DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 8 and 9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shiner et al. (“Shiner”) (US Patent Application Publication No. 2022/0166683).
Regarding claim 1, Shiner discloses a device, comprising: a processing system including a processor and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations (fig. 2 and paragraphs 0038-0041 and fig. 5 and paragraph 0083), the operations comprising: collecting network information about physical parameters of an optical transport network (fig. 5, Network State and paragraph 0084 in light of paragraphs 0013-0015 and 0072-0074), the optical transport network including multiple network elements, the network elements including at least one of a Central Unit (CU), a Distributed Unit (DU) and a Radio Unit (RU), forming collected network information (fig. 1 and paragraph 0035, where elements 110n reads on distributed units), developing a machine learning (ML) model for characterizing at least a portion of the optical transport network, wherein the developing the ML model is based at least in part on the collected network information (fig. 5 element 400 and paragraphs 0083-0084); selecting parameters for a communication channel between two network elements, forming selected parameters (fig. 7 element 508 and paragraphs 0109-0110), and launching the channel between the two network elements according to the selected parameters (fig. 7 element 510 and paragraph 0110 in light of fig. 3 elements 254 and 2565 and paragraphs 0002 and 0055-0056, where the RMSA and defragmentation includes re-routing and/or recoloring channels).
Regarding claim 2, Shiner discloses the device of claim 1, wherein the operations further comprise: determining a functional failure by a particular network element of the multiple network elements, and modifying one or more of spectrum usage and spectrum definition for the communication channel to correct the functional failure (paragraphs 0030 and 0054-0056, where defragmentation is used to address a fault and defragmentation includes recoloring as applicable).
Regarding claim 3, Shiner discloses the device of claim 2, wherein the determining the functional failure by the particular network element comprise: identifying a performance parameter of an existing channel that fails to meet a published specification (paragraph 0030, the RL process identifying a network fault inherently requires identifying that the respective performance parameter fails to meet some specification). Note: the term “published” is non-limiting intended use. The processing that identifies faults based on some specification is not functionally tied to whether the specification per se is published somewhere or not.
Regarding claim 8, Shiner discloses the device of claim 1, wherein the operations further comprise: detecting a change in network performance due to a physical variation in a network element or a network route of the optical transport network, and selecting, by the ML model, revised parameters for the communication channel to compensate for the change in network performance due to the physical variation (paragraphs 0030 and 0055-0056, where re-routing and/or recoloring for defragmentation is a response to fragmentation, which is caused by physical changes over time, paragraphs 0002 and 0046).
Regarding claim 9, Shiner discloses the device of claim 1, wherein the selecting parameters for the communication channel between the two network elements comprises: automatically selecting a number of channels and a spectrum location for each respective channel of the number of channels (paragraphs 0030, 0052, 0055-0056 and 0088, where the RL process, fig. 5, implementing “continuous” learning,” for re-routing and/or recoloring for defragmentation of channels, reads on “automatic”).
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 4, 10-13 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shiner (US Patent Application Publication No. 2022/0166683) in view of Soltani et al. (“Soltani”) (US Patent Application Publication No. 2024/0305371).
Regarding claim 4, Shiner discloses the device of claim 2, but does not specifically disclose that the operations further comprise: identifying the functional failure between one of a respective CU and a respective DU, the functional failure occurring because a distance between the respective CU and the respective DU exceeds a permitted threshold for reliable communication, identifying an improved channel between the respective CU and the respective DU having optimal spectral efficiency to correct the functional failure; and launching the improved channel for communication between the respective CU and the respective DU. Shiner is not tied to particular topology, but the RL engine account for topology (paragraph 0065), including topology changes (paragraph 0107) and topology sensitivities (paragraph 0114). Soltani discloses a conventional network where the topology includes a core network, through backhaul, and further all the way through to radio nodes communicating with UEs, the topology including conventional respective CU, DU and RU nodes (fig. 1 and paragraph 0037), and discloses both non-realtime and near-realtime SDN control of the network using machine learning workflows (paragraph 0042). Further, Shiner is considering path length relative to shortest path, latency, difficult to route among network state quantifications (paragraph 0115), and the claimed failure type, due to excessive distance causing unreliable communication by loss and/or noise, is an inherent failure type of any communication network. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the RL network control system of Shiner and the network type of Soltani, to provide the adaptivity of the RL network control for the entire network from core to UE, including using the re-routing and/or recoloring function for signal failures due to excessive distance (i.e. due to signal loss and/or noise).
