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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 93-97, 100-102, 105-108, and 111-112 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Pub. 2023/0116757 to Zavesky et al. (hereinafter Zavesky).
In regard claim 93, Zavesky teaches or discloses an apparatus (see Figs. 3 and 8) comprising:
at least one processor (see Fig. 3); and
at least one memory storing instructions that, when executed by the at least one processor (see Fig. 3) , cause the apparatus at least to perform (see Fig. 3):
receive, from a network node of a wireless communications system, information indicative of an association (see paragraph [0038], XR management engine 200 may receive local environment information 322 from XR user system 202) between (i) a plurality of implementations for a network element of the wireless communications system to perform a respective task and (ii) required allocations of resources for the network element to perform the respective task in accordance with one or more respective implementations of the plurality of implementations (see paragraphs [0047], [0067], computing environment 600 can be used in the implementation of network elements 150, 152, 154, 156, access terminal 112, base station or access point 122, switching device 132, media terminal 142, and/or VNEs 530, 532, 534, etc. selecting an XR object for presentation on an XR display of the XR user system based on the local environment information, the user preferences, and the historical profile, and allocating compute resources to facilitate a rendering of the XR object, wherein the allocated compute resources are selected from a compute resource pool comprising local compute resources of the XR user system and edge compute resources of a network);
select a first implementation of the plurality of implementations to perform the respective task based at least in part on available resources for the network element to perform the respective task relative to the required allocation of resources to perform the respective task in accordance with the first implementation (see paragraphs [0047], selecting an XR object for presentation on an XR display of the XR user system based on the local environment information, the user preferences, and the historical profile, and allocating compute resources to facilitate a rendering of the XR object, wherein the allocated compute resources are selected from a compute resource pool comprising local compute resources of the XR user system and edge compute resources of a network); and
perform the respective task in accordance with the first implementation (see paragraphs [0013], [0025], [0048], [0050], [0073], and [0085], compute resources of an edge network node may be used to perform computational tasks associated with presenting an XR object at an XR user system. perform particular tasks or implement particular abstract data types).
In regard claim 94, Zavesky teaches or discloses the apparatus according to claim 93, wherein the instructions, when executed by the at least one processor, further cause the apparatus to at least:
cause transmission, to the network node, of information indicative of the first implementation selected to perform the respective task (see paragraphs [0013], and [0029], an XR management engine 220 may administer the selection and allocation of resources for XR application 205, and the communication/coordination between XR user system 202 and MEC edge node(s) 212 required for this purpose. The nature of the distribution of operations/ functions of XR management engine 220 across XR user system 202 and the one or more other devices or systems may vary from embodiment, such that tasks of XR management engine 220 that are handled at XR user system 202 in some embodiments may be handled remotely).
In regard claims 95 and 106, Zavesky teaches or discloses the apparatus according to claim 93, wherein the plurality of implementations correspond to one or more of:
one or more implementations for the network element associated with respective artificial intelligence or machine learning enabled features or feature groups (see paragraphs [0067], and [0087], a n embodiment 700 of a mobile network platform 710 is shown that is an example of network elements 150, 152, 154, 156, and/or VNEs 530, 532, 534, etc. For example, platform 710 can facilitate in whole or in part identifying user preferences for an XR application executing at an XR user system, wherein the user preferences are associated with an XR application user, accessing a historical profile associated with the XR application user, receiving local environment information from a sensor array of the XR user system, selecting an XR object for presentation on an XR display of the XR user system based on the local environment information, the user preferences, and the historical profile, and allocating compute resources to facilitate a rendering of the XR object, wherein the allocated compute resources are selected from a compute resource pool comprising local compute resources of the XR user system and edge compute resources of a network. Some of the embodiments described herein can also employ artificial intelligence (AI) to facilitate automating one or more features described herein. The embodiments (e.g., in connection with automatically identifying acquired cell sites that provide a maximum value/benefit after addition to an existing communication network) can employ various AI-based schemes for carrying out various embodiments thereof);
one or more implementations for the network element associated with respective artificial intelligence or machine learning enabled functionalities; or
one or more implementation variants for the network element associated with a given artificial intelligence or machine learning enabled functionality.
In regard claims 96 and 107, Zavesky teaches or discloses the apparatus according to claim 93, wherein the plurality of implementations comprise one or more implementations for performing the respective task relying on artificial intelligence or machine learning (see paragraph [0087], can also employ artificial intelligence (AI) to facilitate automating one or more features described herein).
In regard claims 97 and 108, Zavesky teaches or discloses the apparatus according to claim 96, wherein two or more implementations of the plurality of implementations are associated with different types of artificial intelligence or machine learning models (see paragraphs [0087], and [0093], the embodiments (e.g., in connection with automatically identifying acquired cell sites that provide a maximum value/benefit after addition to an existing communication network) can employ various AI-based schemes for carrying out various embodiments thereof. It should be appreciated that such terms can refer to human entities or automated components supported through artificial intelligence (e.g., a capacity to make inference based, at least, on complex mathematical formalisms), which can provide simulated vision, sound recognition and so forth).
