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 .
Claims 1-19 are pending in the application under prosecution and have been examined.
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors.
The specification should be amended to reflect the status of all related application, whether patented or abandoned. Therefore, applications noted by their serial number and/or attorney docket number should be updated with correct serial number and patent number if patented.
The first instance of all acronyms or abbreviation should be spelled out for clarity, whether or not considered well known in the art.
In the response to this Office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
37 C.F.R. § 1.83(a) requires the Drawings to illustrate or show all claimed features.
Applicant must clearly point out the patentable novelty that they think the claims present, in view of the state of the art disclosed by the references cited or the objections made, and must also explain how the amendments avoid the references or objections. See 37 C.F.R. § 1.111(c).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7 and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites the limitation " an embedding table of the deep learning recommendation model". There is insufficient antecedent basis for the limitation “the deep learning recommendation model” in the claim.
Claim 16, similar to claim 17, recites the limitation " an embedding table of the deep learning recommendation model". There is insufficient antecedent basis for the limitation “the deep learning recommendation model” in the claim.
Allowable Subject Matter
Claims 1-6, 8-15, and 18-19 are allowed.
Claims 7 and 16 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
The present invention features: a computer-implemented method for processing embedding layers of a model, comprising: receiving, by a computer processor, a historical data set of items accessed by users of a computer system, where each entry in the historical data set indicates a subset of items accessed by a given user;
Related prior art include:
US 20240303514 A1 (SINGH et al) teaching graph-based techniques including: generating a network graph for an entity or entity class based on a plurality of interaction data objects for the entity; the network graph including a plurality of nodes and a plurality of edges; each node corresponding to a particular interaction code of at least one of the plurality of interaction data objects; each edge connecting a node pair that is associated with a particular interaction data object, the nodes and edges weighted to enable the clustering of the network graph for an entity class.
US 8762298 B1 (RANJAN et al) teaching method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a connectivity graph based feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model representing the labels as a function of the values of the connectivity graph based feature.
US 9654593 B2 (GARG et al) teaching plurality of data sources for electronic communications between users analyzed and assigned a relative importance value, a weight also assigned to each of the connections between the users, the weight being an encoded value computed based on a link structure of the connections where the link structure includes metadata indicating a category and a status of the respective connection.
US 9292884 B2 (MARLOW et al) teaching system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: access a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the plurality of nodes corresponding to a plurality of users associated with an online social network, respectively; identify a plurality of clusters in the social graph using graph clustering, each cluster comprising a discrete set of nodes from the plurality of nodes.
US 7,903,537 (TAO et al) teaching method for allocating channel resources in an orthogonal frequency-division multiple access network including a set of base stations and a set of mobile stations (MS) for each base station, comprising the steps of: constructing an interference graph, in which nodes in the interference graph represent mobile stations, and each edge between a pair of nodes represents a potential interference between the mobile stations represented by the pair of nodes; a weight assigned to each edge, which reflects interference between the two MSs connected by the edge, the interference graph partitioned into non-overlapping clusters of nodes based on a structure of the interference graph, the potential interference, so that a sum of the weights of the edges between each cluster is maximized.
US 20260154435 A1 (CELLA et al) teaching system to include a data classification module configured to classify data into classified data based on predefined sensitivity levels and regulatory compliance requirements with determination of k clusters within an input graph data set and an identification of the nodes and/or edges that are included in each cluster, the graph neural network configured to determine clusters based on a k-means clustering analysis.
US 12481629 B1 (WOSNER et al) teaching system to include a data classification module configured to classify data into classified data based on predefined sensitivity levels and regulatory compliance requirements, the system adjusting the edge weights between the entity names according to transactional data related to the entities to create a final entity graph.
US 20250181602 A1 (KHAN et al) teaching predictive model using training data to detect data redundancies from two or more datasets stored to data storage location(s), the training including testing the predictive model by predicting a target variable and iteratively adjusting weights and calculations during each subsequent iteration to improve predictability of a target variable.
Y. Cui et al., "Accelerating Graph Neural Network Inference in Heterogeneous Computing Environments," 2025 IEEE International Conference on Big Data (BigData), Macau, China, 2025, pp. 7311-7320.
P. Fang et al., "OMeGa: Boosting Large-scale Graph Embeddings with Heterogeneous Memory Processing," 2025 IEEE 41st International Conference on Data Engineering (ICDE), Hong Kong, Hong Kong, 2025, pp. 3369-3383.
Z. Shen, W. Zhao, B. Wang, Z. Wang and W. Shang, "CAGR: A Cross-Accelerator Graph Optimization Framework for Efficient Recommender System Inference," in IEEE Access, vol. 14, pp. 38544-38562, 2026.
However, none of the prior art of record teaches solely or renders obvious the features recited in the claims, taken in light of the disclosure; specifically:
constructing, by the computer processor, a graph from the historical data set, where a node in the graph represents a given item, an edge between nodes represents an occurrence of the items being accessed together, and a weight assigned to an edge in the graph indicates a frequency of the items being accessed together; clustering, by the computer processor, nodes in the graph to form one or more clusters of nodes; for each cluster in the one or more cluster of nodes, computing partial sums for the nodes assigned to a given cluster; and storing, by the computer processor, the partial sums in a cache memory.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE MICHEL BATAILLE whose telephone number is (571)272-4178. The examiner can normally be reached Monday - Thursday 7-6 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TIM VO can be reached at (571) 272-3642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PIERRE MICHEL BATAILLE/Primary Examiner, Art Unit 2138
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