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 AIA .
Status of Claims
This communication is a Non-Final office action in response to RCE filed on 12/03/2025. Claims 1, 9 and 17 have been amended. Claims 5, 12, 16 and 19 have been canceled. Claims 23-24 have been newly added. Therefore, claims 1-4, 6-11, 13-15, 17-18 and 20-24 are currently pending and have been addressed below.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/03/2025 has been entered.
Response to Amendment
Applicant has amended claim 17 to overcome the 112(a) rejections. Therefore, the 112(a) rejections are withdrawn for claims 17-20.
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 1-4, 6-11, 13-15, 17-18 and 20-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1-4, 6-8, 17-18, 20-24 are directed to a method (i.e. a process). Claims 9-11 and 13-15 are directed to a system (i.e. a machine). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1-4, 6-11, 13-15, 17-18 and 20-24 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1 recites: A method for generating a user path, the method comprising: classifying a plurality of nodes in a knowledge graph, each of the plurality of nodes representative of an education medium; establishing a plurality of links in the knowledge graph based on content of the education medium, each of the plurality of links connecting to at least two nodes of the plurality of nodes, each of the plurality of links representative of an education relationship between the at least two nodes; generating comprising the plurality of nodes and the plurality of links, visually differentiates types of education media by displaying different visual indicators around each node; transmitting associated with a user; causing to display; receiving data representative of qualifications associated with the user; determining, based on the qualifications associated with the user, a first set of nodes from the plurality of nodes, the first set of nodes representative of education mediums known by the user; alter the plurality of nodes and the plurality of links by filtering out the first set of nodes such that the first set of nodes are no longer visible; creating a user path through the knowledge graph, the user path comprising a second set of nodes from the plurality of nodes, each node in the second set of nodes connected by a link from the plurality of links; generating a modified comprising the second set of nodes connected by the plurality of links; transmitting the modified; causing to highlight the user path; determining, based on metadata, a chronological identity associated with each node in the second set of nodes, wherein the determined chronological identities arrange the second set of nodes in ascending order of difficulty; and assigning, to each node in the second set of nodes, the chronological identity representative of an order by which the user proceeds through the user path, wherein the user path is representative of a learning curriculum comprising the education medium associated with each of the second set of nodes.
Independent claim 9 recites: classify a plurality of nodes in a knowledge graph, each of the plurality of nodes representative of an education medium; establish a plurality of links in the knowledge graph, each of the plurality of links connecting to at least two nodes of the plurality of nodes, each of the plurality of links representative of an education relationship between the at least two nodes; receive an education medium and associated metadata comprising at least a runtime and a level of difficulty; store the education medium and automatically store the metadata in association with a corresponding node in the knowledge graph; receive, associated with a user, data representative of qualifications and user inputs associated with the user, determine, based on the qualifications and the user inputs associated with the user, a first set of nodes from the plurality of nodes, the first set of nodes representative of education mediums known by the user; create a user path through the knowledge graph, the user path comprising a second set of nodes from the plurality of nodes, each node in the second set of nodes connected by a link from the plurality of links; dynamically modify the user path based on environmental data associated with the user by removing additional nodes from the second set of nodes; and assign, to each node in the second set of nodes, a chronological identity representative of an order by which the user proceeds through the user path, wherein the user path is representative of a learning curriculum comprising the education medium associated with each of the second set of nodes.
Independent claim 17 recites: A method of generating a learning curriculum, the method comprising: receiving, associated with a contributor, (i) a first education medium comprising data representative of a contents of the first education medium and (ii) metadata comprising at least a runtime and a level of difficulty of the first education medium; storing the first education medium; classifying, based on the data representative of the contents of the first education medium, the first education medium into a first node in a knowledge graph, the first node including a query path to the first education medium in the database; automatically storing the metadata in association with the first node; establishing, based on the contents of the first education medium, one or more links from the first node to one or more nodes in the knowledge graph, each of the one or more links representative of a relationship between the first education medium and one or more education media associated with the one or more nodes
The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under at least the abstract groupings of:
Mental Processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion (independent claim 1 and 9 recites for example, “generating a user path”, “classifying a plurality of nodes in a knowledge graph”, “establishing a plurality of links in the knowledge graph”, “display the plurality of nodes and the plurality of links, “receiving data representative of qualifications associated with the user”, “determining, based on the qualifications associated with the user”, “filter out the first set of nodes”, “creating a user path through the knowledge graph”, “highlight the user path”, “assigning, to each node in the second set of nodes, a chronological identity representative of an order by which the user proceeds through the user path, wherein the user path is representative of a learning curriculum comprising the education medium”; claim 17 recites: “generating a learning curriculum”, “receiving, a first education medium comprising data representative of a contents of the first education medium”, “storing the first education medium”, “classifying, based on the data representative of the contents of the first education medium, the first education medium into a first node in a knowledge graph”, “establishing, based on the contents of the first education medium, one or more links from the first node to one or more nodes in the knowledge graph, each of the one or more links representative of a relationship between the first education medium and one or more education media associated with the one or more nodes”.) Concepts performed in the human mind as mental processes because the steps of generating, classifying, establishing, determining, filtering, assigning, and presenting data mimic human thought processes of observation, evaluation, judgement and opinion, perhaps with paper and pencil, where data interpretation is perceptible in the human mind. See In re TLI Commc’ns LLCPatentLitig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)). Certain methods of organizing human activity (commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations; (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)). As claims 1 and 9 discuss a method for generating a user path including assigning a chronological identity representative of an order by which the user proceeds through the user path, wherein the user path is representative of a learning curriculum comprising an education medium; claim 17 discusses a method of generating a learning curriculum, which is one of certain methods of organizing human activity.
