DETAILED ACTION
This Office Action is in response to Applicant's Communication received on 12/29/2023 for application number 18/400,535.
Claims 1-20 are presented for examination. Claims 1, 14, and 17 are independent claims.
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 Objections
Claim 10 is objected to because of the following informalities: Claim 10 recites abbreviations “MME, UPF, SMF, PGW, SGW” without setting forth their full terms. Appropriate correction is required.
Claims 12 and 20 are objected to because of the following informalities: These claims recite “the input to the second Al model,” which has no antecedent basis. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-13 are directed to a method, Claims 14-16 are directed to a system, and Claims 17-20 are directed to a computer-readable medium and fall within one of the statutory categories (process, machine, articles of manufacture) and are eligible under Step 1.
Step 2A Prong 1
Independent Claim
Claim 1 recites:
A method of identifying conditions, the method comprising: identify a network function (NF) type indicated by the collected data; based on the identified NF type, generate a prompt for input to a generative pre-trained transformer (GPT) model, wherein the prompt is usable to identify a condition in the telecommunications network, the condition related to the NF type; identify the condition in the telecommunications network, perform root cause analysis and generate recommendations related to performance and system functionality in the telecommunications network - these limitations encompass mental processes. Identifying network function type from collected data, formulating a written inquiry/ prompt from that classification to identify a condition, identifying condition, performing root cause analysis, and generating recommendations are acts of observation, evaluation, and judgement that can be practically performed in a human mind or with pen and paper, and thus recites mental process.
Claims 14 and 17 recite:
based on the collected data, identify a network function (NF) type; based on the NF type, generate a prompt for a generative pre-trained transformer (GPT) model; and identify a condition in the telecommunications network, perform root cause analysis and generate recommendations related to performance and system functionality in the telecommunications network - these limitations encompass mental processes. Identifying network function type from collected data, formulating a written inquiry/ prompt from that classification to identify a condition, identifying condition, performing root cause analysis, and generating recommendations are acts of observation, evaluation, and judgement that can be practically performed in a human mind or with pen and paper, and thus recites mental process.
Accordingly, the claims recites an abstract idea that falls under “mental process” grouping.
Step 2A Prong 2
Independent Claim
Additional elements
Claim 1 recites:
a virtualized computing environment providing a telecommunications network running a plurality of network functions - these limitations are recited at a high-level of generality such that it amounts to no more than generally linking the use of a judicial exception to the technological environment of telecommunications networks (see MPEP § 2106.05(h)).
receiving, by a computing system, data collected from the telecommunications network, wherein the data comprises test data from tests executed in the telecommunications network or live production data from the telecommunications network - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)).
inputting the collected data to a first artificial intelligence (AI) model; using a second AI model; inputting the prompt to the GPT model, wherein the GPT model is trained with 3rd Generation Partnership Project (3GPP) documentation to provide telecommunications design and requirements knowledge - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)). These limitations can also be viewed as generally linking the use of a judicial exception to the technological environment of telecommunications network (see MPEP § 2106.05(h)).
initiating an action at the telecommunications network based on the identified condition - this limitation is a broad post-solution activity recited at a high level of generality without any technical detail or mechanism, thus, merely reciting an idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result (see MPEP § 2106.05(f)).
Claims 14 and 17 recite:
receiving, by a computing system, data collected from a telecommunications network, wherein the data comprises test data from tests run in the telecommunications network or live production data from the telecommunications network - these limitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)).
using a first artificial intelligence (Al) model; using a second AI model; inputting the prompt to the GPT model, wherein the GPT model is trained with 3rd Generation Partnership Project (3GPP) documentation to provide telecommunications design and requirements knowledge - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)). These limitations can also be viewed as generally linking the use of a judicial exception to the technological environment of telecommunications network (see MPEP § 2106.05(h)).
