DETAILED ACTION
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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 18/389,706 filed on February 17, 2026. Claims 1-16 and 18-21 are pending with the application.
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.
Claim(s) 1, 2, 11, and 19-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, and further in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman.
As to claim 1:
Johns discloses:
A method comprising:
obtaining, by a processing system including at least one processor, network operational data of a communication network [Paragraph 0026 teaches at setup, machine learning system 106 uses such historical sets of time series data metrics associated with the computer communication network 102 for learning and training. Paragraph 0036 teaches here, data ingestion layer 206 receives the network data, processes the data, transforms, validates, analyzes, sanitizes and/or normalizes the network data for ingestion by machine learning system 106. Paragraph 0078 teaches in addition to applying historical time series data metrics for training of machine learning model 308, method 600 can utilize historical network anomaly event data for training.];
transforming, by the processing system, the network operational data into a text-based format comprising one or more sentences [Paragraph 0002 teaches Visibility is so important that it stands alone as a sentence in any document, text, or presentation on network performance. Paragraph 0059 teaches FIG. 17 highlights in view 260 whether particular client stations are operating satisfactorily. View 260 shows, for example, client station 262 as a green color to indicate satisfactory operation. On the other hand, client station 264 has a red color showing less than satisfactory operation. There are other colorations and visualizations that the present disclosure may provide for client stations, such as client stations 262 and 264, including textual information stating the IP address for the client station, as well as other relevant information. as seen through the present visualization system to indicate a quality of service problem occurring at the particular client station.];
receiving, by the processing system, a query pertaining to the network operational data [Paragraph 0053 teaches a means could be provided by which one could launch a web application to view various information about a server's performance by clicking on that server from within the visualization. Paragraph 0053 teaches FIG. 21 depicts in view 300 the ability the present system to isolate or filter a particular set of servers 302 for further analysis. Information 304 about a client region or server is written directly onto its texture map.];
and presenting, by the processing system, the textual output that is generated in response to the query [Paragraph 0058 teaches directing the dynamic, three-dimensional performance display system to isolate certain client stations 242, show only those client stations 242 that may be operating, or provide textual and other information regarding particular client stations within a network. Paragraph 0059 teaches There are other colorations and visualizations that the present disclosure may provide for client stations, such as client stations 262 and 264, including textual information stating the IP address for the client station, as well as other relevant information... as seen through the present visualization system to indicate a quality of service problem occurring at the particular client station. Note: User isolating a specific station (query) to output textual in response to that query reads on the claims.]
Johns discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose training, by the processing system, a generative machine learning model implemented by the processing system using the network operational data, wherein the generative machine learning model comprises a deep neural network language model, wherein the training comprises a reinforcement learning process, applying, by the processing system, the query to the generative machine learning model implemented by the processing system to generate a textual output in response to the query, wherein the textual output comprises an additional one or more sentences.
Juneja discloses:
training, by the processing system, a generative machine learning model implemented by the processing system using the network operational data [Paragraph 0026 teaches at setup, machine learning system 106 uses such historical sets of time series data metrics associated with the computer communication network 102 for learning and training. Paragraph 0036 teaches here, data ingestion layer 206 receives the network data, processes the data, transforms, validates, analyzes, sanitizes and/or normalizes the network data for ingestion by machine learning system 106. Paragraph 0078 teaches in addition to applying historical time series data metrics for training of machine learning model 308, method 600 can utilize historical network anomaly event data for training.]
wherein the generative machine learning model comprises a deep neural network language model [Paragraph 0064 teaches machine learning model 308 may be based on machine learning methods such as multi-layer perceptron.]
wherein the training comprises a reinforcement learning process [Paragraph 0060 teaches network event data 4 is determined to be the active indicator while network event data 5 is false positive. Once the network event data are correlated into patterns of active and false positive network event data, the correlated patterns can be utilized as a training set for the machine learning model 308.]