Dependent claims add additional limitations, for example: (claims 2 and 10), retrieving each education medium associated with each node in the second set of nodes; and transmitting each education medium to the user, thereby displaying each education medium associated with the user; (claims 3 and 11) receiving environmental data associated with the user at a current time; and analyzing, based on the environmental data and the current time associated with the user, a level of engagement associated with the user; (claim 4) removing one or more nodes from the second set of nodes based on the level of engagement; (claim 13) wherein the environmental data comprises one or more of: a time of day, a day of a week, a location, or a workload (claims 6 and 14) wherein the creating the user path comprises: receiving a set of constraints indicative of a desired learning curriculum; and selecting the second set of nodes such that each of the second set of nodes matches the set of constraints; (claims 7 and 15) wherein each of the plurality of nodes in the knowledge graph comprises metadata associated with the education medium, the metadata comprises one or more of: a type of content, a level of difficulty, an intended audience, a time to complete, or an author of the education medium; (claim 8) wherein the chronological identity of each of the second set of nodes is representative of a level of difficulty of each learning medium associated with each node in the second set of nodes; (claim 18) a display comprising the first node and the one or more links to one or more nodes; receiving, from a second computing device associated with a user, data representative of qualifications associated with the user; creating, based on the qualifications, a user path through the knowledge graph, the user path comprising the first node and the one or more links to the one or more nodes; and assigning, to the first node and the one or more nodes, a chronological identity representative of an order by which the user proceeds through the user path, wherein the user path is representative of a learning curriculum comprising the first education medium and the one or more education media; (claim 20) receiving environmental data associated with the user at a current time; and analyzing, based on the environmental data and the current time associated with the user, a level of engagement associated with the user; (claim 21) wherein the qualifications comprise one or more of a job, family, years of experience, current time availability, current project time, or combinations thereof, and wherein the qualifications are determined from metadata associated with the user; (claim 22) wherein determining the first set of nodes based on the qualifications associated with the user further comprises: identifying known subject matter or basic subject matter for the user in the plurality of nodes; and filtering the plurality of nodes by selecting a subset of nodes from the plurality of nodes as the first set of nodes, wherein the subset of nodes do not comprise the identified known subject matter or the identified basic subject matter; (claim 23) wherein the visual indicators comprise at least one of: solid lines, dashed lines, mixed dotted lines, or combinations thereof; (claim 24) wherein the visual indicators comprise at least one of: solid lines for video recordings, dashed lines for slide decks, or mixed dotted and dashed lines for quizzes, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of methods of mental processes and organizing human activity but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – (claim 1) a computing device, a graphical user interface (claim 9) A knowledge graph system, a processor; a database; and a memory (claim 17) computing device, database, graphical user interface (GUI). These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use.
Furthermore, claims 1-4, 6-11, 13-15, 17-18 and 20-24 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. The claims are ineligible Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 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 non-obviousness.
Claims 1-4, 6-11, 13-15, 17-18 and 20-24 are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. “Learning Path Generator Based on Knowledge Graph”, Published June 13th, 2021, ACM Digital Library, https://dl.acm.org/doi/10.1145/3450148.3450155, hereinafter “Gao”, over Zaslavsky et al. (US 2015/0242978 A1), hereinafter “Zaslavsky”.
Regarding Claim 1, Gao teaches A method for generating a user path, the method comprising: (Gao, Abstract, page 27, Col 2, teaches learning path generator based on knowledge graph; page 33, Col 1, the generator can produce effectively a learning path which is highly similar to that constructed by experts. As a result, the method is expected to help teachers to construct learning paths automatically in e-learning systems for a learner);
classifying a plurality of nodes in a knowledge graph, each of the plurality of nodes representative of … ; (Gao, page 27, Col 2, teaches to construct a knowledge graph to model the domain knowledge and learning objects; Figure 1 teaches the knowledge graph and the relations and attributes);
establishing a plurality of links in the knowledge graph…, each of the plurality of links connecting to at least two nodes of the plurality of nodes, each of the plurality of links representative of an education relationship between the at least two nodes; (Gao, Figure 1 teaches the knowledge graph and the relations; Gao, Figure 2, teaches a knowledge graph and its links);
receiving, … , data representative of qualifications associated with the user; (See at least Gao, page 27, Col 2, teaches user’s characteristics; learning difficulty and importance);
determining, based on the qualifications associated with the user, a first set of nodes from the plurality of nodes, the first set of nodes representative of … known by the user; (Gao, Figures 1 and 2, teaches knowledge graph including nodes representing learning objects); alter the plurality of nodes and the plurality of links by filtering… the first set of nodes (See at least Gao, page 32, Section 5, teaches filtering the knowledge points (Examiner notes knowledge points are nodes));
creating a user path through the knowledge graph, the user path comprising a second set of nodes from the plurality of nodes, each node in the second set of nodes connected by a link from the plurality of links; (Gao, pages 32, Section 5, page 33, Col 1, the learning path generator has two stages, in the second stage the sequence of learning objects is given, where the problem of learning path generator is transformed (Examiner notes second stage is second set of nodes); Gao, Figure 2, teaches a knowledge graph and its links);
a chronological identity ... chronological identities... assigning, to each node in the second set of nodes, the chronological identity representative of an order by which the user proceeds through the user path (See at least Gao Figure 1 and pages 32, Section 5, page 33, Col 1, teaches learning path generator with sequence; Gao, page 30, column 2, teaches a learning order of the knowledge points; Gao, page 28, Section 3.1, Learning object and knowledge point have four attributes: ID, name, importance and difficulty (Examiner notes identity assigned to nodes));
wherein the user path is representative of a learning curriculum comprising the … associated with each of the second set of nodes (Gao, page 27, The goal of learning path generator is to design a best sequence of knowledge units for learners to follow).
Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches
an education medium… based on content of the education medium (Zaslavsky, Abstract, para 0003, e-learning systems include digital versions of traditional course materials (e.g., digital text and images that mimic those of a traditional, printed textbook), digital self-assessment tools (e.g., digital flash cards, quizzes, etc.), and simple tracking (e.g., quiz scoring, tracking of which lessons have been completed, timers to track time spent, etc.). Some, more recent e-learning systems have added more sophisticated functions. For example, newer digital course materials can include hyperlinks, videos, etc.) generating a graphical user interface (GUI) comprising the plurality of nodes and the plurality of links; wherein the GUI visually differentiates types of education media by displaying different visual indicators around each node; (Zaslavsky, Abstract, para 0003, e-learning systems include digital versions of traditional course materials (e.g., digital text and images that mimic those of a traditional, printed textbook), digital self-assessment tools (e.g., digital flash cards, quizzes, etc.), and simple tracking (e.g., quiz scoring, tracking of which lessons have been completed, timers to track time spent, etc.); See at least Zaslavsky, Figures 1-2 and 12, para 0038, teaching displays nodes and links of the knowledge entities; Zaslavsky, Figure 2, teaches different visual indicators for example a solid line and a dashed line around each entity); transmitting the GUI to a computing device associated with a user; causing the computing device to display the GUI; … from the computing device via the GUI (See at least Zaslavsky, para 0026, authors and students with the functionality of the course backend processor (e.g., by providing graphical user interfaces, etc.); Figures 1-2 and 12, displays nodes and links) causing the computing device to … from the GUI … such that the first set of nodes are no longer visible on the GUI; (See at least Zaslavsky, para 0048, some of the subset (Examiner notes as some of the subset are visible, this means some of the subset are no longer visible) of knowledge entities can be displayed to a student via the course consumption platform in accordance with the course definition, a course flow defined by the respective knowledge edges of the subset of knowledge entities); generating a modified GUI comprising the second set of nodes connected by the plurality of links; transmitting the modified GUI to the computing device; causing the computing device to highlight, on the modified GUI, the user path; and (See at least Zaslavsky, para 0026, functionality of the course backend processor (e.g., by providing graphical user interfaces, etc.); Zaslavsky, Figure 12, shows multiple courses are defined; Examiner notes the nodes are connected by links and highlighted) determining, based on metadata, ... associated with each node in the second set of nodes, wherein the determined ... arrange the second set of nodes in ascending order of difficulty; and (See at least Zaslavsky, para 0042, multiple statistics courses, for example, of different difficulty levels (e.g., basic versus advanced); para 0055, teaches step objects can be categorized and assigned to “baskets.” Each lesson step object or knowledge entity can contain one or more baskets... Each basket can represent a difficulty level, learning type, or any other useful categorization. The basket assignment can be implemented at the knowledge entity level (e.g., using the node attributes of knowledge entities or edge attributes of knowledge edges), at the object level (e.g., using practice step object attributes or practice edge attributes), or using course-level metadata or other metadata; Zaslavsky, para 0037, the course flow relationship considers a set of destination knowledge entities as a group of co-concepts falling within a macro-concept of a source knowledge entity 210 (e.g., as a bucket, macro-object, etc.)... it may be important that some combination of those knowledge entities is consumed as a prerequisite for consuming a next knowledge entity; Zaslavsky, para 0146, teaches the challenges given in order to adjust for yielding optimal student performance (e.g., a student may exhibit a monotonously increasing performance as the challenges difficulty levels increase to 120% (Examiner notes an ascending level of difficulty)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with an education medium …based on content of the education medium generating a graphical user interface (GUI) comprising the plurality of nodes and the plurality of links; wherein the GUI visually differentiates types of education media by displaying different visual indicators around each node; transmitting the GUI to a computing device associated with a user; causing the computing device to display the GUI; … from the computing device via the GUI … generating a modified GUI comprising the second set of nodes connected by the plurality of links; transmitting the modified GUI to the computing device; causing the computing device to highlight, on the modified GUI, the user path ... determining, based on metadata, a ... associated with each node in the second set of nodes, wherein the determined ... arrange the second set of nodes in ascending order of difficulty; as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004). The Gao invention now incorporating the Zaslavsky invention, has all the limitations of claim 1.
Regarding Claim 2, Gao, now incorporating Zaslavsky, teaches The method of Claim 1 … in the second set of nodes; and (Gao, pages 32-33, the learning path generator has two stages, in the second stage the sequence of learning objects is given (Examiner notes second stage is second set of nodes)).
Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches further comprising: retrieving, from a database, each education medium associated with each node (Zaslavsky, para 0080 and 0085, teaches relational database management system (RDBMS), etc.), Examiner notes data can be retrieved from RDBMS; para 0003, e-learning systems include digital versions of traditional course materials (e.g., digital text and images that mimic those of a traditional, printed textbook), digital self-assessment tools (e.g., digital flash cards, quizzes, etc.), and simple tracking (e.g., quiz scoring, tracking of which lessons have been completed, timers to track time spent, etc.). Some, more recent e-learning systems have added more sophisticated functions. For example, newer digital course materials can include hyperlinks, videos, etc.); transmitting each education medium to the user, thereby displaying each education medium on the GUI of the computing device associated with the user (Zaslavsky, para 0153, software or instructions may also be transmitted; See at least, para 0048, knowledge entities can be displayed to a student via the course consumption platform).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with further comprising: retrieving, from a database, each education medium associated with each node; and transmitting each education medium to the user, thereby displaying each education medium on the GUI of the computing device associated with the user as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 3, Gao, now incorporating Zaslavsky, teaches The method of Claim 1. Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches further comprising: receiving, from the computing device associated with the user, environmental data associated with the user at a current time; and (See at least Zaslavsky, para 0145, certain times of the day (or week, month, etc.)) analyzing, based on the environmental data and the current time associated with the user, a level of engagement associated with the user (See at least Zaslavsky, para 0145, performance trends; detected correlations between certain times of day, frequencies, etc. For example, when a clear correlation is determined between certain times of the day (or week, month, etc.), frequency and/or content; detecting that the student is experiencing a positive or negative streak, or the like). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with further comprising: receiving, from the computing device associated with the user, environmental data associated with the user at a current time; and analyzing, based on the environmental data and the current time associated with the user, a level of engagement associated with the user as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 4, Gao, now incorporating Zaslavsky, teaches the method of Claim 3, further comprising … second set of nodes (Gao, pages 32-33, the learning path generator has two stages, in the second stage the sequence of learning objects is given (Examiner notes second stage is second set of nodes)).
Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches
removing one or more nodes from the … based on the level of engagement (See at least Zaslavsky, para 0145, performance trends; detected correlations between certain times of day, frequencies, etc. For example, when a clear correlation is determined between certain times of the day (or week, month, etc.), frequency and/or content; detecting that the student is experiencing a positive or negative streak, or the like; Zaslavsky, para 0095, data graph microstructure can be removed… it can be desirable to ensure that the full set of practice data graph microstructures falls within a reasonable range of difficulty levels (e.g., those falling outside an acceptable range can be removed)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with removing one or more nodes from the … based on the level of engagement as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 6, Gao, now incorporating Zaslavsky, teaches The method of Claim 1, wherein the creating the user path comprises: … a set of constraints indicative of a desired learning curriculum; and selecting the second set of nodes such that each of the second set of nodes matches the set of constraints (Gao, pages 32-33, the learning path generator has two stages, in the second stage the sequence of learning objects is given (Examiner notes second stage is second set of nodes); Gao, page 28, Col 2, teaches constraints including difficulty; Examiner notes this is analogous to Applicant’s own specification, para 0018, which recites: “constraints can include, for example, a level of difficulty”. Gao, page 33, Col 1, the generator can produce effectively a learning path which is highly similar to that constructed by experts. As a result, the method is expected to help teachers to construct learning paths automatically in e-learning systems for a learner).
Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches
receiving, from the computing device associated with the user (See at least Zaslavsky, Figure 1 and para 0026, for the computing environment including a server computer, desktop computer, laptop computer, tablet computer, smart phone).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with receiving, from the computing device associated with the user as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 7, Gao, now incorporating Zaslavsky, teaches The method of Claim 1, wherein each of the plurality of nodes in the knowledge graph comprises metadata associated with the education medium, the metadata comprises one or more of: … a level of difficulty, an intended audience (Gao, page 28, Section 3.1, there are two types of nodes in the knowledge graph: learning object and knowledge point. Learning object and knowledge point has attributes including difficulty).
Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches
a type of content (Zaslavsky, para 0117, type of content item) or an author of the education medium (See at least Zaslavsky, para 0042 and 0117, teaches author; See at least Zaslavsky, para 0003, e-learning systems include digital versions of traditional course materials (e.g., digital text and images that mimic those of a traditional, printed textbook), digital self-assessment tools (e.g., digital flash cards, quizzes, etc.), and simple tracking (e.g., quiz scoring, tracking of which lessons have been completed, timers to track time spent, etc.). Some, more recent e-learning systems have added more sophisticated functions. For example, newer digital course materials can include hyperlinks, videos, etc.).a time to complete (Zaslavsky, para 0137, particular timeframe for completion).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with a type of content … or an author of the education medium … a time to complete as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 8, Gao, now incorporating Zaslavsky, teaches the method of Claim 1, wherein the chronological identity of each of the second set of nodes is representative of a level of difficulty of each … associated with each node in the second set of nodes (Gao, pages 32-33, the learning path generator has two stages, in the second stage the sequence of learning objects is given (Examiner notes second stage is second set of nodes); Gao, page 28, Section 3.1, Learning object and knowledge point have four attributes: ID, name, importance and difficulty (Examiner notes identity assigned to nodes).
Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches
learning medium (See at least Zaslavsky, para 0003, e-learning systems include digital versions of traditional course materials (e.g., digital text and images that mimic those of a traditional, printed textbook), digital self-assessment tools (e.g., digital flash cards, quizzes, etc.), and simple tracking (e.g., quiz scoring, tracking of which lessons have been completed, timers to track time spent, etc.). Some, more recent e-learning systems have added more sophisticated functions. For example, newer digital course materials can include hyperlinks, videos, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with learning medium as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding claim 9, the claims are an obvious variant to claim 1 above, and are therefore rejected on the same premise. Zaslavsky further teaches a processor, a database and a memory storing instructions that are executed by the processor. See at least Zaslavsky computing system Figure 1 and para 0026, teaches processor can be implemented as any suitable computational system, such as a server computer, desktop computer, laptop computer, tablet computer, para 0078, teaches memory; para 0080 and 0085, teaches database(s). With respect to claim 9 limitations: “dynamically modify the user path based on environmental data associated with the user by removing additional nodes from the second set of nodes”. The primary reference Gao teaches dynamically modify the user path … by removing additional nodes from the second set of nodes (See at least Gao, page 32, Section 5, teaches filtering the knowledge points. (Examiner notes knowledge points are nodes. Filtering manipulates data or information by selectively removing specific elements.)) Zaslavsky teaches “based on environmental data associated with the user”. See at least Zaslavsky, para 0095, teaches data graph microstructure can be removed; para 0145, teaches performance trends; detected correlations between certain times of day, frequencies, etc. For example, when a clear correlation is determined between certain times of the day (or week, month, etc.), frequency and/or content; detecting that the student is experiencing a positive or negative streak. Examiner notes this is analogous to Applicant’s own specification, para 0046, recites “examples of environmental data that can be used and obtained by the ED sensor 308 can include but are not limited to: a time of day, a day of the week”. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with based on environmental data associated with the user as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004). With respect to claim 9 newly added limitations: receive an education medium and associated metadata comprising at least a runtime and a level of difficulty; store the education medium in the database and automatically store the metadata in association with a corresponding node in the knowledge graph; (Zaslavsky, para 0052-0053, content resources (e.g., text, images, video, interactive animations, etc.) and/or dynamic content resources (e.g., content that adapts to other tags, contexts, etc.); teaches runtime into a specific intensity... Each object or response can also be associated with metadata; Zaslavsky, para 0042, multiple statistics courses, for example, of different difficulty levels (e.g., basic versus advanced); and user inputs ..., wherein the user inputs comprise one or more of a click, a scroll, a tap, a press, typing, or combinations thereof; ... and the user inputs (See at least Zaslavsky, para 0078 and para 0083, teaches one or more input devices (e.g., a mouse, a keyboard, etc.). Examiner notes a user types on a keyboard; Further, See Zaslavsky, para 0098, teaches text input; Even further, see Zaslavsky, para 0032, teaches pressing a button; Even further, Zaslavsky, para 0059, teaches a user clicks a button). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with receive an education medium and associated metadata comprising at least a runtime and a level of difficulty; store the education medium in the database and automatically store the metadata in association with a corresponding node in the knowledge graph; and user inputs ..., wherein the user inputs comprise one or more of a click, a scroll, a tap, a press, typing, or combinations thereof; ... and the user inputs as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 10, Gao, now incorporating Zaslavsky, teaches The knowledge graph system of Claim 9, wherein the instructions further cause the knowledge graph system to: … in the second set of nodes; (Gao, pages 32-33, the learning path generator has two stages, in the second stage the sequence of learning objects is given (Examiner notes second stage is second set of nodes)).
Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches retrieve, from the database, each education medium associated with each node (Zaslavsky, para 0080 and 0085, teaches relational database management system (RDBMS), etc.), Examiner notes data can be retrieved from RDBMS; para 0003, e-learning systems include digital versions of traditional course materials (e.g., digital text and images that mimic those of a traditional, printed textbook), digital self-assessment tools (e.g., digital flash cards, quizzes, etc.), and simple tracking (e.g., quiz scoring, tracking of which lessons have been completed, timers to track time spent, etc.). Some, more recent e-learning systems have added more sophisticated functions. For example, newer digital course materials can include hyperlinks, videos, etc.) and transmit each education medium to the user, thereby granting the user access to each education medium (Zaslavsky, para 0153, software or instructions may also be transmitted; See at least, para 0048, knowledge entities can be displayed to a student via the course consumption platform; para 0131, apply appropriate access controls (e.g., privileges, etc.) to the content, so that the content becomes accessible to students.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with retrieve, from the database, each education medium associated with each node … and transmit each education medium to the user, thereby granting the user access to each education medium as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 11, the claim recites analogous limitations to claim 3 above, and is therefore rejected on the same premise.
Regarding Claim 13, the claim recites analogous limitations to claim 5 above, and is therefore rejected on the same premise.
Regarding Claim 14, the claim recites analogous limitations to claim 6 above, and is therefore rejected on the same premise.
Regarding Claim 15, the claim recites analogous limitations to claim 7 above, and is therefore rejected on the same premise.
Regarding Claim 17, Gao teaches A method of generating a learning curriculum, the method comprising: (Gao, Abstract, page 27, Col 2, teaches learning path generator based on knowledge graph; page 33, Col 1, the generator can produce effectively a learning path which is highly similar to that constructed by experts. As a result, the method is expected to help teachers to construct learning paths automatically in e-learning systems for a learner);
classifying, based on the data representative of the contents of the … into a first node in a knowledge graph, the first node including a query path to the (Gao, page 27, col 2, teaches to construct a knowledge graph to model the domain knowledge and learning objects; Figure 1 teaches the knowledge graph and the relations and attributes);
establishing, based on the contents of the … , one or more links from the first node to one or more nodes in the knowledge graph, each of the one or more links representative of a relationship between the … associated with the one or more nodes (Gao, Figure 1 teaches the knowledge graph and the relations; Gao, Figure 2, teaches a knowledge graph and its links)
link the at least two nodes based on the type of relationship …(Gao, page 27, col 2, teaches to construct a knowledge graph to model the domain knowledge and learning objects; Figures 1 and 2 teaches the knowledge graph and the relations and attributes)
assigning a chronological identity to each of the first node and the one or more nodes, the chronological identity representative of an order by which a user proceeds through a user path; and (See at least Gao Figure 1 and pages 32, Section 5, page 33, Col 1, teaches learning path generator with sequence; Gao, page 30, column 2, teaches an output of a learning order of the knowledge points; Gao, page 28, Section 3.1, Learning object and knowledge point have four attributes: ID, name, importance and difficulty (Examiner notes identity assigned to nodes)).
Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches
receiving, from a first computing device associated with a contributor, (i) a first education medium comprising data representative of a contents of the first education medium and (ii) metadata comprising at least a runtime and a level of difficulty of the first education medium; storing, in a database, the first education medium; first education medium, the first education medium … first education medium in the database; automatically storing the metadata in association with the first node; … first education medium … first education medium and one or more education media (Zaslavsky, para 0080 and 0085, teaches databases; Zaslavsky, para 0052-0053, content resources (e.g., text, images, video, interactive animations, etc.) and/or dynamic content resources (e.g., content that adapts to other tags, contexts, etc.); teaches runtime into a specific intensity... Each object or response can also be associated with metadata; Zaslavsky, para 0042, multiple statistics courses, for example, of different difficulty levels (e.g., basic versus advanced; Abstract, para 0003, e-learning systems include digital versions of traditional course materials (e.g., digital text and images that mimic those of a traditional, printed textbook), digital self-assessment tools (e.g., digital flash cards, quizzes, etc.), and simple tracking (e.g., quiz scoring, tracking of which lessons have been completed, timers to track time spent, etc.). Some, more recent e-learning systems have added more sophisticated functions. For example, newer digital course materials can include hyperlinks, videos, etc.) sequentially displaying, on a graphical user interface (GUI), the first education medium and the one or more education media to the user based on the corresponding ..., wherein each education medium is retrieved from the database and presented to the user on the GUI as the user progresses through the user path (See at least Zaslavsky, para 0055, teaches objects can be categorized and assigned to “baskets.” Each lesson step object or knowledge entity can contain one or more baskets... Each basket can represent a difficulty level, learning type, or any other useful categorization. The basket assignment can be implemented at the knowledge entity level (e.g., using the node attributes of knowledge entities or edge attributes of knowledge edges), at the object level (e.g., using practice step object attributes or practice edge attributes), or using course-level metadata or other metadata; Zaslavsky, para 0037, the course flow relationship considers a set of destination knowledge entities as a group of co-concepts falling within a macro-concept of a source knowledge entity (e.g., as a bucket, macro-object, etc.)... it may be important that some combination of those knowledge entities is consumed as a prerequisite for consuming a next knowledge entity; Zaslavsky, para 0146, teaches given in order to adjust for yielding optimal student performance (e.g., a student may exhibit a monotonously increasing performance as the challenges difficulty levels increase to 120% (Examiner notes user progresses through the user path). Further, See Zaslavsky, Figures 1-2; Zaslavsky, para 0080 and 0085, teaches relational database management system (RDBMS), etc.), Examiner notes data can be retrieved from RDBMS; Zaslavsky, para 0153, software or instructions may also be transmitted; See at least, para 0048, knowledge entities can be displayed to a student via the course consumption platform). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with receiving, from a first computing device associated with a contributor, (i) a first education medium comprising data representative of a contents of the first education medium and (ii) metadata comprising at least a runtime and a level of difficulty of the first education medium; storing, in a database, the first education medium; first education medium, the first education medium … first education medium in the database; automatically storing the metadata in association with the first node; … first education medium … first education medium and one or more education media ... sequentially displaying, on a graphical user interface (GUI), the first education medium and the one or more education media to the user based on the corresponding ..., wherein each education medium is retrieved from the database and presented to the user on the GUI as the user progresses through the user path as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004). The Gao invention now incorporating the Zaslavsky invention, has all the limitations of claim 17.