Regarding claim 10, Shiner discloses the device of claim 9, but does not disclose that the automatically selecting a number of channels and a spectrum location comprises: selecting the number of channels and the spectrum location based on a distance between a respective CU, a respective DU and a respective RU. Shiner is not tied to particular topology, but the RL engine account for topology (paragraph 0065), including topology changes (paragraph 0107) and topology sensitivities (paragraph 0114). Soltani discloses a conventional network where the topology includes a core network, through backhaul, and further all the way through to radio nodes communicating with UEs, the topology including conventional respective CU, DU and RU nodes (fig. 1 and paragraph 0037), and discloses both non-realtime and near-realtime SDN control of the network using machine learning workflows (paragraph 0042). Further, Shiner is considering path length relative to shortest path, latency, difficult to route among network state quantifications (paragraph 0115). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the RL network control system of Shiner and the network type of Soltani, to provide the adaptivity of the RL network control for the entire network from core to UE, including using the re-routing and/or recoloring function, and considering path length and latency (distance) between nodes.
Regarding claim 11, Shiner discloses a non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations (fig. 2 and paragraphs 0038-0041 and fig. 5 and paragraph 0083), the operations comprising: collecting information about physical parameters of an optical transport network in a network (fig. 5, Network State and paragraph 0084 in light of paragraphs 0013-0015 and 0072-0074), the optical transport network including multiple network elements, the network elements including at least one of a Central Unit (CU), a Distributed Unit (DU) and a Radio Unit (RU), forming collected network information (fig. 1 and paragraph 0035, where elements 110n reads on distributed units); detecting a network failure in the optical transport network (paragraphs 0030 and 0054-0056, where defragmentation is used to address a fault and defragmentation includes recoloring as applicable); receiving, from a model, suggested parameters for a modified channel in the optical transport network, the suggested parameters determined by the model to correct the network failure in the optical transport network (paragraphs 0030 and 0054-0056, where re-routing and/or recoloring for defragmentation is a response to fragmentation including faults), and launching the modified channel according to the suggested parameters (fig. 7 element 510 and paragraph 0110 in light of fig. 3 elements 254 and 2565 and paragraphs 0002 and 0055-0056, where the RMSA and defragmentation includes re-routing and/or recoloring channels). Shiner does not disclose that the optical transport network in a mobility network, or providing radio communication services to user equipment (UE) devices in a service area because Shiner is not tied to particular topology. But the RL engine accounts for topology (paragraph 0065), including topology changes (paragraph 0107) and topology sensitivities (paragraph 0114). Soltani discloses a conventional network where the topology includes a core network, through backhaul, and further all the way through to radio nodes communicating with UEs, the topology including conventional respective CU, DU and RU nodes (fig. 1 and paragraph 0037), and discloses both non-realtime and near-realtime SDN control of the network using machine learning workflows (paragraph 0042). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the RL network control system of Shiner and the network type of Soltani, to provide the adaptivity of the RL network control for the entire network from core to UE.
Regarding claim 12, the combination of Shiner and Soltani discloses the non-transitory machine-readable medium of claim 11, wherein the collecting information about physical parameters of the optical transport network comprises: capturing information about respective network elements of the multiple network elements, and capturing information about respective network routes between respective network elements (Shiner: fig. 5, Network State and paragraph 0084 in light of paragraphs 0013-0015 and 0072-0074); modeling a network topology of the optical transport network, wherein the modeling the network topology is based on the information about the respective network elements and the information about respective network routes, and storing information defining the network topology for access by the model (Shiner: paragraphs 0067, 0107 and 0114 and fig. 2 element 208, and figs. 2 elements 208 and paragraphs 0042-0043, where accounting for topology and changes in topology inherently requires storing topology information).
Regarding claim 13, the combination of Shiner and Soltani discloses the non-transitory machine-readable medium of claim 12, wherein the operations further comprise: predicting a spectral efficiency for the respective network routes, wherein the predicting the spectral efficiency is based on the information about physical parameters of the optical transport network (Shiner: paragraphs 0014 and 0056, where predicting what routes, defined by wavelength at the physical channel layer, will be contentious in the future reads on predicting spectral efficiency for routes).
