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 .
Information Disclosure Statement
The examiner has considered the information disclosure statements (IDS) submitted on 09/22/2025 and 12/22/2025.
Response to Amendment
The amendment filed 12/15/2025 has been entered. Claims 1-24 remain pending in the application.
Response to Arguments
Applicant’s arguments, filed 12/15/2025, with respect to the rejections of the claims under 101 have been fully considered and persuasive. Therefore, the rejection has been withdrawn.
Applicant’s arguments, filed 12/15/2025, with respect to the rejections of the claims under 103 have been fully considered and are persuasive because of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Mimassi (US Pub. 2022/0292346) in view of Hardy et al. (MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets – Applicant provided NPL from the IDS) and further in view of Miyamura (US Pub. 2022/0103620).
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 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 1-2, 4-7, 9-10, 12-15, 17-18 and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Mimassi (US Pub. 2022/0292346) in view of Hardy et al. (MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets – Applicant provided NPL from the IDS) and further in view of Miyamura (US Pub. 2022/0103620).
As per claim 1, Mimassi teaches a computer-implemented method at a first edge server [paragraph 0008, “system for intelligent service intermediation is disclosed, comprising: one or more service edge device(s)”], comprising operations for:
receiving a global that has been trained with common data of a cloud data center [paragraph 0008, “send the updated machine and deep learning global models to the one or more service edge devices”; paragraph 0048, “the cloud-based intermediation server 110 which has a global awareness of the overall state of the distributed service edges”];
determining that area data of the first edge server is imbalanced using the global, wherein the global discriminator compares the area data with the common data and outputs a negative result to indicate that the area data is imbalanced [paragraph 0009, “receiving updated machine and deep learning global model parameters; applying the updated machine and deep learning global model parameters to the local models stored in the service edge device … feeding the received local data as input into one or more of the updated local machine and deep learning models to generate output actions responsive to a service edge device user query … updating the machine and deep learning global models”; paragraph 0055, “the newly updated advanced global model is downloaded 330 to service edge devices 300 which may then use the downloaded model as its new updated local model”; The specification in paragraphs 0027, 0032 and 0035 recites “For example, the common data 112 may include …, Examples of area data 132a ... 132n that is imbalanced data …, and The common data (which may be described as a training data set or an initial data set) serves as the initial training data for the global generator Ge and the global discriminator”. Since the specification and the claim does not positively define what can be area data, common data or area data is imbalance, examiner interprets area data as the output data generated from the edge device, interprets common data as the desired input/output that serve as the training data of the global model, and “area data is imbalance” as error data that cause the error/difference from actual output of the model and desired output. It can be seen that the edge device when receiving the updated global models, feeding inputs into the global model (new updated local model) to generate output, the system then compares the generated output to the desired output for identifying the error/imbalance/difference between actual and desired values (comparing the area data to the common data), and the identified error/imbalance/difference may be used to modify the global model];
training a local with the area data to generate a first result [paragraph 0009, “training and update local machine and deep learning models using the received local data”; paragraph 0055, “The updated local models continue learning and training on locally gathered data in order to refine the local model parameters”; It can be seen that after training the input data with the global model (new updated local model), the edge device update the local model parameters and retraining the local model with the local input (edge device user queries) to generate an output];
training the exchanged local with the area data to generate a second result [paragraph 0055, “a service intermediary server 320 may select a subset of service edge devices 300 to be designated as "learners" … Each device within the selected subset of edge devices uploads its local model updates 310 to service intermediary server 320 which aggregates 322 the received local model update parameters (e.g., weights, thresholds, biases, etc.) and then computes new global model parameters using the aggregated local updates. The computed new global model parameters are then applied to update the advanced global model. Then the newly updated advanced global model is downloaded 330 to service edge devices 300 which may then use the downloaded model as its new updated local model … The updated local models continue learning and training on locally gathered data in order to refine the local model parameters”; It can be seen that the other edge device within the selected subset of edge devices (second edge device) also use its local model to train, learn and retrain the local model based on the error to generate the better output];
determining that the first result and the second result indicate that the first edge server and the second edge server are proximate [paragraphs 0059-0063, “system may utilize a federated awareness architecture where groups of selected service edge devices are placed in an awareness layer … For example, awareness layer 505 comprises restaurants A 506, B 507, and C 508 and those restaurants may have been grouped together … the intelligent service intermediary system may utilize it artificial intelligence capabilities, supported by machine and deep learning models contained in both service edge devices and the service intermediary server, in order to optimize the grouped restaurant's service and operations. For example, restaurant A 506 may need to order more to-go containers, but its distributor has already made its deliveries for the day, whereas restaurant B 507 may have extra to-go containers, send a suggestion to the manager of restaurant B 507 to sell X amount of to-go containers for Y price to restaurant A 506 … a group of restaurants may be located in the same geographic location and they may all purchase from one or more of the same distributors. In this case, the intelligent service intermediary system may suggest that distributor drivers 516 deliver to the all the restaurants in a given geographic location in order to reduce time and resources used by the distributor … By implementing a federated awareness architecture with awareness layers, the intelligent service intermediary system can operate as a single central intelligence that is aware of the overall state of all participants, without necessitating all participants be aware of all other services and service edge devices. This allows service edge devices and the machine and deep learning models contained therein to be agile operating on local data”; It can be seen that the first edge server and the second edge server (for example, restaurant A 506 and restaurant B 507) are located in the same geographic location, the edge servers (restaurants) utilize the local models to operate the restaurants with local inputs to generate outputs such as items to order for the restaurants (to-go containers), which distributors to purchase items, etc., and since the system determines both of the restaurants locate in the same location, they may purchase items from a same distributor, and purchased items can be the same, groups them into the same set];
