prediction model and to use the prediction model along with monitored user activities to predict time required to address each message and to recommend messages based, for example, on an amount of time available to review messages. To enable these operations, the messaging system 102 comprises the training system 104 and the runtime system 106, each including (or associated with) supporting engines or components all of which are configured to communicate with each other (e.g., over a bus, shared memory, or a switch) in accordance with an example embodiment. [0079] In some example embodiments, the machine 1000 may be a portable computing device and have one or more additional input components (e.g., sensors or gauges)... Inputs harvested by any one or more of these input components may be accessible and available for use by any of the modules described herein.) It would have been obvious to one of ordinary skill in the art to modify Brinton to add Qiu’s teachings of using data from aggregated computing devices as opposed to just one.
- c) computing a rating determined at least in part by the item minimal duration; (Qiu [0027] The analysis engine 204 processes the raw data and signals for model training. In example embodiments, the analysis engine 204 computes features (e.g., computing bucketed feature values such as message body length, whether user is mentioned in message, whether sender is in report line, whether sender is frequent correspondent, whether message is marked as high importance, user triage pattern (e.g., historical action distribution), historical respond speed, predicted focused ratio of the mail) and labels (reading and reply time for each message for each user) that may be needed as inputs for model training. Further still, the analysis engine 204 removes outlier data to de-noise the input stream of raw data and removes duplicate data or partial data if there is any. In some embodiments, the analysis engine 204 determines people relationship scores, which is input into the prediction model to predict user action time. [0030] In example embodiments, the training engine 206 uses one or more of the monitored actual action times (e.g., signals) determined by the analysis engine 204 for each message. This gives the training engine 206 a ground truth label that is compared with the predicted action time. In one embodiment, a loss function is determined using an L2 norm of a total distance between the predicted action time and the ground truth label (e.g., actual read plus reply time if any) for all message for all users in a particular category. The training engine 206 attempts to reduce this loss function. [0050] predicts a total action time needed for each message for the users in each category.) Qiu’s predicted total action time is also mapped to the “rating.” Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to modify Brinton’s calculation of the utility score (“based on at least three component scores (but could include more)”), by adding Qiu’s predicted action time as a component score. By simply substituting the predicted action time as one of the utility score components one would predictably arrive at computing a rating based on the focus measure, actual presentation duration and the item minimal duration. One of ordinary skill would have been motivated to add Qiu’s predicted action time, as it provides the benefit of allowing users to manage the content they access based on what they currently have the capacity for. (Qiu [0060] The user may also specify an amount of time that they are available for reading and replying to messages. The activation of the time management recommendation option causes the messaging system 102 to recommend messages based on available time and importance of messages. In some cases, the user may also activate a context-based recommendation trigger that enables recommendations based on both time availability, importance, and context of messages.)
- In step c), and aggregating results across the one or more computing devices, (Qiu [0022] Moreover, any two or more of the systems or machines illustrated in FIG. 1 may be combined into a single system or machine, and the functions described herein for any single system or machine may be subdivided among multiple systems or machines. Additionally, any number and types of client devices 110 may be embodied within the environment 100. [0023] FIG. 2 is a block diagram illustrating an example embodiment of components within the messaging system 102. In example embodiments, the messaging system 102 performs operations to generated and train a prediction model and to use the prediction model along with monitored user activities to predict time required to address each message and to recommend messages based, for example, on an amount of time available to review messages. To enable these operations, the messaging system 102 comprises the training system 104 and the runtime system 106, each including (or associated with) supporting engines or components all of which are configured to communicate with each other (e.g., over a bus, shared memory, or a switch) in accordance with an example embodiment. [0079] In some example embodiments, the machine 1000 may be a portable computing device and have one or more additional input components (e.g., sensors or gauges)... Inputs harvested by any one or more of these input components may be accessible and available for use by any of the modules described herein.) It would have been obvious to one of ordinary skill in the art to modify Brinton to add Qiu’s teachings of using data from aggregated computing devices as opposed to just one.
-and d) providing the rating for display. (Qiu [0036] In some embodiments, the message engine 208 also causes display of the predicted time to the user. [0068] FIG. 6 illustrates an example screenshot of a message user interface showing a labeled message 600. The labeled message 600 includes a predicted user action time “label” 604 that indicates an amount of time predicted for the user to read and, in some cases, respond to the message. In the present example, the predicted user action time is 5 minutes.)
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the present disclosure to modify Brinton by adding Qiu’s calculation of an item minimal duration based on data type and by using text length across aggregate computing devices, and displaying the rating to the user. One of ordinary skill would have found it obvious to add a prediction of a time of completion to Brinton’s rating system as it would result in the predictable outcome of measuring the person’s attentiveness level by comparing their actual presentation time to a predicted presentation time. One of ordinary skill in the art would have been motivated to combine as it would provide the benefit of optimizing the content provided to the user, which is a motivation in both Qiu [0015] and Brinton [0005].
However, neither Brinton nor Qiu teach or suggest:
-that wherein, for each of the sub-content items, the analyzing determines a text complexity measure and a text length for determining the item minimal duration for the sub-content item;(Qiu teaches determining the item minimal direction using text length, however, text complexity is not one of the measures.)
However, Stucker discloses reading time indicators by calculating the estimated reading time of an authored document based on reading metrics such as spelling/construction complexity. Stucker teaches:
-and wherein, for each of the sub-content items, the analyzing determines a text complexity measure and a text length for determining the item minimal duration for the sub-content item;(Stucker [0014] The reading time estimator 150 is configured to provide reading time estimates, based on the preferences and the metrics in relation to the text 120, for provision in the authoring application 110 and in relation to the document. [0021] The metrics store 140 provides, according to user preferences, various metrics by which an estimated reading time for the text 120 is to be generated... The various metrics specify sub-vocalization times for interpreting segments of the text 120 based on the characters and words present, as well as the surrounding effects of punctuation, capitalization, sentence structure, ruby characters/pronunciation guides, word length, spelling/construction complexity, and whitespace on the speed at which the text 120 can be interpreted by a reader. [0032] Proceeding to OPERATION 330, reading metrics for use in calculating reading time estimates are identified. In various aspects, the reading preferences specify a set of reading metrics by which to evaluate how long it will take to read the text 120, such as a reading speed. The reading speed may define a baseline speed at which syllables may be read, multipliers for longer words, (e.g., reading six one-syllable words may be faster than reading one six-syllable word), effects that capitalization, formatting, punctuation, spelling/construction complexity (e.g., “through” versus “threw”), whitespace, punctuation markers, etc., have on reading speed and the like.) “Document” falls within the scope of “sub-content item,” “reading time” falls within the scope of item minimal duration. Furthermore, Stucker teaches using text complexity and length to determine the item minimal duration therefore, the limitation has been satisfied.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify the combination of Brinton and Qiu by adding the teachings of Stucker, particularly using text complexity and text length as a measure to determine item minimal duration. By simply adding text complexity to the combination’s evaluations, the combination would arrive at the predictable outcome of the limitations as claimed. One of ordinary skill in the art would have been motivated to perform this combination by the Stucker’s benefit of improving content limit indicators to more accurately determine consumption time. (Stucker [0001] When authoring content, authors often are faced with content limits. For example, a word limit or a page limit may be placed on a document to keep the document brief. Page count indicators and word count indicators are therefore provided to authors in content authoring applications to gauge the amount of content that has been authored. However, authors can be faced with content limits beyond word and page counts. [0013] Estimates of reading times for the content item as a whole, and of sections thereof, are created as the author manipulates content within a document. These estimates are displayed as indicators within the content authoring application and the document, informing the author of how long it is expected for a reader to read and process the content. The functionality of the computing device running the content authoring application is thus improved by providing content limit indicators related to consumption time.
Regarding Claim 2:
The combination of Brinton, Qiu, and Stucker teach or suggest the method of claim 1
Furthermore, Brinton teaches:
- behaviors by an ordinary person to consume the respective sub-content item using a focused attention,(Brinton [0084] Consequently, the present invention tracks behaviors, compares behaviors to those of a known population, and identifies adjustments (recommendations) for a user based on a combination of behaviors of populations with high and low average CFA scores so as to determine how to adjust material delivered to a student. Of course, at least some of those changes are automatically implemented and the instructor is given indication of those changes as well as recommendations for other changes. [0064] (ii) Discounting intervals: Clickstream logs are the most detailed accounts of a student’s video-watching behavior that are available for online courses today. Even so, it is not possible to determine with complete certainty if a student actually watched or focused on the video for the duration of time in-between the occurrence of two events. Still, we can identify two situations. The first is if the duration between events is extremely long; the user was obviously engaging in some off-task behavior during this time.) Brinton compares the user’s actual interaction data with ordinary person’s to determine the focused attention state, but does not teach an item minimal duration comprises an amount of time required to consume the respective sub-content item.
