DETAILED ACTION
Applicants amended claims 1, 3, 6-8, 10-12, 14, 18, and cancelled claim 5 in the amendment dated 6/24/2026.
Claims 1-4, 6-20 are pending.
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 information disclosure statement (IDS) submitted on 7/28/2026 is being considered by the examiner.
Response to Arguments
Applicant’s arguments with respect to claims 1-4, and 6-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-4, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly (US 12,120,138 B1) in view of Wieman (US 2021/0329444 A1).
With regards to Claim 1, Kelly teaches a communication method, comprising: sending, by a first device, a first message to a second device, wherein the first message contains application model information related to a third device (i.e., …The software agent sends the modified neighboring device data (e.g., in the standardized format) to the scan engine, Col. 3, Lines 35-45; software agent (first device), scan engine (second device), neighboring device (third device); The software agent installed on a particular device responds to requests from the scan engine to provide information about the particular device, such as, for example, user data (e.g., usernames and corresponding permissions), file data (e.g., file names, file contents, file metadata, and the like), group data (e.g., group names, group members, group permissions, and the like), currently installed software applications, and other types of device-related information, Col. 3, Lines 1-14).
However, Kelly does not explicitly disclose wherein: the method further comprises: activating or deactivating, by the first device based on a first preset condition, an application model corresponding model data of which is stored in the first device, wherein the first preset condition comprises that: the first device receives an activation or deactivation instruction sent by the second device to activate or deactivate the application model.
Wieman does teach wherein: the method further comprises: activating or deactivating, by the first device based on a first preset condition, an application model corresponding model data of which is stored in the first device, wherein the first preset condition comprises that: the first device receives an activation or deactivation instruction sent by the second device to activate or deactivate the application model (i.e., a flow diagram illustrating an example embodiment in a gNB, according to some embodiments. At step 600, the gNB receives a request for a context setup from the AMF. This procedure follows an initial context setup request procedure…, Paragraphs 157-169;; Figure 10; First Device (gNB) receives activation (context setup) from Second Device (AMF); gNB updates AMF with model information (UE capability/3rd device)) in order to obtain most up to date functionality information of a device (Paragraph 5). Therefore, based on Kelly in view of Wieman, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Wieman with the system of Kelly in order to obtain most up to date functionality information of a device.
With regards to Claim 2, Kelly teaches wherein the application model information comprises at least one of: identity information of at least one application model supported by the third device; identity information of at least one application model corresponding model data of which is stored in the third device; identity information of at least one application model corresponding model data of which is not stored in the third device; usage specification information of the at least one application model corresponding model data of which is stored in the third device; identity information of at least one application model that has been activated for use by the third device; or activation indication information of the at least one application model corresponding model data of which is stored in the third device, wherein the activation indication information of the at least one application model indicates whether the at least one application model has been activated for use (i.e., In some embodiments, the software agent 126 may be configured to collect a set of machine characteristics of individual computing devices 102. The collected machine characteristics may include information that indicates the machine's operating system (OS) version, OS patches installed on the machine, installed applications and their version information, patches, settings, and metadata, files or file contents on the machine, and configuration data such as the machine's registry entries, security settings, usage data, etc., among other information, Col. 7, Line 60-Col. 8, Line 2).
With regards to Claim 3, Kelly teaches herein the usage specification information of the at least one application model corresponding model data of the which is stored in the third device comprises at least one of: functionality information of the at least one application model, indicating a function associated with the at least one application model; scenario information of the at least one application model, indicating a scenario associated with the at least one application model; usage-range control information of the at least one application model, indicating a usage range of the at least one application model; an input information set of the at least one application model, indicating a type and/or number of input information of the at least one application model; an output information set of the at least one application model, indicating a type and/or number of output information of the at least one application model; input processing information of the at least one application model, indicating a pre-processing manner for the input information of the at least one application model; or output processing information of the at least one application model, indicating a post-processing manner for the output information of the at least one application model (i.e., In some embodiments, the software agent 126 may be configured to collect a set of machine characteristics of individual computing devices 102. The collected machine characteristics may include information that indicates the machine's operating system (OS) version, OS patches installed on the machine, installed applications and their version information, patches, settings, and metadata, files or file contents on the machine, and configuration data such as the machine's registry entries, security settings, usage data, etc., among other information, Col. 7, Line 60-Col. 8, Line 2; i.e., In some embodiments, the software agent 126 may be configured to collect a set of machine characteristics of individual computing devices 102. The collected machine characteristics may include information that indicates the machine's operating system (OS) version, OS patches installed on the machine, installed applications and their version information, patches, settings, and metadata, files or file contents on the machine, and configuration data such as the machine's registry entries, security settings, usage data, etc., among other information, Col. 7, Line 60-Col. 8, Line 2, Col. 9, Lines 17-25).