in response to determining that the first result and the second result indicate that the first edge server and the second edge server are proximate, adding the first edge server and the second edge server to an edge server group list [paragraph 0055, “a service intermediary server 320 may select a subset of service edge devices 300 to be designated as "learners”; paragraph 0058, “The subset of edge devices may be chosen from the set of edge devices subscribed to a given service intermediation server”; paragraphs 0059-0063, “system may utilize a federated awareness architecture where groups of selected service edge devices are placed in an awareness layer … For example, awareness layer 505 comprises restaurants A 506, B 507, and C 508 and those restaurants may have been grouped together … the intelligent service intermediary system may utilize it artificial intelligence capabilities, supported by machine and deep learning models contained in both service edge devices and the service intermediary server, in order to optimize the grouped restaurant's service and operations. For example, restaurant A 506 may need to order more to-go containers, but its distributor has already made its deliveries for the day, whereas restaurant B 507 may have extra to-go containers, send a suggestion to the manager of restaurant B 507 to sell X amount of to-go containers for Y price to restaurant A 506 … a group of restaurants may be located in the same geographic location and they may all purchase from one or more of the same distributors. In this case, the intelligent service intermediary system may suggest that distributor drivers 516 deliver to the all the restaurants in a given geographic location in order to reduce time and resources used by the distributor … By implementing a federated awareness architecture with awareness layers, the intelligent service intermediary system can operate as a single central intelligence that is aware of the overall state of all participants, without necessitating all participants be aware of all other services and service edge devices. This allows service edge devices and the machine and deep learning models contained therein to be agile operating on local data”; It can be seen that the first edge server and the second edge server (for example, restaurant A 506 and restaurant B 507) are located in the same geographic location, the edge servers (restaurants) utilize the local models to operate the restaurants with local inputs to generate outputs such as items to order for the restaurants (to-go containers), which distributors to purchase items, etc., and since the system determines both of the restaurants locate in the same location, they may purchase items from a same distributor, and purchased items can be the same, groups them into the same set/list];
updating at least one of an application model and a configuration of an application from one of the first edge server and the second edge server on the edge server group list [paragraph 0059, “groups of selected service edge devices are placed in an awareness layer … For example, awareness layer 505 comprises restaurants A 506, B 507, and C 508”; paragraphs 0040-0041, “The VA operating on a service edge device 130 may further receive service actions from service intermediary server 110 in order to optimize the experience or role of the edge device user. For example, a service edge device user may be getting ready to leave work and asks the VA to order dinner for pickup from an Italian restaurant that is on her path home. The VA could access the user's preferences stored on the edge device to identify Italian meals the user prefers to order as well as any preferred Italian restaurants that the user likes to dine at … intelligent service intermediary system 100 may be connected to the Italian restaurant and can track the preparation of the user's meal, and may be able to send a service action to the user's device to inform the user to leave work at a certain time in order to ensure the meal is just being placed in to-go containers as the user arrives to the Italian restaurant. Alternatively, the device user may run into traffic on her way from work to the Italian restaurant, and the server 110, receiving the traffic data from the edge device, has awareness of the unexpected traffic and could send a service action suggestion to the kitchen staff to delay preparation of the user's order by ten minutes in order to ensure the food is hot and ready when the edge device user arrives to the restaurant”; the examiner interprets the restaurant as service edge devices, the application is to prepare user’ meal, the configuration of the application can be having the meal ready for the user on time, wherein, and the updating is to delay the meal preparation for a reason];
executing the application [paragraphs 0040-0041, “ensure the meal is just being placed in to-go containers as the user arrives to the Italian restaurant”].
Mimassi does not teach
receiving a global discriminator;
training a local discriminator;
receiving an exchanged local discriminator from a second edge server;
in response to determining that a load is not high at the first edge server, executing the application at the first edge server; and
in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server.
Hardy teaches
receiving a global discriminator [Fig. 1b, page 3, Col. 1 last paragraph to Col. 2, first paragraph discloses federated learning to GANs, where a machine learning model is trained using a set of workers. Fig. 1b shows a server includes a global discriminator which is sent to the workers (green dots), and each worker includes its own discrimination Dn];
training a local discriminator [page 3, section IV, “Each worker n performs L learning iterations on its discriminator Dn … Each worker n computes an error feedback Fn on Xn(g) by using Dn and sends this error to the server];
receiving an exchanged local discriminator from a second edge server [Fig. 1a, page 4, Col. 1, first paragraph, “swaps discriminators between workers in a peer-to-peer fashion … workers start a peer-to-peer swapping process for their discriminators, using function SWAP()”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for service intermediation that connects to a plurality of edge devices of Mimassi to include training the machine learning model on local discriminators of Hardy. Doing so would help training a generative adversarial network using local discriminators on the edge devices (Hardy, page 1, Col. 2).
Mimassi and Hardy do not teach
in response to determining that a load is not high at the first edge server, executing the application at the first edge server; and
in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server.
Miyamura teaches
in response to determining that a load is not high at the first edge server, executing the application at the first edge server [abstract, “An operation management system capable of implementing optimum deployment of applications and data is provided”; paragraph 0041, “Under this circumstance, if the applications, data, and so on increase in the edge servers 120, deviation of resource loads, deviation of I/O loads, shortage of the storage capacity, and so on occur at every edge server 120. In order to solve them, the operation management server 110 issues an instruction to the relevant edge server 120 to migrate (deploy) an application in an edge server 120 with a high resource load to an edge server 120 with a low resource load”; paragraph 0240-0241, “judgment unit … that judges whether a load on resources provided in an edge server included by the above-mentioned operation management system exceeds a threshold value or not … deploys a deployment target application which is provided in the edge server judged by the judgment unit that the load on its resources exceeds the threshold value in an edge server different from the above-mentioned edge server … if the application is deployed in another edge server and the load on resources provided in the other edge server exceeds the threshold value, the application will be deployed in a further another edge server which is different from the other edge server”; It can be seen that if determining that the load in an edge server (first edge server) is not high, the target application will be deployed to the first edge server for executing, but if the load in an edge server (first edge server) is high, the target application will be deployed to other/second edge server to execute]; and
in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server [paragraph 0240-0241, “judgment unit … that judges whether a load on resources provided in an edge server included by the above-mentioned operation management system exceeds a threshold value or not … deploys a deployment target application which is provided in the edge server judged by the judgment unit that the load on its resources exceeds the threshold value in an edge server different from the above-mentioned edge server … if the application is deployed in another edge server and the load on resources provided in the other edge server exceeds the threshold value, the application will be deployed in a further another edge server which is different from the other edge server”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for service intermediation that connects to a plurality of edge devices of Mimassi to include in response to determining that a load is not high at the first edge server, executing the application at the first edge server, and in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server of Miyamura. Doing so would help avoiding the situation where the performance of the specific edge server will degrade (Miyamura, 242).