Claims 14 recites:
A computing system, comprising: one or more processors; and a computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the processor, cause the computing system to perform operations - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 17 recites:
A computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by a processor of a computing system, cause the computing system to perform operations - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2B
Independent Claim
Additional elements
Claim 1 recites:
a virtualized computing environment providing a telecommunications network running a plurality of network functions - these limitations are recited at a high-level of generality such that it amounts to no more than generally linking the use of a judicial exception to the technological environment of telecommunications networks (see MPEP § 2106.05(h)).
receiving, by a computing system, data collected from the telecommunications network, wherein the data comprises test data from tests executed in the telecommunications network or live production data from the telecommunications network - these limitations amount to insignificant extra-solution activity of mere data gathering , which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”).
inputting the collected data to a first artificial intelligence (AI) model; using a second AI model; inputting the prompt to the GPT model, wherein the GPT model is trained with 3rd Generation Partnership Project (3GPP) documentation to provide telecommunications design and requirements knowledge - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)). These limitations can also be viewed as generally linking the use of a judicial exception to the technological environment of telecommunications network (see MPEP § 2106.05(h)).
initiating an action at the telecommunications network based on the identified condition - this limitation is a broad post-solution activity recited at a high level of generality without any technical detail or mechanism, thus, merely reciting an idea of a solution or outcome with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result (see MPEP § 2106.05(f)).
Claims 14 and 17 recite:
receiving, by a computing system, data collected from a telecommunications network, wherein the data comprises test data from tests run in the telecommunications network or live production data from the telecommunications network - these limitations amount to insignificant extra-solution activity of mere data gathering , which is well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”).
using a first artificial intelligence (Al) model; using a second AI model; inputting the prompt to the GPT model, wherein the GPT model is trained with 3rd Generation Partnership Project (3GPP) documentation to provide telecommunications design and requirements knowledge - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)). These limitations can also be viewed as generally linking the use of a judicial exception to the technological environment of telecommunications network (see MPEP § 2106.05(h)).
Claims 14 recites:
A computing system, comprising: one or more processors; and a computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the processor, cause the computing system to perform operations - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 17 recites:
A computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by a processor of a computing system, cause the computing system to perform operations - these limitations are recited at a high-level of generality such that it amount to no more than using generic computer components to apply the judicial exception (see MPEP § 2106.05(f)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claim is patent ineligible.
Step 2A Prong 1
Dependent Claims
Claim 2:
parsing, editing, and organizing the collected data to generate a machine-readable structured format -these limitations encompass mental process of organizing and formatting information, which are steps that can be practically performed in the human mind or by a human using a pen and paper.
Claim 3:
concatenating tables to spatially map the collected data -these limitations encompass mental process and mathematical concepts of organizing and manipulating information. Claims 4 and 15:
identify telemetry information, generate the prompt -these limitations encompass mental process.
Claim 5:
the telemetry information comprises one or more of events, metadata, tags, error messages, or metrics -these limitations merely further the mental process of identifying telemetry information .
Claim 10:
the network functions comprise MME, UPF, SMF, PGW, or SGW -these limitations merely furthers the mental process by specifying network functions.
Thus, the claims recite the abstract idea.
Step 2A Prong 2
Dependent Claims
Additional elements
Claims 4 and 15:
the first Al model is further used - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claims 6 and 16:
the first Al model comprises a classification model - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 7:
the classification model comprises a decision tree, K-nearest neighbor, multi-class SVM, or a convolutional neural network - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claims 8 and 18:
the first Al model is a natural language processing (NLP) model - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 9:
the NLP model as a Named Entity Recognition (NER) model trained with datasets of NF names and NF descriptions - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claims 11 and 19:
the first Al model comprises a plurality of recognition models for each network function - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claims 12 and 20:
the input to the second Al model further comprises, for each NF type: a log type and data associated with the log type; and log content extracted for each log type - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 13:
the prompt is appended with a context extracted by the second Al model - these limitations amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)) and insignificant extra-solution data manipulation (see MPEP § 2106.05(g)) using the already-recited generic model.
Accordingly, these additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to the abstract idea.
