DETAILED ACTION
The instant application having Application No. 18/710,385 filed on is presented for examination by the examiner.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Information Disclosure Statement
As required by M.P.E.P. 609, the applicant’s submissions of the Information Disclosure Statement dated 03/06/2025 and 05/15/2024 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending.
Claim Objections
Claims 10 and 13 are objected to because of the following informalities:
As per claims 10 and 13, the limitation "the terminal" does not have proper antecedence basis.
Appropriate correction is required.
Response to Amendment
In the instant amendment, claims 3-6, 11-15, and 18-21 have been amended.
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 1-15 and 18-21 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Step 1: Claims 1 and 8 recites “A method for…”; the claim recites a series of steps and therefore is a process. Claims 18-19 recite “a network task processing device…” therefore the claim is a machine. Claims 20-21 recite “A CRM…”, therefore the claim is a manufacture.
Step 2A Prong One: Claims 1, 8, and 18-21 recite the limitations "determining …" This limitation is processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a "database" or "computer systems", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. The claim recites the additional elements "stopping processing", “forwarding …” and “processing …” The limitations amount to a data gathering step and a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)).
The network task processing device, a processor, a memory and one or more non-transitory computer-readable storage media in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). The claim is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations "stopping processing" and “processing...." are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc....
Regarding claim 2, under prong 2, the “transmitting”, and “receiving” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 3, under prong 2, the “stopping processing”, and “processing” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 4, under prong 2, the “forwarding”, “processing”, and “receiving …” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 5, under prong 2, the “stopping processing”, and “processing” and limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 6, the limitation “determine …” is an additional metal process under prong 1. Under prong 2, the “forwarding”, “processing …” and “obtaining” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 9, the limitation “determining …” is an additional metal process under prong 1. Under prong 2, the “receiving” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 10, under prong 2, the “transmitting” are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 11, under prong 2, the “receiving” and “stopping” are limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 12, under prong 2, the “forwarding”, “processing” and “stopping” are limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 13, under prong 2, the “forwarding”, “processing” and “receiving” are limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 14, under prong 2, the “forwarding” and “processing” are limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Regarding claim 15, the limitation “determining” is an additional metal process under prong 1. Under prong 2, the “forwarding”, “processing” and “receiving” limitations are additional elements that recite insignificant extra solution activity which do not amount to a practical application, nor amount to significantly more under step 2B as explained above.
Allowable Subject Matter
Claims 6-7 and 13-15 would be allowable if rewritten to overcome the rejection(s) under 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following prior art made of record and not relied upon is cited to establish the level of skill in the applicant’s art and those arts considered reasonably pertinent to applicant’s disclosure. See MPEP 707.05(c).
Prior arts:
US 2023/0337043 to Pateromicchelakis
1) a RAN node (e.g., gNB, MN, SN) obtaining QoS flow configuration parameters based on an expected QoS profile adaptation for a QoS flow (e.g., QoS profile transitions over a time period, time validity, area for which the change applies, enforcement, and/or recommendation indication); 2) the RAN node may decide whether to apply a QoS flow remapping or whether to solve any expected QoS changes using RAN-level decisions (e.g., scheduling, QoS flow to DRB remapping);
US 2020/0289937 to Osman
executed platform front-end 109 may perform operations that delete any previously selected one of the machine learning or artificial intelligence process, the corresponding analytical period, the one or more selected features, the corresponding ranges of feature values, and/or the number of interpolation points.
US 2020/0289937 to Osman
When the user input 1′ is received from the client device 1 via the computer network 102 by the distributed game engine system 106 immediately after receiving the user input 1, the distributed game engine system 106 stops applying a physics prediction engine, an AI prediction engine, a graphics prediction engine, and an audio prediction engine to generate image frames and audio frames for the predicted state 2 and starts applying a graphics engine, an AI engine, a graphics engine, and an audio engine to generate image frames and audio frames for the state 1′.
US 2020/0272899 to Dunne
a sensor-rich programmable artificial intelligence (AI) inference and compute platform that is suitable for deployment at the extreme network edge, from the oceans of earth to low earth orbit, geosynchronous orbit and deep space. The AI inference and compute platform, alternatively termed the AI inference engine or the AI engine, may use machine learning accelerators, neural network accelerators, convolutional neural network accelerators, neuromorphic accelerators or a combination thereof, or may contain solely general compute.
