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 is a reply to the application filed on 12/31/2024, in which, claim(s) 1-45 are pending. Claim(s) 1, 23 and 45 are independent.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/14/2025, has been reviewed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the examiner is considering the information disclosure statement.
Drawings
The drawings filed on 12/31/2024 are accepted by The Examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 1-45 are non-provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over:
Claims 1-20 of Patent 12,189,782.
Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 1-45 are anticipated by claims 1-20 of Patent 12,189,782.
Instant Application 19/007,181
Patent US 12,189,782 B2
Claim 1. A method for translating a natural language user query into a graph database query comprising: receiving a first input from a user comprising a natural language query regarding data in a graph database;
inputting the natural language query into a first machine learning model trained to predict an intent of the user based on the natural language query;
inputting the natural language query into a second machine learning model trained to predict entities and relationships between the entities based on the natural language query;
determining a set of corresponding graph database properties based on the predicted entities and relationships between the entities using a similarity algorithm; and
generating a graph database query based on the set of corresponding graph database properties and the predicted intent of the user.
Claim 1. A computer-implemented method for translating a natural language user query into a graph database query comprising:
receiving a first input from a user comprising a natural language query regarding data in a graph database;
processing the natural language query using a named entity recognition (NER) machine learning model to extract named entities from the natural language query and tag them according to an entity type;
processing the tagged named entities using a word similarity algorithm to identify corresponding nodes and edges, and their associated properties, in the graph database;
processing the natural language query using an intent classification machine learning model to determine a user intent for the natural language query; and
applying a user intent-based template to the identified nodes and edges to formulate a graph database query that corresponds to the natural language query.
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 of this title, 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.
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.
Claims 1-5, 7-15, 17-27, 29-37 and 39-45 are rejected under 35 U.S.C. 103 as being unpatentable over Tomkins et al. (US 2021/0295822 A1) in view of Kim et al. (US 2020/0279002 A1).
Regarding Claims 1, 23 and 45, Tomkins discloses
receiving a first input from a user comprising a natural language query regarding data in a graph database ([0096], “obtaining a query during a session…the query may include a natural language query”, [0013], “retrieve data based on a set of ontology graphs (database)”);
inputting the natural language query into a second machine learning model trained to predict entities and relationships between the entities based on the natural language query ([0096], “the query may include a natural language query”, [0099], “determining one or more learned representations based on the query… As described elsewhere in this disclosure, a learned representation may include a quantitative value, a category, a vector, a list of data objects, or the like… Some embodiments may use the same machine learning model”);
determining a set of corresponding graph database properties based on the predicted entities and relationships between the entities using a similarity algorithm ([0099], “determining one or more learned representations based on the query… As described elsewhere in this disclosure, a learned representation may include a quantitative value, a category, a vector, a list of data objects, or the like… Some embodiments may use the same machine learning model”, [0077], “In some embodiments, a determination that a pair of vector are part of a same cluster may indicate a degree of semantic similarity between the n-grams represented by the pair of vectors, where a lesser distance may be correlated with an increased degree of semantic similarity”); and
generating a graph database query based on the set of corresponding graph database properties ([0101], “a set of query scores for a query may be combined to form a query score vector”).
Tomkins does not explicitly teach but Kim teaches
inputting the natural language query into a first machine learning model trained to predict an intent of the user based on the natural language query and generating based on the predicted intent of the user (Abstract, “receiving a query based on an utterance of a user and a context associated with the user; analyzing an intent of the user for the query based on a natural language understanding (NLU) for the query…predicting the intent of the user using a deep learning based probabilistic model having the query”);
Tomkins and Kim are analogous art as they are in the same field of endeavor of information security. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Kim with the disclosure of Tomkins. The motivation/suggestion would have been for processing an unclear intent query (Kim, [0002]).
Regarding Claims 2 and 24, the combined teaching of Tomkins and Kim teaches
wherein the first machine learning model and the second machine learning model are trained using datasets comprising phrases chosen from a particular application domain (Tomkins, [0271], “Some embodiments may generate a set of phrase vectors or a set of sentence vectors for phrases or sentences”, [0273], “some embodiments may include user profiles that categorize users as being associated with at least one of three different domains of knowledge, labeled with the list of category values ‘[“infrastructure”, “cybersecurity”, “development”]”)
Regarding Claims 3 and 25, the combined teaching of Tomkins and Kim teaches
wherein the particular application domain is any of: a cyber security application domain (Tomkins, [0273], “some embodiments may include user profiles that categorize users as being associated with at least one of three different domains of knowledge, labeled with the list of category values ‘[“infrastructure”, “cybersecurity”, “development”]”).
Regarding Claims 4 and 26, the combined teaching of Tomkins and Kim teaches
wherein the second machine learning model is trained to tag the predicted entities according to an entity type, and wherein the set of corresponding graph database properties are determined based at least in part on the entity type (Tomkins, [0036], “rank the relevance of retrieved data based on a number of matches with exact terms or metadata tags”, [0043], “database may refer to various types of data structures”, [0050-0053], “a concept may be represented by a first vertex, a label or another type of category value”, “various types of data”).
Regarding Claims 5 and 27, the combined teaching of Tomkins and Kim teaches
wherein the first machine learning model is trained to predict a plurality of intents of the user based on the natural language query (Kim, Abstract, “predicting the intent of the user using a deep learning based probabilistic model having the query”).
Regarding Claims 7 and 29, the combined teaching of Tomkins and Kim teaches
displaying the graph database query on a display device and receiving a second input comprising feedback from the user (Tomkins, [0036], “displaying a plurality of expanded queries”, [0188], “a UI that includes UI elements enabling a user to provide feedback”).
