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 .
DETAILED ACTION
This action is responsive to the following communication: Non-Provisional Application filed Jan. 31, 2023.
Claims 1-20 are pending in the case. Claims 1, 14 and 20 are independent claims.
Claim Objections
Claims 10-12 are objected to because of their dependency from rejected independent claims. Claims 10-12 would be allowable if written in independent form.
Claim Rejections - 35 USC § 112
The term “super” in claim 3 is a relative term which renders the claim indefinite. The term “super” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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.
Claims 1-9 and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Malhotra et al. (hereinafter Malhotra) U.S. Patent Publication No. 2019/0340945 in view of Badawy et al. (hereinafter Badawy) U.S. Patent Publication No. 2020/0382586.
With respect to independent claim 1, Malhotra teaches a method for intent-driven adaptive learning delivery, the method comprising:
receiving a learning query comprising a learning topic and a learning intent (see e.g., Para [52]-[62] – “The user may specify a particular career goal or a career goal may be inferred based on data about the user. A career goal may be a particular set of one or more skills, a particular job title, a particular job listing, a particular job function, or a particular job position … In an embodiment, learning path generator 134 generates an ILP in response to explicit user input requesting one, but where the input does not indicate any particular career goal. In such a scenario, learning path generator 134 generates one or more ILPs, one for each inferred career goal.”);
obtaining a metadata graph representative of an asset catalog; filtering, based on the learning topic, the metadata graph to identify a node subset (see e.g., Fig. 3 Para [31]-[51][85]-[98] – “After training a prediction model and validating the prediction model, it may be determined that a subset of the features have little correlation or impact on the final output. In other words, such features have low predictive power. Thus, machine-learned weights for such features may be relatively small, such as 0.01 or −0.001. In contrast, weights of features that have significant predictive power may have an absolute value of 0.2 or higher. Features with little predictive power may be removed from the training data. Removing such features can speed up the process of training future prediction models and making predictions.”);
selecting, based on the learning intent, a reduced node subset from the node subset (see e.g., Para [35]-[51] - “Output of the machine-learned model is a confidence score. Skill pairs whose score is above a confidence threshold are included in (or become part of) the skill dependency graph. Conversely, skill pairs whose score is below the confidence threshold are excluded from the skill dependency graph. In a related embodiment, if a confidence score of a skill pair is within a certain range (e.g., between 0.4 and 0.7), then such a skill pair may be marked for manual review and verification.”).
generating a graph using the reduced node subset; and creating a learning curriculum based on the graph (see e.g., Para [60]-[71] – “learning path generator 134 generates an individualized learning path for a user based on data about the user, a career goal associated with the user, and a skill dependency graph (that is generated, for example, based on online activities of multiple users). An individualized learning path (ILP) for a user may be generated in response to explicit input from the user. For example, a user is prompted to select a job listing or a future job title that the user desires to hold. In response to the user selecting a job listing or a job title, learning path identifier 134 generates an ILP for the user.”).
Malhotra does not expressly show that the graph is a k-partite metadata graph. However, Badawy teaches the above feature (see e.g. para [63] – “the identity management data may be faithfully represented by a k-partite graph, with k types of entities (nodes/vertices, e.g., identity-id, title, location, entitlement, etc.) and stored in a graph data store. It will be noted that graph data store 132 may be stored in any suitable format and according to any suitable storage, including, for example, a graph store such a Neo4j, a triple store, a relational database, etc.”). Both Malhotra and Badawy are directed to graph based artificial intelligence systems. Accordingly, it would have been obvious to the skilled artisan before the effective filing date of the claimed invention having Malhotra and Badawy in front of them to modify the system of Malhotra to include the above feature. The motivation to combine Malhotra and Badawy comes from Badawy. Badawy discloses the motivation to use k-partite metadata graph so that computational burden is reduced (see e.g. para [10][13]-[15][39]).
With respect to dependent claim 2, the modified Malhotra teaches creating the learning curriculum based on the k-partite metadata graph, comprises: identifying at least one key node in the k-partite metadata graph; determining, based on the at least one key node, a learning path traversing at least a portion of the k-partite metadata graph (see e.g. Abstract, Claim 1 and para [60]-[71] – “A skill dependency graph is generated that indicates, for each pair of connecting nodes in the graph, a first skill in the pair as a prerequisite of a second skill in the pair. A set of destination skills is determined that a user is to obtain to achieve a possible career goal.” Also see Badawy Para [63]-[73]); identifying a set of learning path nodes positioned along the learning path; and creating the learning curriculum comprising a manifest of assets listing a set of assets corresponding, respectively, to the set of learning path nodes (see e.g. para [71]-[79] Also see Badawy Para [63]-[73]).
