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
Response to Amendment
No claims have been amended, canceled or added.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, see Remarks filed 7/28/2026, with respect to the rejection(s) of claims 1-20 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of TRAN (2022/0237368).
Claim Rejections - 35 USC § 103
I. 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 1-13 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over TRAN (2022/0237368) in view of WEBER, JR. et al (US 2023/0336456).
Per claim 1, TRAN teaches a method comprising:
receiving, by a cellular network monitoring software tool, a natural language query from an agent of a mobile operator operating a fifth or later generation cellular network regarding operational status of a network function on the fifth or later generation cellular network (paras 0095, 0099, 0101-102, 0186, 0275-276, 0283-286, 0299, 0304, 0309, 0332, 0414—receiving NL query from mobile 5G/6G cellular network, deploying chatbot and agents for processing queries and responses collecting data);
vectorizing, by the cellular network monitoring software tool, the natural language query through vector embedding such that the query is standardized (paras 0069, 0073, 0095-97, 0171-172, 0331-332, 0415—word vectorization or embedding to covert words into language by natural language processing NLP with ML models for use in search query);
determining whether the standardized query is cached (paras 0164, 0211-218, 0221, 0245, 0378, 0414—searching database to locate documents matching text input checking database for query/input submission);
performing, in response to determining whether the standardized query is cached: retrieving, based on determining that the standardized query is cached, a cached response of previously generated code from a network status query database (paras 0164-176, 0378, 0407-410—checking database and inventory by querying location of the display space, retrieving documents matching the first text input when the query match); or
applying, based on determining that the standardized query is not cached, a generative artificial intelligence model to the natural language query such that newly generated code is generated (paras 0021, 0053, 0055-58, 0186—applying artificial intelligence software to detect similarity of functions, generate documentation for the code on behalf of the developer to improve code documentation, the AI text suggestion system to generate topically relevant text suggestions), and
presenting, via the graphical user interface to the agent of the mobile operator, a response to executing the previously generated code from the cached response or the newly generated code such that a status of the network function is indicated (paras 0130-134, 0136, 0431—generate targeted and context-sensitive responses, presenting data within GUI).
TRAN teaches the limitations as applied above, yet fail to explicitly teach “a cellular network health monitoring software tool”. However, WEBER, JR. et al teach cellular network health monitoring, performing a test query based on the input of the cellular core function via 5G (paras 0002-7, 0027), employing a graphical user interface GUI for presenting and identifying the monitored status, functions and data of the cellular network (paras 0046-49).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of TRAN and WEBER, JR. et al for the purpose of the implementing a cellular network health monitoring system, which is well-known in the art for tracking and maintaining the operational health of a network by monitoring its function and status.
Claims 17 and 19 contain limitations that are substantially equivalent to the limitations of claim 1 and are therefore rejected under the same basis.
Per claim 2, TRAN and WEBER, JR. et al teach the method of claim 1, WEBER, JR. et al further teach the method further comprising applying a data input layer that receives data for processing by the cellular network health monitoring software tool (para 0033—data layers of cellular network slices receiving QOS parameters and other network configurations for particular types of data).
Per claim 3, TRAN and WEBER, JR. et al teach the method of claim 2, TRAN further teaches wherein the data input layer comprises a user input interface that receives the natural language query or a log file interface that receives network log data (paras 0003, 0008, 0068, 0275-276, 0285-286, 0387, 0415—input text for generative natural language query, code repositories, logging and database for storage of networked data, semantic tags to aid a search engine in locating best matching responses that are in natural language).
Per claim 4, TRAN and WEBER, JR. et al teach the method of claim 1, TRAN further teaches the method further comprising applying a pre-processing layer that transforms log data for subsequent analysis by a modeling layer (paras 0064, 0068, 0094-95, 0158, 0415—creating predictive models using pre-processing and model tuning via resampling, text preprocessing is the first step for the given dataset to prepare it to be an input to the classification model).
