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 .
This office correspondence is in responsive to the application filed on November 18, 2024. Claims 1-15, and 17 are amended, and claims 16, and 19-30 are canceled as per preliminary amendment filed on 11/18/2024. Claims 16, and 19-30 are canceled as per preliminary amendment.
Claims 1-15, and 17-18 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/26/2024 was filed after the mailing date of the instant application on 11/18/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 18 is rejected under 35 U.S.C. 101 because the claim invention is directed to non-statutory subject matter. A computer program comprising instructions is just software module, not any hardware or tangible module. Hence, the “system” is reasonably interpreted by one of ordinary skill as just software, it is a system of software, per se. The function of the system is just software not any hardware. Warmerdam, 33 F.3d at 1361, 31 USPQ2d at 1760 (claim to a data structure per se held nonstatutory). Such claimed data structures do not define any structural and functional interrelationships between the data structure and other claimed aspects of the invention which permit the data structure’s functionality to be realized. Similarly, computer program claimed as computer instructions per se, i.e., the descriptions or expressions of the programs, are not physical “things.” They are neither computer components nor statutory processes, as they are not “acts” being performed. Such claimed computer programs modules do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer which permit the computer program’s functionality to be realized. Accordingly, it is important to distinguish claims that define descriptive material per se from claims that define statutory inventions. So, it does not appear that a claim reciting software module with functional descriptive material falls within any of the categories of patentable subject matter set forth in § 101.
Claim Rejections - 35 USC § 103
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 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.
Claims 1-15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al. (US Publication 2023/0123074) hereafter Ahmed, in view of Mody et al. (US Publication 2023/0209437) hereafter Mody.
As per claim 1, Ahmed discloses a communications network device comprising: at least one processor; and at least one memory including computer program code (paragraphs 10-12:); the at least one memory and the computer program code configured to, with the at least one processor, cause the communications network device at least to: decompose a communications network into service level agreement, SLA, coverage overlap regions, SCORs, according to mobility relations between logical network entity, LNE, pairs within the communication network, said SCOR comprising at least one LNE pair (paragraphs 13-14, 42: machine learning-based optimization algorithm to identify an optimal assignment of the incoming network request to a selected SFC); and assign a machine learning agent to at least one of the decomposed SCORs, wherein said machine learning agent is configured to apply a deep reinforcement learning model to solve an optimization (paragraphs 20-21, 24-25: metrics collector may be configured to collect various telemetry data relating to the handling of incoming flows by various SFCs). Although, Ahmed discloses machine learning based on optimized SFC assignment for network request of a given network service type, but he fails to expressly disclose machine learning agent is configured to apply a deep reinforcement learning model to solve an optimization problem related to a self-organizing network, SON, function within its assigned SCOR.
However, in the same field of endeavor, Mody discloses the claimed imitation of machine learning agent is configured to apply a deep reinforcement learning model to solve an optimization problem related to a self-organizing network, SON, function within its assigned SCOR (paragraphs 83, 131-133: collecting historical data and applying ML techniques to long-term behavior of the network, the INSPiRE Agent may reduce the priority of video applications).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Modys’ teaching of network optimization solutions provided as software agents (applications) executed on various network nodes, with teachings of Ahmed. One would be motivated for optimization based on distributed multi-agent machine learning with minimal inter-agent dependency.
As per claim 2, Ahmed discloses the communications network device wherein LNE pairs in a SCOR comprising at least two LNE pairs are strongly coupled, and dependency between the SCORs is low (paragraphs 18-20).
As per claim 3, Ahmed discloses the communications network device wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the communications network device to decompose the communications networklogical network graph into subgraphs, said subgraphs representing SCORs comprising strongly coupled LNE pairs (paragraphs 24-25).
As per claim 4, Ahmed discloses the communications network device wherein vertices of the logical network graph comprise the LNE pairs, and weights of edges of the logical network graph reflect a mobility relationship between two LNE pairs (paragraphs 25, 27).
As per claim 5, Ahmed discloses the communications network device to wherein the at least one memory and the computer program code are further configured to, with the at least one processor cause the communications network device at least to generate a profile for said subgraph, said profile comprising an adjacency matrix or an adjacency list representing the respective subgraph (paragraphs 14, 18-19).