As per claim 2, Mimassi, Hardy and Miyamura teach the computer-implemented method of claim 1.
Miyamura teaches
receiving the request to execute the application at the first edge server from an edge device [paragraph 0135, “the analysis unit 312 creates a list of candidates for an edge server 120 (migration destination) to migrate the migration target application 321. For example, the analysis unit 312 selects an edge server 120 which has an amount of free resources (free resource amount) equal to or more than the required resource amount of the migration target application 321 as the candidate for the migration destination”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for service intermediation that connects to a plurality of edge devices of Mimassi to include receiving the request to execute the application at the first edge server from an edge device of Miyamura. Doing so would help executing the application using the created list of candidates for an edge server (Miyamura, 0135).
Claims 10 and 18 are rejected by the same reason as of claim 2, since these claims recite the similar limitations.
As per claim 4, Mimassi, Hardy and Miyamura teach the computer-implemented method of claim 1.
Mimassi further teaches
an edge device maintains a visited edge servers list while traversing a path that passes by at least one of the first edge server and the second edge server [paragraphs 0057-0058, “a federated network has devices called service intermediary servers 400, 410, and 420. A large plurality of service edge devices 405a-e, 4l5a-e, 425a-e, acting as nodes, may be subscribed to the service intermediary servers 400, 410, and 420 … each server in the network may store the same global models and when one server updates its global model parameters, it can send the updated global models to the other servers in the network … if service intermediary server 410 were to become disconnected from the system network, any of the remaining service intermediary servers 400, 420 may pick up the slack and connect with service edge devices 415a-e to facilitate continued network viability while maintaining the same global models and thus the same network functionality”; It can be seen that the edge device/user maintains a list of edge servers to maintain services even when one of the servers is disconnected].
Claims 12 and 20 are rejected by the same reason as of claim 4, since these claims recite the similar limitations.
As per claim 5, Mimassi, Hardy and Miyamura teach the computer-implemented method of claim 1.
Hardy further teaches
receiving an exchanged local discriminator from a third edge server [Fig. 1a, “MD-GAN swaps discriminators between workers in a peer-to-peer fashion”; page 5, algorithm 1];
training the exchanged local discriminator from the third edge server with the area data to generate a third result [page 5, section C, “Each worker n hosts a discriminator Dn and a training dataset Bn … 1) The swapping of discriminators: Each discriminator n solely uses Bn to train its parameters ϴn. If too many iterations are performed on the same local dataset, the discriminator tends to over specialize (which decreases its capacity of generalization). This effect, called overfitting, is avoided in MD-GAN by swapping the parameters of discriminators ϴn between workers after E epochs. The swap is implemented in a gossip fashion, by choosing randomly for every worker another worker to send its parameters to”];
determining that the first result and the third result indicate that the first edge server and the third edge server are not proximate [page 5, section C, “Each worker n hosts a discriminator Dn and a training dataset Bn … 1) The swapping of discriminators: Each discriminator n solely uses Bn to train its parameters ϴn. If too many iterations are performed on the same local dataset, the discriminator tends to over specialize (which decreases its capacity of generalization). This effect, called overfitting, is avoided in MD-GAN by swapping the parameters of discriminators ϴn between workers after E epochs. The swap is implemented in a gossip fashion, by choosing randomly for every worker another worker to send its parameters to”; page 9, Col. 2, last paragraph, “The swapping process between workers leads to better results”; It can be seen that for the same parameters which send to another worker that comprises a different discriminator (third discriminator), the result would be different (better result)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for service intermediation that connects to a plurality of edge devices of Mimassi to include training the exchanged local discriminator and determining that the results are not proximate of Hardy. Doing so would help obtaining a better result by swapping discriminators between edge servers (Hardy, page 9, Col. 2, last paragraph).
Claims 13 and 21 are rejected by the same reason as of claim 5, since these claims recite the similar limitations.
As per claim 6, Mimassi, Hardy and Miyamura teach the computer-implemented method of claim 1.
Hardy teaches
the global discriminator [Fig. 1];
Mimassi (as modified) further teaches
wherein the global discriminator outputs a positive result to indicate that the area data is not imbalanced [paragraph 0055, “a service intermediary server 320 may select a subset of service edge devices 300 to be designated as "learners" … Each device within the selected subset of edge devices uploads its local model updates 310 to service intermediary server 320 which aggregates 322 the received local model update parameters (e.g., weights, thresholds, biases, etc.) and then computes new global model parameters using the aggregated local updates. The computed new global model parameters are then applied to update the advanced global model 324 stored and operating within the server 320. Once the global model has been updated, the model may be tested to validate accuracy of the model under the influence of the aggregated and computed new global model parameters. Manual verification may be conducted using a set of test data selected and prepared by a data scientist or analyst affiliated with a business enterprise. In other embodiments, the server 310 may perform autonomous verification by using a set of test data and determining if a predefined output accuracy threshold is surpassed or not”; It can be seen that if the global model output is greater than the accuracy threshold, the result indicates that the area data is not imbalanced, if the output is less than the accuracy threshold, the result indicates the area data is imbalanced].