Regarding claim 17, Shiner discloses a method, comprising: receiving, by a processing system including a processor (fig. 2 and paragraphs 0038-0041 and fig. 5 and paragraph 0083), network information about physical parameters of a fronthaul network (fig. 5, Network State and paragraph 0084 in light of paragraphs 0013-0015 and 0072-0074), the fronthaul network including multiple network elements including optical communication elements, forming collected network information (fig. 1 and paragraph 0035, where elements 110n); receiving, by the processing system, from a deep learning (DL) model (fig. 5 element 400 and paragraphs 0083-0084), operating parameters for a channel in the fronthaul network, the operating parameters determined by the DL model for improved spectral efficiency in the channel between a first element and a second element in the fronthaul network (fig. 7 element 508 and paragraphs 0109-0110 in light of paragraph 0056), and launching, by the processing system, the channel between the first element and the second element, wherein the launching the channel is according to the operating parameters (fig. 7 element 510 and paragraph 0110 in light of fig. 3 elements 254 and 2565 and paragraphs 0002 and 0055-0056, where the RMSA and defragmentation includes re-routing and/or recoloring channels). Shiner does not disclose that the fronthaul network is a mobility network, or that the network elements are of a radio access network of the mobility network. Soltani discloses a conventional network where the topology includes a core network, through backhaul, and further all the way through to radio network elements communicating with UEs, which is “radio access”, the topology including conventional respective CU, DU and RU network elements (fig. 1 and paragraph 0037), and discloses both non-realtime and near-realtime SDN control of the network using machine learning workflows (paragraph 0042). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the RL network control system of Shiner and the network type of Soltani, to provide the adaptivity of the RL network control for the entire network from core to UE.
Regarding claim 18, the combination of Shinar and Soltani discloses the method of claim 17, further comprising: receiving, by the processing system, information about respective network elements of the fronthaul network, receiving, by the processing system, information about respective optical network routes between the network elements of the fronthaul network (Shiner: fig. 5, Network State and paragraph 0084 in light of paragraphs 0013-0015 and 0072-0074); modeling, by the processing system, a network topology of the fronthaul network, wherein the modeling the network topology is based on the information about the respective network elements and the information about respective optical network routes, and storing information defining the network topology for access by the model (Shiner: paragraphs 0067, 0107 and 0114 and fig. 2 element 208, and figs. 2 elements 208 and paragraphs 0042-0043, where accounting for topology and changes in topology inherently requires storing topology information).
Regarding claim 19, the combination of Shinar and Soltani discloses the method of claim 17, comprising: predicting, by the processing system, a spectral efficiency for the respective optical network routes, wherein the predicting the spectral efficiency is based on the information about the physical parameters of the fronthaul network (Shiner: paragraphs 0014 and 0056, where predicting what routes, defined by wavelength at the physical channel layer, will be contentious in the future reads on predicting spectral efficiency for routes).
Regarding claim 20, the combination of Shinar and Soltani discloses the method of claim 17, wherein the receiving the operating parameters comprises: receiving, by the processing system, a spectrum location for the channel in the fronthaul network (fig. 7 element 508 and paragraphs 0109-0110 in light of paragraph 0056, where the “recoloring” assignment is a wavelength assignment, i.e., spectrum location).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Shiner (US Patent Application Publication No. 2022/0166683) in view of Soltani (US Patent Application Publication No. 2024/0305371) as applied to claim 4 above, and further in view of Paraschis et la. (“Paraschis”) (US Patent Application Publication No. 2018/0220210).
Regarding claim 5, the combination of Shiner and Soltani discloses the device of claim 4, wherein the identifying the improved channel comprises: selecting a wavelength of the improved channel, the wavelength selected from available choices to improve spectral efficiency of the improved channel (Shiner: paragraphs 0014 and 0056, as applicable for the combination; defragmenting spectral fragmentation reads on improved spectral efficiency, and the recoloring is inherently from among available wavelength choices). The combination of Shiner and Soltani is an SDN using WDM, but does not disclose that the wavelengths are flex grid wavelengths. Paraschis discloses that using flex-grid WDM is consistently with the flexibility provided by analytics based SDN (paragraph 0038). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use flex-grid WDM for the WDM of the combination, since it can provide greater routing and/or recoloring than static-grid WDM.
Claims 6 and 7 rejected under 35 U.S.C. 103 as being unpatentable over Shiner (US Patent Application Publication No. 2022/0166683) in view of Soltani (US Patent Application Publication No. 2024/0305371) and further in view of Dai et al. (“Dai”) (US Patent Application Publication No. 2010/0329319).
