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 .
Claims 1-20 are pending.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 4, 8, 9 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ramachandran et al. (hereinafter Ramachandran) (US 20250156759 A1) in view of Tong et al. (hereinafter Tong) (US 20240022927 A1) .
As to claim 1, Ramachandran teaches Edge network equipment [FIG. 2: UE 110-1], comprising:
at least one processor [FIG. 1: processor 120]; and
at least one memory [memory 125] that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, comprising:
identifying data regarding facilitation of broadband cellular communications at the edge network equipment, wherein the edge network equipment is part of a broadband cellular network [0019] [UEs in wireless communication networks wirelessly communicate with RAN node to send or receive data. Sensing is a process of obtaining information about a device's surroundings. Sensing can also be used to detect information about an object such as its location, speed, distance, orientation, shape, texture, etc. This information can be used to improve communications in the network, as well as for other application-specific purposes.];
processing the data using a first local artificial intelligence model to produce embeddings that correspond to the data, wherein the embeddings differ from artificial intelligence model gradients [0035: “FIG. 2 shows UEs (110-1, 110-2, 110-3) serving as local learners and a gNB 170 functioning as an aggregator node. After training a local model, each individual learner (110-1, 110-2, 110-3) transfers (at 204-1, 204-2, 204-3) its local model parameters (202-1, 202-2, 203-3), instead of a raw training dataset, to an aggregating unit 170.”] [0048: “Step 1: A grouping is performed first on the cells using a similarity criterion. Embodiment #1: The similarity criteria here is specifically decided based on the embeddings sent out by each local node together with the local model.”];
sending the embeddings to a central scaling platform that aggregates the embeddings into a group of embeddings, and that trains a global artificial intelligence model based on the group of embeddings, to produce trained global artificial intelligence model, wherein respective embeddings of the group of embeddings are received from respective edge network equipment of a group of edge network equipment that comprise the edge network equipment [0035: “The aggregating unit 170 utilizes the local model parameters (202-1, 202-2, 203-3) to update a global model 206…”];
receiving, from the central scaling platform, an update to a second local artificial intelligence model based on the trained global artificial intelligence model [0035: “The aggregating unit 170 utilizes the local model parameters (202-1, 202-2, 203-3) to update a global model 206 which may eventually be fed back (at 208-1, 208-2, 208-3) to the local learners (110-1, 110-2, 110-3) for further iterations until global model 206 converges.”]; and
implementing a scale up operation or a scale down operation of the edge network equipment based on an output of the second local artificial intelligence model [0035][0036: “The central server aggregates the received models and sends back the model updates to the nodes.”] [Apparently, UE performs operation based on the updated local model.].
Ramachandran does not teach that the data output from the local model in the UE is anonymized.
Tong teaches that the data output from the local model in the UE is anonymized [0400: “ It should be noted that, in the present disclosure, AI-related data that may be communicated to the network node 731 (e.g., from the UE 710 and/or system node 720) may include either or both of the following: raw (i.e., unprocessed or minimally processed) local data (e.g., raw network data), processed local data (e.g., local model parameters, inferred data generated by local AI model(s), and anonymized network data, etc.). Raw local data may be unprocessed network data that can include sensitive user data (e.g., user photographs, user videos, etc.), and thus it may be important to provide a secure logical layer for communication of such sensitive AI-related data.”].
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teaching of anonymizing output data from the local model as suggested in Tong to implement federated learning. One having ordinary skill in the art would have been obvious to make such modification to improve the security of the data.
As to claim 3, Ramachandran teaches wherein the broadband cellular network is associated with a first telecommunications service provider, and wherein the global artificial intelligence model is trained based on a group of data from a group of telecommunications service providers that comprises the first telecommunications service provider [0020] [0035].
As to claim 4, Ramachandran teaches wherein the operations further comprise: iteratively updating the second local artificial intelligence model based on a group of updates received from the central scaling platform, wherein the group of updates comprises the update [0035: “The aggregating unit 170 utilizes the local model parameters (202-1, 202-2, 203-3) to update a global model 206 which may eventually be fed back (at 208-1, 208-2, 208-3) to the local learners (110-1, 110-2, 110-3) for further iterations until global model 206 converges.”].
As to claim 8, Ramachandran teaches wherein the operation further comprises: performing at least one iteration of the identifying and the processing [0035: “The aggregating unit 170 utilizes the local model parameters (202-1, 202-2, 203-3) to update a global model 206 which may eventually be fed back (at 208-1, 208-2, 208-3) to the local learners (110-1, 110-2, 110-3) for further iterations until global model 206 converges.”].
As to claims 9, it relates to method claim comprising the similar subject matters claimed in claim 1. Therefore, it is rejected under the same reasons applied to claim 1.
As to claim 17, it relates to computer-readable medium claim comprising the similar subject matters claimed in claim 1. Therefore, it is rejected under the same reasons applied to claim 1.
Allowable Subject Matter
Claims 2, 5-7, 10-16 and 18-20 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 XUXING CHEN whose telephone number is (571)270-3486. The examiner can normally be reached M-F 9-5:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jaweed Abbaszadeh can be reached at 571-270-1640. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/XUXING CHEN/Primary Examiner, Art Unit 2176
Prosecution Insights