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 .
Introduction
This office action is in response to communication filed 08/13/2026. Claims 1-2, 4-9, 11-16, 18-22, 24 and 26-30, filed 08/04/026, are pending and likewise have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/13/2026 has been entered.
Response to Amendment
Amendment filed 08/13/2026 (claims filed 08/04/2026), has been fully considered by Examiner.
Response to Arguments
Applicant’s arguments, see Remarks, Pg 10-14, filed 08/04/2026, with respect to the rejections of Claims 1-2, 4-9, 11-16, 18-22, 24 under 35 U.S.C. 101 have been fully considered and are persuasive. The rejections of Claims 1-2, 4-9, 11-16, 18-22, 24 under 35 U.S.C. 101 has been withdrawn.
Applicant's arguments, see Remarks, Pg 14-22, filed 08/04/2026, with respect to the rejections of Claims 22 and 24 under 35 U.S.C. 103 have been fully considered and are persuasive. The rejections of Claims 22 and 24 under 35 U.S.C. 103 has been withdrawn.
Applicant's arguments, see Remarks, Pg 14-22, filed 08/04/2026, with respect to the rejections of Claims 1-2, 4-9, 11-16 and 18-21 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Examiner believes the prior art of record teaches the claimed limitations.
Applicant argues the cited portions of Chen do not describe converting “requirements, prompts, and other requested features in the text-based service request into the model and data descriptions”.
Examiner believes the prior art of record does teach these limitations. Chen, Pg 3, III. Native AI Framework for 6G Network, Para 1, Ln 1-17, The cloud identifies user intent by processing queries from the end-user device using the intent-aware PFM. It then manages task orchestration and resource allocation for the edge or end-user device by utilizing algorithms within the edge algorithm toolkit. Pg 4, Col 1, Para 2, Ln 1-1, two prerequisites, namely, the dataset and fine-tuning scheme for the PFM. For the former, we endeavor to construct an expert knowledge library by aggregating non-private data from multiple clouds. To fully exploit the standardized characteristics of wireless communication processes, we categorize user requests based on various criteria, such as the type/target of the task, processing workflow, and signal processing methodologies. Then, an expert knowledge graph can be established with a tree structure based on the existing structured data knowledge, as illustrated in Fig. 2. See Fig 2 . Pg 4, Col 1, Para 3, Ln 1-5, The latter requires a meticulously designed fine-tuning method due to the massive scale and resource requirements of the majority of foundation models. Thus, we implement the native intent-aware PFM by fine-tuning the existing PFM with parameter-efficient fine-tuning methods. Examples available in Pg 4, Col 1, Para 4 - Col 2 Para 3: Prompt tuning, Prefix Tuning, LoRA and Adaptor tuning. Pg 4, Col 2, Para 3, Adaptor Tuning, Ln 1-8, The adaptor tuning method entails the insertion of additional trainable parameters at each layer of the PFM. These parameters are proficient in modifying the outputs to better align with specific tasks without altering the fundamental structure or weights of the model. This strategy aims to retain the majority of pre-trained knowledge while permitting a degree of fine-tuning to enhance performance on specific tasks.
The requirements, prompts, and other requested features are not defined in the claims in a way that differentiates them from the described prompts in Fig 2. and the text-based service request into the model and data descriptions are not defined in the claims in a way that differentiates them from the Prompt tuning, Prefix Tuning, LoRA and Adaptor tuning, and expert knowledge graph.
With regard to the arguments that the prior art of record does not teach generating “model and data specifications” from” automated generative translations of the service request”, and the generated model/data descriptions or specifications being used with the AI task capacity profile, Examiner believes these limitations are not in Claims 1, 8 and 15 specifically, and are unique to Claims 22 and 24, which are no longer rejected. In other words, Claims 1, 8 and 15, do not have the limitations that Claim 22 and 24 have, which uses the results of the model/data descriptions step, in the AI task capacity profile step. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Examiner has not relied on Sindwani, Xu, Persia or Zawish for the above limitations.
Applicant argues, with regard to Claims 5, 12 and 19 that Xu does not teach “a compatibility list that comprises hardware and software mismatches between a potential AI model and the edge device or potential bottlenecks in memory, CPU, GPU, or software infrastructure of the edge device”.
Examiner argues that this limitation is a group of optional limitations and Xu does teach GPU memory requirements and determinations of model existing on edge devices. (Pg 2, Col 2, Para 1, Ln 1-13, we consider an edge intelligence system model……cloud data center and edge servers can serve generative AI services. The cloud data center is represented by 0 and the set of edge servers is represented by N = {1,2,...,N}. In this system, edge servers and the cloud center provide generic AI services such as AIGC, depending on different PFMs. Pg 3, Col 1, Para 1, Ln 1-16, To offer AI services based on PFMs, we propose a joint foundation model caching and inference framework. Edge servers need to make model caching and request offloading decisions to utilize the existing edge computing resources for accommodating generative AI service requests of mobile users…….the binary variable indicating whether model m of application i is cached at edge server n. Pg 3, Col 1, Para 2, Ln 1-5, The generative AI service requests of users can be executed at edge servers if the required components of models are loaded at the GPU memories. Let Gn denote the capacity of GPU memory of edge server n).