US 2020/0143265 to Jonnalagadda
the neural encoder may include three functional tasks: natural language understanding (including intent classification and named entity recognition), inference (which includes learning policies and implementation of these policies appropriate to the objective of the conversation system using reinforcing learning or a precomputed policy), and natural language generation (by taking into account an action/decision made based upon the intent and incorporating AI models for emotion and knowledge sets).
The prior art of record does not disclose and/or fairly suggest at least claimed limitations recited in such manners in dependent claims 6-7 and 13-15 15.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 5, 8-10, 12 and 18-21 are rejected under 35 U.S.C. 102(a1) as being anticipated by US 2022/0400521 to Wei et al. (hereafter “Wei”)
As per claim 1, Wei discloses a network task processing method based on artificial intelligence (AI), performed by a terminal (paragraphs 0037, 0085-0087, 0096-0099 and 0124: “The ML algorithm provides, for example, the prediction of available resources and the outputs are sent to the admission controller, i.e. the admission control layers, which decides on admission control and send the signaling to a network control plane (AMF/RRM “Access and Mobility Management Function”/“Radio Resource Management”). As a result, new call/traffic may be restricted. If, for example, the ML algorithm outputs unexpected results or something wrong, the admission controller may override it with rule-based policy by predetermined admission control rules.” [Wingdings font/0xE0] a connection requests are received and determined by AI/machine learning in a controller), and comprising:
determining an AI processing decision in response to a cell access operation occurring during a process of performing (paragraphs 0037, 0093, 0095-0098, with an AI mode, a network task by the terminal (paragraphs 0037, 0085-0087, 0096-0099, 0124 and 0127: “The ML algorithm provides, for example, the prediction of available resources and the outputs are sent to the admission controller, i.e. the admission control layers, which decides on admission control and send the signaling to a network control plane (AMF/RRM “Access and Mobility Management Function”/“Radio Resource Management”). As a result, new call/traffic may be restricted. If, for example, the ML algorithm outputs unexpected results or something wrong, the admission controller may override it with rule-based policy by predetermined admission control rules.” [Wingdings font/0xE0] a connection requests are received and determined by AI/machine learning in a controller) [Wingdings font/0xE0] the controller based on AI decides an admission permission or an admission rejection (decisions as claimed)), wherein the AI processing decision comprises at least one of: stopping processing, based on an AI inference result, a task, forwarding the AI inference result to a target node by a node currently performing an inference task (FIGs. 3 and 5; paragraphs 0227: “The neural network 30 corresponds to the trained neural network 20 of FIG. 3 and is deployed in the network entity 7 for inferencing, wherein the input layer 32, the intermediate layer 33 and the output layer 34 have the same structure as in FIG. 3. The neural network 30 obtains actual (real-time) data 31 and outputs the predictions to an admission controller 35 including three admission control layers: a service level layer, a network level layer and a user level layer. The admission controller 35 determines an admission permission or admission rejection and sends the signaling to a network control plane (AMF/RRM) 36. As a result, a new call or traffic is restricted.”), or forwarding an AI inference model and inference data to a node subsequently performing the inference task by the node currently performing the inference task; and
processing, based on the AI processing decision, the network task (FIGs. 3 and 5; paragraphs 0227: “The neural network 30 corresponds to the trained neural network 20 of FIG. 3 and is deployed in the network entity 7 for inferencing, wherein the input layer 32, the intermediate layer 33 and the output layer 34 have the same structure as in FIG. 3. The neural network 30 obtains actual (real-time) data 31 and outputs the predictions to an admission controller 35 including three admission control layers: a service level layer, a network level layer and a user level layer. The admission controller 35 determines an admission permission or admission rejection and sends the signaling to a network control plane (AMF/RRM) 36. As a result, a new call or traffic is restricted.” [Wingdings font/0xE0] i.e., as a result, a new call or traffic is restricted/allowed [Wingdings font/0xE0] performing the request).