Per claim 5, TRAN and WEBER, JR. et al teach the method of claim 4, TRAN further teaches wherein the pre-processing layer comprises: a data cleaning operation that removes errors from the log data; a data storage operation that structures the cleaned log data into a data frame for efficient processing; or a pattern mining operation that identifies recurring patterns in the log data (paras 0094-95, 0387, 0405, 0408-410—text preprocessing is the first step for the given dataset to prepare it to be an input to the classification model, logging network data, monitoring network errors, delete anomalies in the data likely to represent errors or non-conforming asset structures and data cleansing).
Per claim 6, TRAN and WEBER, JR. et al teach the method of claim 1, TRAN further teaches wherein the previously generated code from the cached response or the newly generated code is executed by a modeling layer (paras 0057, 0067-69, 0088-91—neural network is trained from large-scale data sources, such as code repositories with neural network is trained from large-scale data sources, such as code repositories executing code to extract noun phrases, train a neural network to generate description and databases for information on the descriptive text; WEBER, JR. et al: para 0055—in response to a request from a user, may obtain the identifiers of each cellular network core function within a particular cloud computing sub-region).
Claims 18 and 20 contain limitations that are substantially equivalent to the limitations of claim 6 and are therefore rejected under the same basis.
Per claim 7, TRAN and WEBER, JR. et al teach the method of claim 6, TRAN further teaches wherein the modeling layer comprises a code generation operation or a code execution operation (paras 0057, 0067-69—generating code and algorithms, machine learning used to create software capable of creating its own software).
Per claim 8, TRAN and WEBER, JR. et al teach the method of claim 7, TRAN further teaches wherein the code generation operation comprises: a caching operation that performs text embedding on the standardized query such that semantic meaning is captured in a vector space; a caching operation that calculates a similarity score between the standardized query and cached queries such that similar past queries are identified; a smart search operation that performs named entity recognition on the standardized query such that a recognized name from the query is identified; or a large language model-based code generation operation that generates a data frame or chart based on the standardized query at least in part by translating the query into executable code (paras 0003, 0057, 0095, 0415—natural language processing NLP systems such as rule-based and/or ML machine learning-based classifiers and/or regressors, machine learning used to create software capable of creating its own software, semantic word vector spaces can be used in search query can be used where a vector generated from co-occurrence statistics of a word, process natural language text with ML and supports language detection).
Per claim 9, TRAN and WEBER, JR. et al teach the method of claim 6, TRAN further teaches wherein the modeling layer comprises: a code testing operation that removes jailbreaking attempts, unsafe scripts, or non-whitelisted libraries such that a safety level of executing code is improved; a code execution operation that runs the previously generated code or the newly generated code on the network log data; or a cache updating operation that stores the standardized query and corresponding generated code for future use (paras 0055, 0276, 0281, 0335, 0363, 0415—code generation for larger models converge to lower test error in fewer gradient updates than smaller models, large models achieve higher accuracy faster for training and speed during inference is achieved using model compression, apply a knowledge-based library of concepts to help search engines detect different businesses or entities; WEBER, JR. et al: paras 0050-51, 0054, 0061, 0063—perform tests and checks that are specific to the network function being tested, cellular network administrator may select a particular test configuration file, a customized test query can be performed, such as based on a file that contains commands specific to the network function, vendor, version, and/or pod from a function test library).
Per claim 10, TRAN and WEBER, JR. et al teach the method of claim 1, WEBER, JR. et al further teach further comprising applying a graphical user interface layer that presents the response indicating the status of the network function to the agent of the mobile operator (para 0048—graphical user interface GUI for cellular network administrator monitoring; TRAN: paras 0130-134, 0136, 0431—generate targeted and context-sensitive responses, presenting data within GUI).