Applicant argues the prior art of record does not teach deploying the resource-optimal AI model to the edge device based on the AI task capacity profile”.
Examiner believes Chen does teach these limitations (Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices……..resource scheduling and task orchestration for a cell have shifted from its edge server to the cloud-based intent aware PFM within this framework. See Pg 5, Fig 3, Edge AI models toolkit. Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks. Pg 4, Col 2, B. Edge Intelligence with AI Toolkit, Para 2, Ln 1 – Pg 5, Col 1, Para 1, Ln 12, Due to the limited storage capacity available on intelligent edges, the edge algorithm toolkit cannot accommodate algorithms for every possible task. Therefore, when an intelligent edge node requires an algorithm not available in the toolkit, it requests it from the cloud and updates its internal stored algorithms. As a result, a tool selection strategy for the edge algorithm toolkit must be implemented within this framework. Ideally, the algorithms within the toolkit should adequately address the requirements of the intelligent edge nodes it serves. To achieve this, each intelligent edge node examines, records, and anticipates task demands, while also analyzing and tracking the various job types and frequencies it supports).
Applicant argues there is no rational for the combinations of Chen, Sindwani, Xu, Persia and Zawish, and the office action does not explain why or how a person of ordinary skill would have modified the cited references.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, the rational provided for each reference, cited by applicant in their arguments, constitute a motivation to combine the references.
For these reasons Examiner believes the prior art of record teaches the claimed limitations.
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.
Claim(s) 1-2 and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al “Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End Collaboration”, hereinafter Chen, and further in view of Sindwani et al. (US 12541687 B1).
Regarding Claim 1:
Chen teaches a computer-implemented method comprising: receiving a text-based service request for an artificial intelligence (AI) model for an edge device(Pg 3, Col 2, Para 2, Ln 1-6, The cloud identifies user intent by processing queries from the end-user device using the intent-aware PFM. It then manages task orchestration and resource allocation for the edge or end-user device);
generating model and data descriptions using the text-based service request, wherein generating the model and data descriptions comprises translating requirements, prompts, and other requested features in the text-based service request into the model and data descriptions(Pg 4, Col 1, Para 2, Ln 1-1, two prerequisites, namely, the dataset and fine-tuning scheme for the PFM. For the former, we endeavor to construct an expert knowledge library by aggregating non-private data from multiple clouds. To fully exploit the standardized characteristics of wireless communication processes, we categorize user requests based on various criteria, such as the type/target of the task, processing workflow, and signal processing methodologies. Then, an expert knowledge graph can be established with a tree structure based on the existing structured data knowledge, as illustrated in Fig. 2. See Fig 2 . Pg 4, Col 1, Para 3, Ln 1-5, The latter requires a meticulously designed fine-tuning method due to the massive scale and resource requirements of the majority of foundation models. Thus, we implement the native intent-aware PFM by fine-tuning the existing PFM with parameter-efficient fine-tuning methods. Examples available in Pg 4, Col 1, Para 4 - Col 2 Para 3: Prompt tuning, Prefix Tuning, LoRA and Adaptor tuning. Pg 4, Col 2, Para 3, Adaptor Tuning, Ln 1-8, The adaptor tuning method entails the insertion of additional trainable parameters at each layer of the PFM. These parameters are proficient in modifying the outputs to better align with specific tasks without altering the fundamental structure or weights of the model. This strategy aims to retain the majority of pre-trained knowledge while permitting a degree of fine-tuning to enhance performance on specific tasks.);
generating an AI task capacity profile, wherein the Al task capacity profile is generated based on a capacity profile of the edge device, and wherein the capacity profile of the edge device comprises……and at least one of supported Al model formats of the edge device and available inference engines of the edge device(Pg 4, Col 2, Para 5, Ln 1-6, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices. See Fig 3, Edge, traditional algs toolkit, tool AI models toolkit);
selecting a resource-optimal AI model for deployment on the edge device based on the AI task capacity profile(Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices……..resource scheduling and task orchestration for a cell have shifted from its edge server to the cloud-based intent aware PFM within this framework. See Pg 5, Fig 3, Edge AI models toolkit. Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks);
and deploying the resource-optimal AI model to the edge device based on the AI task capacity profile(Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices……..resource scheduling and task orchestration for a cell have shifted from its edge server to the cloud-based intent aware PFM within this framework. See Pg 5, Fig 3, Edge AI models toolkit. Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks. Pg 4, Col 2, B. Edge Intelligence with AI Toolkit, Para 2, Ln 1 – Pg 5, Col 1, Para 1, Ln 12, Due to the limited storage capacity available on intelligent edges, the edge algorithm toolkit cannot accommodate algorithms for every possible task. Therefore, when an intelligent edge node requires an algorithm not available in the toolkit, it requests it from the cloud and updates its internal stored algorithms. As a result, a tool selection strategy for the edge algorithm toolkit must be implemented within this framework. Ideally, the algorithms within the toolkit should adequately address the requirements of the intelligent edge nodes it serves. To achieve this, each intelligent edge node examines, records, and anticipates task demands, while also analyzing and tracking the various job types and frequencies it supports).