Regarding Claims 8 and 30, the combined teaching of Tomkins and Kim teaches
wherein the second input comprising feedback from the user is a confirmation that the graph database query is an accurate translation of the natural language query (Tomkins, [0226], “For example some embodiments may receive a feedback message indicating that a summary is accurate”).
Regarding Claims 9 and 31, the combined teaching of Tomkins and Kim teaches
wherein the second input comprising feedback from the user is a correction to the graph database query (Tomkins, [0226], “some embodiments may receive a feedback message indicating that a summary is inaccurate and, in response, some embodiments may decrease a preference weight associated with the set of ontologies used to generate the summary”).
Regarding Claims 10 and 32, the combined teaching of Tomkins and Kim teaches
submitting the graph database query to the graph database (Tomkins, [0309], “The user may then tap on a UI element such as a button labeled “submit,” causing the client computing device to send a web message that includes the n-gram “coughing” and a set of update values including “tuberculosis” and “relType1” to a server performing one or more operations described in this disclosure”).
Regarding Claims 11 and 33, the combined teaching of Tomkins and Kim teaches
returning a graph database result following submission of the graph database query to the graph database (Tomkins, [0050], “using one or more of the operations described in this disclosure may provide search results that match or exceeds other language models in general language tasks”).
Regarding Claims 12 and 34, the combined teaching of Tomkins and Kim teaches
wherein the graph database result comprises a visual display of nodes and edges in a subgraph of the graph database that match the graph database query (Tomkins, [0355], “FIG. 23 is a diagram of an example user interface displaying a representation of a decision tree… The decision tree 2300 includes a decision tree root node 2302”).
Regarding Claims 13 and 35, the combined teaching of Tomkins and Kim teaches
wherein the graph database result comprises numbers or text (Tomkins, [0188], “Some embodiments may present the results of the AI services 1234 to a UI that includes UI elements enabling a user to provide feedback. For example, some embodiments may display a generated text to a user in a UI”).
Regarding Claims 14 and 36, the combined teaching of Tomkins and Kim teaches
wherein the second machine learning model comprises a supervised learning model (Tomkins, [0235], “performing a set of supervised learning operations”).
Regarding Claims 15 and 37, the combined teaching of Tomkins and Kim teaches
wherein the second machine learning model comprises a recurrent neural network (RNN) (Tomkins, [0067], “some embodiments may use bidirectional long short term memory (LSTM) neural networks or another recurrent neural network”).
Regarding Claims 17 and 39, the combined teaching of Tomkins and Kim teaches
wherein the training of the second machine learning model is updated according to the feedback received from the user (Tomkins, [0188], “some embodiments may update a set of named entity recognition operations based on the updated feedback”).
Regarding Claims 18 and 40, the combined teaching of Tomkins and Kim teaches
wherein the first machine learning model comprises a supervised learning model (Tomkins, [0235], “performing a set of supervised learning operations”).
Regarding Claims 19 and 41, the combined teaching of Tomkins and Kim teaches
wherein the first machine learning model comprises a Bidirectional Encoder Representations from Transformers (BERT) model, a long short-term memory (LSTM) model, or a Naive Bayes model (Tomkins, [0067], “some embodiments may use bidirectional long short term memory (LSTM) neural networks”).
Regarding Claims 20 and 42, the combined teaching of Tomkins and Kim teaches
wherein the first machine learning model is trained using a set of intent-labeled natural language queries (Kim, [0051], “a query intent classifier 330” as a set of intent-labeled).
Regarding Claims 21 and 43, the combined teaching of Tomkins and Kim teaches
wherein the training of the first machine learning model is updated according to the feedback received from the user (Tomkins, [0188], “some embodiments may update a set of named entity recognition operations based on the updated feedback”).
Regarding Claims 22 and 44, the combined teaching of Tomkins and Kim teaches
wherein no prior knowledge of the underlying graph database schema is required of the user (Tomkins, [0049], “a knowledge graph organized by the ontology data model”, i.e., no prior knowledge of the underlying graph database schema).
Claims 6, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Tomkins et al. (US 2021/0295822 A1) in view of Kim et al. (US 2020/0279002 A1) further in view of Zadeh et al. (US 2020/0184278 A1).
Regarding Claims 6, and 28, the combined teaching of Tomkins and Kim does not explicitly teach but Zadeh teaches
analyzing paths in a graph data model stored in the graph database to automatically determine a number of edge connections between a pair of nodes included in the corresponding graph database properties ([2751], “determine a match in the user's (and/or the user's contacts/connections') annotated facial database”),
Tomkins, Kim and Zadeh are analogous art as they are in the same field of endeavor of information security. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zadeh with the combined teaching of Tomkins and Kim. The motivation/suggestion would have been to provide very efficient and fast algorithms for image processing, learning machines, NLP, pattern recognition (Zadeh, [0234]).
Claims 16, and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Tomkins et al. (US 2021/0295822 A1) in view of Kim et al. (US 2020/0279002 A1) further in view of Robert Jose et al. (US 2021/0279264 A1).
Regarding Claims 16, and 38, the combined teaching of Tomkins and Kim does not explicitly teach but Robert Jose teaches
wherein the second machine learning model is trained using a set of annotated natural language queries ([0062], “The training data may comprise a set of natural language search queries that have been previously received and manually annotated”),
Tomkins, Kim and Robert Jose are analogous art as they are in the same field of endeavor of information security. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Robert Jose with the combined teaching of Tomkins and Kim. The motivation/suggestion would have been for interpreting natural language search queries that account for contextual relevance of words of the search query that would ordinarily not be processed (Robert Jose, [0003]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHENG-FENG HUANG whose telephone number is (571)272-6186. The examiner can normally be reached Monday-Friday: 9 am - 5 pm.
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/CHENG-FENG HUANG/Primary Examiner, Art Unit 2497
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