Claims 14 and 22 are rejected by the same reason as of claim 6, since these claims recite the similar limitations.
As per claim 7, Mimassi, Hardy and Miyamura teach the computer-implemented method of claim 1.
Hardy teaches
the global discriminator [Fig. 1];
Mimassi (as modified) further teaches
the global discriminator is trained at a cloud node and deployed to the first edge server [paragraph 0048, “the cloud-based intermediation server 110 which has a global awareness of the overall state of the distributed service edges”; paragraph 0007, “a service intermediation server, which stores advanced global machine and deep learning models”; paragraph 0008, “upload trained and updated local model parameters to a service intermediary server … aggregate the received updated local model parameters … compute the average value of the local model parameters; update the machine and deep learning global models using the computed average values … send the updated machine and deep learning global models to the one or more service edge devices”].
Claims 15 and 23 are rejected by the same reason as of claim 6, since these claims recite the similar limitations.
As per claim 9, Mimassi teaches a computer program product of a first edge server, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by at least one processor to perform operations for [paragraph 0008, “system for intelligent service intermediation is disclosed, comprising: one or more service edge device(s) comprising at least a plurality of programming instructions stored in a memory of, and operating on at least one processor of, a computing device, wherein the plurality of programming instructions, when operating on the at least one processor, cause the computing device to”]:
receiving a global that has been trained with common data of a cloud data center [paragraph 0008, “send the updated machine and deep learning global models to the one or more service edge devices”; paragraph 0048, “the cloud-based intermediation server 110 which has a global awareness of the overall state of the distributed service edges”];
determining that area data of the first edge server is imbalanced using the global, wherein the global discriminator compares the area data with the common data and outputs a negative result to indicate that the area data is imbalanced [paragraph 0009, “receiving updated machine and deep learning global model parameters; applying the updated machine and deep learning global model parameters to the local models stored in the service edge device … feeding the received local data as input into one or more of the updated local machine and deep learning models to generate output actions responsive to a service edge device user query … updating the machine and deep learning global models”; paragraph 0055, “the newly updated advanced global model is downloaded 330 to service edge devices 300 which may then use the downloaded model as its new updated local model”; The specification in paragraphs 0027, 0032 and 0035 recites “For example, the common data 112 may include …, Examples of area data 132a ... 132n that is imbalanced data …, and The common data (which may be described as a training data set or an initial data set) serves as the initial training data for the global generator Ge and the global discriminator”. Since the specification and the claim does not positively define what can be area data, common data or area data is imbalance, examiner interprets area data as the output data generated from the edge device, interprets common data as the desired input/output that serve as the training data of the global model, and “area data is imbalance” as error data that cause the error/difference from actual output of the model and desired output. It can be seen that the edge device when receiving the updated global models, feeding inputs into the global model (new updated local model) to generate output, the system then compares the generated output to the desired output for identifying the error/imbalance/difference between actual and desired values (comparing the area data to the common data), and the identified error/imbalance/difference may be used to modify the global model];
training a local with the area data to generate a first result [paragraph 0009, “training and update local machine and deep learning models using the received local data”; paragraph 0055, “The updated local models continue learning and training on locally gathered data in order to refine the local model parameters”; It can be seen that after training the input data with the global model (new updated local model), the edge device update the local model parameters and retraining the local model with the local input (edge device user queries) to generate an output];
training the exchanged local with the area data to generate a second result [paragraph 0055, “a service intermediary server 320 may select a subset of service edge devices 300 to be designated as "learners" … Each device within the selected subset of edge devices uploads its local model updates 310 to service intermediary server 320 which aggregates 322 the received local model update parameters (e.g., weights, thresholds, biases, etc.) and then computes new global model parameters using the aggregated local updates. The computed new global model parameters are then applied to update the advanced global model. Then the newly updated advanced global model is downloaded 330 to service edge devices 300 which may then use the downloaded model as its new updated local model … The updated local models continue learning and training on locally gathered data in order to refine the local model parameters”; It can be seen that the other edge device within the selected subset of edge devices (second edge device) also use its local model to train, learn and retrain the local model based on the error to generate the better output];
determining that the first result and the second result indicate that the first edge server and the second edge server are proximate [paragraphs 0059-0063, “system may utilize a federated awareness architecture where groups of selected service edge devices are placed in an awareness layer … For example, awareness layer 505 comprises restaurants A 506, B 507, and C 508 and those restaurants may have been grouped together … the intelligent service intermediary system may utilize it artificial intelligence capabilities, supported by machine and deep learning models contained in both service edge devices and the service intermediary server, in order to optimize the grouped restaurant's service and operations. For example, restaurant A 506 may need to order more to-go containers, but its distributor has already made its deliveries for the day, whereas restaurant B 507 may have extra to-go containers, send a suggestion to the manager of restaurant B 507 to sell X amount of to-go containers for Y price to restaurant A 506 … a group of restaurants may be located in the same geographic location and they may all purchase from one or more of the same distributors. In this case, the intelligent service intermediary system may suggest that distributor drivers 516 deliver to the all the restaurants in a given geographic location in order to reduce time and resources used by the distributor … By implementing a federated awareness architecture with awareness layers, the intelligent service intermediary system can operate as a single central intelligence that is aware of the overall state of all participants, without necessitating all participants be aware of all other services and service edge devices. This allows service edge devices and the machine and deep learning models contained therein to be agile operating on local data”; It can be seen that the first edge server and the second edge server (for example, restaurant A 506 and restaurant B 507) are located in the same geographic location, the edge servers (restaurants) utilize the local models to operate the restaurants with local inputs to generate outputs such as items to order for the restaurants (to-go containers), which distributors to purchase items, etc., and since the system determines both of the restaurants locate in the same location, they may purchase items from a same distributor, and purchased items can be the same, groups them into the same set];