Chen does not teach and wherein the capacity profile of the edge device comprises operating system constraints.
In the same field of edge computing, Sindwani teaches and wherein the capacity profile of the edge device comprises operating system constraints(Col 12, Ln 44-60, execute respective ones of the plurality of useable versions of the trained ML models on a plurality of different compute instance types using the model evaluation data to produce model evaluation results for a plurality of different combinations of the useable versions of the trained ML model and the plurality of different compute instance types. Compute instance types may include features and/or capabilities of specific or particular compute resources for running the trained ML model. In some aspects, different compute instance types may include different virtual machine instance types specified based on an operating system type).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify Chen with the edge computing system of Sindwani, as it helps minimize cost(Col 2, Ln 25-30).
Regarding Claim 2:
The combination of Chen and Sindwani teaches the computer-implemented method of claim 1, and Chen teaches wherein the text-based service request comprises a description of an AI task, a description of an AI model architecture, a description of an input to the AI model, a description of an output of the AI model, an example of a deployment scenario of the AI model, an example of a specific use-case for the AI model, an example of a re-use of the AI model, a list of performance requirements of the AI model, or a list of generative prompts to the AI model(Pg 5, Fig 3: Application 1, Application 2).
Regarding Claim 26:
The combination of Chen and Sindwani teaches the computer-implemented method of claim 1, and Chen teaches wherein deploying the resource-optimal AI model to the edge device comprises transmitting, to a Foundation Model as a Service (FMaaS) provisioning instance configured to provision the resource-optimal AI model to the edge device, a selection of the resource-optimal AI model(Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices……..resource scheduling and task orchestration for a cell have shifted from its edge server to the cloud-based intent aware PFM within this framework. See Pg 5, Fig 3, Edge AI models toolkit. Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks. Pg 4, Col 2, B. Edge Intelligence with AI Toolkit, Para 2, Ln 1 – Pg 5, Col 1, Para 1, Ln 12, Due to the limited storage capacity available on intelligent edges, the edge algorithm toolkit cannot accommodate algorithms for every possible task. Therefore, when an intelligent edge node requires an algorithm not available in the toolkit, it requests it from the cloud and updates its internal stored algorithms. As a result, a tool selection strategy for the edge algorithm toolkit must be implemented within this framework. Ideally, the algorithms within the toolkit should adequately address the requirements of the intelligent edge nodes it serves. To achieve this, each intelligent edge node examines, records, and anticipates task demands, while also analyzing and tracking the various job types and frequencies it supports).
Claim(s) 4 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Chen and Sindwani as applied to claim 1 above, and further in view of Xu et al. “Joint Foundation Model Caching and Inference of Generative AI Services for Edge Intelligence”, hereinafter Xu.
Regarding Claim 4:
The combination of Chen and Sindwani teaches the computer-implemented method of claim 1, and Chen teaches wherein generating the AI task capacity profile further comprises: retrieving a capacity profile of the edge device(Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices);
The combination of Chen and Sindwani does not specifically teach identifying performance and resource parameters by comparing the capacity profile of the edge device to an AI model requirements mapping; and generating the AI task capacity profile using the performance and resource parameters.
In the same field of AI Edge Computing, Xu teaches identifying performance and resource parameters by comparing the capacity profile of the edge device to an AI model requirements mapping(Pg 2, Col 2, Para 1, Ln 1-13, we consider an edge intelligence system model……cloud data center and edge servers can serve generative AI services. The cloud data center is represented by 0 and the set of edge servers is represented by N = {1,2,...,N}. In this system, edge servers and the cloud center provide generic AI services such as AIGC, depending on different PFMs. Pg 3, Col 1, Para 1, Ln 1-16, To offer AI services based on PFMs, we propose a joint foundation model caching and inference framework. Edge servers need to make model caching and request offloading decisions to utilize the existing edge computing resources for accommodating generative AI service requests of mobile users…….the binary variable indicating whether model m of application i is cached at edge server n. Pg 3, Col 1, Para 2, Ln 1-5, The generative AI service requests of users can be executed at edge servers if the required components of models are loaded at the GPU memories. Let Gn denote the capacity of GPU memory of edge server n);
and generating the AI task capacity profile using the performance and resource parameters(Pg 3, Col 1, Para 1, Ln 1-16, To offer AI services based on PFMs, we propose a joint foundation model caching and inference framework. Edge servers need to make model caching and request offloading decisions to utilize the existing edge computing resources for accommodating generative AI service requests of mobile users).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Chen and Sindwani with the Edge Computing system of Xu, as it can help improve model performance(Pg 6, Col 1, Para 3, Ln 1-12).
Regarding Claim 5:
The combination of Chen and Sindwani teaches the computer-implemented method of claim 1, but does not specifically teach wherein the AI task capacity profile comprises a compatibility list that comprises hardware and software mismatches between a potential AI model and the edge device or potential bottlenecks in memory, CPU, GPU, or software infrastructure of the edge device.