-the nature including text complexity and text length(Brinton [0015] The purposes of User Modeling, at a high level, is (1) estimating a student’s knowledge state and/or content preferences with respect to each topic as the student proceeds through the course, while (2) determining whether Path Switching is needed. That is, there exists (or the system creates) a user model for each student. The model is prepared and updated through analysis of all or a subset of the behaviors the student exhibits, derived both from their measurements generated on the course material and their performance on the assessments pertaining to each topic, both within the currently shown content Files as well as Files shown previously. The model is intended at least in part to be a formulated understanding of the student’s skills, comprehension, and/or understanding of the course material, as well as the student’s presentation preferences. As the student progresses through the course, the student’s mastery of the subject matter and preferences are tracked by the user model, which evolves with more data, indicating topics needing further instruction or remediation, and presentation preferences. As a simple example, answering a test question correctly could signify an increase in content knowledge on the corresponding topic. As another example, exhibiting high engagement in a certain File of a Module may be interpreted as an increase in preference for this content type (e.g., video vs. text) in explaining the topics that are covered by this File.) This excerpt in Brinton shows the estimation of a student’s knowledge state based on their engagement with the content.
However, Brinton fails to teach:
-wherein item minimal duration comprises an amount of time required to consume the respective sub-content item,
-based on an amount and nature of information in the sub-content item;
- the rating (Brinton shows metrics that indicate likely knowledge but does not teach the rating of claim 1)
Alternatively, Qiu teaches: -wherein item minimal duration comprises an amount of time required to consume the respective sub-content item, (Qiu [0027] The analysis engine 204 processes the raw data and signals for model training. In example embodiments, the analysis engine 204 computes features (e.g., computing bucketed feature values such as message body length, whether user is mentioned in message, whether sender is in report line, whether sender is frequent correspondent, whether message is marked as high importance, user triage pattern (e.g., historical action distribution), historical respond speed, predicted focused ratio of the mail) and labels (reading and reply time for each message for each user) that may be needed as inputs for model training. Further still, the analysis engine 204 removes outlier data to de-noise the input stream of raw data and removes duplicate data or partial data if there is any. In some embodiments, the analysis engine 204 determines people relationship scores, which is input into the prediction model to predict user action time. [0030] In example embodiments, the training engine 206 uses one or more of the monitored actual action times (e.g., signals) determined by the analysis engine 204 for each message. This gives the training engine 206 a ground truth label that is compared with the predicted action time. In one embodiment, a loss function is determined using an L2 norm of a total distance between the predicted action time and the ground truth label (e.g., actual read plus reply time if any) for all message for all users in a particular category. The training engine 206 attempts to reduce this loss function. [0050] predicts a total action time needed for each message for the users in each category. [0068] Qiu The labeled message 600 includes a predicted user action time “label” 604 that indicates an amount of time predicted for the user to read)
-based on an amount and nature of information in the sub-content item; (Qiu [0056] In operation 404, the extraction engine 210 extracts message features/properties from the new message. In some embodiments, the extraction engine 210 extracts message information such as message content (e.g., header, body) and sender of message, along with other message properties (e.g., has attachment or not, marked as high importance or not, user being mentioned in the message or not). The extraction engine 210 then extracts message features based on this information. [0057] In operation 406, the modeling engine 212 applies the prediction model corresponding to the user (e.g,, message recipient) to predict user action time. The modeling engine 212 takes the message features from the extraction engine 210 as inputs and applies the prediction model. The result is the predicted user action time for the message. The modeling engine 212 sends the result back to the message engine 208. [0025] The data engine 202 manages data used to train the prediction model. In example embodiments, the data engine 202 extracts the data by querying user activity (e.g., querying Exchange Substrate services) and message storage services. For example, the data engine 202 uses MARS (Exchange Map Reduce System) to extract raw data for each message. The raw data comprises, for example, user identifier, message identifier, message body length, “at mentioned” or not (e.g., was the user mentioned in the message), whether message is marked as high importance or not, whether the message was sent to a distribution list or not, and so forth. The raw data is provided to the analysis engine 204 for processing.) The message length is mapped to “amount” and the message marked as “high importance or not” is an example of “nature of information.”
-the rating (Qiu [0036] In some embodiments, the message engine 208 also causes display of the predicted time to the user. [0068] FIG. 6 illustrates an example screenshot of a message user interface showing a labeled message 600. The labeled message 600 includes a predicted user action time “label” 604 that indicates an amount of time predicted for the user to read and, in some cases, respond to the message. In the present example, the predicted user action time is 5 minutes.)
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the present disclosure to modify Brinton by adding Qiu’s calculation of an item minimal duration, based on the length and nature of the message. One of ordinary skill would have found it obvious to add Qiu’s prediction of time based on content to Brinton’s analysis of behaviors by an ordinary person to consume the respective sub-content item using a focused attention as it would provide the predictable result of determining the item minimal duration of an ordinary person using a focused attention based on the length and content of the material. One of ordinary skill in the art would have been motivated to combine as it would provide the benefit of optimizing the content provided to the user, which is a motivation in both Qiu [0015] and Brinton [0005].
Regarding Claim 3:
The combination of Brinton, Qiu, and Stucker teaches the method of claim 1:
Furthermore, Brinton teaches:
-comprising: determining a subject for the content; (Brinton [0143] The first step in Content Tagging 101 is ingestion. During content ingestion, the system potentially breaks files into smaller “building blocks,” referred to previously as content Segments. Segments form the basis for building the adaptive versions of content by piecing them together in various sequences. In a preferred embodiment, content tags are assigned to each Segment, with each tag being a textual or quantitative summary of the topics covered in the Segment and the degree to which each is covered.)
-and providing the subject for display with the rating. (Brinton [0143] In a preferred embodiment, content tags are assigned to each Segment, with each tag being a textual or quantitative summary of the topics covered in the Segment and the degree to which each is covered. With the ability to break down a course to the Segment-level and reorder how that Segment-level content is presented, only Segments that a specific learner needs to learn from are shown to a particular student. This process thus saves students time and allows them to focus on topics that need their attention, such as those which need to be reinforced individually.) Brinton’s “quantitative summary of the topics covered” is mapped to ratings associated with subjects(topics). Since these tags are associated with teach segment of topics, the limitation has been taught.
However, Brinton fails to teach:
-associating the rating with the subject;
Alternatively, Qiu teaches:
-associating the rating with the subject; (Qiu [0036] In some embodiments, the message engine 208 also causes display of the predicted time to the user. [0068] FIG. 6 illustrates an example screenshot of a message user interface showing a labeled message 600. The labeled message 600 includes a predicted user action time “label” 604 that indicates an amount of time predicted for the user to read and, in some cases, respond to the message. In the present example, the predicted user action time is 5 minutes. See also Fig. 6 where the rating “ 604 time needed: 5 min” is visibly associated next to RE: Subject 3. )
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the present disclosure to modify Brinton by adding Qiu’s display of the rating(based on minimal item duration) next to the subject. One of ordinary skill would have found it obvious to add Qiu’s prediction of time based on content to Brinton’s analysis of behaviors by an ordinary person to consume the respective sub-content item using a focused attention as it would provide the predictable result of determining the item minimal duration of an ordinary person using a focused attention based on the length and content of the material. One of ordinary skill in the art would have been motivated to combine as it would provide the benefit of optimizing the content provided to the user, which is a motivation in both Qiu [0015] and Brinton [0005].
