DETAILED ACTION
This is in response to the amendment filed on March 2nd 2026.
Response to Arguments
Applicant’s arguments, see pg. 9, filed 3/2/26, with respect to the drawing objection have been fully considered and are persuasive. The drawing objection of Fig. 2 has been withdrawn.
Applicant’s arguments, see pg. 9-10, with respect to the rejection(s) of claim(s) 1-20 under 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ahmed et al. US 11,729,636 B1.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kass et al. US 2019/0102716 A1 in view of S. Luo, X. Chen, Z. Zhou, X. Chen and W. Wu, "Incentive-Aware Micro Computing Cluster Formation for Cooperative Fog Computing," in IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2643-2657, April 2020, hereinafter “Luo” and Ahmed et al. US 11,729,636 B1.
Regarding claim 1, Kass discloses a method for secured closed group services over a cellular network (paragraphs 1-2, Fig. 1), comprising:
receiving, by one or more processors, a request from a device within a … network, wherein the request indicates a recommendation for other devices within the cellular network that are available to form a closed group of devices capable of performing a task (receive project – paragraph 39, Fig. 3)
determining, by the one or more processors, devices that are within a specified distance of the device such that a network can be established between the device and the devices (consider geographic proximity when selecting cells – paragraphs 42-43; also resource descriptors include location information – paragraph 18, Fig. 1; and consider close geographical location – paragraph 57);
determining, by the one or more processors and one or more machine learning (ML) models (ML engine – Fig. 1, paragraph 46) the other devices that are within the specified distance (devices/resources are connected via a network – see Fig. 1; thus the distributed resources are “within a specified distance such that a network can be established between the devices”) and that at least in combination with the device are able to perform the task (determine cell group of resources/devices for project task – paragraphs 13, 20, 42, 51, Fig. 3); wherein the one or more machine learning (ML) models identify the other devices based on … measured capabilities associated with the devices (use machine learning to determine groups – Fig. 1 item 130, paragraphs 27 and 46; consider resource capability, capacity and available – paragraphs 18, 42; also see paragraphs 30 and 46 which teaches the ML engine considers any data to update or propose resource assignments);
generating, by the one or more processors, a response that identifies the other devices that, at least in combination with the device, are able to participate with the device to perform the task (project template or team sourcing layer identifies resources that are able to participate in the project task – paragraphs 14, 42, 46);
sending, by the one or more processors, the response to the device (communicate resource data for display on user interface – see paragraphs 21, 60-61, and Figs 1, 6-7).
Kass does not explicitly disclose a cellular network but this is taught by Luo as creating clusters of devices using 5G cellular mobile devices (see Section I, Fig. 1). Luo also discloses sending a response identifying other devices to the device (MCC formation includes sending the core solution including a list of devices to all the devices – see Section V).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kass to operate with a cellular network as taught by Luo. Mobile devices connected via cellular network is very well-known in the art. Luo suggests that efficiency is increased by task offloading and multiple mobile device collaboration (Sections I-II).
The combination of Kass and Luo does not explicitly disclose identify devices based on real-time network performance metrics but this is taught by Ahmed as a machine learning model performs clustering of network elements in a radio (5G) network based on input data which includes user data and network data, this data has real-time network performance metrics (see abstract, Fig. 5 and col. 7 ln. 14-29). Ahmed also teaches using location data to cluster the network elements (abstract, Fig. 3; this reads on “determining … devices that are within a specified distance of the device such that a network can be established”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Kassa and Luo to consider real-time network performance information when clustering as taught by Ahmed for the purpose of determining devices to perform a task. One of ordinary skill in the art would understand network performance to be an important metric to consider when sending and receiving data to a group of devices. Ahmed also suggests that data can be used to optimize network performance for different cells/groups of devices based on observed measurements (col. 2 ln. 29-54).
Regarding claim 2, Kass discloses the response causes the device to establish the closed group with one or more of the other device (create cell resource group – Figs. 3-4, 9).
Kass does not explicitly disclose and directly communicate with the other one or more of the other devices to perform the task but this is taught by Luo (cluster formation for cooperative fog computing includes using device-to-device (D2D) communication – Section I, Fig. 1, and Section III). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kass to use direct communication (e.g. D2D) as taught by Luo for the purpose of collaborative task processing. Direct or D2D communication provides low-latency and efficient communications between mobile devices (see Luo abstract, Section I).
Regarding claim 3, Kass discloses establishing a network that includes the one or more other devices (establish groups of devices via network – see Figs. 1, 9). Kass does not explicitly disclose establishing a “mesh” network but this is taught by Luo as a fog network (Sections I-III). Under the BRI, the term “mesh” network includes a network having devices that are at least partially interconnected (e.g. see Fig. 3 of pending application). The fog network computing taught by Luo is equivalent in this regard because it refers to a plurality of interconnected mobile devices arranged in a cluster that communicate directly/D2D (Sections I-III). The motivation to combine is the same as that given above in the rejection of claim 2 (i.e. the same benefits of D2D apply).