In the same field of AI Edge Computing, Xu teaches wherein the AI task capacity profile comprises a compatibility list that comprises hardware and software mismatches between a potential AI model and the edge device or potential bottlenecks in memory, CPU, GPU, or software infrastructure of the edge device(Pg 2, Col 2, Para 1, Ln 1-13, we consider an edge intelligence system model……cloud data center and edge servers can serve generative AI services. The cloud data center is represented by 0 and the set of edge servers is represented by N = {1,2,...,N}. In this system, edge servers and the cloud center provide generic AI services such as AIGC, depending on different PFMs. Pg 3, Col 1, Para 1, Ln 1-16, To offer AI services based on PFMs, we propose a joint foundation model caching and inference framework. Edge servers need to make model caching and request offloading decisions to utilize the existing edge computing resources for accommodating generative AI service requests of mobile users…….the binary variable indicating whether model m of application i is cached at edge server n. Pg 3, Col 1, Para 2, Ln 1-5, The generative AI service requests of users can be executed at edge servers if the required components of models are loaded at the GPU memories. Let Gn denote the capacity of GPU memory of edge server n).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Chen and Sindwani with the Edge Computing system of Xu, as it can help improve model performance(Pg 6, Col 1, Para 3, Ln 1-12).
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Chen and Sindwani as applied to claim 1 above, and further in view of Zawish et al. “Complexity-Driven Model Compression for Resource-Constrained Deep Learning on Edge”.
Regarding Claim 6:
The combination of Chen and Sindwani teaches the computer-implemented method of claim 1, and Chen teaches wherein selecting the resource-optimal AI model for deployment on the edge device based on the AI task capacity profile further comprises: identifying an AI model family using the model and data descriptions and the AI task capacity profile(Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks. Pg 4, Col 2, Para 5, Ln 1-6, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices. Pg 4, Col 1, Para 2, Ln 1-8, two prerequisites, namely, the dataset and fine-tuning scheme for the PFM. For the former, we endeavor to construct an expert knowledge library by aggregating non-private data from multiple clouds. To fully exploit the standardized characteristics of wireless communication processes, we categorize user requests based on various criteria, such as the type/target of the task, processing workflow, and signal processing methodologies. Pg 4, Col 1, Para 3, Ln 1-5, The latter requires a meticulously designed fine-tuning method due to the massive scale and resource requirements of the majority of foundation models. Thus, we implement the native intent-aware PFM by fine-tuning the existing PFM with parameter-efficient fine-tuning methods);
The combination of Chen and Sindwani does not teach and selecting a model variant of the AI model family based on the AI task capacity profile and resources of the edge device.
In the same field of AI Edge Computing, Zawish teaches and selecting a model variant of the AI model family based on the AI task capacity profile and resources of the edge device(Pg 3892, Col 2, Para 2, Memory Aware Pruning, Ln 1-13, reduction in memory of a CNN is critical when the aim is to achieve both computation and energy efficiency…..Moreover, in order for the deep models to run at the edge, they must fit within the target device’s RAM without disrupting the IoT application at the runtime. To achieve this, the memory-based complexity of each convolutional layer k can be calculated using. Pg 3894, Col 1, Para 1, Ln 1-2, model M must be either less than or equal to the desired complexityCr).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Chen and Sindwani with the Pruning methods of Zawish, as it can improve computation and energy efficiency of the model(Pg 3892, Col 2, Para 2, Memory Aware Pruning, Ln 1-13).
Regarding Claim 7:
The combination of Chen, Sindwani and Zawish teaches the computer-implemented method of claim 6, but does not teach wherein the model variant is a compressed, pruned, or quantized AI model to correspond to resources of the edge device.
In the same field of AI Edge Computing, Zawish teaches wherein the model variant is a compressed, pruned, or quantized AI model to correspond to resources of the edge device(Pg 3892, Col 2, Para 2, Memory Aware Pruning, Ln 1-13, reduction in memory of a CNN is critical when the aim is to achieve both computation and energy efficiency…..Moreover, in order for the deep models to run at the edge, they must fit within the target device’s RAM without disrupting the IoT application at the runtime. To achieve this, the memory-based complexity of each convolutional layer k can be calculated using. Pg 3894, Col 1, Para 1, Ln 1-2, model M must be either less than or equal to the desired complexityCr).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Chen, Sindwani and Zawish with the Pruning methods of Zawish, as it can improve computation and energy efficiency of the model(Pg 3892, Col 2, Para 2, Memory Aware Pruning, Ln 1-13).
Claim(s) 8-9, 15-16 and 27-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen, and further in view of Persia et al. (US 20220327442 A1), and further in view of Sindwani.