As per claim 6, Ahmed discloses the communications network device wherein said profile further comprises at least one of: a number of vertices, a number of edges, a number of involved LNEs, a degree distribution, a distribution of edge weights, a distribution of summed weights of edges incident to a vertex, or at least one LNE specific feature for the respective subgraph including at least one of a deployment type, an LNE type, an associated user mobility distribution, position information, or an LNE load state (paragraphs 23, 33).
As per claim 7, Ahmed discloses the communications network device wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the communications network device at least to obtain the deep reinforcement learning model as pretrained from a SON node device (paragraphs 15, 20, 38).
As per claim 8, Ahmed discloses the communications network device wherein states of said assigned machine learning agent comprise at least one of: LNE -specific metrics, LNE pair specific metrics, or contextual information for capturing at least one of temporal or spatial correlations (paragraphs 15, 30, 34).
As per claim 9, Ahmed discloses the communications network device wherein an action space of said assigned machine learning agent comprises a discrete action space or a continuous action space (paragraphs 30, 34).
As per claim 10, Ahmed discloses the communications network device wherein rewards for said assigned machine learning agent are based on at least one of: LNE pair -specific handover performance metrics, LNE -specific quality of service, QoS, performance metrics, or LNE pair specific QoS performance metrics (paragraphs 15, 20, 38).
As per claim 10, Ahmed discloses the communications network device wherein the SON function comprises a mobility robustness optimization, MRO, function, a coverage and capacity optimization function, or a mobility load balancing function (paragraphs 20-21, 24).
As per claim 12, Ahmed discloses the communications network device wherein the MRO function comprises optimization of one or more handover parameters (paragraphs 16-20).
As per claim 13, Ahmed discloses the communications network device wherein said SCOR further comprises a group of physical cell boundaries, a group of logical cell boundaries, or a group of physical cell boundaries and logical cell boundaries (paragraphs 23-24, 33).
As per claim 14, Ahmed discloses the communications network device wherein the LNEs comprise at least one of cells, slices, or QoS flows (paragraphs 15, 20-22).
As per claim 15, Ahmed discloses the communications network device wherein the generating of the logical network graph comprises generating the logical network graph based on historical LNE data, statistical mobility data, or an SLA coverage map (paragraphs 19-20, 44).
Claim 17 is an Independent claim with similar limitation but different in preamble and hence are rejected based on the rejection provided in claim 17.
Claim 18 is an Independent claim with similar limitation but different in preamble and hence are rejected based on the rejection provided in claim 17.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
KAPLAN et al. (US Publication 2023/0297907) discloses allocating resources for a plurality of time intervals, including: receiving a forecasted workload and at least one required service metric value; applying a search algorithm to identify an initial allocation assignment; inputting the assignment to a machine learning algorithm, the machine learning algorithm trained on historic data of past intervals; predicting an expected service metric value provided by the initial allocation assignment; adjusting the initial allocation assignment based on a difference between the expected service metric value and the corresponding required service metric value; iteratively repeating the applying, inputting, predicting, and adjusting operations until one of: the expected service metric value predicted for an adjusted allocation assignment is within a predetermined distance of the corresponding at least one required service metric value for the interval; or a predetermined time has elapsed.
Roy et al. (US Publication 2023/0139623) discloses designing a data path circuit such as a parallel prefix circuit with reinforcement learning are described. A method can include receiving a first design state of a data path circuit, inputting the first design state of the data path circuit into a machine learning model, and performing reinforcement learning using the machine learning model to output a final design state of the data path circuit, wherein the final design state of the data path circuit has decreased area, power consumption and/or delay as compared to conventionally designed data path circuits.
Sarbajit K. Rakshit (US Publication 2022/0398131) discloses technology for revising a smart contract, including a set of machine logic based rules for job management of jobs to be performed under an SLA (service level agreement). The machine learning algorithm is refined and optimized dynamically based on intermittently received context data (historical, relevant operational data—may be augmented with projections regarding future events and/or operations). Also, the SLA is self-evolving so that its terms also change based an analysis of the context data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARZANA B HUQ whose telephone number is (571)270-3223. The examiner can normally be reached Monday - Friday: 8:30-5:30 ET.
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, Emmanuel L Moise can be reached at 571-272-3865. 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.
/FARZANA B HUQ/Primary Examiner, Art Unit 2455
Prosecution Insights