Regarding Claim 18, Gao, now incorporating Zaslavsky, teaches The method of Claim 17, further comprising: receiving, …, data representative of qualifications associated with the user; (See at least Gao, page 27, col 2, teaches user’s characteristics; learning difficulty and importance);
creating, based on the qualifications, a user path through the knowledge graph, the user path comprising the first node and the one or more links to the one or more nodes; and (Gao, Abstract, teaches Learning path offers personalized guidance in order to improve learners’ performance in e-learning systems with a method of a learning path generator based on knowledge graph; Gao, Figure 2, teaches a knowledge graph and its links);
assigning, to the first node and the one or more nodes, a chronological identity representative of an order by which the user proceeds through the user path, (See at least Gao Figure 1 and pages 32, Section 5, page 33, Col 1, teaches learning path generator with sequence; Gao, page 30, column 2, teaches an output of a learning order of the knowledge points; Gao, page 28, Section 3.1, Learning object and knowledge point have four attributes: ID, name, importance and difficulty (Examiner notes identity assigned to nodes));
wherein the user path is representative of a learning curriculum … (Gao, page 27, The goal of learning path generator is to design a best sequence of knowledge units for learners to follow).
Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches
displaying, on a graphical user interface (GUI) at a second computing device associated with a user, a display comprising the first node and the one or more links to one or more nodes (See at least Zaslavsky, para 0026, teaches graphical user interfaces; Figures 1-2 and 12, displays nodes and links) from a second computing device associated with a user (Zaslavsky, para 0026, any suitable computational system, such as a server computer, desktop computer, laptop computer, tablet computer, smart phone) comprising the first education medium and the one or more education media (Zaslavsky, Abstract, para 0003, e-learning systems include digital versions of traditional course materials (e.g., digital text and images that mimic those of a traditional, printed textbook), digital self-assessment tools (e.g., digital flash cards, quizzes, etc.), and simple tracking (e.g., quiz scoring, tracking of which lessons have been completed, timers to track time spent, etc.). Some, more recent e-learning systems have added more sophisticated functions. For example, newer digital course materials can include hyperlinks, videos, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with displaying, on a graphical user interface (GUI) at a second computing device associated with a user, a display comprising the first node and the one or more links to one or more nodes; … from a second computing device associated with a user … comprising the first education medium and the one or more education media as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 20, Gao, now incorporating Zaslavsky, teaches The method of Claim 18. Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches further comprising: receiving, from the second computing device associated with the user, environmental data associated with the user at a current time; and (See at least Zaslavsky, para 0145, certain times of the day (or week, month, etc.)) analyzing, based on the environmental data and the current time associated with the user, a level of engagement associated with the user (See at least Zaslavsky, para 0145, performance trends; detected correlations between certain times of day, frequencies, etc. For example, when a clear correlation is determined between certain times of the day (or week, month, etc.), frequency and/or content; detecting that the student is experiencing a positive or negative streak, or the like). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with further comprising: receiving, from the second computing device associated with the user, environmental data associated with the user at a current time; and analyzing, based on the environmental data and the current time associated with the user, a level of engagement associated with the user.as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 21, Gao, now incorporating Zaslavsky, teaches The method of Claim 1, wherein the qualifications comprise one or more of (See at least Gao, page 27, Col 2, teaches user’s characteristics; learning difficulty and importance).
Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches a job, family, years of experience, current time availability, current project time, or combinations thereof, and wherein the qualifications are determined from metadata associated with the user
(See at least Zaslavsky, para 0031, the node attributes of the datagraph nodes can include various types of metadata. Some metadata in the node attributes can be used to describe the respective datagraph node …, the metadata can include …different knowledge levels of particular students (e.g., whether the concept(s) included in the datagraph node are considered new knowledge, a review of prior knowledge, a preview of future knowledge, etc.) different student context (e.g., student age, geography, affiliation, gender, socioeconomics, etc.), etc. Examiner notes affiliation is family.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with a job, family, years of experience, current time availability, current project time, or combinations thereof, and wherein the qualifications are determined from metadata associated with the user as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 22, Gao, now incorporating Zaslavsky, teaches The method of Claim 1, wherein determining the first set of nodes based on the qualifications associated with the user further comprises: … and filtering the plurality of nodes by selecting a subset of nodes from the plurality of nodes as the first set of nodes, wherein the subset of nodes do not comprise (See at least Gao, page 27, Col 2, teaches user’s characteristics; learning difficulty and importance. See at least Gao, page 32, Section 5, teaches filtering the knowledge points. Examiner notes knowledge points are nodes. Filtering manipulates data or information by selectively removing specific elements). Yet, Gao does not appear to explicitly teach and in the same field of endeavor Zaslavsky teaches identifying known subject matter or basic subject matter for the user in the plurality of nodes; …the identified known subject matter or the identified basic subject matter (See at least Zaslavsky, para 0031, the node attributes of the datagraph nodes can include various types of metadata. Some metadata in the node attributes can be used to describe the respective datagraph node …, the metadata can include …different knowledge levels of particular students (e.g., whether the concept(s) included in the datagraph node are considered new knowledge, a review of prior knowledge, a preview of future knowledge, etc.))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Gao with identifying known subject matter or basic subject matter for the user in the plurality of nodes; …the identified known subject matter or the identified basic subject matter as taught by Zaslavsky with the motivation for facilitation of dynamic course creation and increased course adaptability; improved measurement of student knowledge acquisition and retention, and of student and teacher performance (Zaslavsky, para 0004).
Regarding Claim 23, Gao, now incorporating Zaslavsky, teaches The method of Claim 1, wherein the visual indicators comprise at least one of: solid lines, dashed lines, mixed dotted lines, or combinations thereof (See at least Zaslinksy, Figure 2, shows visual indicator with solid line (for example, knowledge entity 210a); visual indicator with dashed line (knowledge entity 210b2)).