in response to determining that the first result and the second result indicate that the first edge server and the second edge server are proximate, adding the first edge server and the second edge server to an edge server group list [paragraph 0055, “a service intermediary server 320 may select a subset of service edge devices 300 to be designated as "learners”; paragraph 0058, “The subset of edge devices may be chosen from the set of edge devices subscribed to a given service intermediation server”; paragraphs 0059-0063, “system may utilize a federated awareness architecture where groups of selected service edge devices are placed in an awareness layer … For example, awareness layer 505 comprises restaurants A 506, B 507, and C 508 and those restaurants may have been grouped together … the intelligent service intermediary system may utilize it artificial intelligence capabilities, supported by machine and deep learning models contained in both service edge devices and the service intermediary server, in order to optimize the grouped restaurant's service and operations. For example, restaurant A 506 may need to order more to-go containers, but its distributor has already made its deliveries for the day, whereas restaurant B 507 may have extra to-go containers, send a suggestion to the manager of restaurant B 507 to sell X amount of to-go containers for Y price to restaurant A 506 … a group of restaurants may be located in the same geographic location and they may all purchase from one or more of the same distributors. In this case, the intelligent service intermediary system may suggest that distributor drivers 516 deliver to the all the restaurants in a given geographic location in order to reduce time and resources used by the distributor … By implementing a federated awareness architecture with awareness layers, the intelligent service intermediary system can operate as a single central intelligence that is aware of the overall state of all participants, without necessitating all participants be aware of all other services and service edge devices. This allows service edge devices and the machine and deep learning models contained therein to be agile operating on local data”; It can be seen that the first edge server and the second edge server (for example, restaurant A 506 and restaurant B 507) are located in the same geographic location, the edge servers (restaurants) utilize the local models to operate the restaurants with local inputs to generate outputs such as items to order for the restaurants (to-go containers), which distributors to purchase items, etc., and since the system determines both of the restaurants locate in the same location, they may purchase items from a same distributor, and purchased items can be the same, groups them into the same set/list];
updating at least one of an application model and a configuration of an application from one of the first edge server and the second edge server on the edge server group list [paragraph 0059, “groups of selected service edge devices are placed in an awareness layer … For example, awareness layer 505 comprises restaurants A 506, B 507, and C 508”; paragraphs 0040-0041, “The VA operating on a service edge device 130 may further receive service actions from service intermediary server 110 in order to optimize the experience or role of the edge device user. For example, a service edge device user may be getting ready to leave work and asks the VA to order dinner for pickup from an Italian restaurant that is on her path home. The VA could access the user's preferences stored on the edge device to identify Italian meals the user prefers to order as well as any preferred Italian restaurants that the user likes to dine at … intelligent service intermediary system 100 may be connected to the Italian restaurant and can track the preparation of the user's meal, and may be able to send a service action to the user's device to inform the user to leave work at a certain time in order to ensure the meal is just being placed in to-go containers as the user arrives to the Italian restaurant. Alternatively, the device user may run into traffic on her way from work to the Italian restaurant, and the server 110, receiving the traffic data from the edge device, has awareness of the unexpected traffic and could send a service action suggestion to the kitchen staff to delay preparation of the user's order by ten minutes in order to ensure the food is hot and ready when the edge device user arrives to the restaurant”; the examiner interprets the restaurant as service edge devices, the application is to prepare user’ meal, the configuration of the application can be having the meal ready for the user on time, wherein, and the updating is to delay the meal preparation for a reason];
executing the application [paragraphs 0040-0041, “ensure the meal is just being placed in to-go containers as the user arrives to the Italian restaurant”].
Mimassi does not teach
receiving a global discriminator;
training a local discriminator;
receiving an exchanged local discriminator from a second edge server;
in response to determining that a load is not high at the first edge server, executing the application at the first edge server; and
in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server.
Hardy teaches
receiving a global discriminator [Fig. 1b, page 3, Col. 1 last paragraph to Col. 2, first paragraph discloses federated learning to GANs, where a machine learning model is trained using a set of workers. Fig. 1b shows a server includes a global discriminator which is sent to the workers (green dots), and each worker includes its own discrimination Dn];
training a local discriminator [page 3, section IV, “Each worker n performs L learning iterations on its discriminator Dn … Each worker n computes an error feedback Fn on Xn(g) by using Dn and sends this error to the server];
receiving an exchanged local discriminator from a second edge server [Fig. 1a, page 4, Col. 1, first paragraph, “swaps discriminators between workers in a peer-to-peer fashion … workers start a peer-to-peer swapping process for their discriminators, using function SWAP()”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for service intermediation that connects to a plurality of edge devices of Mimassi to include training the machine learning model on local discriminators of Hardy. Doing so would help training a generative adversarial network using local discriminators on the edge devices (Hardy, page 1, Col. 2).
Mimassi and Hardy do not teach
in response to determining that a load is not high at the first edge server, executing the application at the first edge server; and
in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server.
Miyamura teaches
in response to determining that a load is not high at the first edge server, executing the application at the first edge server [abstract, “An operation management system capable of implementing optimum deployment of applications and data is provided”; paragraph 0041, “Under this circumstance, if the applications, data, and so on increase in the edge servers 120, deviation of resource loads, deviation of I/O loads, shortage of the storage capacity, and so on occur at every edge server 120. In order to solve them, the operation management server 110 issues an instruction to the relevant edge server 120 to migrate (deploy) an application in an edge server 120 with a high resource load to an edge server 120 with a low resource load”; paragraph 0240-0241, “judgment unit … that judges whether a load on resources provided in an edge server included by the above-mentioned operation management system exceeds a threshold value or not … deploys a deployment target application which is provided in the edge server judged by the judgment unit that the load on its resources exceeds the threshold value in an edge server different from the above-mentioned edge server … if the application is deployed in another edge server and the load on resources provided in the other edge server exceeds the threshold value, the application will be deployed in a further another edge server which is different from the other edge server”; It can be seen that if determining that the load in an edge server (first edge server) is not high, the target application will be deployed to the first edge server for executing, but if the load in an edge server (first edge server) is high, the target application will be deployed to other/second edge server to execute]; and
in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server [paragraph 0240-0241, “judgment unit … that judges whether a load on resources provided in an edge server included by the above-mentioned operation management system exceeds a threshold value or not … deploys a deployment target application which is provided in the edge server judged by the judgment unit that the load on its resources exceeds the threshold value in an edge server different from the above-mentioned edge server … if the application is deployed in another edge server and the load on resources provided in the other edge server exceeds the threshold value, the application will be deployed in a further another edge server which is different from the other edge server”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for service intermediation that connects to a plurality of edge devices of Mimassi to include in response to determining that a load is not high at the first edge server, executing the application at the first edge server, and in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server of Miyamura. Doing so would help avoiding the situation where the performance of the specific edge server will degrade (Miyamura, 242).