Step 2B
Dependent Claims
Additional elements
Claims 4 and 15:
the first Al model is further used - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claims 6 and 16:
the first Al model comprises a classification model - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 7:
the classification model comprises a decision tree, K-nearest neighbor, multi-class SVM, or a convolutional neural network - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claims 8 and 18:
the first Al model is a natural language processing (NLP) model - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 9:
the NLP model as a Named Entity Recognition (NER) model trained with datasets of NF names and NF descriptions - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claims 11 and 19:
the first Al model comprises a plurality of recognition models for each network function - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claims 12 and 20:
the input to the second Al model further comprises, for each NF type: a log type and data associated with the log type; and log content extracted for each log type - these limitations are recited at a high-level of generality such that it amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)).
Claim 13:
the prompt is appended with a context extracted by the second Al model - these limitations amount to no more than using generic computing tool/ models to apply the judicial exception (see MPEP § 2106.05(f)) and insignificant extra-solution data manipulation (see MPEP § 2106.05(g)) using the already-recited generic model.
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, the claims are patent ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 CFR § 1.111(c) to consider these references fully when responding to this action.
Bogineni et al. (US 2020/0196155 A1) teaches: the core NWDAF device, the edge NWDAF device, and the RAN NWDAF device can receive analytics data from an analytics repository (e.g., in real time, near-real time, periodically, and/or the like); the analytics data can include data identifying trends and/or patterns associated with the network; data indicating bit rates through network resources; data indicating collision and packet drop rates associated with the network; flow data associated with the network; and/or the like; the device can process the analytics data (from the core domain, edge domain, and the RAN), the core data, the edge data, and the RAN data, with domain-specific machine learning models, to determine one or more actions that are to be performed with respect to one or more of the core domain of the network, the edge domain of the network, and/or the RAN; the core domain of the network can include one or more core devices, such as an authentication server function (AUSF) device, a network slice selection function (NSSF) device, a policy control function (PCF) device, a network function repository function (NRF) device, an access and mobility management function (AMF) device, and/or the like; the edge domain of the network can include one or more edge devices, such as a PCF device, a session management function (SMF) device, an AMF device, and/or the like; the NWDAF device can cause one or more actions to be performed in the core domain of the network, the edge domain of the network, and/or the RAN; causing the one or more actions to be performed can include causing one or more core devices to rebalance a network slice and/or a network function associated with the network (see [0008], [0017], [0012], [0028]). Puente Pestana (US 2023/0153685 A1) teaches: the NWDAF 100 comprises a data collection module 102, which collects data from one or more network functions 110 of the network; the collected data is passed to one or more training modules 104, which utilize machine-learning algorithms to generate and train respective models 106; the models 106 may be classifier models or, as in the illustration, prediction models; the second data analytics entity is then enabled to update the accuracy of its model through communication with the network function repository function (NRF); the second data analytic entity thus transmits a discovery request message to the NRF comprising an indication of one or more of: the network function type to be discovered (see [0010], [0052], [0061]); Campo Trapero et al. (US 2025/0016072 A1) teaches: computing one or more analytics related to deviation in processing load or capacity of the target NF instances, based on the information obtained from the OAM NF and on a capacity dimensioning model for each target NF instance; an identifier of a target NF instance or a target NF type; an indication of whether a capacity dimensioning model is requested for the identified target NF instance or type (see [0018] , [0019]); Mitelman et al. (US 2020/0382527 A1) teaches: the event data may be data representing or derived from hypertext protocol (HTTP) logs, domain name service (DNS) logs, virtual private network (VPN) logs, netflow traffic, intrusion detection system (IDS) logs, and so forth; classify the network devices using supervised or unsupervised machine learning; apply one or multiple models for purposes of classifying the network device into one or multiple classes; the supervised machine learning engine 122 may be a neural network (a deep learning neural network, a recurrent neural network (RNN) (see [0017], [0028], [0030], [0044]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REJI KARTHOLY whose telephone number is (571)272-3432. The examiner can normally be reached on Monday - Thursday from 7:30 am to 3:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch, can be reached at telephone number 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REJI KARTHOLY/Primary Examiner, Art Unit 2143