Regarding Claim 8:
Chen teaches receiving a text-based service request for an artificial intelligence (AI) model for an edge device(Pg 3, Col 2, Para 2, Ln 1-6, The cloud identifies user intent by processing queries from the end-user device using the intent-aware PFM. It then manages task orchestration and resource allocation for the edge or end-user device);
generating model and data descriptions using the text-based service request, wherein generating the model and data descriptions comprises translating requirements, prompts, and other requested features in the text-based service request into the model and data descriptions(Pg 4, Col 1, Para 2, Ln 1-1, two prerequisites, namely, the dataset and fine-tuning scheme for the PFM. For the former, we endeavor to construct an expert knowledge library by aggregating non-private data from multiple clouds. To fully exploit the standardized characteristics of wireless communication processes, we categorize user requests based on various criteria, such as the type/target of the task, processing workflow, and signal processing methodologies. Then, an expert knowledge graph can be established with a tree structure based on the existing structured data knowledge, as illustrated in Fig. 2. See Fig 2 . Pg 4, Col 1, Para 3, Ln 1-5, The latter requires a meticulously designed fine-tuning method due to the massive scale and resource requirements of the majority of foundation models. Thus, we implement the native intent-aware PFM by fine-tuning the existing PFM with parameter-efficient fine-tuning methods. Examples available in Pg 4, Col 1, Para 4 - Col 2 Para 3: Prompt tuning, Prefix Tuning, LoRA and Adaptor tuning. Pg 4, Col 2, Para 3, Adaptor Tuning, Ln 1-8, The adaptor tuning method entails the insertion of additional trainable parameters at each layer of the PFM. These parameters are proficient in modifying the outputs to better align with specific tasks without altering the fundamental structure or weights of the model. This strategy aims to retain the majority of pre-trained knowledge while permitting a degree of fine-tuning to enhance performance on specific tasks.);
generating an AI task capacity profile, wherein the AI task capacity profile is generated based on a capacity profile of the edge device, and wherein the capacity profile of the edge device comprises……and at least one of supported AI model formats of the edge device and available inference engines of the edge device; (Pg 4, Col 2, Para 5, Ln 1-6, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices. See Fig 3, Edge, traditional algs toolkit, tool AI models toolkit);
selecting a resource-optimal AI model for deployment on the edge device based on the AI task capacity profile(Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices……..resource scheduling and task orchestration for a cell have shifted from its edge server to the cloud-based intent aware PFM within this framework. See Pg 5, Fig 3, Edge AI models toolkit. Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks).
and deploying the resource-optimal AI model to the edge device based on the AI task capacity profile(Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices……..resource scheduling and task orchestration for a cell have shifted from its edge server to the cloud-based intent aware PFM within this framework. See Pg 5, Fig 3, Edge AI models toolkit. Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks. Pg 4, Col 2, B. Edge Intelligence with AI Toolkit, Para 2, Ln 1 – Pg 5, Col 1, Para 1, Ln 12, Due to the limited storage capacity available on intelligent edges, the edge algorithm toolkit cannot accommodate algorithms for every possible task. Therefore, when an intelligent edge node requires an algorithm not available in the toolkit, it requests it from the cloud and updates its internal stored algorithms. As a result, a tool selection strategy for the edge algorithm toolkit must be implemented within this framework. Ideally, the algorithms within the toolkit should adequately address the requirements of the intelligent edge nodes it serves. To achieve this, each intelligent edge node examines, records, and anticipates task demands, while also analyzing and tracking the various job types and frequencies it supports).
Chen does not explicitly teach a system comprising: a memory having computer readable instructions; and one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising.
Persia teaches a system comprising: a memory having computer readable instructions; and one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising(Para [0083], Ln 1-14, Processor 620 is implemented in hardware, firmware, or a combination of hardware and software. In some implementations, processor 620 includes one or more processors capable of being programmed to perform a function. Memory 630 includes a random access memory):
It would have been obvious for one skilled in the art, at the effective time of filing, to modify Chen with the computer components of Persia, as it provides an environment for the system to be realized(Para [0082], Ln 1-13, Para [0083], Ln 1-14).
The combination of Chen and Persia does not teach and wherein the capacity profile of the edge device comprises operating system constraints.
In the same field of edge computing, Sindwani teaches and wherein the capacity profile of the edge device comprises operating system constraints(Col 12, Ln 44-60, execute respective ones of the plurality of useable versions of the trained ML models on a plurality of different compute instance types using the model evaluation data to produce model evaluation results for a plurality of different combinations of the useable versions of the trained ML model and the plurality of different compute instance types. Compute instance types may include features and/or capabilities of specific or particular compute resources for running the trained ML model. In some aspects, different compute instance types may include different virtual machine instance types specified based on an operating system type).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Chen and Persia with the edge computing system of Sindwani, as it helps minimize cost(Col 2, Ln 25-30).
Regarding Claim 9:
The combination of Chen, Persia and Sindwani teaches the system of claim 8, and Chen teaches wherein the text-based service request comprises a description of an AI task, a description of an AI model architecture, a description of an input to the AI model, a description of an output of the AI model, an example of a deployment scenario of the AI model, an example of a specific use-case for the AI model, an example of a re-use of the AI model, a list of performance requirements of the AI model, or a list of generative prompts to the AI model(Pg 5, Fig 3: Application 1, Application 2).