As per claim 17, Mimassi teaches a first edge server [paragraph 0008, “system for intelligent service intermediation is disclosed, comprising: one or more service edge device(s)] comprising:
one or more processors, one or more computer-readable memories and one or more computer-readable, tangible storage devices; and program instructions, stored on at least one of the one or more computer-readable, tangible storage devices for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, to perform operations comprising [paragraph 0008, “system for intelligent service intermediation is disclosed, comprising: one or more service edge device(s) comprising at least a plurality of programming instructions stored in a memory of, and operating on at least one processor of, a computing device, wherein the plurality of programming instructions, when operating on the at least one processor, cause the computing device to”]:
receiving a global that has been trained with common data of a cloud data center [paragraph 0008, “send the updated machine and deep learning global models to the one or more service edge devices”; paragraph 0048, “the cloud-based intermediation server 110 which has a global awareness of the overall state of the distributed service edges”];
determining that area data of the first edge server is imbalanced using the global, wherein the global discriminator compares the area data with the common data and outputs a negative result to indicate that the area data is imbalanced [paragraph 0009, “receiving updated machine and deep learning global model parameters; applying the updated machine and deep learning global model parameters to the local models stored in the service edge device … feeding the received local data as input into one or more of the updated local machine and deep learning models to generate output actions responsive to a service edge device user query … updating the machine and deep learning global models”; paragraph 0055, “the newly updated advanced global model is downloaded 330 to service edge devices 300 which may then use the downloaded model as its new updated local model”; The specification in paragraphs 0027, 0032 and 0035 recites “For example, the common data 112 may include …, Examples of area data 132a ... 132n that is imbalanced data …, and The common data (which may be described as a training data set or an initial data set) serves as the initial training data for the global generator Ge and the global discriminator”. Since the specification and the claim does not positively define what can be area data, common data or area data is imbalance, examiner interprets area data as the output data generated from the edge device, interprets common data as the desired input/output that serve as the training data of the global model, and “area data is imbalance” as error data that cause the error/difference from actual output of the model and desired output. It can be seen that the edge device when receiving the updated global models, feeding inputs into the global model (new updated local model) to generate output, the system then compares the generated output to the desired output for identifying the error/imbalance/difference between actual and desired values (comparing the area data to the common data), and the identified error/imbalance/difference may be used to modify the global model];
training a local with the area data to generate a first result [paragraph 0009, “training and update local machine and deep learning models using the received local data”; paragraph 0055, “The updated local models continue learning and training on locally gathered data in order to refine the local model parameters”; It can be seen that after training the input data with the global model (new updated local model), the edge device update the local model parameters and retraining the local model with the local input (edge device user queries) to generate an output];
training the exchanged local with the area data to generate a second result [paragraph 0055, “a service intermediary server 320 may select a subset of service edge devices 300 to be designated as "learners" … Each device within the selected subset of edge devices uploads its local model updates 310 to service intermediary server 320 which aggregates 322 the received local model update parameters (e.g., weights, thresholds, biases, etc.) and then computes new global model parameters using the aggregated local updates. The computed new global model parameters are then applied to update the advanced global model. Then the newly updated advanced global model is downloaded 330 to service edge devices 300 which may then use the downloaded model as its new updated local model … The updated local models continue learning and training on locally gathered data in order to refine the local model parameters”; It can be seen that the other edge device within the selected subset of edge devices (second edge device) also use its local model to train, learn and retrain the local model based on the error to generate the better output];
determining that the first result and the second result indicate that the first edge server and the second edge server are proximate [paragraphs 0059-0063, “system may utilize a federated awareness architecture where groups of selected service edge devices are placed in an awareness layer … For example, awareness layer 505 comprises restaurants A 506, B 507, and C 508 and those restaurants may have been grouped together … the intelligent service intermediary system may utilize it artificial intelligence capabilities, supported by machine and deep learning models contained in both service edge devices and the service intermediary server, in order to optimize the grouped restaurant's service and operations. For example, restaurant A 506 may need to order more to-go containers, but its distributor has already made its deliveries for the day, whereas restaurant B 507 may have extra to-go containers, send a suggestion to the manager of restaurant B 507 to sell X amount of to-go containers for Y price to restaurant A 506 … a group of restaurants may be located in the same geographic location and they may all purchase from one or more of the same distributors. In this case, the intelligent service intermediary system may suggest that distributor drivers 516 deliver to the all the restaurants in a given geographic location in order to reduce time and resources used by the distributor … By implementing a federated awareness architecture with awareness layers, the intelligent service intermediary system can operate as a single central intelligence that is aware of the overall state of all participants, without necessitating all participants be aware of all other services and service edge devices. This allows service edge devices and the machine and deep learning models contained therein to be agile operating on local data”; It can be seen that the first edge server and the second edge server (for example, restaurant A 506 and restaurant B 507) are located in the same geographic location, the edge servers (restaurants) utilize the local models to operate the restaurants with local inputs to generate outputs such as items to order for the restaurants (to-go containers), which distributors to purchase items, etc., and since the system determines both of the restaurants locate in the same location, they may purchase items from a same distributor, and purchased items can be the same, groups them into the same set];