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 teaching of the cited references and modify the invention as taught by Juneja, by incorporating training of machine learning model by utilizing historical network event data, as taught by Johns (see Paragraph 0026, 0036, 0060, 0064, and 0078), because the two applications are directed to data analysis; incorporating training of machine learning model by utilizing historical network event data increases model accuracy (see Juneja Paragraph 0063).
Johns and Juneja discloses most of the limitations as set forth in claim 1 but does not appear to expressly disclose applying, by the processing system, the query to the generative machine learning model implemented by the processing system to generate a textual output in response to the query, wherein the textual output comprises an additional one or more sentences.
Jayaraman discloses:
applying, by the processing system, the query to the generative machine learning model implemented by the processing system to generate a textual output in response to the query, wherein the textual output comprises an additional one or more sentences [Column 23 Lines 55-65 teach incident report 800 consists of a number of fields… field 806 may be a free-form text string containing anywhere from a few words to several sentences or more. Column 24 Lines 30-31 teach a text query may be entered into web interface 900. Column 24 Lines 49-51 teach the incident reports with the problem descriptions of “My email client is not downloading new emails”, “Email crashed”, and “Can't connect to email” may be provided. Column 27 Lines 52-56 teaches at step 7, an input text string is received and provided, word-by-word, to encoder 1102. The input text string may have been typed into a web interface by a user and may be, for example, a problem description of an incident. Column 28 Lines 26-30 teach for each of the identified text string vectors, the associated text string may be looked up in database 902 and provided as an output text string. In some cases, the associated incident reports may be provided as well. Column 33 Lines 23-25 teach Then, ANN 1200 is trained, resulting in encoder 1102 being able to produce a paragraph vector representation of the new incident text. Note: In response to input text strings that are queries, outputting from artificial neural network textual reports that include several sentences based on paragraph representations reads on the claims.];
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 teaching of the cited references and modify the invention as taught by Johns and Juneja, by incorporating in response to input text strings that are queries, outputting from artificial neural network textual reports that include several sentences based on paragraph representations, as taught by Jayaraman (see Column 23 Lines 55-65, Column 24 Lines 30-31, Column 24 Lines 49-51, Column 27 Lines 52-56, Column 28 Lines 26-30, and Column 33 Lines 23-25), because the three applications are directed to query processing; incorporating in response to input text strings that are queries, outputting from artificial neural network textual reports that include several sentences based on paragraph representations improves the enterprise's operations and workflow for IT, HR, CRM, customer service, application development, and security (see Jayaraman Column 6 Lines 29-31).
Claims 19 and 20 are similarly rejected because they are similar in scope.
As to claim 2:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1.
Juneja also discloses:
The method of claim 1, wherein the network operational data comprises at least one of: network performance indicator data [Paragraph 0044 teaches the historical time series data metrics are sequences of data measurements associated with the computer communication network 102. The data metrics may indicate the performance, quality, health, or available resource of a system at any point in time. Paragraph 0045 teaches, the historical time series data metrics may be work metrics that indicate the health of the communication network.]; or configurable setting values for one or more network settings.
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 teaching of the cited references and modify the invention as taught by Juneja, by incorporating training of machine learning model by utilizing historical network event data, as taught by Johns (see Paragraph 0026, 0036, 0060, 0064, and 0078), because the two applications are directed to data analysis; incorporating training of machine learning model by utilizing historical network event data increases model accuracy (see Juneja Paragraph 0063).
Claim(s) 21 is/are similarly rejected because it is similar in scope.
As to claim 11:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1.
Johns also discloses:
The method of claim 1, wherein the query pertaining to the network operational data comprises: a question pertaining to the network operational data [Paragraph 0053 teaches a means could be provided by which one could launch a web application to view various information about a server's performance by clicking on that server from within the visualization. Paragraph 0053 teaches FIG. 21 depicts in view 300 the ability the present system to isolate or filter a particular set of servers 302 for further analysis. Information 304 about a client region or server is written directly onto its texture map.] a classification request; a summarization request;; a prediction and forecasting request; an anomaly detection and root-cause analysis request; or a network setting recommendation request.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman, and further in view of Shah et al. (U.S. Publication No.: US 20230036289 A1) hereinafter Shah.
