DETAILED ACTION
This action is in response to claims filed 30 April 2024 for application 18651460 filed 30 April 2024. Currently claims 1-20 are pending.
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 .
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-, 8-13, and 16-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Negandhi et al. (US 20240311847 A1).
Regarding claims 1, 10 and 18, Negandhi discloses:
A method for automatically recommending navigation actions within a user interface of a software application, the method comprising:
generating a knowledge graph comprising a plurality of nodes and a plurality of edges, each of the plurality of nodes representing a different user interface element of a plurality of user interface elements of the user interface, each of the plurality of edges representing a relationship among two or more nodes of the knowledge graph (“The data structures, in some embodiments, are used to construct a knowledge graph. The activity tracker can capture filters the user applied during the exploratory analysis, such as a data drill down operation performed, and annotations the user made to specific data indicating a noteworthy pattern. As used herein, “drill down” means a computer-implemented user activity that allows a user to shift from one level of data to more detailed, granular level of data within the same dataset, the data at the detailed, granular level providing more specific or more detailed information than that of the data at the prior level. An example of a drill down activity is accessing database information starting from a general category and moving (in response to processor-executed instructions) through a data hierarchy of field, file, and record. The drill down, in the context of exploratory network analysis to detect fraud, for example, can change a user's focus within a network of transactions by shifting perspective from a first person to another person or another part of the network.” [0025], knowledge graph comprises nodes and edges [0027], “In certain embodiments, a knowledge graph corresponding to an existing pattern can be presented via a graphical user interface (GUI) visually to a user engaged in a current exploratory analysis. The visual presentation can present nodes and edges in a manner (e.g., different colorings, shadings, and/or markings) that indicate significant aspects determined from prior exploratory network analyses.” [0070]);
receiving user action data associated with a user interacting with the user interface (“In still other embodiments, the inventive arrangements include a next-action recommender. Based on information captured in the knowledge component, the next-action recommender recommends one or more activities based on a current set of actions performed by a user. Recommended activities are presented sequentially as best next actions. Each best next action is one most likely, given other existing conditions, to lead to a correct exploratory network analysis. The likelihood is determined by the inventive arrangements implementing one or more machine learning models. In some arrangements, the next-action recommender optionally recommends a final decision based on the current activities performed by the user and correlating the current activities to the activities in the knowledge graph corresponding to a past decision that previously proved to be correct.” [0029]);
dynamically updating the knowledge graph based on the user action data (each action updates the graph [0029]);
determining a recommended navigation action based on the dynamically updated knowledge graph (each action leads to a next action or final recommendation [0029]);
providing input to a generative language processing machine learning model based on the recommended navigation action, the input comprising a prompt requesting the generative language processing machine learning model generate natural language guidance indicative of the recommended navigation action (“Pattern recognition engine 204, in certain embodiments, implements a generative model for generating knowledge components 214. In some embodiments, pattern recognition engine 204 optionally constructs a knowledge graph (FIGS. 4A through 4D) from knowledge components 214. As described in greater detail below, the knowledge graph can be input to a machine learning model that predicts a best next action and, during a user's on-going exploratory network analysis, recommends the action to the user.” [0053]); and
displaying the natural language guidance to the user (“Visual display of knowledge graph 508 shows one or more recommended best next actions for the user to take, the recommendation generated by the machine learning model in response to input 510 of a set of current user activities. Next-action recommender 206 can also generate output 512. Output 512 gives a likelihood of fraud, which with a given confidence level, is predicted based on data and meta data generated by the exploratory network analysis activities of the user.” [0073]).
Regarding claims 2, 11 and 19, Negandhi discloses: The method of Claim 1, further comprising: receiving feedback data from the user regarding the natural language guidance; and updating the knowledge graph based on the feedback data ([0029] and Fig5a, the user selecting an action is interpreted as feedback).
Regarding claim 3, Negandhi discloses: The method of Claim 1, wherein the user action data comprises a sequence of user interface elements with which the user interacted (each action is one in a series/pattern of activity [0029], “Next-action recommender 206 correlates decision 410 based on the network analysis performed with pattern of activities 400 and predicts that repeating the remaining activities A3 and A4 after activities A1 and A2 are 75 percent likely to escalate the case. Optionally, at the conclusion of the exploratory network analysis, next-action 206 generates a final decision based on the results of the full pattern of activities. The final decision can indicate that money laundering likely occurred. Next-action recommender 206 can generate the final decision based on the current activities performed and by correlating the current activities to past activities (e.g., comprising a knowledge graph) and decisions corresponding to the past activities.” [0068]).
Regarding claims 4, 12 and 20, Negandhi discloses: The method of Claim 1, wherein dynamically updating the knowledge graph comprises adjusting one or more weights associated with one or more edges of the plurality of edges of the knowledge graph (Fig 5A next activity percentage is interpreted as a weight on the edge to the next activity ).