Regarding Claim 24, Gao, now incorporating Zaslavsky, teaches The method of Claim 1, wherein the visual indicators comprise at least one of: solid lines for video recordings, dashed lines for slide decks, or mixed dotted and dashed lines for quizzes (See at least Zaslavsky, Abstract, para 0003, e-learning systems include digital versions of traditional course materials (e.g., digital text and images that mimic those of a traditional, printed textbook), digital self-assessment tools (e.g., digital flash cards, quizzes, etc.)... digital course materials can include hyperlinks, videos, etc.); Zaslinksy, Figure 2, shows visual indicator with solid line (for example, knowledge entity 210a); visual indicator with dashed line (knowledge entity 210b2)).
Examiner notes that the visual indicators of solid line, dashed lines and mixed dots are labels for video recordings, slide decks, and quizzes, and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the specific type of information) which does not explicitly alter or impact the steps of the method does not patentably distinguish the claimed invention from the prior art in terms of patentability. See MPEP 2111.05
Response to Arguments
Applicants arguments filed on 12/03/2025 have been fully considered but they are not persuasive.
Regarding 35 U.5.C. § 101 rejections: Examiner has updated the 101 rejection in light of the most recent claim amendments and maintains the 101 rejection. Applicant’s arguments have been fully considered but are found unpersuasive.
Applicant remarks:
“A. Claims 1-11, 13-15 and 17-22 Are Patent Eligible Because They Are Not Directed to an Abstract Idea Under Step One of Alice …
1. Claims 1-11, 13-15 and 17-22 Are Patent Eligible Because They Do Not Fall Under Enumerated Groupings of Abstract Idea …
Here, the Office Action characterizes the instant claims as being directed to a concept "certain methods of organizing human activity," that are allegedly directed to the abstract idea.
Office Action at p.7 Applicant respectfully disagrees with this characterization. Nonetheless, to more clearly highlight the patent-eligible nature of the claims, Applicant hereby amends independent Claims 1, 9, and 17 to include additional features of, for example, "generating a graphical user interface (GUI) comprising the plurality of nodes and the plurality of links, wherein the GUI visually differentiates types of education media by displaying different visual indicators around each node," "transmitting the GUI to a computing device associated with a user," "determining, based on metadata, a chronological identity associated with each node in the second set of nodes, wherein the determined chronological identities arrange the second set of nodes in ascending order of difficulty," "assigning, to each node in the second set of nodes, the chronological identity representative of an order by which the user proceeds through the user path," and "wherein the user path is representative of a learning curriculum comprising the education medium associated with each of the second set of nodes" which represent improvements to the capability of the method and system as a whole.
The Office Action alleges that "the claims only recite the additional elements . . . [and] it amounts to no more than mere instructions to apply the exception using generic computer components." Office Action at 9. Applicant disagrees. The instant claims, which relate to a specific implementation of generating a customized user path… “
Examiner has considered all arguments and respectfully disagrees.
With respect to the abstract idea, the claimed invention falls within at least the abstract groupings of mental processes and organizing human activity as explained in the above 101 analysis. Further, Examiner again respectfully notes, Applicant appears to be confusing the additional elements (e.g. a computing device, a graphical user interface, etc.) under Step 2A: Prong One: Abstract Ideas. The additional elements are analyzed in Step 2A: Prong Two and Step 2B of the 101 analysis.
Further, with respect to Applicant’s remarks on DDR Holdings, the decision of DDR Holdings does not apply as, unlike DDR Holdings, the claimed invention is not "deeply rooted in the technology". See Step 2A: Prong Two and Step 2B of the 101 analysis.
Applicant further argues:
“2. Claims 1-11, 13-15 and 17-22 Are Patent Eligible Because They Recite a Practical Application…
Applicant respectfully submits that the claims are patent-eligible because the claims integrate the alleged abstract idea by using a computing system that includes various components including "a processor," "a memory," "a database," "a computing device," and "a graphical user interface (GUI)" in a "meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.... “
Examiner has considered all arguments and respectfully disagrees.
Applicant asserts the claims impose a meaningful limit on the abstract idea because the claims do not monopolize the judicial exception. Examiner respectfully does not find this assertion persuasive because Applicant does not explain how or why the quoted limitations of the claims do not monopolize the judicial exception, Applicant only makes the assertion. As such, Examiner finds Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patent eligible invention without specifically pointing out how the language of the claims reflect a practical application (e.g., how the claims reflect an improvement).
Further, with respect to integration of the abstract idea into a practical application, the computing elements (computing device, graphical user interface, processor; database; and a memory) are additional elements to perform the steps and amount to no more than mere instructions to apply the exception using generic computer components. Examiner has reviewed Applicants claims and specification and has found only generic computing elements. Examiner fails to see how the generic recitations of these most basic computer components and/or of a system so integrates the judicial exception as to “impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” Guidance, 84 Fed. Reg. at 53. Thus, Examiner finds that the claims recite the judicial exception of certain methods of organizing human activity and mental process and is not integrated into a practical application.
Applicant further argues:
“B. Claims 1-11, 13-15 and 17-22 Are Patent Eligible Under Step Two of Alice Because They Recite a Combination of Elements that Is Significantly More than an Abstract Idea …”
Examiner has considered all arguments and respectfully disagrees.
With respect to Applicant’s remarks on BASCOM, in BASCOM, as in the instant case, the claimed invention was directed to an abstract idea, and only contained additional elements not amounting to significantly more when considered individually. However, the distinction between BASCOM and the instant case is that the claimed invention had a “non-conventional and non-generic arrangement of the additional elements.” This non-conventional arrangement was “installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user.” (BASCOM, 827 F.3d at 1345). In the instant case, there is no indication that the arrangement is non-conventional or non-generic. Instead, the computer appears to be a generic computer (Figure 3 of Applicants specification), used to generate a user path. Therefore, the arrangement of elements is conventional and generic.
In summary, the computer is merely a platform on which the abstract idea is implemented. Simply executing an abstract concept on a computer does not transform a patent-ineligible claim into a patent-eligible one. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1280 (Fed. Cir. 2012). There are no improvements to another technology or technical field, no improvements to the functioning of the computer itself, transformation or reduction of a particular article to a different state or thing or any other meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment as a result of performing the claimed method. The claimed sequence of steps comprises only “conventional steps, specified at a high level of generality,” which is insufficient to supply an “inventive concept.” Id. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1297, 1300). Also the addition of merely novel or non-routine components to the claimed idea does not necessarily turn an abstraction into something concrete (See Ultramercial, Inc. v. Hulu, LLC, _ F.3d_, 2014 WL 5904902, (Fed. Cir. Nov. 14, 2014). Hence the claims do not recite significantly more than an abstract idea. Examiner maintains the 101 rejection with respect to these and all depending claims unless otherwise indicated.