Regarding Claim 4:
The combination of Brinton, Qiu, and Stucker teaches the method of claim 3
Furthermore, Brinton teaches:
-comprising accumulating the rating with past ratings associated with the subject (Brinton [0189] Fifth, in an embodiment, only Segments that have similar topic composition to the Desired Topic Remediation 500 are considered. Others may be deemed as irrelevant and therefore a distraction or harmful to the remediation needed here. Topic similarity is calculated in the same manner as the similarity score that will be described next. [0200] FIG. 7 shows how the utility values will be updated in an online manner 700 as the historical score is updated. In order to update the historical measure over time, one decision to be made is what exactly constitutes an “effective” as opposed to an “ineffective” observation; there are a number of possibilities for this. In one embodiment, the difference would be the amount of improvement shown in the user model after visiting the candidate sequence, or a binary measure of whether it is expected to improve at all or not. In another embodiment, the difference is tied to subsequent behavior or performance in the course, for example, whether the learner answers the next quiz questions associated with these topics correctly, or whether the learner exhibits the same motifs that showed signs of necessary remediation in the first place.) In Brinton, a topic similarity score is generated along with historical scores to determine how effective a particular piece of content is for a particular topic. This covers the limitation because it is an accumulation of a rating with past ratings associated with the topic to determine the effective of the subject.
- to determine a ranking for the subject (Brinton [0133] the backend will take the distribution for this content unit and compute cosine similarity between this particular distribution and the distributions extracted from the previous content files in the course. With a matrix like this in hand, for each file, the other files are ranked from most similar (highest similarity value) to least similar (lowest similarity value), not including (i) the file itself (it is not practical to route the user back to the same material they have struggled on), and (ii) future files (files appearing later in the syllabus have not been covered yet, and may contain more complicated material the instructor has not yet taught). According to this logic, in FIG. 5, File 1’s closest neighbor is File 2, File 2’s is File 1, and so on (entries bolded). The most similar one here, for example, could be the video from the unit where the test question occurs. Then the web application will display that video to the user. By doing so, the web application forms an individualized and customized user learning experience across different learning modes.)
-and providing the ranking for display. (Brinton [0216] The system must also specify the number of results that should be returned from each search engine. Typically, an API will provide the results in a ranked order of relevance and importance to the query.)
Regarding Claim 5:
The combination of Brinton, Qiu, and Stucker teach the method of claim 4,
Furthermore, Brinton teaches:
- i) at least one of the rating, past ratings are stored in association with the user. (Brinton [0203] Either way remediation sequences are selected 709, efficacy is observed 708, and the User Model and Utility are updated 707 and any new Sequence Utilities are stored in Sequence Utilities database 703. With each of the three scores—similarity, distance, and historical—calculated, the system can combine them together at any point in time for a total utility score. This provides the system with a final score measuring how effective each Segment is at helping remediate the behavior at hand, and with it, the system now has the information necessary to select candidate sequences to show.)
-and wherein the method comprises providing an interface to verify the at least one of the rating, past ratings and ranking of the user; (Bradley [0105] At times, the system may suggest recommendations to the instructor. For example, a particular student’s data might be inconsistent with known patterns or might not yield sufficient confidence to implement a change. In such circumstances, the system might present data regarding a student or regarding an entire class, or something in between, indicating confidence intervals around various options. ) The instructor interface presenting data regarding a student’s progress teaches the limitation.
Regarding Claim 8:
The combination of Brinton, Qiu, and Stucker teaches or suggests the method of claim 1,
Furthermore, Brinton teaches:
-wherein step a) comprises processing the sub-content items by data type (Brinton [0009] For the purpose of this application, one can think of a course divided into Modules, each Module serving to include all learning material for at least one portion of the course such as a syllabus topic or sub-topic. A Module is formed of one or more Files, each File being of one or more types of content medium, such as video, images, or text, and including at least some portion of a representation of requisite content for a Module.)
-and wherein: for a video or audio data type, processing comprises determining a play back length and applying a video or audio factor to the length; (Brinton [0099] 6. Average playback rate (avgPBR): The time-average of user selected playback rates. [0100] 7. Standard deviation of playback rate (stdPBR): The standard deviation of the playback rates selected over time. [0103] These quantities can also form a special motif, where each “action” is a summary of actions on a specific learning mode; e.g., completing 50% of a video, followed by fast forwarding on the video twice, followed by skipping over 20% of an article.) The avgPBR and stdPBR is an example of video factors to the length.
-for an image data type, processing comprises using image processing to determine whether the image is an infographic, comprising text and processing the text as a text data type; and (Brinton [0148] The present invention also includes a method for ensuring that image-based content, whether in a slide, PDF, or other format, is preserved in the Segment or Segments where it is referenced and/or appears. For example, if a PDF Segment mentions a figure in its text, then the accompanying image is stored together with the Segment for concurrent display, and if a slide references an image from a previous slide, it will be replicated in the current Segment as well. To accomplish this in the present invention, images in the slides and PDFs are extracted and tagged based on the Segment they are accompanied with and any other figure information that can be used for reference. Images may also be passed through OCR methods to extract text that may be stored in a pictorial format; this text is appended to the Segment so that it can be used in content analysis, but not to be served up as custom content, since the information is already contained in the image.)
-for a text data type, processing comprises determine text length, text complexity, (Brinton
[0145] Segment formats can differ substantially, both between content types and between courses. In one embodiment, each File is divided into discrete homogeneous Segment lengths, e.g., taking every paragraph in a PDF, A textbook with 100 chapters might have a Segment set as a single chapter, whereas a course with 10 PDF documents might also have a Segment defined as a single chapter. [0152] With the course broken down into Segments and ordered accordingly, the last part of Content Tagging 101 as shown in FIG. 2 is Segment Topic Modeling 205, which is the assignment of textual and numerical tags to each Segment. In a preferred embodiment, this is done through NLP techniques of Artificial Intelligence (AI) that extract key words from a collection of documents and then represent each document as the collection of key words it possesses. [0188] Fourth, only Segments through the current course Module are considered. Segments appearing beyond the Module in which Path Switching 103 was executed may contain more advanced versions of content that the user is not yet prepared for.)
-and optionally any of text sentiment (Brinton [[0166] g. Information on each post made in discussion forums, including its content, whether it was meant as a question, answer, or comment, and the number of up-votes it received from other users or the instructor. Discussion forum posts are analyzed using NLP techniques which are able to detect sentiment as well as if a post is a question or statement)
However, Brinton fails to teach:
-that “for a video or audio data type, processing comprises determining a play back length and applying a video or audio factor to the length” is done to determine the item minimal duration
-applying one or more text factors to determine the item minimal duration.
Alternatively, Qiu teaches:
-item minimal duration (Qiu [0035] The modeling engine 212 takes the message properties as inputs and applies the prediction model to determine a predicted user action time for the new message. The predicted user action time is a predicted amount of time that the messaging system 102 believes it will take the user to read the message and perform a corresponding action (e.g., reply to the message, close the message). In example embodiments, the modeling engine 212 detects the message features extracted for the new message and obtains a corresponding value for each feature. Each value is computed in real time. For example, as the message arrives, the training system 104 “listens” to message arrival event(s) and extracts the relevant information that is needed based on corresponding logic code to compute the value. The modeling engine 212 then multiplies the message feature’s value with a corresponding parameter of the prediction model. For example, an algorithm used to predict the action time may be: (parameterA×x feature1)+(parameterB×feature 2)+ . . . , where, for example, feature1 is a value corresponding to how long the message is, feature2 is a value corresponding to whether the user is mentioned in the message, feature3 is a value corresponding to whether the sender is in the report line, and so forth. A result is the predicted user action time for the new message. In example embodiments, the modeling engine 212 runs in sub-second (e.g., near real-time manner).
-applying one or more text factors to determine the item minimal duration. (Qiu [0040] In some embodiment, the recommendation engine 214 performs a time-based prediction on other aspects of the message and gives a context-based message recommendation. Thus, when the user enables a time and context-aware view of messages, the recommendation engine 214 performs a context-based recommendation process to pin contextual-aware messages to the top of the list during a particular timeframe. In various embodiments, the context-based recommendations can be based on one or more of location awareness, time awareness, artifact awareness, and people awareness.) A context aware view is an example of text factors to determine the item minimal duration(user action predicted time.)
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the present disclosure to modify Brinton by adding Qiu’s calculation of an item minimal duration, based on text factors and video/audio factors. One of ordinary skill would have found it obvious to add Qiu’s prediction of time using an average video playback, since an average duration to read a message(Qiu) can be substituted for an average duration to playback a video(Brinton), and still provide the predictable outcome of a predicted total user action time. One of ordinary skill in the art would have been motivated to combine as it would provide the benefit of optimizing the content provided to the user, which is a motivation in both Qiu [0015] and Brinton [0005].