As to claim 3:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose obtaining a plurality of documents from at least one network knowledge repository wherein the training further comprises training the generative machine learning model implemented by the processing system using the plurality of documents.
Shah discloses:
The method of claim 1, further comprising: obtaining a plurality of documents from at least one network knowledge repository [Paragraph 0016 teaches the training data 116 may comprise text, numerical values, documents, images, or any other suitable type data that can be input into a machine learning model 112. Paragraph 0029 teaches the network device 104 determines performance metrics for the machine learning models 112. The performance metrics identify the performance of the network device 104 while executing each machine learning model 112 and/or the performance of each machine learning model 112. Note: Obtaining documents from a network device (network knowledge repository) reads on the claims.], wherein the training further comprises training the generative machine learning model implemented by the processing system using the plurality of documents [Paragraph 0016 teaches the training data 116 may comprise text, numerical values, documents, images, or any other suitable type data that can be input into a machine learning model 112. Paragraph 0029 teaches the network device 104 determines performance metrics for the machine learning models 112. The performance metrics identify the performance of the network device 104 while executing each machine learning model 112 and/or the performance of each machine learning model 112. Note: Obtaining documents from a network device (network knowledge repository) to train a machine learning model reads on the claims.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating obtaining documents from a network device (network knowledge repository) to train a machine learning model, as taught by Shah (see Paragraph 0016 and 0029), because the four applications are directed to network device analysis; incorporating obtaining documents from a network device (network knowledge repository) to train a machine learning model improves the operation of a device (see Shah Paragraph 0003).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman, in view of Napoli (U.S. Publication No.: US 20180225795 A1) hereinafter Napoli, and further in view of Booth et al. (U.S. Publication No.: US 20220075929 A1) hereinafter Booth.
As to claim 4:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose obtaining flowchart data from at least one network knowledge repository; and transforming the flowchart data into the text-based format, wherein the training further comprises training the generative machine learning model implemented by the processing system using the flowchart data in the text-based format.
Napoli discloses:
The method of claim 1, further comprising: obtaining flowchart data from at least one network knowledge repository [Paragraph 0100 teaches the status of one or more machines 102, 106 in the network 104 is monitored, generally as part of network management… this information may be identified by a plurality of metrics, and the plurality of metrics can be applied at least in part towards… any aspects of operations of the present solution described herein. Paragraph 0112 teaches the smart task GUI can automatically generate a flow chart and/or display a flow chart for an execution sequence, such as the flow chart illustrated in FIG. 3B. Note: The execution sequence associated with monitored network data (flowchart data) that used to generate a flowchart reads on the claims.]; and
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating the execution sequence associated with monitored network data (flowchart data) that used to generate a flowchart, as taught by Napoli (see Paragraph 0100 and 0112), because the four applications are directed to data analysis; incorporating the execution sequence associated with monitored network data (flowchart data) that used to generate a flowchart improvements to the logical flow of distribution and real-time information and analytics (see Napoli Paragraph 0004).
Johns, Juneja, Jayaraman, and Napoli discloses all of the limitations as set forth in claim 1 and some of 4 but does not appear to expressly disclose transforming the flowchart data into the text-based format, wherein the training further comprises training the generative machine learning model implemented by the processing system using the flowchart data in the text-based format.