As per claim 2, Wei discloses wherein the determining the AI processing decision comprises:
transmitting decision-related information to a network device (paragraphs 0093-0100 and 0123-0124: “different kinds of outputs are described (which can be each implemented alone or in any combination with each other). In some embodiments, the output includes a plurality of predictions and/or probabilities of, for example, (future) network traffic, (future) incoming UEs and services, (future) available resources and the like. In some embodiments, the output of the machine learning algorithm includes a prediction of future connection requests and their service requirements. In such embodiments, the ML algorithm can provide input for separate admission control algorithms (admission control layers). In some embodiments, the output includes connection restrictions, such as a type of restricted service, based on a monitoring of various network parameters, i.e. the ML algorithm calculates admission control criteria for the plurality of admission control layers.” [Wingdings font/0xE0] output from the ML to network entity/base controller), wherein the decision-related information comprises at least one of: remaining computing power, storage information, a task real-time requirement (paragraphs 0093-0094, 0099 and 0123-0124: “the output includes connection restrictions, such as a type of restricted service, based on a monitoring of various network parameters, i.e. the ML algorithm calculates admission control criteria for the plurality of admission control layers. In some embodiments, the output of the machine learning algorithm includes generated admission control rules. In such is embodiments, the output includes a dynamically generated QoS policy and the policy is distributed to a PCRF (“Policy and Charging Rules Function”) server.”), or a task accuracy requirement; and
receiving the AI processing decision transmitted by the network device (paragraphs 0092-0099 and 0123-0124: the base network entity/controller receives the output from the ML), wherein the AI processing decision is determined by the network device based on the decision-related information (paragraphs 0092-0099 and 0123-0124: AI decision restricting/unrestricting the network requests based on the ML output).
As per claim 5, Wei discloses wherein the AI processing decision comprises forwarding the AI inference result to the target node by the node currently performing the inference task (paragraphs 0093-0100 and 0123-0124: “different kinds of outputs are described (which can be each implemented alone or in any combination with each other). In some embodiments, the output includes a plurality of predictions and/or probabilities of, for example, (future) network traffic, (future) incoming UEs and services, (future) available resources and the like. In some embodiments, the output of the machine learning algorithm includes a prediction of future connection requests and their service requirements. In such embodiments, the ML algorithm can provide input for separate admission control algorithms (admission control layers). In some embodiments, the output includes connection restrictions, such as a type of restricted service, based on a monitoring of various network parameters, i.e. the ML algorithm calculates admission control criteria for the plurality of admission control layers.” [Wingdings font/0xE0] output from the ML to network entity/base controller), and the processing, based on the AI processing decision, the network task comprises:
receiving the AI inference result transmitted by the target node (paragraphs 0092-0099 and 0123-0124: the base network entity/controller receives the output from the ML), and performing AI-based processing for the network task based on the AI inference result (paragraphs 0092-0099 and 0123-0124: AI decision restricting/unrestricting the network requests based on the ML output).
As per claim 8, Wei discloses a network task processing method based on artificial intelligence (AI), performed by a network device (paragraphs 0037, 0085-0087, 0096-0099 and 0124: “The ML algorithm provides, for example, the prediction of available resources and the outputs are sent to the admission controller, i.e. the admission control layers, which decides on admission control and send the signaling to a network control plane (AMF/RRM “Access and Mobility Management Function”/“Radio Resource Management”). As a result, new call/traffic may be restricted. If, for example, the ML algorithm outputs unexpected results or something wrong, the admission controller may override it with rule-based policy by predetermined admission control rules.” [Wingdings font/0xE0] a connection requests are received and determined by AI/machine learning in a controller), and comprising:
determining an AI processing decision (paragraphs 0037, 0093, 0095-0098, with an AI mode, a network task by the terminal (paragraphs 0037, 0085-0087, 0096-0099, 0124 and 0127: “The ML algorithm provides, for example, the prediction of available resources and the outputs are sent to the admission controller, i.e. the admission control layers, which decides on admission control and send the signaling to a network control plane (AMF/RRM “Access and Mobility Management Function”/“Radio Resource Management”). As a result, new call/traffic may be restricted. If, for example, the ML algorithm outputs unexpected results or something wrong, the admission controller may override it with rule-based policy by predetermined admission control rules.” [Wingdings font/0xE0] a connection requests are received and determined by AI/machine learning in a controller) [Wingdings font/0xE0] the controller based on AI decides an admission permission or an admission rejection (decisions as claimed)), wherein the AI processing decision comprises at least one of: stopping processing, based on an AI inference result, a task, forwarding the AI inference result to a target node by a node currently performing an inference task (FIGs. 3 and 5; paragraphs 0227: “The neural network 30 corresponds to the trained neural network 20 of FIG. 3 and is deployed in the network entity 7 for inferencing, wherein the input layer 32, the intermediate layer 33 and the output layer 34 have the same structure as in FIG. 3. The neural network 30 obtains actual (real-time) data 31 and outputs the predictions to an admission controller 35 including three admission control layers: a service level layer, a network level layer and a user level layer. The admission controller 35 determines an admission permission or admission rejection and sends the signaling to a network control plane (AMF/RRM) 36. As a result, a new call or traffic is restricted.”), or forwarding an AI inference model and inference data to a node subsequently performing the inference task by the node currently performing the inference task; and
processing, based on the AI processing decision, a network task (FIGs. 3 and 5; paragraphs 0227: “The neural network 30 corresponds to the trained neural network 20 of FIG. 3 and is deployed in the network entity 7 for inferencing, wherein the input layer 32, the intermediate layer 33 and the output layer 34 have the same structure as in FIG. 3. The neural network 30 obtains actual (real-time) data 31 and outputs the predictions to an admission controller 35 including three admission control layers: a service level layer, a network level layer and a user level layer. The admission controller 35 determines an admission permission or admission rejection and sends the signaling to a network control plane (AMF/RRM) 36. As a result, a new call or traffic is restricted.” [Wingdings font/0xE0] i.e., as a result, a new call or traffic is restricted/allowed [Wingdings font/0xE0] performing the request).