Regarding Claim 10:
The combination of Brinton, Qiu, and Stucker teaches or suggests teaches the method of claim 1
Furthermore, Brinton teaches:
-comprising receiving the tracking data, the tracking data defined during a presentation of the content via a display device to the user; (Brinton [0082] There are other examples of user data collection and use as well. The method of the present invention may involve one or more of the following non-exclusive approaches for tracking student behavior and making recommendations based on the tracked behavior. Described below are a series of tracked behaviors, ranging from clicks, durations between clicks, clicks in a series, duration at particular videos, clicks of varying types during video play, and so on. In some cases, tracked behaviors may be analyzed as individual behaviors, as collections of behaviors, or a sequence of behaviors, any or all of which can be used to generate recommendations. [0070] In an embodiment, each end user device has an interaction recorder (IR) loaded into memory, to monitor user interaction with the various learning modalities. In at least one embodiment, this IR is embedded in a GUI. For example, in a video, the time interval between two successive click actions (e.g., play, pause, jump, end of video, switching away from the video view, or closing the course application) is measured by the IR, as well as the UNIX Epoch time, starting position, and interval duration for each case.)
-and wherein: the tracking data comprises loqqed data for:
-i) the viewport; and (Brinton [0070] For example, in a video, the time interval between two successive click actions (e.g., play, pause, jump, end of video, switching away from the video view, or closing the course application) is measured by the IR, as well as the UNIX Epoch time, starting position, and interval duration for each case. The specific type of click is captured as well including, for example, clicks away from the course material.) The viewport is the portion of the application that is viewable, therefore any interaction with the screen is an interaction with the viewport.
-ii) any interactivity in the viewport; (Brinton [0070] As another example, for textual content, the time the user has spent viewing a page will be recorded by the IR each time she flips the page or switches away from the current text view.)
-wherein the logged data is associated with a timestamp; (Brinton [0073] a. Play, pause, stop, fast forward, rewind, playback rate change, exit, and any other video player events, as well as corresponding timestamps, durations, and any other information that specifies user interaction with the video player.)
- and determining a focus measure comprises determining which sub-content item is presented in the viewport and (Brinton [0110] d. Recommendations to revisit specific portions of a learning mode where the level of focus, as dictated by the quantities in (a)-(c), is exceedingly high or low. [0111] e. Depictions of learning style preferences, including the percentage of focus placed on each of the different modes (video, text, audio, and/or social learning), clusters of users based on these preferences. [0128] h. Heat maps, which indicate the level of focus of learners at specific points within the content modes, and annotations on top of these heat maps to depict motifs.) The percentage of focus places on a particular mode of sub-content viewed in the viewport is taught by Brinton in this excerpt.
-a behavioral measure associated with a scroll rate for the sub- content item presented in the viewport. (Brinton [0085] The events that form a motif can consist of any combination of behavioral action collected from a learner as he/she interacts with the course application, such as, but not limited to, play, pause, skip backwards, skip forward, rate change faster, rate change slower on a video or interactive slide presentation, scrolling up or down in an article or resizing the view, creating or sharing a note, and mouse movements. [0022] These (among other) various interactions are captured by the system of the present invention and processed into “behaviors” so as to determine the student's overall strengths and weaknesses and specific positives and negatives relative to the topic material. In another example, the approach used by the student may be considered, such as recognizing when the student may be reflecting on video content (e.g., pauses in playing video), reviewing content, skimming content, or speeding through content (such as at a faster than default rate). [0118] l. Depiction of the identified motifs (e.g., reflecting, revising, speeding, skimming), which users/content modes have exhibited these motifs, and how often they occur.)
Regarding Claim 13:
The combination of Brinton, Qiu, and Stucker teaches or suggests teaches the method of claim 1
Furthermore, Brinton teaches:
-wherein: the content is presented in at least two separate sessions of a computing device : (Brinton [0134] Second, the present invention may re-route a user to multiple content files within the same reviewing session, either sequentially (i.e., one file at a time, in sequence) or concurrently (i.e., within the same view), with the next decision point occurring after the user has finished visiting all of the content on the alternate path. An example would be a user triggered a re-routed individualization that leads to, for example, two PDF that come from different modules. The IIC player may display two PDFs side by side concurrently.)
-step c) determines a partial rating for the first session and adds to the partial rating for each separate session. (Brinton [0194] For example, suppose the course consists of 1,000 Segments, ordered 1, 2, . . . , and a candidate sequence is composed of Segments 10, 20, 21, 22, 35, 36, 60. Out of seven neighboring pairs in the candidate sequence, three (20 and 21, 21 and 22, 35 and 36) are neighboring in the original course, so the fraction 3/7 could be taken as the distance score in this case. Another technique factors in the actual distance between Segments in the candidate sequence, with increasing penalty for further distance. In the above example, for instance, the first pair 10 and 20 may get 1/(20−10)=1/10, the second 20 and 21 would get 1/(21−20)=1, and so on through the last pair 36 and 30 which would get 1/(60−36)=1/24, for a total score of 3.22/7. The distances could be penalized in different ways, too, so that even far-away Segments are still counted more than a non-negligible amount.) Scores being separately taken for each session then added to the next rating teaches the element above.
However, Brinton fails to teach:
-wherein: the content is presented in at least two separate sessions by at least two different computing device
-step b) is performed for respective tracking data for each separate session responsive to a setup of the computing device used for the separate session; and
Alternatively, Qiu teaches:
-wherein: the content is presented in at least two separate sessions by at least two different computing devices(Qiu [0029] The training engine 206 generates and trains the prediction model. In example embodiments, based on user readership/reply distribution, the training engine 206 clusters users into multiple categories (e.g., users that are heavy message users, users that rarely interact with messages). For each category, the training engine 206 trains a prediction model (e.g., a SVM regression model) by taking input features (e.g., user identifier, message identifier, user read time, user reply time, message body length, people relationship score, “at mentioned” or not, whether messages are marked as high importance or not, whether a message was sent to a distribution list or not, in focused inbox or not) for all the users in the category and predicts a total action time needed for each message for the users in each category (e.g., using tensorflow SVM regression package). [0045] Furthermore, according to various example embodiments, components described herein as being implemented within a single machine, database, or device may be distributed across multiple machines, databases, or devices.)
-step b) is performed for respective tracking data for each separate session responsive to a setup of the computing device used for the separate session; and (Qiu[0029] The training engine 206 generates and trains the prediction model. In example embodiments, based on user readership/reply distribution, the training engine 206 clusters users into multiple categories (e.g., users that are heavy message users, users that rarely interact with messages). For each category, the training engine 206 trains a prediction model (e.g., a SVM regression model) by taking input features (e.g., user identifier, message identifier, user read time, user reply time, message body length, people relationship score, “at mentioned” or not, whether messages are marked as high importance or not, whether a message was sent to a distribution list or not, in focused inbox or not) for all the users in the category and predicts a total action time needed for each message for the users in each category (e.g., using tensorflow SVM regression package). [0045] Furthermore, according to various example embodiments, components described herein as being implemented within a single machine, database, or device may be distributed across multiple machines, databases, or devices. The scheduling system 102 may comprise other components not pertinent to example embodiments that are not shown or discussed. Further still, one or more of the components of the scheduling system 102 may be located at one or more of the client devices.) Qiu teaches the performance of step B on various devices.
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the present disclosure to modify Brinton by adding Qiu’s ability to track the interactions across multiple devices. One of ordinary skill in the art would have been motivated by the fact that it would allow a user to complete Brinton’s modules across different devices and geographic locations without losing progress. (Qiu [0091] The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor-implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations.)
Regarding Claim 14:
The combination of Brinton, Qiu and Stucker teaches the method of claim 1
Furthermore, Brinton teaches:
-wherein step b) uses setup data of the computing device with which to determine that a particular one of the sub-content items is actually presented and (Brinton [0211] In a preferred embodiment, the content used for individualization in the system is not confined to what is contained in the original, non-adapted course. Files external to the course may also be pulled in and delivered through the Player 105, depending on the access provided to the Individualization System 100. Sources of external content could include, but are not limited to, other Files on the system intranet, such as other courses being hosted by the same institution, and content on the Internet, such as HTML pages that can be accessed through a public URL. Types of external content include, but are not limited to, videos, articles, journals, online books, encyclopedia references, and any other type of information. In FIG. 1, this type of content is represented by the External Database 106.)