Booth discloses:
transforming the flowchart data into the text-based format, wherein the training further comprises training the generative machine learning model implemented by the processing system using the flowchart data in the text-based format [Paragraph 0197 teaches receive a text-based input and generate a flowchart, which would then be converted into legal text based on various templates. Paragraph 0199 teaches an NER module can be trained based on patent claims to identify common words and identify points to segment claims and identify logical breaks in claims. An NER module may also be used to analyze language and generate a sequence diagram illustrating communication between different devices such as FIG. 3 of the instant disclosure.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, Jayaraman, and Napoli, by incorporating a training NER module using legal text from a flowchart, as taught by Booth (see Paragraph 0197 and 0199), because the five applications are directed to data analysis; incorporating a training NER module using legal text from a flowchart provides improving the machine-user interface, which provides further inputs into a natural language processing server and yields a more robust output (see Booth Paragraph 0030).
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman, and further in view of McClintock et al. (U.S. Patent No.: US 9342796 B1) hereinafter McClintock.
As to claim 5:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose applying an anonymization process to the network operational data in the text-based format to remove personal information and sensitive information.
McClintock discloses:
The method of claim 1, further comprising: applying an anonymization process to the network operational data in the text-based format to remove personal information and sensitive information [Column 12 Lines 40-48 teach the decontextualization operation(s) 402 may include a redaction, editing, or alteration of the text to remove or obscure user names, user addresses, user telephone numbers, computer system hostnames or addresses, domain names, network names, date/time stamps, product names, company names, trademarks, other data that may be sensitive, personal, private, or other data that may be associated with the operations of a business or organization.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating a redaction, editing, or alteration of the text to remove or obscure personal information and sensitive information, as taught by McClintock (see Paragraph 0100 and 0112), because the four applications are directed to data analysis; incorporating a redaction, editing, or alteration of the text to remove or obscure personal information and sensitive information efficiently analyzes data (see McClintock Column 19 Lines 34-35).
As to claim 14:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose applying an anonymization process to the textual output to remove personal information.
McClintock discloses:
The method of claim 1, wherein the presenting of the textual output that is generated in response to the query comprises: applying an anonymization process to the textual output to remove personal information [Column 12 Lines 40-48 the decontextualization operation(s) 402 may include a redaction, editing, or alteration of the text to remove or obscure user names, user addresses, user telephone numbers, computer system hostnames or addresses, domain names, network names, date/time stamps, product names, company names, trademarks, other data that may be sensitive, personal, private, or other data that may be associated with the operations of a business or organization.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating a redaction, editing, or alteration of the text to remove or obscure personal information and sensitive information, as taught by McClintock (see Paragraph 0100 and 0112), because the four applications are directed to data analysis; incorporating a redaction, editing, or alteration of the text to remove or obscure personal information and sensitive information efficiently analyzes data (see McClintock Column 19 Lines 34-35).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman, in view of McClintock et al. (U.S. Patent No.: US 9342796 B1) hereinafter McClintock, and further in view of Kothari et al. (U.S. Patent No.: US 8694646 B1) hereinafter Kothari.
As to claim 6:
Johns, Juneja, Jayaraman, and McClintock discloses all of the limitations as set forth in claim 1 and 5 but does not appear to expressly disclose wherein the anonymization process replaces personal information with a generic token.
Kothari discloses:
The method of claim 5, wherein the anonymization process replaces personal information with a generic token [Column 7 Lines 62-64 teach the anonymization module 408 is configured to intercept any data to be transmitted from a user computer to the hosted cloud. Column 8 Lines 1-4 teach the anonymization module 408 is configured to perform anonymization of one or more data fields using one or more of the tokenization module 412. Column 9 Lines 47-48 teach in this technique, for data contained in each data field, a corresponding token is created.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, Jayaraman, and McClintock, by incorporating an anonymization module replacing data from a user (personal information) with a corresponding token, as taught by Kothari (see Column 7 Lines 62-64, Column 8 Lines 1-4, and Column 9 Lines 47-48), because the five applications are directed to data analysis; incorporating an anonymization module replacing data from a user (personal information) with a corresponding token provides improved overall performance of an anonymization system (see Kothari Column 18 Lines 56-57).
Claim(s) 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman, and further in view of Cui et al. (U.S. Publication No.: US 20240086637 A1) hereinafter Cui.