With regards to Claim 4, Kelly teaches wherein before sending, by the first device, the first message to the second device, the method further comprises: receiving, by the first device, a second message sent by the second device, wherein the second message is used to trigger the first device to send the first message (i.e., After the scan engine 108 determines that the computing device 102(N) that is being scanned has the software agent 126 installed, the scan engine 108 sends the request 120 to the software agent 126 to provide information about the neighboring devices of the computing device 102(N), Col. 6, Lines 47-61).
With regards to Claim 13, Kelly teaches wherein the first device is a first terminal device, the second device is a network device or a second terminal device, and the first terminal device and the second terminal device are different terminal devices; or the first device is a first network device, the second device is a second network device or a terminal device, and the first network device and the second network device are different network devices; or the third device is the first terminal device (i.e., …The software agent sends the modified neighboring device data (e.g., in the standardized format) to the scan engine, Col. 3, Lines 35-45; software agent (first device), scan engine (second device), neighboring device (third device); Figure 1).
The limitations of Claim 14 are rejected in the analysis of Claim 1 above, and the claim is rejected on that basis.
The limitations of Claim 15 are rejected in the analysis of Claim 2 above, and the claim is rejected on that basis.
The limitations of Claim 16 are rejected in the analysis of Claim 4 above, and the claim is rejected on that basis.
The limitations of Claim 17 are rejected in the analysis of Claim 13 above, and the claim is rejected on that basis.
The limitations of Claim 18 are rejected in the analysis of Claim 1 above, and the claim is rejected on that basis.
The limitations of Claim 19 are rejected in the analysis of Claim 2 above, and the claim is rejected on that basis.
The limitations of Claim 20 are rejected in the analysis of Claim 13 above, and the claim is rejected on that basis.
Claim Rejections - 35 USC § 103
Claims 6-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly (US 12,120,138 B1) in view of Wieman (US 2021/0329444 A1) and further in view of Patel (US 2023/0107309 A1).
With regards to Claim 6, Kelly and Wieman teach the above disclosed subject matter. However, Kelly and Wieman do not explicitly disclose wherein the first preset condition comprises at least one of: in a case where a source device of the application model provides the model data of the application model for the first device, the source device indicates to the first device whether the application model is to be activated; in a case where the source device of the application model provides the model data of the application model for the first device, the application model is in an inactive state by default or the application model is in an active state by default; in a case where the source device of the application model provides the model data of the application model for the first device, the source device provides an activation condition and/or a deactivation condition associated with the application model for the first device; and in a case where the activation condition is satisfied or the deactivation condition is not satisfied, the first device activates the application model or keeps the application model in the active state, and/or in a case where the deactivation condition is satisfied or the activation condition is not satisfied, the first device deactivates the application model or keeps the application model in the inactive state; the first device storing the model data of the application model autonomously determines to activate or deactivate the application model.
Patel does teach wherein the first preset condition comprises at least one of: in a case where a source device of the application model provides the model data of the application model for the first device, the source device indicates to the first device whether the application model is to be activated; in a case where the source device of the application model provides the model data of the application model for the first device, the application model is in an inactive state by default or the application model is in an active state by default; in a case where the source device of the application model provides the model data of the application model for the first device, the source device provides an activation condition and/or a deactivation condition associated with the application model for the first device; and in a case where the activation condition is satisfied or the deactivation condition is not satisfied, the first device activates the application model or keeps the application model in the active state, and/or in a case where the deactivation condition is satisfied or the activation condition is not satisfied, the first device deactivates the application model or keeps the application model in the inactive state; the first device storing the model data of the application model autonomously determines to activate or deactivate the application model (i.e., …In one embodiment, iterative performance of examining and MLM model selection can be dependent on and triggered by a sensed condition, e.g., a sensed migration of an MLM or query generating application 20, as set forth herein. The triggering of model examining and selection can be incorporated into tactical autonomic language (TAL) functionality of a 5G service orchestration layer, an example of which is shown in FIG. 5., Paragraph 109) in order to select machine learning model in dependence on the determined model requirement (Paragraph 4). Therefore, based on Kelly in view of Wieman and further in view of Patel, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Patel with the system of Kelly and Wieman in order to select machine learning model in dependence on the determined model requirement.