Regarding Claim 15:
Chen teaches receiving a text-based service request for an artificial intelligence (AI) model for an edge device(Pg 3, Col 2, Para 2, Ln 1-6, The cloud identifies user intent by processing queries from the end-user device using the intent-aware PFM. It then manages task orchestration and resource allocation for the edge or end-user device);
generating model and data descriptions using the text-based service request, wherein generating the model and data descriptions comprises translating requirements, prompts, and other requested features in the text-based service request into the model and data descriptions(Pg 4, Col 1, Para 2, Ln 1-1, two prerequisites, namely, the dataset and fine-tuning scheme for the PFM. For the former, we endeavor to construct an expert knowledge library by aggregating non-private data from multiple clouds. To fully exploit the standardized characteristics of wireless communication processes, we categorize user requests based on various criteria, such as the type/target of the task, processing workflow, and signal processing methodologies. Then, an expert knowledge graph can be established with a tree structure based on the existing structured data knowledge, as illustrated in Fig. 2. See Fig 2 . Pg 4, Col 1, Para 3, Ln 1-5, The latter requires a meticulously designed fine-tuning method due to the massive scale and resource requirements of the majority of foundation models. Thus, we implement the native intent-aware PFM by fine-tuning the existing PFM with parameter-efficient fine-tuning methods. Examples available in Pg 4, Col 1, Para 4 - Col 2 Para 3: Prompt tuning, Prefix Tuning, LoRA and Adaptor tuning. Pg 4, Col 2, Para 3, Adaptor Tuning, Ln 1-8, The adaptor tuning method entails the insertion of additional trainable parameters at each layer of the PFM. These parameters are proficient in modifying the outputs to better align with specific tasks without altering the fundamental structure or weights of the model. This strategy aims to retain the majority of pre-trained knowledge while permitting a degree of fine-tuning to enhance performance on specific tasks.);
generating an AI task capacity profile, wherein the AI task capacity profile is generated based on a capacity profile of the edge device, and wherein the capacity profile of the edge device comprises ……..and at least one of supported AI model formats of the edge device and available inference engines of the edge device(Pg 4, Col 2, Para 5, Ln 1-6, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices. See Fig 3, Edge, traditional algs toolkit, tool AI models toolkit);
selecting a resource-optimal AI model for deployment on the edge device based on the AI task capacity profile(Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices……..resource scheduling and task orchestration for a cell have shifted from its edge server to the cloud-based intent aware PFM within this framework. See Pg 5, Fig 3, Edge AI models toolkit. Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks).
and deploying the resource-optimal AI model to the edge device based on the AI task capacity profile(Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices……..resource scheduling and task orchestration for a cell have shifted from its edge server to the cloud-based intent aware PFM within this framework. See Pg 5, Fig 3, Edge AI models toolkit. Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks. Pg 4, Col 2, B. Edge Intelligence with AI Toolkit, Para 2, Ln 1 – Pg 5, Col 1, Para 1, Ln 12, Due to the limited storage capacity available on intelligent edges, the edge algorithm toolkit cannot accommodate algorithms for every possible task. Therefore, when an intelligent edge node requires an algorithm not available in the toolkit, it requests it from the cloud and updates its internal stored algorithms. As a result, a tool selection strategy for the edge algorithm toolkit must be implemented within this framework. Ideally, the algorithms within the toolkit should adequately address the requirements of the intelligent edge nodes it serves. To achieve this, each intelligent edge node examines, records, and anticipates task demands, while also analyzing and tracking the various job types and frequencies it supports).
Chen does not explicitly teach a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising.
Persia teaches a computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising(Para [0083], Ln 1-14, Processor 620 is implemented in hardware, firmware, or a combination of hardware and software. In some implementations, processor 620 includes one or more processors capable of being programmed to perform a function. Memory 630 includes a random access memory):
It would have been obvious for one skilled in the art, at the effective time of filing, to modify Chen with the computer components of Persia, as it provides an environment for the system to be realized(Para [0082], Ln 1-13, Para [0083], Ln 1-14).
The combination of Chen and Persia does not teach and wherein the capacity profile of the edge device comprises operating system constraints.
In the same field of edge computing, Sindwani teaches and wherein the capacity profile of the edge device comprises operating system constraints(Col 12, Ln 44-60, execute respective ones of the plurality of useable versions of the trained ML models on a plurality of different compute instance types using the model evaluation data to produce model evaluation results for a plurality of different combinations of the useable versions of the trained ML model and the plurality of different compute instance types. Compute instance types may include features and/or capabilities of specific or particular compute resources for running the trained ML model. In some aspects, different compute instance types may include different virtual machine instance types specified based on an operating system type).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Chen and Persia with the edge computing system of Sindwani, as it helps minimize cost(Col 2, Ln 25-30).
Regarding Claim 16:
Claim 16 contains similar limitations as Claim 9, and is therefore rejected for the same reasons.