As to claim 7:
Johns discloses:
The method of claim 1, wherein the transforming of the network operational data [Paragraph 0002 teaches Visibility is so important that it stands alone as a sentence in any document, text, or presentation on network performance. Paragraph 0059 teaches FIG. 17 highlights in view 260 whether particular client stations are operating satisfactorily. View 260 shows, for example, client station 262 as a green color to indicate satisfactory operation. On the other hand, client station 264 has a red color showing less than satisfactory operation. There are other colorations and visualizations that the present disclosure may provide for client stations, such as client stations 262 and 264, including textual information stating the IP address for the client station, as well as other relevant information. as seen through the present visualization system to indicate a quality of service problem occurring at the particular client station.]
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose transforming data into the text-based format is in accordance with at least a first artificial intelligence model that is configured to transform the network operational data into the text-based format
Cui discloses:
transforming data into the text-based format is in accordance with at least a first artificial intelligence model that is configured to transform the network operational data into the text-based format [Paragraph 0053 teaches the method for normalizing text of feature (1), in which the statistical model further includes a bidirectional encoder representations from transformer (BERT) model as a baseline model. Paragraph 0055 teaches transforming at least one compound phrase containing a combination of text, numbers, marks, and metrics, into text. Note: A BERT model (artificial intelligence model) used to convert metric data to text reads on the claims.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating a BERT model (artificial intelligence model) used to convert metric data to text, as taught by Cui (see Paragraph 0053 and 0055), because the four applications are directed to data analysis; incorporating a BERT model (artificial intelligence model) used to convert metric data to text efficiently transforms data (see Cui Abstract).
As to claim 8:
Johns discloses:
The method of claim 7, wherein the transforming of the network operational data [Paragraph 0002 teaches Visibility is so important that it stands alone as a sentence in any document, text, or presentation on network performance. Paragraph 0059 teaches FIG. 17 highlights in view 260 whether particular client stations are operating satisfactorily. View 260 shows, for example, client station 262 as a green color to indicate satisfactory operation. On the other hand, client station 264 has a red color showing less than satisfactory operation. There are other colorations and visualizations that the present disclosure may provide for client stations, such as client stations 262 and 264, including textual information stating the IP address for the client station, as well as other relevant information. as seen through the present visualization system to indicate a quality of service problem occurring at the particular client station.]
Johns, Juneja, Jayaraman, and Cui discloses all of the limitations as set forth in claims 1 and 7.
Cui also discloses:
wherein the at least the first artificial intelligence model comprises at least a first machine learning model that is trained to transform the network operational data into the text-based format [Paragraph 0022 teaches the character based BERT model 120 may be fully trained and labels each character by predefined tags. Paragraph 0053 teaches The method for normalizing text of feature (1), in which the statistical model further includes a bidirectional encoder representations from transformer (BERT) model as a baseline model. Paragraph 0055 teaches transforming at least one compound phrase containing a combination of text, numbers, marks, and metrics, into text.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating a BERT model (artificial intelligence model) used to convert metric data to text, as taught by Cui (see Paragraph 0022, 0053, and 0055), because the four applications are directed to data analysis; incorporating a BERT model (artificial intelligence model) used to convert metric data to text efficiently transforms data (see Cui Abstract).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman, in view of Cui et al. (U.S. Publication No.: US 20240086637 A1) hereinafter Cui, and further in view of Kockman et al. (U.S. Publication No.: 20240283820 A1) hereinafter Kockman.
As to claim 9:
Johns, Juneja, Jayaraman, and Cui discloses all of the limitations as set forth in claim 1 and 7.