-the actual presentation duration thereof. (Brinton [0218] For external content that is not broken down and delivered through the Player 105, i.e., that which is displayed through a browser, the entire File can be treated as a candidate sequence. The disadvantage to external content displayed through a browser is that the behavior of a user on that content cannot be captured. Historical measures tied to whether certain behaviors are exhibited during remediation are therefore not applicable. However, aggregate measures such as total time spent and total number of clicks to that browser page can be tracked. Total time spent is based upon the timestamp when a user clicks on the content, and how long the Player window remains in the background thereafter before returning to the internal content. The behavior after returning to the internal course content can be tracked, too.)
Regarding Claim 20:
The combination of Brinton, Qiu, and Stucker teaches the method of claim 1
Furthermore, Brinton teaches:
-wherein the content comprises any of a web page or an electronic document.(Brinton [0211] Types of external content include, but are not limited to, videos, articles, journals, online books, encyclopedia references, and any other type of information [0210] FIGS. 9A-9B give examples of the Individualization System 100 modifying the GUI for remediation in an embodiment. FIG. 9A is an original course Module (titled “An Alphabet Soup”) and FIG. 9B (titled “An Alphabet Soup—Review”) gives an example of a remediation Module generated for a user based on his/her User Model at the end of the course Module. Both cases contain both video and PDF files, but the remediation Module contains far fewer Segments than would be expected of a Module in the original course content.)
Regarding Claim 22:
The combination of Brinton, Qiu, and Stucker teaches the method of claim 1 and performing steps a) to d)
Furthermore, Brinton teaches:
-for a plurality of content and respective interactions with the plurality of content by respective users; (Brinton [0007] Behaviors are used to model a student, compare the student to one or more other students, or compare the student to the same student’s behaviors in prior courses, or both, and adjust course delivery based, at least in part, on known success approaches for different behaviors. [0022] The present invention includes several novel attributes such as the ability to customize the selection of Modules and content within Modules to be delivered to a student while the course is progressing and based on the student’s interaction with the course. The student interaction (“measurements”) can take the form of mouse clicks, durations between mouse clicks, sequences of mouse clicks, selection of topics to review, durations on particular screens, quizzes and results, and physical body (and/or eye) movements, as observed by cameras and/or audio recording instrumentation. These (among other) various interactions are captured by the system of the present invention and processed into “behaviors” so as to determine the student’s overall strengths and weaknesses and specific positives and negatives relative to the topic material. In another example, the approach used by the student may be considered, such as recognizing when the student may be reflecting on video content (e.g., pauses in playing video), reviewing content, skimming content, or speeding through content (such as at a faster than default rate). Once determined, a next Module, aligned with the course syllabus, is delivered to the student, where the content contained in the Module is that most likely to be in line with the student’s strengths and abilities.)
-storing content attribute data for each content of the plurality; (Brinton [0019] Attributes of Modules and Segments that are understood in advance, including their topics, media types, and past utility in being displayed to students, can be used by the present invention to determine how a student’s learning path should be adjusted. In a simple example, a Module might be animation-oriented and have a File that has been demonstrably exciting to learners; if a student has a preference for animations and is struggling on learning the topics covered by this module, the student’s path may be adjusted to include this Module or File and it may supplement or replace one or more Modules, Files, or Segments. [0044] From a hardware system perspective, the present invention includes a server architecture that contains one or more databases for storage of user and content information, which may or may not be updated over time, as well as storage of behavioral data. A content store contains the content items such as videos and/or references to external (stored external to the system of the present invention but accessible by the present invention) content that is available from third party content providers.)
-for each user interacting with respective ones of the content, storing, in association with the content attribute data, interaction attribute data for each interaction; and (Brinton [0070] In an embodiment, each end user device has an interaction recorder (IR) loaded into memory, to monitor user interaction with the various learning modalities. In at least one embodiment, this IR is embedded in a GUI. For example, in a video, the time interval between two successive click actions (e.g., play, pause, jump, end of video, switching away from the video view, or closing the course application) is measured by the IR, as well as the UNIX Epoch time, starting position, and interval duration for each case. The specific type of click is captured as well including, for example, clicks away from the course material. As another example, for textual content, the time the user has spent viewing a page will be recorded by the IR each time she flips the page or switches away from the current text view.)
- providing an interface to obtain insight data responsive to the content attribute data and interaction attribute data, wherein the insight data is for a particular user based on the particular user’s interaction with the content,( Brinton [0068] In the present invention, the user interface can transform data to an aggregated and easy to comprehend form, deliver conclusions based on the data including student actions, and deliver specific suggestions for course adjustments by the instructor, among other items. Such data and its analysis can be delivered based on individual students or an aggregation of student. [0119] The output can also be customized by/for an instructor so as to, for example, provide further granularity. That is, an instructor may pre-set displays. See [0070] interactivity recorder for “insight data.”)
- and optionally either or both of: i) the insight data provides a comparison to insight data for an aggregate of interactions with the content by a plurality of users; or ii) the insight data is in the form of trend data for a period of time. [0120] The present invention includes a plurality of ways to visualize these outputs on the instructor interface. These include, but are not limited to, the following: [0121] a. Scatterplot of points, in 2 or 3 dimensions, where the dimensions of interest are selected by the instructor. [0122] b. Time-series plots of a quantity, where the time interval and granularity of measurement are selected by the instructor. [0123] c. Histogram plots, which are graphical representations of the distribution of a quantity of interest. There can be one or two independent variables on top of which this variation is measured, and they must take continuous values (e.g., intervals of a video). [0124] d. Bar graphs, which are representations of how a quantity of interest varies over one or two discrete sets (e.g., set of students). [0125] e. Box and whisker plots, which show the distribution of a set of points and emphasize the median, quartiles, and outliers of the dataset. They are typically depicted side-by-side for multiple datasets, to show the difference in distributions. [0126] f. Network graph structures, consisting of nodes, links between the nodes (either directed or undirected), and possibly weights on the links, which may be color coded to represent different ranges of values. These graphs can emphasize various network substructures, such as clusters, cliques, or the most central nodes. [0127] g. Popups and notifications, which are included in the various modules for recommendations and early detection as appropriate. [0128] h. Heat maps, which indicate the level of focus of learners at specific points within the content modes, and annotations on top of these heat maps to depict motifs. [0129] In an embodiment, each of these visuals is interactive, meaning that the instructor can select the quantities, dimensions, datasets, and graph plotting properties specified above. They are also real-time in two senses: the displays may update instantaneously when the instructor makes a new selection, and any new input data will be processed immediately and the corresponding display re-rendered.)
Claims 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Brinton (US 20210049923 A1) in view of Qiu (US 20200153776 A1) further in view of Stucker et al. (US 20180246866 A1), further in view of Bradley et al. (US 20190082224 A1) hereinafter Bradley.
Regarding Claim 15:
The combination of Brinton, Qiu, and Stucker teach the method of claim 1,(including steps a) to d)
However, neither Brinton, Qiu, nor Stucker teach or suggest:
- comprising processing the content to verify the content is valid prior to performing steps a) to d).
Alternatively, Bradley discloses a method of rating content that verifies the content before the rating process which teaches: - comprising processing the content to verify the content is valid prior to performing the rating(Bradley[0050] Next, the system ranks the biases for the content and the news sources (step 208). The biases may be ranked on one or more scales (e.g., 1-10, far right, conservative, moderate conservative, neutral, moderate liberal, liberal, far left, color spectrums, etc.). Any number of ranking systems, including text, numeric/mathematical, visual, audio, or otherwise may be utilized and presented to users that access the system. By aggregating and evaluating bias, the system may measure a total tally of tone and bias of content as it is released from each site. The biases for the content as well as the source may be determined during step 208. For example, the system may determine websites that are reporting a political skew that is a mix of balanced news stories and those that have lower instances of biased language in the respective content. During step 208, the system may also validate and verify content and news sources. For example, the system may cross reference content between multiple sources to determine whether provided information is deemed to be accurate over time. [0052] For example, an inaudible tone may verify that the content is verified or from a verified source. The inclusion of the tone allows users to quickly verify if content is actually from an approved or confirmed source.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify Brinton by adding Bradley’s verification process, prior to performing the full process. One of ordinary skill in the art would have been motivated by the benefit of giving a clear understanding of the truthfulness of content, with applications in various fields. (Bradley 0074] In one example, job seekers and employers may utilize the services of the system to ensure the profile of a user/employer is true and accurate.)
Regarding Claim 16:
The combination of Brinton, Qiu, and Stucker teach the method of claim 1,
Furthermore, Brinton teaches:
- comprising: determining a content contribution by the user related with the content; (Brinton[0115] i. Depictions of the social network of users, obtained from their post and comment relations on the discussion forums, and their sharing of notes, both in aggregate across all material and for individual sections of content.)