Regarding claim 4, Kass discloses obtaining performance data relating to the performance of the task by one or more other devices of the closed group; updating at least one of the one or more ML models using at least a portion of the performance data as a training input; (monitor performance metrics/tracking data – abstract, paragraphs 13-15, 26, Figs. 1-3; machine learning receives the performance data and makes updates based on the performance – paragraphs 27-30). Kass does not explicitly disclose and deploying an updated ML model but this is well-known in the art (e.g. feedback loops are known to improve system function, one well known example in ML art is “supervised learning”) and taught by Luo as using a model to determine fog offloading clusters (Section III), wherein the algorithm updates as iterations are performed in order to improve task execution in the resulting group cluster (Section V).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kass to use feedback/update model as taught by Luo and generally known in the art. Luo teaches that by updating/iteratively executing the model, optimal results are obtained (Sections III and VI-VII).
Regarding claim 5, Kass discloses establishing the closed group includes establishing a … network that includes the device and the one or more other devices; wherein the device communicates with the one or more other devices (Figs. 1, 3-4, 9).
Kass does not explicitly disclose a “mesh” network but this is taught by Luo as a fog network (Sections I-III). Under the BRI, the term “mesh” network includes a network having devices that are at least partially interconnected (e.g. see Fig. 3 of pending application). The fog network computing paradigm taught by Luo is equivalent in this regard because it refers to a plurality of interconnected mobile devices arranged in a cluster that communicate directly/D2D (Sections I-III). The motivation to combine is the same as that given above in the rejection of claim 2 (i.e. the same benefits of D2D apply).
Kass does not explicitly teach using at least one of unicast messages, or multi-cast messages but it at least suggests this by disclosing the use of communicating over network with dynamic IP address ranges (paragraph 19). One of ordinary skill in the art would understand that an IP address provides a unicast message, and an IP address range allows for multi-cast messaging. Thus, this would have been obvious to one of ordinary skill in the art before the effective filing date based on the teachings of Kass.
Regarding claim 6, Kass does not explicitly disclose the other devices are 5G connected IoT devices but this is taught by Luo (Section I, Fig. 1). The motivation to combine is the same as that given in the rejection of claim 1.
Regarding claim 7, Kass discloses wherein determining the other devices comprises determining one or more groups of devices that are configured to perform at least a portion of the task (groups of devices perform portions of tasks – paragraph 13, Fig. 1, Fig. 9).
Regarding claim 8, it is a system claim that corresponds to the method of claim 1. Therefore, it is rejected for the same reasons and Kass discloses a device wirelessly connected (Fig. 1).
Regarding claim 9, Kass discloses the device is configured to: determine selected devices from the other devices; and establish the closed group with selected devices (create cell resource group – Figs. 3-4, 9).
Regarding claim 10, Kass discloses the device is configured to: establish a closed group with one or more of the other devices (create cell resource group – Figs. 3-4, 9).
Kass does not explicitly disclose directly communicate with the one or more of the other devices to perform the task, but this is taught by Luo as explained above in the rejection of claim 2. The motivation to combine is the same.
Regarding claim 11, it corresponds to claim 3 so it is rejected for the same reasons.
Regarding claim 12, it corresponds to claim 4 so it is rejected for the same reasons.
Regarding claim 13, it corresponds to claim 5 so it is rejected for the same reasons.
Regarding claim 14, it corresponds to claim 6 so it is rejected for the same reasons.
Regarding claim 15, it corresponds to claim 7 so it is rejected for the same reasons.
Regarding claim 16, it is a non-transitory medium claim that corresponds to the method of claim 1; therefore, it is rejected for the same reasons.
Regarding claims 17-18, they correspond to claims 2-3; thus they are rejected for the same reasons.
Regarding claim 19, Kass discloses the device and the one or more other devices communicate (Fig. 1). Kass does not explicitly disclose using secured communication channels but this is taught by Luo as using D2D communication such as ZigBee and Bluetooth (Section V.B.) One of ordinary skill in the art would understand these protocols to be “secured” because they use encryption and security keys. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kass to use secure communication as taught by Luo. Secure communication provides manifest benefits.
Regarding claim 20, it corresponds to claim 6 so it is rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kedalagudde et al. US 2023/0171168 A1 discloses a 5G cellular network and using a ML model to request and create a group of UE resources for a session to execute a task (abstract, paragraphs 18-19, Figs. 1 and 6).
Duo et al. US 2020/0396613 A1 discloses a mesh network with secure transmission paths/channels (title, abstract, paragraphs 5, 21-22, 86, Figs. 1-2).
Moradi et al. US 12,165,022 B2 discloses a machine learning model obtaining network condition information for a plurality of client nodes in order to cluster the nodes to perform a task (abstract).
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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/JASON D RECEK/Primary Examiner, Art Unit 2458
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