As per claim 9, Wei discloses wherein the determining the AI processing decision comprises:
receiving decision-related information (paragraphs 0093-0100 and 0123-0124: “different kinds of outputs are described (which can be each implemented alone or in any combination with each other). In some embodiments, the output includes a plurality of predictions and/or probabilities of, for example, (future) network traffic, (future) incoming UEs and services, (future) available resources and the like. In some embodiments, the output of the machine learning algorithm includes a prediction of future connection requests and their service requirements. In such embodiments, the ML algorithm can provide input for separate admission control algorithms (admission control layers). In some embodiments, the output includes connection restrictions, such as a type of restricted service, based on a monitoring of various network parameters, i.e. the ML algorithm calculates admission control criteria for the plurality of admission control layers.” [Wingdings font/0xE0] output from the ML to network entity/base controller), wherein the decision-related information comprises at least one of: remaining computing power, storage information, a task real-time requirement (paragraphs 0093-0094, 0099 and 0123-0124: “the output includes connection restrictions, such as a type of restricted service, based on a monitoring of various network parameters, i.e. the ML algorithm calculates admission control criteria for the plurality of admission control layers. In some embodiments, the output of the machine learning algorithm includes generated admission control rules. In such is embodiments, the output includes a dynamically generated QoS policy and the policy is distributed to a PCRF (“Policy and Charging Rules Function”) server.”), or a task accuracy requirement; and
determining, based on the decision-related information, the AI processing decision (paragraphs 0092-0099 and 0123-0124: AI decision restricting/unrestricting the network requests based on the ML output).
As per claim 10, Wei discloses transmitting the AI processing decision to the terminal (paragraphs 0037, 0085-0087, 0096-0099 and 0124: “The ML algorithm provides, for example, the prediction of available resources and the outputs are sent to the admission controller, i.e. the admission control layers, which decides on admission control and send the signaling to a network control plane (AMF/RRM “Access and Mobility Management Function”/“Radio Resource Management”). As a result, new call/traffic may be restricted. If, for example, the ML algorithm outputs unexpected results or something wrong, the admission controller may override it with rule-based policy by predetermined admission control rules.” [Wingdings font/0xE0] a connection requests are received and determined by AI/machine learning in a controller).
As per claim 12, Wei discloses wherein the AI processing decision comprises forwarding the AI inference result to the target node by the node currently performing the inference task (paragraphs 0093-0100 and 0123-0124: “different kinds of outputs are described (which can be each implemented alone or in any combination with each other). In some embodiments, the output includes a plurality of predictions and/or probabilities of, for example, (future) network traffic, (future) incoming UEs and services, (future) available resources and the like. In some embodiments, the output of the machine learning algorithm includes a prediction of future connection requests and their service requirements. In such embodiments, the ML algorithm can provide input for separate admission control algorithms (admission control layers). In some embodiments, the output includes connection restrictions, such as a type of restricted service, based on a monitoring of various network parameters, i.e. the ML algorithm calculates admission control criteria for the plurality of admission control layers.” [Wingdings font/0xE0] output from the ML to network entity/base controller), and
the processing, based on the AI processing decision, the network task (paragraphs 0092-0099 and 0123-0124: AI decision restricting/unrestricting the network requests based on the ML output) comprises:
forwarding, in response to the network device being the node currently performing the inference task, the AI inference result to the target node (paragraphs 0093-0100 and 0123-0124: “different kinds of outputs are described (which can be each implemented alone or in any combination with each other). In some embodiments, the output includes a plurality of predictions and/or probabilities of, for example, (future) network traffic, (future) incoming UEs and services, (future) available resources and the like. In some embodiments, the output of the machine learning algorithm includes a prediction of future connection requests and their service requirements. In such embodiments, the ML algorithm can provide input for separate admission control algorithms (admission control layers). In some embodiments, the output includes connection restrictions, such as a type of restricted service, based on a monitoring of various network parameters, i.e. the ML algorithm calculates admission control criteria for the plurality of admission control layers.” [Wingdings font/0xE0] output from the ML to network entity/base controller).