-wherein a content contribution in association with the content comprises, by the user, any of:
- reacting; commenting; adding notes; shares. (Brinton [0166] g. Information on each post made in discussion forums, including its content, whether it was meant as a question, answer, or comment, and the number of up-votes it received from other users or the instructor. Discussion forum posts are analyzed using NLP techniques which are able to detect sentiment as well as if a post is a question or statement. [0165] f. Position and content of notes taken on video or text at specific locations, or on a slide, as well as whether these notes were either shared publically, shared with a specific set of users, or not shared.)
However, neither Brinton, Qiu, nor Stucker teach or suggest:
-defining a content contribution rating responsive to the content contribution; and
providing the content contribution rating for display;
Alternatively, Bradley teaches:
-defining a content contribution rating responsive to the content contribution; and
providing the content contribution rating for display; and (Bradley [0022] In one embodiment, the system may present a specialized website, application, browser-add in or extension, rating platform, or other tool for receiving user selections including rating content for truthfulness, up or down voting the content (e.g., like/dislike, thumbs up/thumbs down, up vote/down vote, etc.), rating the content for bias (e.g., liberal/conservative, capitalist/socialist, pro-gun/anti-gun, pro-choice/pro-life, etc.), receiving comments, and displaying or otherwise communicating the content accordingly. [0099] The comments may be aggregated and displayed as described herein. In addition, individual comments may be rated up/down, true/false (or percentage true or numerical value), or liberal conservative (or percentage or numerical value liberal/conservative). The system may utilize any number of databases and associated fields to add/record/write, update, manage, and access the applicable information, such as content information, content identifiers, ratings, views, shares, and so forth without limitation. In one embodiment, the process of FIG. 7 may be utilized for a report page (e.g., “Bled Report” showing both blue/liberal and red/conservative content) for separating and displaying content with the applicable user ratings, values, comments, data, and information.) Bradley’s displaying content with the applicable user ratings teaches the limitation above.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify Brinton by adding Bradley’s rating of content contributions such as comments and likes. One of ordinary skill in the art would have been motivated by the fact that the rating of the content for display would allow users to ascertain the credibility and truthfulness of a source based on the interactions concerning the source. (Bradley [0022] In one embodiment, the system may present a specialized website, application, browser-add in or extension, rating platform, or other tool for receiving user selections including rating content for truthfulness, up or down voting the content (e.g., like/dislike, thumbs up/thumbs down, up vote/down vote, etc.), rating the content for bias (e.g., liberal/conservative, capitalist/socialist, pro-gun/anti-gun, pro-choice/pro-life, etc.), receiving comments, and displaying or otherwise communicating the content accordingly.)
Regarding Claim 17:
The combination of Brinton, Qiu, Stucker and Bradley teach the method of claim 16,
Furthermore, Brinton, Qiu, and Stucker fail to teach or suggest:
- following a making available of the content contribution to a recipient:
-evaluating recipient interaction to the content contribution; and
-defining a content contribution rating or an update thereto responsive to the evaluating.
Alternatively, Bradley teaches:
- following a making available of the content contribution to a recipient: (Bradley [0124] Sharing indicators 1010 may be utilized to share the user's personal selections and or the overall selections by numerous users available through the user interface 1000. The sharing indicators 1010 may be utilized to perform social media posts, text messages, email, in application messages, or so forth. [0090] Next, the system shares the content and associated user rating as requested by the user (step 610). The user may share the content and associated rating utilizing any number of messages (e.g., text, email, etc.), social media post, snapshot/image, or other similar process. In one embodiment, the content may be shared utilizing a hyperlink.) The broadest reasonable interpretation of “following a making available of content contribution to a recipient” is that it means prior to sharing content with a recipient...
-evaluating recipient interaction to the content contribution; and defining a content contribution rating (Bradley [0090] The rating information may specify how the user upvoted or downvoted the content, rated/ranked bias, and the truthfulness assigned to the content by the user. The rating information may also show how all other users have rated the content. To the extent user profiles or associated information is available, it may be utilized to show ratings by demographics, cohorts, groups, self-selecting individuals, or others may be shown (e.g., forty percent of teenagers voted this false with a 30% liberal bias, 20% of African Americans upvoted this as true with a 25% conservative bias, etc.). [0091] The process of FIG. 6 may be performed repeatedly. For example, the user may be navigating content available through a browser or application and may choose content to rate as a public service, for fun, based on emotion, based on shared content (e.g., friends, family, acquaintances, etc.).) The above are examples of interactions prior to the sharing of the content.
-or an update thereto responsive to the evaluating.(Bradley [0112] [0112] The available information may be retrieved and updated automatically. For example, data may be updated continuously, periodically, at set intervals, based on specified events, or so forth. In another embodiment, user input may be required to enter information or verify the provided information. As noted, the illustrative embodiments automatically determine bias, lean, skew, deviations, variations, or so forth from the truth, norms, price, standards, thresholds, or so forth. For example, the illustrative embodiments may be utilized to determine political lean and bias for web content. [0126] Likewise, the commentary and opinion may show commentary that is associated with a particular bias. The commentary may also be reviewed and an initial categorization of the content (e.g., liberal, conservative, etc.) may change based on the user ratings.) This excerpt shows that the ratings update with each additional interaction.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present disclosure to further modify Brinton by adding Bradley’s feature of using interactions with content contributions(such as comments likes and shares) to define or update a content contribution rating as taught by Brinton. One of ordinary skill in the art would have been motivated to perform this combination as it would yield the benefit of the categorization and rating of the content being updated through comments, keeping the rating up to date and accurate. (Bradley [0126])
Claim 7 and 21 is rejected under 35 U.S.C. 103 as being unpatentable over Brinton (US 20210049923 A1) in view of Qiu (US 20200153776 A1), further in view of Stucker (US 20180246866 A1) further in view of Morisset (US 20150010894 A1).
Regarding Claim 7:
The combination of Brinton, Qiu, and Stucker teach the method of claim 4.
However, neither Brinton, Qiu, nor Stucker teach:
- comprising at least one of: associating a reward to the rating and providing a service according to the reward;
-and associating a reward to the ranking and providing a service according to the reward.
Alternatively, Morisset discloses a learning management system that accounts for various factors such the expected time to complete a task based on the complexity, and provides rewards according to various achievements. Morisset teaches:
-associating a reward to the rating and providing a service according to the reward; (Morisset [0014] Furthermore, training providers and instructional designers who develop continuing education programs bear the responsibility to ensure the training program’s time duration estimates are sufficient and that the units mentioned on the associated training certificates awarded to successful students reflect both the achievement of a learning goal 35 and an accurate estimated time of study to reach that goal. [0043] rewarding learning scenario, [0044] With the course content 66 completed, the developer is able to dynamically adjust durations 54 and points 56 associated with each section 38-50 of each learning activity 36 (624). This allows the developer to selectively give more or less weight to reward activities that the course provider wants to value more or less. It is therefore possible to award certificates of completion for users who achieve a general level of proficiency or a higher level of proficiency using the same course 24 and content 66 without the need to develop a new course 24.) Morisset’s points are mapped to ratings, which result in a reward. Awarding a certificate of completion is mapped to “providing a service according to the reward.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify Brinton by providing rewards as taught by Morisset when certain ratings are reached. One of ordinary skill in the art would have been motivated to perform this combination as it would provide the benefit of ensuring that the users are properly incentivized for the proper amount of time credits for a course. (Morisset [0014] Such a lack of standardization is a major concern for professional organizations since this can result in significant discrepancies between training programs, allowing some professionals to easily fulfill training requirements by utilizing a training program that does not meet qualitative expectations in terms of educational value and learning goal 35 achievement.)
Regarding Claim 21:
The combination of Brinton, Qiu, and Stucker teach the method of claim 1, and step a),
However, neither Brinton, Qiu, nor Stucker teach or suggest: -wherein step a) determines for each sub-content item a credit value and
-wherein the rating is further determined in association with a total credit value of all sub-content items.
