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 action is responsive to the following communication: US Patent Application filed on 8/29/2024.
● Claims 1-20 are currently pending.
Information Disclosure Statement
● The information disclosure statement (IDS) submitted on 8/29/2024 compliances with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ramsl (US Patent Application Publication No. 2023/0359825) in view of Edelson et al (US Patent Application Publication No. 2023/0291691), hereinafter Edelson.
Regarding claims 1 and 7, Ramsl teaches [Fig. 9/10; para 00115-0128] a computer implemented method and system comprising processors and memory storing executable instructions, that when executed by the processors, cause the processors to perform training a machine learning model to process time-series data [para 0050 - input time-series data] received from a plurality of sources and generate natural language statistically supported responses to requests received by the machine learning model [para 0038 - machine learning module 270 trains machine learning models to perform various functions based on training data]; generating a workspace that indicates a plurality of data sources that may be used by the machine learning model [para 0036 - knowledge graph]; receiving, at the machine learning model, data from each of the plurality of data sources [para 0032 - input provided to trained machine learning model that generates an output] ; receiving, at the machine learning model, a request [para 0064 -- The machine learning model 545 generates natural language text for the requested portion of the knowledge graph entities 570 and generates the natural text document 560 as output; para 0070 -- receives text as input and outputs an indication of which words and phrases in the input text identify entities]; processing, with the machine learning model, the data from each of the plurality of data sources to determine a statistical relationship between data of at least two of the plurality of data sources that is responsive to the request [para 0086 -- determine a degree of similarity between the text and a second text; para 0088; 0094; 0096]; and providing a natural language response to the request [para 0086; 0088 - using the trained machine learning model, generating an approximation of the text].
Ramsl fails to teach the response includes the statistical relationship.
Edelson teaches managing networks based on weather conditions using historical weather data to determine how a current weather condition will affect the network, where the information regarding the weather effect can be output as an alert, streamed and/or stored [para 0032-0038].
One having ordinary skill in the art at the time of the invention would have recognized the advantages of including the data relationship information, as suggested by Edelson, in the natural language response of Ramsl, and the results would have been predictable in improving the system to provide the user with information that can be used to make adjustments or precautions based on the effects of the weather conditions on networks/systems.
Regarding claims 2 and 9, the combination of Ramsl and Edelson teaches receiving a selection of a weather data source and a communication data source to be included in the plurality of data sources [Edelson's weather and network data -- para 0032-0038]; and wherein: processing the data includes processing a weather data received from the weather data source to determine at least one weather feature of the weather data that impacts a transmission feature of a communication data received from the communication data source [Edelson's weather and network data and processing to determine how the weather condition effects the network-- para 0032-0038]; and the natural language response includes a natural language description that a first change in the weather feature caused a second change in the transmission feature [Edelson's weather and network data and processing to determine how the weather condition effects the network-- para 0032-0038].
Regarding claims 3 and 12, the combination of Ramsl and Edelson teaches determining a confidence interval corresponding to the statistical relationship [Edelson's weather and network data processing with threshold -- para 0032-0038]; and providing, as part of the natural language response, the confidence interval [Edelson's weather and network data processing with threshold and predictions alerts-- para 0032-0038].
Regarding claim 4, the combination of Ramsl and Edelson teaches providing, as part of the natural language response, data supporting the statistical relationship [Edelson's weather and network data processing with threshold and predictions alerts-- para 0032-0038; Ramsl's data and graphs on the UI - Fig 4].
Regarding claims 5 and 10, the combination of Ramsl and Edelson teaches the data supporting the statistical relationship includes a text-based graph data [Ramsl para 0036]; generating, based at least in part on the text-based graph data, a graph [Ramsl Fig4; para 0058]; and providing the graph as data supporting the statistical relationship [Ramsl Fig 4; para 0058].
Regarding claim 6, the combination of Ramsl and Edelson teaches: receiving a selection of a first data source [Ramsl para 0050 - input time-series data; Edelson's weather and network data -- para 0032-0038]; presenting a plurality of features of the first data source [Ramsl para 0050 - input time-series data; Edelson's weather and network data -- para 0032-0038]; receiving a selection of a first feature of the plurality of features [Ramsl para 0050 - input time-series data; Edelson's weather and network data -- para 0032-0038]; and wherein: processing the data includes processing the data of the first feature of the first data source [Ramsl para 0036; para 0086 -- determine a degree of similarity between the text and a second text; para 0088; 0094; 0096; Edelson's weather and network data -- para 0032-0038]; and the natural language response includes a natural language description that is based at least in part on the data of the first feature of the first data source [Edelson's weather and network data processing with threshold and predictions alerts-- para 0032-0038; Ramsl's data and graphs on the UI- - Fig 4].
Regarding claim 8, the combination of Ramsl and Edelson teaches the system is at an edge location that is disconnected from a cloud computing system [Edelson para 0042-processing not limited to cloud computing].
Regarding claims 11-14, the combination of Ramsl and Edelson teaches request for a report [Ramsl para 0064 -- The machine learning model 545 generates natural language text for the requested portion of the knowledge graph entities 570 and generates the natural text document 560 as output; Edelson para 0040 - user specified preferred content]; graph corresponding to statistically supported response [Edelson's weather and network data processing with threshold and predictions alerts-- para 0032-0038; Ramsl's data and graphs on the UI- - Fig 4]; insight and foresight [Edelson's weather and network data processing with historical data, threshold processing and predictions alerts-- para 0032-0038; Ramsl's data and graphs on the UI - Fig 4].
Regarding claim 15, the combination of Ramsl and Edelson teaches receiving selections from a first and second data source [Ramsl para 0086 -- determine a degree of similarity between the text and a second text; Edelson para 0032 – weather data sources, computer models of weather, satellite data, meteorological databases]; process, with the machine learning model, first data from the first data source and second data from the second data source to determine a statistical relationship between the first data and the second data [para 0086 -- determine a degree of similarity between the text and a second text; para 0088; 0094; 0096]; and generate the natural language statistically supported response based at least in part on the statistical relationship [para 0086; 0088 - using the trained machine learning model, generating an approximation of the text; Edelson's weather and network data and processing to determine how the weather condition effects the network-- para 0032-0038].
Regarding claims 16-20, claims 16-20 are rejected under similar rationale as
claims 1-15.
Conclusion
● The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure.
XU et al (US Patent Application Publication NO. 2023/0214679) discloses
extracting and classifying entities from digital content items.
Song (US Patent No.) teaches structural information preserving for
graph-to-text generation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THIERRY L PHAM whose telephone number is (571)272-7439. The examiner can normally be reached M-F, 11-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hai Phan can be reached at (571)272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THIERRY L PHAM/Primary Examiner, Art Unit 2654
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