Regarding Claim 27:
The combination of Chen, Persia and Sindwani teaches the system of claim 8, and Chen teaches wherein deploying the resource-optimal AI model to the edge device comprises transmitting, to a Foundation Model as a Service (FMaaS) provisioning instance configured to provision the resource-optimal AI model to the edge device, a selection of the resource-optimal AI model(Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices……..resource scheduling and task orchestration for a cell have shifted from its edge server to the cloud-based intent aware PFM within this framework. See Pg 5, Fig 3, Edge AI models toolkit. Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks. Pg 4, Col 2, B. Edge Intelligence with AI Toolkit, Para 2, Ln 1 – Pg 5, Col 1, Para 1, Ln 12, Due to the limited storage capacity available on intelligent edges, the edge algorithm toolkit cannot accommodate algorithms for every possible task. Therefore, when an intelligent edge node requires an algorithm not available in the toolkit, it requests it from the cloud and updates its internal stored algorithms. As a result, a tool selection strategy for the edge algorithm toolkit must be implemented within this framework. Ideally, the algorithms within the toolkit should adequately address the requirements of the intelligent edge nodes it serves. To achieve this, each intelligent edge node examines, records, and anticipates task demands, while also analyzing and tracking the various job types and frequencies it supports).
Regarding Claim 28:
Claim 28 contains similar limitations as Claim 27 and is therefore rejected for the same reasons.
Claim(s) 11-12 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Chen, Persia and Sindwani as applied to claim 8 above, and further in view of Xu.
Regarding Claim 11:
The combination of Chen, Persia and Sindwani teaches the system of claim 8, and Chen teaches wherein the operations to generate the AI task capacity profile further comprises: retrieving a capacity profile of the edge device(Pg 4, Col 2, Para 5, Ln 1-15, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices);
The combination of Chen, Persia and Sindwani does not specifically teach identifying performance and resource parameters by comparing the capacity profile of the edge device to an AI model requirements mapping; and generating the AI task capacity profile using the performance and resource parameters.
In the same field of AI Edge Computing, Xu teaches identifying performance and resource parameters by comparing the capacity profile of the edge device to an AI model requirements mapping(Pg 2, Col 2, Para 1, Ln 1-13, we consider an edge intelligence system model……cloud data center and edge servers can serve generative AI services. The cloud data center is represented by 0 and the set of edge servers is represented by N = {1,2,...,N}. In this system, edge servers and the cloud center provide generic AI services such as AIGC, depending on different PFMs. Pg 3, Col 1, Para 1, Ln 1-16, To offer AI services based on PFMs, we propose a joint foundation model caching and inference framework. Edge servers need to make model caching and request offloading decisions to utilize the existing edge computing resources for accommodating generative AI service requests of mobile users…….the binary variable indicating whether model m of application i is cached at edge server n. Pg 3, Col 1, Para 2, Ln 1-5, The generative AI service requests of users can be executed at edge servers if the required components of models are loaded at the GPU memories. Let Gn denote the capacity of GPU memory of edge server n);
and generating the AI task capacity profile using the performance and resource parameters(Pg 3, Col 1, Para 1, Ln 1-16, To offer AI services based on PFMs, we propose a joint foundation model caching and inference framework. Edge servers need to make model caching and request offloading decisions to utilize the existing edge computing resources for accommodating generative AI service requests of mobile users).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Chen, Persia and Sindwani with the Edge Computing system of Xu, as it can help improve model performance(Pg 6, Col 1, Para 3, Ln 1-12).
Regarding Claim 12:
The combination of Chen, Persia and Sindwani teaches the system of claim 8, but does not specifically teach wherein the AI task capacity profile comprises a compatibility list that comprises hardware and software mismatches between a potential AI model and the edge device or potential bottlenecks in memory, CPU, GPU, or software infrastructure of the edge device.
In the same field of AI Edge Computing, Xu teaches wherein the AI task capacity profile comprises a compatibility list that comprises hardware and software mismatches between a potential AI model and the edge device or potential bottlenecks in memory, CPU, GPU, or software infrastructure of the edge device(Pg 2, Col 2, Para 1, Ln 1-13, we consider an edge intelligence system model……cloud data center and edge servers can serve generative AI services. The cloud data center is represented by 0 and the set of edge servers is represented by N = {1,2,...,N}. In this system, edge servers and the cloud center provide generic AI services such as AIGC, depending on different PFMs. Pg 3, Col 1, Para 1, Ln 1-16, To offer AI services based on PFMs, we propose a joint foundation model caching and inference framework. Edge servers need to make model caching and request offloading decisions to utilize the existing edge computing resources for accommodating generative AI service requests of mobile users…….the binary variable indicating whether model m of application i is cached at edge server n. Pg 3, Col 1, Para 2, Ln 1-5, The generative AI service requests of users can be executed at edge servers if the required components of models are loaded at the GPU memories. Let Gn denote the capacity of GPU memory of edge server n).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Chen, Persia and Sindwani with the Edge Computing system of Xu, as it can help improve model performance(Pg 6, Col 1, Para 3, Ln 1-12).
Regarding Claim 18:
Claim 18 contains similar limitations as Claim 11, and is therefore rejected for the same reasons.