Alternatively, Morisset teaches: -wherein step a) determines for each sub-content item a credit value and (Morisset [0017] Using a computing device in communication with the computing system, the computing system is provided with details related to a desired subject matter for which the course is to cover, a total duration of time allotted for the user to complete the course, and a minimum number of total points required for the user to receive a passing score upon completion of the course. Additionally, each section has a duration of time allotted for the user to complete said section and a maximum number of points to potentially be earned by the user for completing said section. The developer is subsequently able to selectively populate each section of each learning activity with content associated with the subject matter of the course. Additionally, the developer is able to selectively adjust the duration of time and points associated with each section of each learning activity. [0038] With continued reference to FIGS. 3 and 4, each of the sections 38-50 has a specified duration 54 (i.e., amount of time afforded to the user to complete the section 38-50) and number of points 56 (i.e., maximum number of points 56 available through completing the associated section 38-50), as defined by the developer of the course 24. [0046] Upon completing the course 24, the computing system 22 transmits reports regarding user access, grade transcripts and certificates to any parties who have been designated to receive such information (636). The computing system 22 is also capable of displaying special mentions on the delivered certificates to report the actual educational value attained from one user to another. Such special mentions can be customized and used by professional organizations as a way to change their standard requirement of continuing education, making more specific requirements with not only units of duration 54 but also with units of educational value (i.e., points 56) attainment, as a way of promoting higher standards.) Morisset’s “maximum number of points to be earned” is mapped to a credit value for each sub-content item since it is a “unit of education value.”
-wherein the rating is further determined in association with a total credit value of all sub-content items.(Morisset [0041] As illustrated in the flow diagram of FIG. 6 and the exemplary user interface 52 of FIG. 4, in at least one embodiment, when creating a new course 24 (600), the developer first provides to the computing system 22 desired developer-related information including but not limited to developer name, company logo, copyright information, preferred color scheme, etc. (602). The developer also provides to the computing system 22 a title 58 of the new course 24 (604), the desired total duration 54 of the course 24 to be allotted (606), and the minimum number of total Points 56 required for a user to receive a passing score (608).) The minimum number of total points is a total credit value of all the sub-content items in a session.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to further modify Brinton by providing points and credit values as taught by Morisset. One of ordinary skill in the art would have been motivated to perform this combination as it would provide the benefit of ensuring that the users are properly incentivized for the proper amount of time credits for a course. (Morisset [0014] Such a lack of standardization is a major concern for professional organizations since this can result in significant discrepancies between training programs, allowing some professionals to easily fulfill training requirements by utilizing a training program that does not meet qualitative expectations in terms of educational value and learning goal 35 achievement.)
Claims 27, & 29 are rejected under 35 U.S.C. 103 as being unpatentable over Brinton (US 20210049923 A1) in view of Qiu (US 20200153776 A1), further in view of Stucker(US 20180246866 A1), further in view of Romagnolo et al. (US 20210049627 A1) hereinafter Romagnolo.
Regarding Claim 27:
The combination of Brinton, Qiu, and Stucker teach The method of claim 22.
Furthermore, Brinton teaches:
-comprising: providing an interface to receive a new content, the new content comprising a plurality of sub-content items; (Brinton [0020] The process of the present invention may alternatively create new Files that are combinations of these remedial Segments, and insert these new Files as the next content in the sequence to be visited on the student’s path. [0042] It is important to recognize that once a course is assembled by amassing a collection of content files, the course can be changed. An instructor, for example, can create and/or add new content files for the course and replace old ones. In addition, as new course files are added, replaced, or removed, the processes of the present invention—tagging and path development—are restarted, especially for the new content.)
-analysing the new content to determine and store content attribute data for the new content; (Brinton [0183] Based on content type, an aggregate content set is selected (text-based, 503a, audio, 503b, and/or video, 503c) is arranged and a remediation module is constructed 504 and sent to Player 105. Equipped with the Desired Topic Remediation 500 distribution summarizing the topics that the student is currently struggling with, the Path Switching 103 must then determine the set of content (Remediation Segments) that the student will be shown next, and the sequence in which those Segments should be presented.) This step in Brinton analyzes the content at improving specific user outcomes.
- and analyzing the new content to maximize a likelihood of a desired interaction with the new content, (Brinton [0184] To do this, the system of the present invention ascertains the utility of showing the user a given sequence of Segments. Utility scores are created and updated over time for different possible sequences of Segments (“candidate sequences”). In a preferred embodiment, this utility score is based on at least three component scores (but could include more) that, in part, determine the effectiveness of this content at improving the specific user’s outcomes at this specific point in time: a similarity score, a distance score, and a historical score, which may be adjusted with new observations via reinforcement learning techniques and machine learning.) Brinton’s analysis of new content does not include optimization recommendations, it merely analyses the outcome.
However, neither Brinton, Qiu, nor Stucker teach or suggest:
-and analyzing the new content to maximize a likelihood of a desired interaction with the new content,
- the optimizing responsive to content attribute data of at least some of the plurality of content and the interaction attribute data associated therewith,
- wherein optimizing comprises providing changes to at least some content attributes of the new content.
Alternatively, Romagnolo discloses a method of evaluating and optimizing media content including an input interface configured to receive a media content for evaluation by users in an online community, and output recommendations to optimize a particular desired outcome. Romagnolo teaches: -and optimizing the new content to maximize a likelihood of a desired interaction with the new content,(Romagnolo [0040] Various embodiments of the subject matter disclosed herein can provide one or more of the following capabilities/features. Media content can be tested, optimized and evaluated based on user responses to the contents on a social media platform, including user behavior such as views, user responses such as comments, and user actions such as votes or other expressions of user opinion. [0113] Just as there may be a big difference in the predictability of box office revenue before an opening weekend to after an opening weekend, the approach of backcasting in some embodiments can seek to collect user behavioral metrics and data in regard to user actions on a social media platform (e.g., numbers of views, numbers of likes, numbers of recommendations to friends, diffusion over a network, and so forth) in order to measure the success of a media content and then predicting on the basis of that measurement the likely success of subsequent development or variation on the content.) Romagnolo’s prediction of success based on number views or likes, is mapped to “optimizing the new content to maximize a likelihood of a desired interaction with the new content.”
- the optimizing responsive to content attribute data of at least some of the plurality of content and the interaction attribute data associated therewith, (Romagnolo [0180] Additional data that can be combined with the data in regard to user activity on the platform can include: [0181] Information from professional reviews [0182] Quantity of views, ratio of likes dislikes, rate of tweets, sentiment of tweets via natural language processing or otherwise. [0183] Views of content on other social media and content platforms [0184] Likes or “+1s” of content on other social media and social network platforms [0185] Views of derivative media content (e.g., TV, theater, video games, etc.) [0214] One approach to train a statistical machine learning model for the purpose of estimating the value of media content from the data harvested from an instrumented social media platform can proceed in a staged way. For example, one approach to the staged training of a model can: [0215] i. Collect data from the platform and look to build a predictive model of views on platform for some subsequent period of time [0216] ii. Collect data from the platform and also from another platform, and look to build a predictive model of views when the same content (e.g., same test format, or same episode) is released to another platform (e.g., YouTube) [0217] iii. Collect data from the platform and look to build a predictive model of views on platform for a sequel to the content, or a reproduced version of the content [0218] iv. Collect data from the platform and also from off platform (e.g., movie theaters) and look to build a predictive model of the value of the media content when released off platform (e.g., to theatrical release)) The excerpt above shows optimizing a model to predict the amount of views (interaction attribute data) for a piece of content.
- wherein optimizing comprises providing changes to at least some content attributes of the new content. (Romagnolo [0124] Rather than solely relying on similarity to other contents (for example, in terms of genre, actors, director, producer, themes, motifs, etc.), or evaluation by an expert or group of experts, the embodiments of the disclosed subject matter allow a media content to be evaluated by engaging a user base with the content, collecting metrics in regard to user behavior, actions, and engagement with the content, and then using statistical machine learning or other predictive approaches to estimate the value of the content, and promote decision making into further refinement and testing and also in regard to investment in the content, optimization of the content, and possible franchise extensions. [0131] Creating “mashable content” and reusing/editing content to create second-order imitations or parodies, including tools to edit and manipulate content. ) Editing content to create second-order imitation is mapped to “providing changes to at least some of the content attributes of the content.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to modify Brinton by adding Romagnolo’s interface for uploading and editing new content with optimization algorithms being run before upload. This combination would yield the predictable result of allowing administrators in Brinton to edit their files prior to upload, based on recommendations generated by Romagnolo’s optimization algorithms. One of ordinary skill would have been motivated to combine to receive benefit of increasing the quality of posts by editing them using predictive models. (Romagnolo [0040] Various embodiments of the subject matter disclosed herein can provide one or more of the following capabilities/features. Media content can be tested, optimized and evaluated based on user responses to the contents on a social media platform, including user behavior such as views, user responses such as comments, and user actions such as votes or other expressions of user opinion. The social media platform can be designed for the express purpose of eliciting informative signals in regard to media content through user behavior and actions. Information can also be incorporated from other sources, including micro-blogs, social networks and news sites. Continual measurement and refinement of media content can be supported, with production of variations on contents made in a way that is responsive to feedback. Methods of statistical machine learning and regression can be used to estimate the value of media content, and provide probabilistic models of value. Value estimates can be used to guide investment decisions and franchise extension decisions, enabling return-on-investment from media content over the whole range of demand. In this way, the user community can be involved in choosing which new content is produced, participating in a democratic process of refining content and promoting content. Behavioral metrics in regard to the affinity of users for media content can also be inferred from data, and used to better understand consumer decision making and in order to improve marketing and distribution. User feedback on original content provided by third parties can be obtained, allowing an exchange for content, where new content is deployed on the supply side, evaluated by the community, and ultimately matched with potential investors on the demand side.)