in response to determining that the first result and the second result indicate that the first edge server and the second edge server are proximate, adding the first edge server and the second edge server to an edge server group list [paragraph 0055, “a service intermediary server 320 may select a subset of service edge devices 300 to be designated as "learners”; paragraph 0058, “The subset of edge devices may be chosen from the set of edge devices subscribed to a given service intermediation server”; paragraphs 0059-0063, “system may utilize a federated awareness architecture where groups of selected service edge devices are placed in an awareness layer … For example, awareness layer 505 comprises restaurants A 506, B 507, and C 508 and those restaurants may have been grouped together … the intelligent service intermediary system may utilize it artificial intelligence capabilities, supported by machine and deep learning models contained in both service edge devices and the service intermediary server, in order to optimize the grouped restaurant's service and operations. For example, restaurant A 506 may need to order more to-go containers, but its distributor has already made its deliveries for the day, whereas restaurant B 507 may have extra to-go containers, send a suggestion to the manager of restaurant B 507 to sell X amount of to-go containers for Y price to restaurant A 506 … a group of restaurants may be located in the same geographic location and they may all purchase from one or more of the same distributors. In this case, the intelligent service intermediary system may suggest that distributor drivers 516 deliver to the all the restaurants in a given geographic location in order to reduce time and resources used by the distributor … By implementing a federated awareness architecture with awareness layers, the intelligent service intermediary system can operate as a single central intelligence that is aware of the overall state of all participants, without necessitating all participants be aware of all other services and service edge devices. This allows service edge devices and the machine and deep learning models contained therein to be agile operating on local data”; It can be seen that the first edge server and the second edge server (for example, restaurant A 506 and restaurant B 507) are located in the same geographic location, the edge servers (restaurants) utilize the local models to operate the restaurants with local inputs to generate outputs such as items to order for the restaurants (to-go containers), which distributors to purchase items, etc., and since the system determines both of the restaurants locate in the same location, they may purchase items from a same distributor, and purchased items can be the same, groups them into the same set/list];
updating at least one of an application model and a configuration of an application from one of the first edge server and the second edge server on the edge server group list [paragraph 0059, “groups of selected service edge devices are placed in an awareness layer … For example, awareness layer 505 comprises restaurants A 506, B 507, and C 508”; paragraphs 0040-0041, “The VA operating on a service edge device 130 may further receive service actions from service intermediary server 110 in order to optimize the experience or role of the edge device user. For example, a service edge device user may be getting ready to leave work and asks the VA to order dinner for pickup from an Italian restaurant that is on her path home. The VA could access the user's preferences stored on the edge device to identify Italian meals the user prefers to order as well as any preferred Italian restaurants that the user likes to dine at … intelligent service intermediary system 100 may be connected to the Italian restaurant and can track the preparation of the user's meal, and may be able to send a service action to the user's device to inform the user to leave work at a certain time in order to ensure the meal is just being placed in to-go containers as the user arrives to the Italian restaurant. Alternatively, the device user may run into traffic on her way from work to the Italian restaurant, and the server 110, receiving the traffic data from the edge device, has awareness of the unexpected traffic and could send a service action suggestion to the kitchen staff to delay preparation of the user's order by ten minutes in order to ensure the food is hot and ready when the edge device user arrives to the restaurant”; the examiner interprets the restaurant as service edge devices, the application is to prepare user’ meal, the configuration of the application can be having the meal ready for the user on time, wherein, and the updating is to delay the meal preparation for a reason];
executing the application [paragraphs 0040-0041, “ensure the meal is just being placed in to-go containers as the user arrives to the Italian restaurant”].
Mimassi does not teach
receiving a global discriminator;
training a local discriminator;
receiving an exchanged local discriminator from a second edge server;
in response to determining that a load is not high at the first edge server, executing the application at the first edge server; and
in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server.
Hardy teaches
receiving a global discriminator [Fig. 1b, page 3, Col. 1 last paragraph to Col. 2, first paragraph discloses federated learning to GANs, where a machine learning model is trained using a set of workers. Fig. 1b shows a server includes a global discriminator which is sent to the workers (green dots), and each worker includes its own discrimination Dn];
training a local discriminator [page 3, section IV, “Each worker n performs L learning iterations on its discriminator Dn … Each worker n computes an error feedback Fn on Xn(g) by using Dn and sends this error to the server];
receiving an exchanged local discriminator from a second edge server [Fig. 1a, page 4, Col. 1, first paragraph, “swaps discriminators between workers in a peer-to-peer fashion … workers start a peer-to-peer swapping process for their discriminators, using function SWAP()”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for service intermediation that connects to a plurality of edge devices of Mimassi to include training the machine learning model on local discriminators of Hardy. Doing so would help training a generative adversarial network using local discriminators on the edge devices (Hardy, page 1, Col. 2).
Mimassi and Hardy do not teach
in response to determining that a load is not high at the first edge server, executing the application at the first edge server; and
in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server.