In regard claims 100 and 111, Zavesky teaches or discloses the apparatus according to claim 93, wherein the instructions, when executed by the at least one processor, further cause the apparatus to at least:
cause transmission, to the network node, of resource availability information, wherein the plurality of implementations are based at least in part on the resource availability information (see paragraphs [0035], and [0069], the action of running by the user 201 is detected by sensor array 210 and the management engine. 220 determines insufficient resources 214 are available for a full XR weather simulation so hailstones are decreased in visual size for the XR display 208).
In regard claim 101, Zavesky teaches or discloses the apparatus according to claim 100, wherein the resource availability information comprises one or more of:
availability information of processing resources for the network element (see paragraphs [0024], [0035], [0042], [0044], and [0045]);
availability information of memory resources for the network element;
battery life information of the network element;
availability information of one or more inputs to use for performing the respective task;
availability information of one or more outputs to use for the respective task;
a connection status of the network element with the wireless communications system;
a connection quality of the network element with the wireless communications system;
or
availability information of one or more communication resources for the network element.
In regard claim 102, Zavesky teaches or discloses the apparatus according to claim 100, wherein the resource availability information indicates whether the resource availability information is dynamic or static, wherein dynamic resource availability information is valid for a imited time duration (see paragraph [0035], XR management engine 220 may dynamically select XR objects to be mapped within the XR environment based on a historical profile 326 associated with user 201. 220 determines insufficient resources 214 are available for a full XR weather simulation so hailstones are decreased in visual size for the XR display 208).
In regard claim 105, Zavesky teaches or discloses an apparatus comprising:
at least one processor (see Fig. 3); and
at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform (see Fig. 3):
cause transmission, towards a network element of a wireless communications system, information indicative of an association (see paragraphs [0066], and [0067], Wi-Fi is a wireless technology similar to that used in a cell phone that enables such devices, e.g., computers, to send and receive data indoors and out; anywhere within the range of a base station. the mobile network platform 710 can generate and receive signals transmitted and received by base stations or access points such as base station or access point 122) between (i) a plurality of implementations for a network element of the wireless communications system to perform a respective task and (ii) required allocations of resources for the network element to perform the respective task in accordance with one or more respective implementations of the plurality of implementations (see paragraphs [0047], [0067], computing environment 600 can be used in the implementation of network elements 150, 152, 154, 156, access terminal 112, base station or access point 122, switching device 132, media terminal 142, and/or VNEs 530, 532, 534, etc. selecting an XR object for presentation on an XR display of the XR user system based on the local environment information, the user preferences, and the historical profile, and allocating compute resources to facilitate a rendering of the XR object, wherein the allocated compute resources are selected from a compute resource pool comprising local compute resources of the XR user system and edge compute resources of a network);
receive, from the network element, information indicative of a first implementation to perform the respective task (see paragraphs [0014], [0015], [0016], [0038], and [0039], XR management engine 200 may receive local environment information 322 from XR user system 202).
In regard claim 112, Zavesky teaches or discloses an apparatus comprising:
means for receiving, from a network node of a wireless communications system, information indicative of an association (see paragraphs [0038], XR management engine 200 may receive local environment information 322 from XR user system 202) between (i) a plurality of implementations for a network element of the wireless communications system to perform a respective task and (ii) required allocations of resources for the network element to perform the respective task in accordance with one or more respective implementations of the plurality of implementations (see paragraphs [0047], [0067], computing environment 600 can be used in the implementation of network elements 150, 152, 154, 156, access terminal 112, base station or access point 122, switching device 132, media terminal 142, and/or VNEs 530, 532, 534, etc. selecting an XR object for presentation on an XR display of the XR user system based on the local environment information, the user preferences, and the historical profile, and allocating compute resources to facilitate a rendering of the XR object, wherein the allocated compute resources are selected from a compute resource pool comprising local compute resources of the XR user system and edge compute resources of a network);
means for selecting a first implementation of the plurality of implementations to perform the respective task based at least in part on available resources for the network element to perform the respective task relative to the required allocation of resources to perform the respective task in accordance with the first implementation (see paragraphs [0047], selecting an XR object for presentation on an XR display of the XR user system based on the local environment information, the user preferences, and the historical profile, and allocating compute resources to facilitate a rendering of the XR object, wherein the allocated compute resources are selected from a compute resource pool comprising local compute resources of the XR user system and edge compute resources of a network); and
means for performing the respective task in accordance with the first implementation (see paragraphs [0013], [0025], [0048], [0050], [0073], and [0085], compute resources of an edge network node may be used to perform computational tasks associated with presenting an XR object at an XR user system. perform particular tasks or implement particular abstract data types).
Allowable Subject Matter
Claims 98, 99, 103, 104, 109, and 110 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHIRIN SAM whose telephone number is (571)272-3082. The examiner can normally be reached Mon - Fri, 10:30am - 5pm.
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Date: 08/24/2026
/PHIRIN SAM/Primary Examiner, Art Unit 2476
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