Johns discloses:
The method of claim 7, transforming the network operational data into the text-based format and wherein the transforming of the network operational data into the text-based format further comprises: [Paragraph 0002 teaches Visibility is so important that it stands alone as a sentence in any document, text, or presentation on network performance. FIG. 17 highlights in view 260 whether particular client stations are operating satisfactorily. View 260 shows, for example, client station 262 as a green color to indicate satisfactory operation. On the other hand, client station 264 has a red color showing less than satisfactory operation. There are other colorations and visualizations that the present disclosure may provide for client stations, such as client stations 262 and 264, including textual information stating the IP address for the client station, as well as other relevant information. as seen through the present visualization system to indicate a quality of service problem occurring at the particular client station.]
Johns, Juneja, Jayaraman, and Cui discloses all of the limitations as set forth in claims 1 and 7.
Cui also discloses:
wherein the at least the first artificial intelligence model is one of a plurality of artificial intelligence models capable of transforming the data into the text-based format [Paragraph 0041 teaches the statistical model may be full size, half size, or other configurations of the BERT model.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, Jayaraman, by incorporating an option of a plurality of BERT models (artificial intelligence model) used to convert metric data to text, as taught by Cui (see Paragraph 0041), because the four applications are directed to data analysis; incorporating an option of a plurality of BERT models (artificial intelligence model) used to convert metric data to text efficiently transforms data (see Cui Abstract).
Johns, Juneja, Jayaraman, and Cui discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose selecting the at least the first artificial intelligence model from among the plurality of artificial intelligence models based upon a performance optimization criterion of the generative machine learning model.
Kockman discloses:
selecting the at least the first artificial intelligence model from among the plurality of artificial intelligence models based upon a performance optimization criterion of the generative machine learning model [Paragraph 0053 teaches the automated machine learning training module selects a machine learning model from the plurality of corresponding machine learning models implemented by the plurality of candidate machine learning pipelines, the selected machine learning model having a higher performance in relation to the performances of other machine learning models in the plurality of corresponding machine learning models.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, Jayaraman, and Cui, by incorporating a user selecting a machine learning model from the plurality of corresponding machine learning models, as taught by Kockman (see Paragraph 0053), because the five applications are directed to data analysis; incorporating a user selecting a machine learning model from the plurality of corresponding machine learning models improves the efficiency of the overall machine learning platform (see Kockman Paragraph 0008).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman, in view of Candel et al. (U.S. Publication No.: US 20190295000 A1) hereinafter Candel, and further in view of Garg et al. (U.S. Publication No.: US 20080104043 A1) hereinafter Garg.
As to claim 10:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose wherein the transforming comprises: converting categorical data to a numeric encoding and transforming the numeric encoding into the text-based format.
Candel discloses:
The method of claim 1, wherein the transforming comprises: converting categorical data to a numeric encoding [Paragraph 0043 teaches cross validation categorical to numeric encoding transformer.]; and
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating a categorical to numeric encoding transformer, as taught by Candel (see Paragraph 0043), because the four applications are directed to data analysis; incorporating a categorical to numeric encoding transformer provides an improvement to the field of machine learning (see Candel Paragraph 0036).
Johns, Juneja, Jayaraman, and Candel discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose transforming the numeric encoding into the text-based format.
Garg discloses:
transforming the numeric encoding into the text-based format [Paragraph 0052 teaches receives a numeric query 108 that is not represented in the numeric-to-text query mappings 102, the numeric-to-text converter 128 can determine an associated text query 118 by using the numeric-to-text converter 128].
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, Jayaraman, and Candel, by incorporating numeric-to-text converter 128, as taught by Garg (see Paragraph 0052), because the five applications are directed to data analysis; incorporating numeric-to-text converter 128 provides more efficient use of information gained from past text (see Garg Paragraph 0063).
Claim(s) 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman, and further in view of Tapia (U.S. Publication No.: US 20180270126 A1) hereinafter Tapia.
As to claim 12:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose obtaining, by the processing system, feedback on the output and retraining, by the processing system, the generative machine learning model using the feedback that is obtained.