As per claim 18, it is a network task processing device claim, which recite(s) the same limitations as those of claim 1. Accordingly, claim 17 is rejected for the same reasons as set forth in the rejection of claim 1.
As per claim 19, it is a network task processing device claim, which recite(s) the same limitations as those of claim 8. Accordingly, claim 19 is rejected for the same reasons as set forth in the rejection of claim 8.
As per claim 20, it is a network task processing device claim, which recite(s) the same limitations as those of claim 1. Accordingly, claim 20 is rejected for the same reasons as set forth in the rejection of claim 1.
As per claim 21, it is a network task processing device claim, which recite(s) the same limitations as those of claim 8. Accordingly, claim 21 is rejected for the same reasons as set forth in the rejection of claim 8.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 3 is rejected under 35 U.S.C. 103(a) as being unpatentable over Wei in view of US 2018/0219959 to Bugenhagen et al. (hereafter “Bugenhagen”)
As per claim 3, Wei discloses wherein the AI processing decision comprises stopping processing, based on the AI inference result, the task (paragraphs 0037, 0093, 0095-0098).
Wei does not explicitly disclose the processing, based on the AI processing decision, the network task comprises: stopping processing, based on the AI inference result, the network task.
Bugenhagen further discloses the processing, based on the AI processing decision, the network task comprises: stopping processing, based on the AI inference result, the network task (paragraphs 0047 and 0053: “performing actions in response to analysis of at least one of the customer network telemetry data or the service provider network telemetry data, restricting traffic to the LAN, restricting traffic from the LAN, redirecting traffic to the LAN, redirecting traffic from the LAN, sending at least one notification through the LAN, executing a software application in the LAN, or stopping execution of a software application in the LAN, and/or the like.”).
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Bugenhagen into Wei’s teaching because it would provide for the purpose of restricting traffic to the first network, restricting traffic from the first network, redirecting traffic to the first network, redirecting traffic from the first network, sending at least one notification through the first network, executing a software application in the first network, or stopping execution of a software application in the first network (Bugenhagen, paragraph 0024).
Claim 4 is rejected under 35 U.S.C. 103(a) as being unpatentable over Wei in view of US 2022/0067580 to Rho et al. (hereafter “Rho”)
As per claim 4, Wei discloses wherein the AI processing decision comprises stopping processing, based on the AI inference result, the task (paragraphs 0037, 0093, 0095-0098).
Wei does not explicitly disclose the processing, based on the AI processing decision, the network task comprises: transmitting a notification message to the node currently performing the inference task, wherein the notification message is configured to instruct the node currently performing the inference task to stop performing AI inference.
Rho further discloses the processing, based on the AI processing decision, the network task comprises: transmitting a notification message to the node currently performing the inference task, wherein the notification message is configured to instruct the node currently performing the inference task to stop performing AI inference (paragraph 0074: “upon selection of “Cancel” icon 214 by analyst 101, executed platform front-end 109 may perform operations that delete any previously selected one of the machine learning or artificial intelligence process, the corresponding analytical period, the one or more selected features, the corresponding ranges of feature values, and/or the number of interpolation points.”).
It would have been obvious to a person having ordinary skill in the art at the time before the effective filling date of the claimed invention to combine a teaching of Rho into Wei’s teaching because it would provide for the purpose of enable the analyst to visualize, via the web-based GUI, data characterizing input features and prediction stability of the trained machine learning or artificial intelligence process and further, to monitor and visualize, via the web-based GUI, evaluation metrics as ground truth data becomes available (Rho, paragraph 0015).
Conclusion
Any inquiry concerning this communication should be directed to examiner Tuan Dao, whose telephone/fax numbers are (571) 270 3387 and (571) 270 4387, respectively. The examiner can normally be reached on every Monday-Thursday, and the second Friday of the bi-week from 7:30AM to 5:00PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do, can be reached at (571) 272 3721.
The fax phone number for the organization where this application or proceeding is assigned is (571) 273 8300.
Any inquiry of a general nature of relating to the status of this application or proceeding should be directed to the TC 2100 Group receptionist whose telephone number is (571) 272 2100.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/TUAN C DAO/ Primary Examiner, Art Unit 2198
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