Regarding claims 5 and 13, Negandhi discloses: The method of Claim 1, wherein dynamically updating the knowledge graph comprises: providing the user action data as an input to a machine learning model configured to analyze the user action data to determine an adjustment to one or more weights associated with one or more edges of the plurality of edges of the knowledge graph (Fig 5A next activity percentage is interpreted as a weight on the edge to the next activity);
obtaining the adjustment to the one or more weights as an output of the machine learning model; and dynamically updating the knowledge graph based on the adjustment (Fig 5A next activity percentage is interpreted as a weight on the edge to the next activity, ML model provides probabilities [0053]).
Regarding claims 8 and 16, Negandhi discloses: The method of Claim 1, wherein: the recommended navigation action comprises a particular navigation path within the user interface for causing the software application to perform a specific task; and the natural language guidance comprises step-by-step instructions on how to traverse the particular navigation path within the user interface (Fig 5a at each node of knowledge graph (fig 4b) the user is presented with recommended next action which is interpreted as step by step instructions to achieve the goal).
Regarding claims 9 and 17, Negandhi discloses: The method of Claim 8, wherein the particular navigation path includes fewer user interface elements than any other navigation path within the user interface and associated with performing the specific task (“Next-action recommender 206 correlates decision 410 based on the network analysis performed with pattern of activities 400 and predicts that repeating the remaining activities A3 and A4 after activities A1 and A2 are 75 percent likely to escalate the case. Optionally, at the conclusion of the exploratory network analysis, next-action 206 generates a final decision based on the results of the full pattern of activities. The final decision can indicate that money laundering likely occurred. Next-action recommender 206 can generate the final decision based on the current activities performed and by correlating the current activities to past activities (e.g., comprising a knowledge graph) and decisions corresponding to the past activities.” [0068]).
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 (i.e., changing from AIA to pre-AIA ) 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 for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Negandhi in view of Jiang et al. (S-GCN-GRU-NN: A novel hybrid model by combining a Spatiotemporal Graph Convolutional Network and a Gated Recurrent Units Neural Network for short-term traffic speed forecasting).
Regarding claims 6 and 14, Negandhi does not explicitly disclose: The method of Claim 5, wherein the machine learning model comprises a hybrid neural network comprising a convolutional neural network configured to analyze the user action data in a spatial dimension and a gated recurrent unit configured to analyze the user action data in a temporal dimension.
Jiang teaches: wherein the machine learning model comprises a hybrid neural network comprising a convolutional neural network configured to analyze the user action data in a spatial dimension and a gated recurrent unit configured to analyze the user action data in a temporal dimension (“In this Section, we propose a novel hybrid model S-GCN GRU-NN for short-term traffic speed forecasting. The S-GCN-GRU-NN model is based on a spatiotemporal relation matrix and composed of our proposed S-GCN model and a GRU-NN model. More details are discussed as follows.” P3 §2, Fig 2).
Negandhi and Jiang are in the same field of endeavor of graph machine learning models and are analogous. Negandhi discloses a knowledge graph recommendation system using a generative model. Jiang teaches a hybrid model comprising a graph convolutional neural network (GCN) and a gated recurrent unit (GRU) for processing spatiotemporal data. It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the generative model of Negandhi with the known spatiotemporal hybrid model as taught by Jiang to yield predictable results of a more stable and accurate model (Jiang abstract).
Claim(s) 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Negandhi in view of Hao et al. (LKPNR: LLM and KG for Personalized News Recommendation Framework).
Regarding claims 9 and 17, Negandhi discloses generative machine learning models, however, does not explicitly disclose: wherein the generative language processing machine learning model comprises a large language model.
Hao teaches: wherein the generative language processing machine learning model comprises a large language model (Fig 2 “The framework of LKPNR. The lower left corner of figure is the input news data into LKPNR, where the words marked in yellow indicates the entities. Candidate news is encoded by LK-Aug news encoder that combines LLM and KG with traditional general news encoder to obtain news representation. To obtain user representation, LK-Aug User Encoder contains several LK-Aug News Encoders, which encodes user’s historical click behaviors”).
Negandhi and Hao are in the same field of endeavor of knowledge graphs and generative models and are analogous. Negandhi discloses a knowledge graph recommendation system using a generative model. Hao discloses the use of knowledge graphs with an LLM in particular. It would have been obvious to one of ordinary skill in the art before the effective filing date to substitute the generative model of Negandhi with the known LLM as taught by Hao to yield predictable results of utilizing the LLMs known powerful text understanding abilities (Hao abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC NILSSON whose telephone number is (571)272-5246. The examiner can normally be reached M-F: 7-3.
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/ERIC NILSSON/ Primary Examiner, Art Unit 2151
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