Regarding 35 U.S.C. § 103 rejections. With respect to the prior art rejections, Applicants arguments have been fully considered but are found unpersuasive. Examiner has updated the rejections in light of the most recent claim amendments. With respect to Applicants remarks that Gao does not teach “"determining, based on metadata, a chronological identity associated with each node in the second set of nodes, wherein the determined chronological identities arrange the second set of nodes in ascending order of difficulty,” (Remarks page 21), Examiner respectfully disagrees as the applied reference Gao is not used alone.
Gao teaches a “chronological identity” associated with each node. See at least Gao, page 28, Section 3.1, Learning object and knowledge point have four attributes: ID, name, importance and difficulty (Examiner notes identity associated with nodes)). Gao, page 30, column 2, teaches learning order of the knowledge points and at least Gao Figure 1 and pages 32, Section 5, page 33, Col 1, teaches learning path generator with sequence.
Examiner has provided the Zaslavsky reference to teach “determining, based on metadata, associated with each node in the second set of nodes, wherein the determined ... arrange the second set of nodes in ascending order of difficulty”. See at least Zaslavsky, para 0042, teaching multiple courses, for example, of different difficulty levels (e.g., basic versus advanced); para 0055, teaches step objects can be categorized and assigned to “baskets.” Each lesson step object or knowledge entity can contain one or more baskets... Each basket can represent a difficulty level, learning type, or any other useful categorization. The basket assignment can be implemented at the knowledge entity level (e.g., using the node attributes of knowledge entities or edge attributes of knowledge edges), at the object level (e.g., using practice step object attributes or practice edge attributes), or using course-level metadata or other metadata; Zaslavsky, para 0037, the course flow relationship considers a set of destination knowledge entities as a group of co-concepts falling within a macro-concept of a source knowledge entity 210 (e.g., as a bucket, macro-object, etc.)... it may be important that some combination of those knowledge entities is consumed as a prerequisite for consuming a next knowledge entity (Examiner notes ascending level of difficulty). Further, Zaslavsky, para 0146, teaches the challenges given in order to adjust for yielding optimal student performance (e.g., a student may exhibit a monotonously increasing performance as the challenges difficulty levels increase to 120% (Examiner notes ascending level of difficulty)).
Applicant further argues that Gao does not teach "receive, from a computing device associated with a user, data representative of qualifications and user inputs associated with the user, wherein the user inputs comprise one or more of a click, a scroll, a tap, a press, typing, or combinations thereof,” (Remarks page 21), Examiner respectfully disagrees as the Zaslavsky reference is used to teach “wherein the user inputs comprise one or more of a click, a scroll, a tap, a press, typing, or combinations thereof”. See at least Zaslavsky, para 0078 and para 0083, teaches one or more input devices (e.g., a mouse, a keyboard, etc.) Examiner notes a user types on a keyboard to enter data. Further, See Zaslavsky, para 0098, teaches text input. Even further, see Zaslavsky, para 0032, teaches pressing a button. Even further, Zaslavsky, para 0059, teaches a user clicks a button. Applicant further argues that Gao does not teach "sequentially displaying, on a graphical user interface (GUI), the first education medium and the one or more education media to the user based on the corresponding chronological identity, wherein each education medium is retrieved from the database and presented to the user on the GUI as the user progresses through the user path,” (Remarks page 21), Examiner respectfully disagrees as the applied reference Gao is not used alone.
Gao teaches a chronological identity associated with each node. See at least Gao, page 28, Section 3.1, Learning object and knowledge point have four attributes: ID, name, importance and difficulty (Examiner notes identity associated with nodes)). Gao, page 30, column 2, teaches learning order of the knowledge points and at least Gao Figure 1 and pages 32, Section 5, page 33, Col 1, teaches learning path generator with sequence.
Examiner has provided the Zaslavsky reference to teach “sequentially displaying, on a graphical user interface (GUI), the first education medium and the one or more education media to the user based on the corresponding ..., wherein each education medium is retrieved from the database and presented to the user on the GUI as the user progresses through the user path”. See at least Zaslavsky, para 0055, teaches objects can be categorized and assigned to “baskets.” Each lesson step object or knowledge entity can contain one or more baskets... Each basket can represent a difficulty level, learning type, or any other useful categorization. The basket assignment can be implemented at the knowledge entity level (e.g., using the node attributes of knowledge entities or edge attributes of knowledge edges), at the object level (e.g., using practice step object attributes or practice edge attributes), or using course-level metadata or other metadata; Zaslavsky, para 0037, the course flow relationship considers a set of destination knowledge entities as a group of co-concepts falling within a macro-concept of a source knowledge entity (e.g., as a bucket, macro-object, etc.)... it may be important that some combination of those knowledge entities is consumed as a prerequisite for consuming a next knowledge entity; Zaslavsky, para 0146, teaches given in order to adjust for yielding optimal student performance (e.g., a student may exhibit a monotonously increasing performance as the challenges difficulty levels increase to 120% (Examiner notes as user progresses through the user path). Further, See Zaslavsky, Figures 1-2; Zaslavsky, para 0080 and 0085, teaches relational database management system (RDBMS), etc.), Examiner notes data can be retrieved from RDBMS; Zaslavsky, para 0153, software or instructions may also be transmitted; See at least, para 0048, knowledge entities can be displayed to a student via the course consumption platform.
Therefore Applicants remarks are found unpersuasive and Examiner has updated and maintains the 103 rejections for all claims.
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Mubarek US 9,262,058 – a node graph - altering the edges, e.g., using different dash styles, colors, shapes, sizes, thicknesses, etc. Similarly, shape (solid line, dashed line, dotted line, etc.) and color of the edges can further define the interaction and/or relationship between respective nodes
Rosenberg US 2020/0174630 – See at least para 0441, “displayed with emphasis (e.g. bolded or solid lines), whereas logical connections to other nodes are displayed with less emphasis (e.g. dashed lines), and other visible nodes may be displayed with even less emphasis (e.g. grayed out).”
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Conclusion
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/R.R.N./Examiner, Art Unit 3629
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626