Regarding Claim 19:
Claim 19 contains similar limitations as Claim 12, and is therefore rejected for the same reasons.
Claim(s) 13-14 and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Chen, Persia and Sindwani as applied to claim 8 above, and further in view of Zawish.
Regarding Claim 13:
The combination of Chen, Persia and Sindwani teaches the system of claim 8, and Chen teaches wherein the operations to select the resource-optimal AI model for deployment on the edge device based on the AI task capacity profile further comprise: identifying an AI model family using the model and data descriptions and the AI task capacity profile(Pg 5, Col 2, Para 1, Ln 1-15, AI models stored in the algorithm toolkits are orchestrated by the well trained PFM to handle tasks across multiple edge/end-user devices……Through intent recognition and unified orchestration, these models are assigned to tasks that align with their capabilities, enabling them to effectively leverage the relationships between tasks. Pg 4, Col 2, Para 5, Ln 1-6, intelligent edge within this framework pools the majority of its local resources. These resources are represented as part of the edge’s status information, which is then transmitted to the cloud along with requests from end-user devices. Pg 4, Col 1, Para 2, Ln 1-8, two prerequisites, namely, the dataset and fine-tuning scheme for the PFM. For the former, we endeavor to construct an expert knowledge library by aggregating non-private data from multiple clouds. To fully exploit the standardized characteristics of wireless communication processes, we categorize user requests based on various criteria, such as the type/target of the task, processing workflow, and signal processing methodologies. Pg 4, Col 1, Para 3, Ln 1-5, The latter requires a meticulously designed fine-tuning method due to the massive scale and resource requirements of the majority of foundation models. Thus, we implement the native intent-aware PFM by fine-tuning the existing PFM with parameter-efficient fine-tuning methods);
The combination of Chen, Persia and Sindwani does not teach and selecting a model variant of the AI model family based on the AI task capacity profile and resources of the edge device.
In the same field of AI Edge Computing, Zawish teaches and selecting a model variant of the AI model family based on the AI task capacity profile and resources of the edge device(Pg 3892, Col 2, Para 2, Memory Aware Pruning, Ln 1-13, reduction in memory of a CNN is critical when the aim is to achieve both computation and energy efficiency…..Moreover, in order for the deep models to run at the edge, they must fit within the target device’s RAM without disrupting the IoT application at the runtime. To achieve this, the memory-based complexity of each convolutional layer k can be calculated using. Pg 3894, Col 1, Para 1, Ln 1-2, model M must be either less than or equal to the desired complexityCr).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Chen, Persia and Sindwani with the Pruning methods of Zawish, as it can improve computation and energy efficiency of the model(Pg 3892, Col 2, Para 2, Memory Aware Pruning, Ln 1-13).
Regarding Claim 14:
The combination of Chen, Persia, Sindwani and Zawish teaches the system of claim 13, but does not teach wherein the model variant is a compressed, pruned, or quantized AI model to correspond to resources of the edge device.
In the same field of AI Edge Computing, Zawish teaches wherein the model variant is a compressed, pruned, or quantized AI model to correspond to resources of the edge device(Pg 3892, Col 2, Para 2, Memory Aware Pruning, Ln 1-13, reduction in memory of a CNN is critical when the aim is to achieve both computation and energy efficiency…..Moreover, in order for the deep models to run at the edge, they must fit within the target device’s RAM without disrupting the IoT application at the runtime. To achieve this, the memory-based complexity of each convolutional layer k can be calculated using. Pg 3894, Col 1, Para 1, Ln 1-2, model M must be either less than or equal to the desired complexityCr).
It would have been obvious for one skilled in the art, at the effective time of filling, to modify the combination of Chen, Persia, Sindwani and Zawish with the Pruning methods of Zawish, as it can improve computation and energy efficiency of the model(Pg 3892, Col 2, Para 2, Memory Aware Pruning, Ln 1-13).
Regarding Claim 20:
Claim 20 contains similar limitations as Claim 13, and is therefore rejected for the same reasons.
Regarding Claim 21:
Claim 21 contains similar limitations as Claim 14, and is therefore rejected for the same reasons.
Allowable Subject Matter
Claim 22, 24 and 29-30 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding Claim 22:
The prior art of record alone or in combination does not teach generating model and data specifications by using automated generative translations of the service request, wherein using the automated generative translations comprises translating requirements, prompts, and other requested features in the service request into the model and data specifications; performing an AI task capacity profiling using the model and data specifications and a capacity profile of the edge device to identify a key performance parameter and a key resource parameter of the AI model, wherein the capacity profile of the edge device comprises operating system constraints and at least one of supported AI model formats of the edge device and available inference engines of the edge device.
See Applicant arguments, see Remarks, Pg 14-22, filed 08/04/2026, with respect to claims 22 and 24 for further details.
For these reasons the prior art of record does not teach the claimed limitations.
Claim 24 contains similar limitations as Claim 22 and therefore also contains allowable subject matter.
Claims 29-30 depend on a claim containing allowable subject matter and therefore also contain allowable subject matter.
Conclusion
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/ALEXANDER G MARLOW/Assistant Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
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