With regards to Claim 7, Kelly and Wieman teach the above disclosed subject matter. However, Kelly and Wieman do not explicitly disclose deleting, by the first device based on a second preset condition, the model data of the application model. Patel does teach deleting, by the first device based on a second preset condition, stored model data of an application model (i.e., …Thus, by running polling process 111, orchestrator 110 updates infrastructure data area 2121 and MLM registry area 2122 in order to include at all times recent and comprehensive infrastructure performance metrics data and MLM performance metrics data for all computing nodes 10…., Paragraph 43) in order to select machine learning model in dependence on the determined model requirement (Paragraph 4). Therefore, based on Kelly in view of Wieman and further in view of Patel, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Patel with the system of Kelly and Wieman in order to select machine learning model in dependence on the determined model requirement.
With regards to Claim 8, Kelly and Wieman teach the above disclosed subject matter. However, Kelly and Wieman do not explicitly disclose wherein the second preset condition comprises at least one of: in a case where the first device storing the model data of the application model receives a deletion instruction sent by the second device, the first device deletes the model data of the application model; in a case where a deletion condition associated with the application model is satisfied, the first device deletes the model data of the application model, wherein the deletion condition is configured by the second device; or the first device storing the model data of the application model autonomously determines to delete the model data of the application model. Patel does teach wherein the second preset condition comprises at least one of: in a case where the first device storing the model data of the application model receives a deletion instruction sent by the second device, the first device deletes the model data of the application model; in a case where a deletion condition associated with the application model is satisfied, the first device deletes the model data of the application model, wherein the deletion condition is configured by the second device; or the first device storing the model data of the application model autonomously determines to delete the model data of the application model (i.e., …Thus, by running polling process 111, orchestrator 110 updates infrastructure data area 2121 and MLM registry area 2122 in order to include at all times recent and comprehensive infrastructure performance metrics data and MLM performance metrics data for all computing nodes 10…., Paragraph 43) in order to select machine learning model in dependence on the determined model requirement (Paragraph 4). Therefore, based on Kelly in view of Wieman and further in view of Patel, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Patel with the system of Kelly and Wieman in order to select machine learning model in dependence on the determined model requirement.
With regards to Claim 9, Kelly and Wieman teach the above disclosed subject matter. However, Kelly and Wieman do not explicitly disclose sending, by the first device, assistance information to the second device, wherein the assistance information comprises at least one of: identity information of at least one application model expected by the first device to be activated; identity information of at least one application model expected by the first device to be deactivated; or identity information of at least one application model expected by the first device to be deleted. Patel does teach sending, by the first device, assistance information to the second device, wherein the assistance information comprises at least one of: identity information of at least one application model expected by the first device to be activated; identity information of at least one application model expected by the first device to be deactivated; or identity information of at least one application model expected by the first device to be deleted (i.e., embodiments herein can provide a mechanism to discover the MLMs by a job submission router in the 5G plane and enquire for all the performance-based functionality of the machine learning models. When any MLM is activated on a server and the infrastructure is allocated to virtual machines in the 5G core (or edge network) cloud, then embodiments herein running in the MLM can collect the information and share it with the router functions…, Paragraph 111) in order to select machine learning model in dependence on the determined model requirement (Paragraph 4). Therefore, based on Kelly in view of Wieman and further in view of Patel, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Patel with the system of Kelly and Wieman in order to select machine learning model in dependence on the determined model requirement.