With respect to dependent claim 3, the modified Malhotra teaches each key node of the at least one key node is one selected from a group of nodes comprising a super node, a most connected node in a subgraph of the k-partite metadata graph, a first node positioned along a longest path traversing the k-partite metadata graph, and a second node positioned along a shortest path traversing the k-partite metadata graph (see e.g. Badawy Para [64][68][90]-[100] – The examiner notes that based on the specification of current application, any node can be a “super node.”).
With respect to dependent claim 4, the modified Malhotra teaches creating the learning curriculum based on the k-partite metadata graph, further comprises: prior to determining the learning path: adjusting an edge weight associated with at least one edge in the k-partite metadata graph to obtain at least one adjusted edge weight, wherein the learning path is determined further based on the at least one adjusted edge weight (see e.g. Badawy Para [10][64][68][90]-[100] – “Each edge (or relationship) of the graph may join two nodes of the graph and be associated with a similarity weight representing a degree of similarity between the identities of the respective nodes. The identity graph may then be pruned to remove weak edges (e.g., those edges whose similarity weight may fall below a pruning threshold). The pruned identity graph can then be clustered into peer groups of identities (e.g., using a graph based community detection algorithm).”).
With respect to dependent claim 5, the modified Malhotra teaches prior to obtaining the metadata graph: obtaining a user profile for an organization user, wherein the learning query originates from the organization user and the user profile comprises user learning preferences associated with the organization user, and wherein the edge weight is adjusted based on the user learning preferences (see e.g., Para [23]-[27]).
With respect to dependent claim 6, the modified Malhotra teaches the user profile further comprises user talent information associated with the organization user (see e.g., Para [23]-[27]).
With respect to dependent claim 7, the modified Malhotra teaches after obtaining the user profile: reducing, based on the learning intent, the user talent information to obtain impactful user talent information, wherein the edge weight is adjusted further based on the impactful user talent information (see e.g., Badawy Para [61] -“ This similarity weight may be generated based on the number of entitlements shared between the two joined nodes. As but one example, the similarity weight could be based on a count of the similarity (e.g., overlap or intersection of entitlements) between the two identities divided by the union of entitlements. For example, in one embodiment, the edges are weighted via a proper similarity function (e.g., Jaccard similarity). In one embodiment, a dissimilarity measure, of entitlement binary vectors, d, may be chosen, then the induced similarity, 1-d(x,y), may be used to assign a similarity weight to the edge joining the nodes, x,y. Other methods for determining a similarity weight between two nodes are possible and are fully contemplated herein.”).
With respect to dependent claim 8, the modified Malhotra teaches after creating the learning curriculum: providing, in response to the learning query, the learning curriculum to the organization user (see e.g., Abstract Para [60]-[64] – “The individualized learning path is presented on a screen of a computing device of the user.”).
With respect to dependent claim 9, the modified Malhotra teaches the user profile further comprises user access permissions associated with the organization user (see e.g., Para [18]-[22] – “server system 130 stores the information in an account that is associated with the user and that is associated with credential data that is used to authenticate the user to server system 130 when the user attempts to log into server system 130 at a later time … Server system 130 determines, based on the name, where the employer and/or user is located. If the employer has multiple offices, then a location of the user may be inferred based on an IP address associated with the user when the user registered with a social network service (e.g., provided by server system 130) and/or when the user last logged onto the social network service.” Malhotra does not expressly show this feature. However, the examiner notes that this feature is well-known in the art.).
With respect to dependent claim 13, the modified Malhotra teaches each asset in the set of assets is a learning material for gaining proficiency in the learning topic and for satisfying the learning intent (see e.g., Para [28]-[34]).
Claim 14 is rejected for the similar reasons discussed above with respect to claim 1.
Claim 15 is rejected for the similar reasons discussed above with respect to claim 2.
Claim 16 is rejected for the similar reasons discussed above with respect to claim 4.
Claim 17 is rejected for the similar reasons discussed above with respect to claim 5.
Claim 18 is rejected for the similar reasons discussed above with respect to claim 6.
Claim 19 is rejected for the similar reasons discussed above with respect to claim 7.
Claim 20 is rejected for the similar reasons discussed above with respect to claim 1.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEIYONG WENG whose telephone number is (571)270-1660. The examiner can normally be reached on Mon.-Fri. 8 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Matthew Ell, can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PEI YONG WENG/Primary Examiner, Art Unit 2141