Per claim 11, TRAN and WEBER, JR. et al teach the method of claim 10, WEBER, JR. et al further teach wherein the graphical user interface layer comprises: a network function selection prompt; a segmentation order selection prompt; a time window selection prompt; a domain name system selection prompt; or a data source selection prompt (paras 0047-50, 0052-53—graphical user interface GUI, user interface can be a command line interface, raw output data back from the network functions and/or pods in response to the commands issued by function analysis engine are presented to the cellular network administrator for review via user interface; TRAN: paras 0187-188, 0274, 0279, 0395, 0415—text prompt and query segmentation).
Per claim 12, TRAN and WEBER, JR. et al teach the method of claim 10, TRAN further teach wherein the graphical user interface layer comprises a heat map that visually represents the status of the network function indicated in the response (paras 0130-134, 0136, 0302, 0306, 0431—generate targeted and context-sensitive responses, presenting data within GUI, sensors for collecting temperature and presenting the data).
Per claim 13, TRAN and WEBER, JR. et al teach the method of claim 12, WEBER, JR. et al further teach wherein the heat map is indexed along one dimension by date or by location of a data center (paras 0019, 0027-28, 0035, 0041-42—data centers providing service to different geographic regions).
CLAIM 14 is rejected under 35 U.S.C. 103 as being unpatentable over TRAN (2022/0237368) in view of WEBER, JR. et al (US 2023/0336456) and BENDECK et al (US 2024/0362248).
Per claim 14, TRAN and WEBER, JR. et al teach the method of claim 10, as applied above, yet fail to explicitly teach the method “wherein the graphical user interface layer displays the original natural language query”. However, BENDECK et al teach a GUI receiving user to input natural language queries and display data visualizations (paras 0092-94, 0097).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of TRAN and WEBER, JR. et al with BENDECK et al for the purpose of using a GUI to display natural language queries, which is well-known in the art for allowing user interaction with the system in translating natural language in structured queries.
CLAIM 15 is rejected under 35 U.S.C. 103 as being unpatentable over TRAN (2022/0237368) in view of WEBER, JR. et al (US 2023/0336456) and JEGARAJAN et al (WO 2024/076651).
Per claim 15, TRAN and WEBER, JR. et al teach the method of claim 10, as applied above, yet fail to explicitly teach the method “wherein the graphical user interface layer displays a chart indicating a pass rate per location for the network function”. However, JEGARAJAN et al teach a GUI dashboard that displays graphs that visualize network traffic flow metrics and usage from a source to destination spanning cloud computer environments across regions (paras 0022, 0024-25, 0032, 0048-52, 0064-74, 0076).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of TRAN and WEBER, JR. et al with JEGARAJAN et al for the purpose of using a GUI to display graphs/charts that visualize the metrics of the network traffic flow usage, which is well-known in the art for illustrating metrics and patterns of collected data.
CLAIM 16 is rejected under 35 U.S.C. 103 as being unpatentable over TRAN (2022/0237368) in view of WEBER, JR. et al (US 2023/0336456) and MACKAY et al (WO 2022/183138).
Per claim 16, TRAN and WEBER, JR. et al teach the method of claim 10, as applied above, yet fail to explicitly teach the method “wherein the graphical user interface layer comprises: a feedback prompt asking whether the response was helpful; or a chatbot dialogue interface for further queries”. However, MACKAY et al teach the implementation of feedback prompts and open-ended dialogue queries for sentiment analysis within a GUI (paras 0292, 0307-335, 0339-340).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of TRAN and WEBER, JR. et al with MACKAY et al for the purpose of provisioning feedback and chat dialog queries in a GUI, which is well-known in the art for allowing user open-ended dialogue interaction with the system.
Conclusion
VI. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure: US 2021/0357850, USPN 11556579.
VII. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTIE D SHINGLES whose telephone number is (571)272-3888. The examiner can normally be reached on Monday-Thursday 10am-7pm.
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/KRISTIE D SHINGLES/
Primary Examiner, Art Unit 2453
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