Tapia discloses:
The method of claim 1, further comprising: obtaining, by the processing system, feedback on the output [Figure 5:512-514 and Paragraph 0071 teaches the performance extrapolation engine 120 may determine whether the machine learning model is to be re-trained. In various embodiments, the machine learning model may be retrained in response to a user command that is inputted by a user. Accordingly, if the performance extrapolation engine 120 determines that the machine learning model is to be retrained (“yes” at decision block 514), the process 500 may proceed to block 516.]; and retraining, by the processing system, the generative machine learning model using the feedback that is obtained [Figure 5:512-514 and Paragraph 0071 teaches the performance extrapolation engine 120 may determine whether the machine learning model is to be re-trained. In various embodiments, the machine learning model may be retrained in response to a user command that is inputted by a user. Accordingly, if the performance extrapolation engine 120 determines that the machine learning model is to be retrained (“yes” at decision block 514), the process 500 may proceed to block 516.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating the machine learning model may be retrained in response to a user command that is inputted by a user, as taught by Tapia (see Figure 5:512-514 and Paragraph 0071), because the four applications are directed to query processing; incorporating the machine learning model may be retrained in response to a user command that is inputted by a user facilitates the performance analyses (see Tapia Paragraph 0046).
Johns, Juneja, and Tapia discloses all of the limitations as set forth in claim 1
Johns discloses:
textual output [Paragraph 0002 teaches visibility is so important that it stands alone as a sentence in any document, text, or presentation on network performance. FIG. 17 highlights in view 260 whether particular client stations are operating satisfactorily. View 260 shows, for example, client station 262 as a green color to indicate satisfactory operation. On the other hand, client station 264 has a red color showing less than satisfactory operation. There are other colorations and visualizations that the present disclosure may provide for client stations, such as client stations 262 and 264, including textual information stating the IP address for the client station, as well as other relevant information. as seen through the present visualization system to indicate a quality of service problem occurring at the particular client station.]
As to claim 13:
Johns, Juneja, Jayaraman, and Tapia discloses all of the limitations as set forth in claim 12 and 1.
Tapia also discloses:
The method of claim 12, wherein the feedback includes a measure of correspondence between the output and one or more outputs of one or more additional generative machine learning models that are internal or external to the communication network [Paragraph 0070 teaches performance extrapolation engine 120 may apply the machine learning model to additional network performance data relevant to a calculation of the QoE metrics to generate a set of extrapolated QoE metrics that includes KPIs for the first set or the second set of one or more user devices. In various embodiments, the additional network performance data may belong to the time period and/or area specified QoE assessment query. In other words, the additional network performance data corresponds to the time period and/or area in which the one or more user devices of the first set or the second set were operating, and for which QoE metrics are being sought. Paragraph 0071 teaches the performance extrapolation engine 120 may determine whether the machine learning model is to be re-trained. In various embodiments, the machine learning model may be retrained in response to a user command that is inputted by a user. Accordingly, if the performance extrapolation engine 120 determines that the machine learning model is to be retrained (“yes” at decision block 514), the process 500 may proceed to block 516. Note: A user response of “yes” (measure of correspondence) representing feedback for two sets of network performance output at different time periods (between the output and one or more outputs) from the machine learning model that is either internal or external reads on the claims.]
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 teaching of the cited references and modify the invention as taught by Johns and Juneja, by incorporating a user response of “yes” (measure of correspondence) representing feedback for two sets of network performance output at different time periods (between the output and one or more outputs) from the machine learning model that is either internal or external, as taught by Tapia (see Figure 5:512-514, Paragraph 0070, and 0071), because both applications are directed to query processing; by a user response of “yes” (measure of correspondence) representing feedback for two sets of network performance output at different time periods (between the output and one or more outputs) from the machine learning model that is either internal or external facilitates the performance analyses (see Tapia Paragraph 0046).
Johns, Juneja, and Tapia discloses all of the limitations as set forth in claims 1 and 12.