With regards to Claim 10, Kelly and Wieman teach the above disclosed subject matter. However, Kelly and Wieman do not explicitly disclose wherein after the first device is shut down or deregistered or deactivated or dormant, the first device performs any one of the following behaviors: not deleting the model data of the application model stored in the first device; deactivating the application model but not deleting the model data of the application model; deleting the model data of the application model; not deleting model data of an application model that satisfies a storage condition and deleting model data of an application model that does not satisfy the storage condition; determining, according to indication information associated with the application model, to delete or not delete the model data of the application model; and determining, according to a default rule, at least one of an application model to be deleted and/or an application model to be stored. Patel does teach wherein after the first device is shut down or deregistered or deactivated or dormant, the first device performs any one of the following behaviors: not deleting the model data of the application model stored in the first device; deactivating the application model but not deleting the model data of the application model; deleting the model data of the application model; not deleting model data of an application model that satisfies a storage condition and deleting model data of an application model that does not satisfy the storage condition; determining, according to indication information associated with the application model, to delete or not delete the model data of the application model; and determining, according to a default rule, at least one of an application model to be deleted and/or an application model to be stored (i.e., Orchestrator 110 running polling process 111 can include orchestrator 110 polling respective data repositories R of respective managers M of the various computing environments A-Z in order to return copies of the returned local infrastructure performance metrics data and MLM registry performance metrics data stored in the respective data repositories R for storage into infrastructure data area 2121 and MLM registry 2122 of data repository 108 of orchestrator 110. Thus, by running polling process 111, orchestrator 110 updates infrastructure data area 2121 and MLM registry area 2122 in order to include at all times recent and comprehensive infrastructure performance metrics data and MLM performance metrics data for all computing nodes 10., Paragraph 43) in order to select machine learning model in dependence on the determined model requirement (Paragraph 4). Therefore, based on Kelly in view of Wieman and further in view of Patel, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Patel with the system of Kelly and Wieman in order to select machine learning model in dependence on the determined model requirement.
With regards to Claim 11, Kelly teaches the above disclosed subject matter. However, Kelly does not explicitly disclose receiving, by the first device, first indication information sent by the second device, wherein the first indication information indicates at least one of the following meanings: whether the first device is allowed to send to the second device the application model information related to the third device; whether the first device is allowed to autonomously determine to activate or deactivate an application model; whether the first device is allowed to autonomously determine to delete model data of the application model; or whether the first device is allowed to send assistance information to the second device. Patel does teach receiving, by the first device, first indication information sent by the second device, wherein the first indication information indicates at least one of the following meanings: whether the first device is allowed to send to the second device the application model information related to the third device; whether the first device is allowed to autonomously determine to activate or deactivate an application model; whether the first device is allowed to autonomously determine to delete model data of the application model; or whether the first device is allowed to send assistance information to the second device (i.e., Embodiments herein recognize there can be many MLMs in the service orchestration plane and a job router function can be configured to have authorization to some or all of the MLMs…., Paragraph 112) in order to select machine learning model in dependence on the determined model requirement (Paragraph 4). Therefore, based on Kelly in view of Wieman and further in view of Patel, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Patel with the system of Kelly and Wieman in order to select machine learning model in dependence on the determined model requirement.
With regards to Claim 12, Kelly and Wieman teach the above disclosed subject matter. However, Kelly and Wieman do not explicitly disclose sending, by the first device, second indication information to the second device, wherein the second indication information indicates at least one of the following meanings: whether the first device has a willingness or capability to provide the application model information related to the third device for the second device; whether the first device has a capability to autonomously determine to activate or deactivate the application model; whether the first device has a capability to autonomously determine to delete the model data of the application model; or whether the first device has a willingness or capability to provide assistance information for the second device. Patel does teach sending, by the first device, second indication information to the second device, wherein the second indication information indicates at least one of the following meanings: whether the first device has a willingness or capability to provide the application model information related to the third device for the second device; whether the first device has a capability to autonomously determine to activate or deactivate the application model; whether the first device has a capability to autonomously determine to delete the model data of the application model; or whether the first device has a willingness or capability to provide assistance information for the second device (i.e., …In one embodiment, iterative performance of examining and MLM model selection can be dependent on and triggered by a sensed condition, e.g., a sensed migration of an MLM or query generating application 20, as set forth herein. The triggering of model examining and selection can be incorporated into tactical autonomic language (TAL) functionality of a 5G service orchestration layer, an example of which is shown in FIG. 5., Paragraph 109) in order to select machine learning model in dependence on the determined model requirement (Paragraph 4). Therefore, based on Kelly in view of Wieman and further in view of Patel, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to utilize the teachings of Patel with the system of Kelly and Wieman in order to select machine learning model in dependence on the determined model requirement.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SURAJ M JOSHI whose telephone number is (571)270-7209. The examiner can normally be reached Monday - Friday 8-6 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Joon Hwang can be reached at (571)272-4036. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SURAJ M JOSHI/Primary Examiner, Art Unit 2447 September 23, 2026