Miyamura teaches
in response to determining that a load is not high at the first edge server, executing the application at the first edge server [abstract, “An operation management system capable of implementing optimum deployment of applications and data is provided”; paragraph 0041, “Under this circumstance, if the applications, data, and so on increase in the edge servers 120, deviation of resource loads, deviation of I/O loads, shortage of the storage capacity, and so on occur at every edge server 120. In order to solve them, the operation management server 110 issues an instruction to the relevant edge server 120 to migrate (deploy) an application in an edge server 120 with a high resource load to an edge server 120 with a low resource load”; paragraph 0240-0241, “judgment unit … that judges whether a load on resources provided in an edge server included by the above-mentioned operation management system exceeds a threshold value or not … deploys a deployment target application which is provided in the edge server judged by the judgment unit that the load on its resources exceeds the threshold value in an edge server different from the above-mentioned edge server … if the application is deployed in another edge server and the load on resources provided in the other edge server exceeds the threshold value, the application will be deployed in a further another edge server which is different from the other edge server”; It can be seen that if determining that the load in an edge server (first edge server) is not high, the target application will be deployed to the first edge server for executing, but if the load in an edge server (first edge server) is high, the target application will be deployed to other/second edge server to execute]; and
in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server [paragraph 0240-0241, “judgment unit … that judges whether a load on resources provided in an edge server included by the above-mentioned operation management system exceeds a threshold value or not … deploys a deployment target application which is provided in the edge server judged by the judgment unit that the load on its resources exceeds the threshold value in an edge server different from the above-mentioned edge server … if the application is deployed in another edge server and the load on resources provided in the other edge server exceeds the threshold value, the application will be deployed in a further another edge server which is different from the other edge server”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for service intermediation that connects to a plurality of edge devices of Mimassi to include in response to determining that a load is not high at the first edge server, executing the application at the first edge server, and in response to determining that the load is high at the first edge server, forwarding a request to execute the application to the second edge server of Miyamura. Doing so would help avoiding the situation where the performance of the specific edge server will degrade (Miyamura, 242).
Claims 3, 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mimassi in view of Hardy et al. in view of Miyamura et al. and further in view of Agrawal et al. (US Pub. 2015/0245160).
As per claim 3, Mimassi, Hardy and Miyamura teach the computer-implemented method of claim 1.
Mimassi, Hardy and Miyamura do not teach
under control of an edge device,
determining that the edge device is approaching an area of coverage of the first edge server;
requesting the edge server group list from the first edge server;
in response to determining that at least one of an application model and a configuration of another application is not from any edge server on the edge server group list, requesting at least one of a new application model and a new configuration from the first edge server; and
executing the another application using the at least one of the new application model and the new configuration.
Agrawal teaches
determining that the edge device is approaching an area of coverage of the first edge server [paragraph 0020, “when a user's request arrives at the edge network”];
requesting the edge server group list from the first edge server [paragraph 0005, “placing the requests among the edge servers”; Since Mimassi In paragraph 0055 teaches “select a subset of service edge devices 300 to be designated as "learners"”, while Agrawal teaches the requests are placed among the edge devices, therefore, the combination of Mimassi and Agrawal teaches the set/list of edge servers is obtained];
in response to determining that at least one of an application model and a configuration of another application is not from any edge server on the edge server group list, requesting at least one of a new application model and a new configuration from the first edge server [paragraph 0020, “checks whether the requested application or service is available locally”; paragraph 0003, “select a set of applications and services and load them at the edge devices. If a mobile client requests a pre-loaded application or service, the edge device will provide the service utilizing local computational resources (e.g., CPU and memory). If the requested service or application is not loaded in the edge device, the request will be forwarded”; paragraph 0077, “by exchanging information with neighboring edge devices, the edge device of interest can estimate the volume of new users that are moving towards its coverage area and their application and service requests”; paragraph 0091, “determine which applications to activate at the edge device and which ones to deactivate/replace”; paragraphs 00092-0097, “receipt of a user request r for an application A, a determination is made in step 702 as to whether application A is already active at the edge server … If the application A is not already active at the edge server … a determination is made as to whether there is time to service the request locally at the edge, and if so, is the cost of running the application A locally at the edge smaller than running the application A at the core. If the answer to these questions is yes, i.e., there is sufficient time to run the application A at the edge and the local cost is smaller, then as per step 708, the request will be serviced at the edge (new application is active at the edge)”]; and
executing the another application using the at least one of the new application model and the new configuration [paragraphs 00092-0097, “receipt of a user request r for an application A, a determination is made in step 702 as to whether application A is already active at the edge server … If the application A is not already active at the edge server … a determination is made as to whether there is time to service the request locally at the edge, and if so, is the cost of running the application A locally at the edge smaller than running the application A at the core. If the answer to these questions is yes, i.e., there is sufficient time to run the application A at the edge and the local cost is smaller, then as per step 708, the request will be serviced at the edge (new application is executing at the edge)”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for service intermediation that connects to a plurality of edge devices of Mimassi to include in response to determining that at least one of an application model and a configuration of another application is not from any edge server on the edge server group list, requesting at least one of a new application model and a new configuration from the first edge server, and executing the another application using the at least one of the new application model and the new configuration of Agrawal. Doing so would help improving the performance of application and service placement (Agrawal, 0018).
Claims 11 and 19 are rejected by the same reason as of claim 3, since these claims recite the similar limitations.
Claims 8, 16 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Mimassi in view of Hardy et al. in view of Miyamura et al. and further in view of Visentini Scarzanella et al. (US Patent 11,010,637).
As per claim 8, Mimassi, Hardy and Miyamura teach the computer-implemented method of claim 1.
Mimassi, Hardy and Miyamura do not teach
a Software as a Service (SaaS) is configured to perform the operations of the computer-implemented method.
Visentini Scarzanella teaches
a Software as a Service (SaaS) is configured to perform the operations of the computer-implemented method [Col. 4, lines 31-49, “FIG. 2 is a block/flow diagram of an exemplary cloud computing environment, in accordance with an embodiment of the present invention … Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources … This cloud model can include at least three service models”; Col. 5, lines 13-18, “Service Models are as follows: Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail)”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for service intermediation that connects to a plurality of edge devices of Mimassi to include a Software as a Service (SaaS) is configured to perform the operations of Visentini Scarzanella. Doing so would help reducing costs and increasing accessibility and convenience via internet-connected devices.
Claims 16 and 24 are rejected by the same reason as of claim 8, since these claims recite the similar limitations.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Ji et al. (US Pub. 2022/0121920) describes a system with federated learning model for medical research applications.
Martins et al. (US Pub. 2023/0058223) describes a multi-agent coordination system.
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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/TRI T NGUYEN/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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