Johns also discloses:
textual output [Paragraph 0002 teaches visibility is so important that it stands alone as a sentence in any document, text, or presentation on network performance. FIG. 17 highlights in view 260 whether particular client stations are operating satisfactorily. View 260 shows, for example, client station 262 as a green color to indicate satisfactory operation. On the other hand, client station 264 has a red color showing less than satisfactory operation. There are other colorations and visualizations that the present disclosure may provide for client stations, such as client stations 262 and 264, including textual information stating the IP address for the client station, as well as other relevant information. as seen through the present visualization system to indicate a quality of service problem occurring at the particular client station.]
Claim(s) 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman, and further in view of Cohen et al. (U.S. Publication No.: US 20080201389 A1) hereinafter Cohen.
As to claim 15:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1 but does not appear to expressly disclose converting, by the processing system, the textual output to a different media format wherein the presenting comprises presenting the textual output in the different media format.
Cohen discloses:
The method of claim 1, further comprising converting, by the processing system, the textual output to a different media format [Paragraph 0094 teaches converting from… a text messaging format. Paragraph 0095 converting to the at least second media format wherein the at least second media format includes one or more of an image media format, a text messaging format, a video media format, an audio media format, a non-voice audio media format, a voice media format, a digital media format, and an analog media format (e.g. conversion module 550 converting to a second media format wherein the media format is one or more of an image media format, a text messaging format, a video media format, an audio media format, a non-voice audio media format, a voice media format, a digital media format, and an analog media format).], wherein the presenting comprises presenting the textual output in the different media format [Paragraph 0055 teaches output device (e.g., conversion module 250 converting the original media type to an alternative media type.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating converting a media format to another media format, as taught by Cohen (see Paragraph 0055, 0094, and 0095), because the three applications are directed to data analysis; incorporating converting a media format to another media format provides speed and accuracy (see Cohen Paragraph 0036).
As to claim 16:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claims 1 and 15 but does not appear to expressly disclose wherein the different media format is selected by a user from among a plurality of available media formats.
Cohen discloses:
The method of claim 15, wherein the different media format is selected by a user from among a plurality of available media formats [Paragraph 0097 teaches optional block 6702 which provides for selecting such additional media format in response to a management request from the designated user and/or the approved device.]
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating selecting such additional media format in response to a management request from the designated user, as taught by Cohen (see Paragraph 0097), because the four applications are directed to data analysis; incorporating selecting such additional media format in response to a management request from the designated user (see Cohen Paragraph 0036).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Johns (U.S. Publication No.: US 20080250356 A1) hereinafter Johns, in view of Juneja et al. (U.S. Publication No.: US 20240187321 A1) hereinafter Juneja, in view of Jayaraman et al. (U.S. Patent No.: US 10459962 B1) hereinafter Jayaraman, and further in view of Sewak et al. (U.S. Publication No.: US 20220414137 A1) hereinafter Sewak.
As to claim 18:
Johns, Juneja, and Jayaraman discloses all of the limitations as set forth in claim 1.
Sewak discloses:
The method of claim 1, wherein the deep neural network language comprises at least one of: a transformer-based language model [Paragraph 0047 teaches a generative Pre-trained Transformer model is generally an autoregressive language model that uses a neural network based on deep learning. Paragraph 0048 teaches a Transformer model is generally a deep learning model that makes use of an attention mechanism to incorporate a broad context of an input in the context of other inputs that may be relevant to a classification decision.] or a large language model
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 teaching of the cited references and modify the invention as taught by Johns, Juneja, and Jayaraman, by incorporating a transformer model that is a language model based on deep neural network learning, as taught by Sewak (see Paragraph 0047 and 0048), because the four applications are directed to data analysis; incorporating a transformer model that is a language model based on deep neural network learning provides improved performance, accuracy, relevance, reliability, and stability (see Sewak Abstract).
Response to Arguments
Applicant’s arguments with respect to the 103 rejection of claim 1-16 and 18-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/EARL LEVI ELIAS/Examiner, Art Unit 2169
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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