Regarding claim 6, Shiner discloses the device of claim 1, but does not disclose that the collecting network information about physical parameters of an optical transport network comprises: collecting information about distances (paragraph 0115), but not distances between the network elements including the at least one of a Central Unit (CU), a Distributed Unit (DU) and a Radio Unit (RU), or collecting information about gain profiles of channels between the network elements, collecting information about absorption losses in optical fibers of the optical transport network, and collecting information about scattering in the optical fibers of the optical transport network. Shiner’s network using optical fibers for transmission (paragraph 0034) and is not tied to particular topology, but the RL engine accounts for topology (paragraph 0065), including topology changes (paragraph 0107) and topology sensitivities (paragraph 0114). Soltani discloses a conventional network where the topology includes a core network, through backhaul, and further all the way through to radio nodes communicating with UEs, the topology including conventional respective CU, DU and RU nodes (fig. 1 and paragraph 0037), discloses both non-realtime and near-realtime SDN control of the network using machine learning workflows (paragraph 0042) and discloses that the types of performance metrics for an SDN to proactively make decisions about include optical amplification gain level metric for different optical amplification types in the network, and optical fiber characteristics (paragraph 0009). Further, one skill in the art would recognize that the claimed absorption loss and scattering are characteristics of the fibers per se. Dai discloses characterizing fibers’ frequency domain characteristics for insertion (absorption) loss and return loss from measuring scattering parameters (paragraph 0059). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the RL network control system of Shiner and the network type of Soltani, including collecting information about gain profiles of channels between the network elements, and absorption losses and scatting in optical fibers, to provide the adaptivity of the RL network control for the entire network from core to UE, defragmenting based on such parameters, to flexibly optimizing the routing of wavelength channels in the network.
Regarding claim 7, the combination of Shiner, Soltani and Dai discloses the device of claim 6, wherein the selecting parameters for the communication channel between two network elements comprises: selecting a spectrum location for the communication channel (Shiner: paragraph 0056, “recoloring” a wavelength channel reads on selecting a spectrum location for the channel).
Claims 14-16 rejected under 35 U.S.C. 103 as being unpatentable over Shiner (US Patent Application Publication No. 2022/0166683) in view of Soltani (US Patent Application Publication No. 2024/0305371) as applicable for claim 11, and further in view of Swinkels et al. (“Swinkels”) (US Patent Application Publication No. 2017/0048018).
Regarding claim 14, the combination of Shiner and Soltani discloses the non-transitory machine-readable medium of claim 11, wherein the collecting information about physical parameters of the optical transport network comprises: identifying respective network routes between respective network elements of the optical transport network and receiving information about available modulation techniques (fig. 5, Network State and paragraph 0084 in light of paragraphs 0013-0015 and 0072-0074), forming received network link information and developing the model as a machine learning model based on the received network link information (Shiner: fig. 5 element 400 and paragraphs 0083-0084). The combination does not disclose that the information includes possible data rates or available FEC techniques. Swinkels discloses an SDN and determining system margin and network optimization based on several of the same parameters as the combination, and including FEC and symbol rate (paragraphs 0007-0008). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to account for available FEC techniques and possible data rates in the information collected by the combination, to enhance the training and decision-making granularity for the RL based rerouting, recoloring and/or defragmentation of the combination.
Regarding claim 15, the combination of Shiner, Soltani and Swinkels discloses the non-transitory machine-readable medium of claim 14, wherein the operations further comprise: receiving, from the model, channel parameters corresponding to an optimal spectral efficiency for the modified channel, the optimal spectral efficiency determined by the model to be a spectral efficiency to correct the network failure in the optical transport network (Shiner: paragraphs 0030 and 0054-0056 where recoloring to correct network failure is based on receiving the new wavelength assignment for the respective channel(s) from the model and reads on spectral efficiency to correct the network failure).
Regarding claim 16, the combination of Shiner, Soltani and Swinkels discloses the non-transitory machine-readable medium of claim 15, wherein the operations further comprise: receiving, from the model, a channel wavelength for the modified channel (Shiner: paragraphs 0030 and 0054-0056 where recoloring to correct network failure is based on receiving the new wavelength assignment for the respective channel(s) from the model).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chen, Xiaoliang and Li, Baojia and Proietti, Roberto and Lu, Hongbo and Zhu, Zuqing and Yoo, S. J. Ben; DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks, Journal of Lightwave Technology, Institute of Electrical and Electronics Engineers (IEEE), August 2019, pages 4155–4163; arXiv:1905.02248.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATHAN M CORS whose telephone number is (571)272-3028. The examiner can normally be reached Monday-Friday.
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/NATHAN M CORS/Primary Examiner, Art Unit 2634