Regarding Claim 29:
The combination of Brinton, Qiu, Stucker and Romagnolo teach The method of claim 27,
Furthermore, Brinton and Qiu fails to teach or suggest:
wherein either or both of: the method comprises annotating the new content with the changes;
-or the at least some of the plurality of content are selected from the plurality of content based on the associated interactive attribute data that maximizes the desired interaction.
Alternatively, Romagnolo teaches:
- the at least some of the plurality of content are selected from the plurality of content based on the associated interactive attribute data that maximizes the desired interaction.(Romangolo [0123] use these predictions to decide on investments in contents, and refinements to contents, for the purpose of continually identifying, refining and selecting media and investing at the right scale to maximize return-on-investment. [0144] Receiving feedback as to which content is gaining social actions, and in regard to the probability that content will be selected for incremental investment. [0334] In some further variations of the implementation illustrated in Example 1, additional steps can include, prior to step (e), the steps of: [0335] g) [democratic] selecting a media content for a recommendation to receive investment, this selection made in a way that depends on the aggregate behavioral and user engagement actions of users; and [0336] h) electronically outputting the media content identity and the recommendation.)
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to modify Brinton by adding Romagnolo’s interface for selecting the most optimal form of media content to maximize a specific outcome. This combination would yield the predictable result of allowing administrators in Brinton to select the most optimal version/form of content that would maximize interactions or revenue. One of ordinary skill would have been motivated to combine to receive benefit of increasing the quality of posts by selecting the best version using predictive models. (Romagnolo [0040])
Response to Arguments
Applicant's arguments filed 11/17/2025 have been fully considered but they are not persuasive. Regarding applicant’s arguments over rejections under 35 U.S.C. 101, the applicant’s arguments alleging that none of the cited references teach or suggest the claim as amended are not persuasive because they are not relevant to the 101 discussion. MPEP 2106.05(I) states,
“Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting “the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101”). As made clear by the courts, the “‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter.” Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) (“a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty.”). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103. See, e.g., BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016) (“The inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art. . . . [A]n inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces.”). Specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. The distinction between eligibility (under 35 U.S.C. 101) and patentability over the art (under 35 U.S.C. 102 and/or 103) is further discussed in MPEP § 2106.05(d).”
The arguments regarding the prior art are reconsidered for prior art purposes, but are not persuasive to the 101 argument. Therefore, the argument that “it is apparent from comparing the claimed subject matter with the cited art and by applying the applicable tests, that the claimed subject matter is more than a mere abstract idea” is not persuasive because in order to assert an improvement upon the conventional functioning of a computer, a technical explanation in the original filed disclosure must describe the invention in such a way that the improvement would be apparent to one of ordinary skill in the art. For example MPEP 2106.05(a) states, “An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.” Furthermore, the claims do not distinguish over the prior art of record especially when considered in combination, and even assuming arguendo that they distinguished from the prior art, the claims must reflect a technical improvement over prior art systems to integrate the abstract idea into a practical application or provide an inventive concept.
Furthermore, the applicant’s argument that “determination in keeping with how content is often consumed by a user – across multiple computing devices” is not persuasive because performing the abstract idea on multiple computers(tracking personal behavior on multiple computer devices) is still equivalent to “apply it.” Even though the cited references may not disclose the exact wording “two separate sessions,” performing “certain methods of organizing human activity,” does not depend on the amount of user’s or amount of computers being interacted with, but it depends on whether the activity itself falls within the category. See MPEP 2106.04(a)(2)(II), “Finally, the sub-groupings encompass both activity of a single person (for example, a person following a set of instructions or a person signing a contract online) and activity that involves multiple people (such as a commercial interaction), and thus, certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. It is noted that the number of people involved in the activity is not dispositive as to whether a claim limitation falls within this grouping. Instead, the determination should be based on whether the activity itself falls within one of the sub-groupings.”
Furthermore, the applicant’s argument asserting that the “complex analysis...results in more tracking data and more content attribute data, which, in turn, allows for more efficient and accurate computation of user interactivity...” is also not persuasive for the following reasons. The scope of the claims does not recite enough particularity to be considered a “complex analysis”, and while the claims are read in view of the specification, particular limitations from the specification are not read into the claims. Therefore, the claims are read in the broadest reasonable interpretation of the plain language, without reading any limitations from the specification into the claims itself. The claims do not reflect a purported improvement because they are too broad to be considered particular enough to be meaningfully limited to a particular technological implementation. Furthermore, the alleged improvements to the efficiency and accuracy of computation, is not a persuasive argument because it is merely claiming improved speed or efficiency inherent with applying the abstract idea on a computer or multiple computers. MPEP 2106.05(f) states, “Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept.” Furthermore, the claims of “improved accuracy of tracking with fewer computational resources,” is merely a bare assertion of an improvement. MPEP 2106.05(a) states, “Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology.” Lastly, the argument that the conclusion has been “validated my multiple persons skilled in the field, including researchers at academic institutions and technical leaders at top industry labs focused on user engagement measurement” is not a convincing argument because the applicant has failed to show where in the specification, the claims purport an improvement to technology. Improvements to “user engagement measurement” still fall within the scope of “managing personal behavior,” and even improved abstract ideas are still directed to an abstract idea.
In view of applicant’s arguments over the prior art, particular over the rejections under 35 U.S.C. 103, the applicant submits that none of the references relates to determining a text complexity measure. In view of the amended claims, an updated search and consideration has yielded an updated combination now including Stucker, which integrates text complexity. Therefore, the applicant’s argument is moot. Furthermore, the applicant’s argument regarding “aggregating over different computers” is not persuasive because the combination renders aggregating over several computers obvious over the prior art, particularly, given that the broadest reasonable interpretation of the claims does not specifically limit how this aggregation over different computers is to be performed.
Circling back to prior art arguments that were provided in the remarks over 101, the applicant’s arguments that neither Britton and Qiu examine text complexity, because Britton uses NPL of Segments to determine topic related information but makes no reference to the complexity, and no regarding to determining an item minimum duration. The examiner acknowledges this, and does not rely on Britton alone to teach text complexity or item minimum duration. Furthermore, the examiner is aware that Qiu models reading time based on text length and training data of read/reply times, but without considering text complexity. The combination which now includes Britton, Qiu and Stucker, teach all of the limitations because Stucker suggests text complexity, which is simply substituted into the combination of Britton and Qiu.
Furthermore, the applicant’s argument that the claimed invention avoids “use of complex and expensive peripheral analysis components such as eye tracker technology (Britton),” however, this argument is not persuasive because one cannot claim an improvement over the prior art as a whole by attacking one of the embodiments of the prior art. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Furthermore, the prior art discussion is not relevant to the eligibility under 35 U.S.C. 101. Therefore, none of the applicant’s arguments over 35 U.S.C. 101 or 35 U.S.C. 103 are persuasive and the rejections stand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
-Catalano et al. (US 20180267955 A1) discloses a computer program for making online text align with the reading level of a user based on determining a difficulty rating of a communication and comparing it to the reading level of a user, replacing the text if necessary.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICO LAUREN PADUA whose telephone number is (703)756-1978. The examiner can normally be reached Mon to Fri: 8:30 to 5:00pm.
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/NICO L PADUA/Junior Patent Examiner, Art Unit 3626
/SANGEETA BAHL/Primary Examiner, Art Unit 3626
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