DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by International Publication No. WO 2020/240039 to Financiera Maderera, S.A. (“Financiera”).
Regarding claim 1, Financiera discloses:
A method comprising (Financiera discloses “a moisture content control system and method for controlling a fibre moisture content in a fibreboard manufacturing process.” See, e.g., Financiera at Abstract):
controlling processing of wood particles into engineered wood products (Financiera discloses that a “first aspect of the invention relates to a moisture content control system for controlling a fibre moisture content in a fibreboard manufacturing process”(“controlling processing of wood particles into engineered wood products”). See, e.g., Financiera at p. 5, lines 10-15.)
by sensing interaction information associated with interaction between a plurality of steps in manufacturing the engineered wood products (Financiera discloses that a “first aspect of the invention relates to a moisture content control system for controlling a fibre moisture content in a fibreboard manufacturing process” (“manufacturing the engineered wood products”). See, e.g., Financiera at p. 5, lines 10-15. Financiera also discloses that [s]uch moisture control system comprises a plurality of sensors where each sensor is configured to monitor a respective parameter [(“interaction information”)] of the fibreboard manufacturing process … [and that] the sensors may be deployed in different points of the fibreboard production line to monitor the corresponding parameters” (“by sensing interaction information associated with interaction between a plurality of steps”). See, e.g., Financiera at p. 5, lines 10-15. Thus, Financiera discloses “controlling processing of wood particles into engineered wood products by sensing interaction information associated with interaction between a plurality of steps in manufacturing the engineered wood products.”) or
or [by sensing] interaction between a plurality of properties associated with materials used to make the engineered wood products (Financiera discloses that the “moisture control unit is also configured to compare the estimate or prediction generated with the pre-defined setpoint for the fibre moisture content [(“properties associated with materials”)] at the output of the drying stage and to modify at least one setpoint associated to a corresponding input drying temperature [(“properties associated with materials”)] of a respective drying unit of the fibreboard manufacturing process based on the result of the comparison.” See, e.g., Financiera at p. 5, line 35 to p. 6, line 2. Thus, Financiera discloses “controlling processing of wood particles into engineered wood products by sensing … interaction between a plurality of properties associated with materials used to make the engineered wood products).),
or [by sensing] interaction between said plurality of steps and said plurality of properties (Financiera disclose that “[d]epending on the number of drying units in the fibreboard production line, the moisture control unit may modify one or more of the setpoints associated to the input drying temperatures of the different drying units in the fibreboard production line.” See, e.g., Financiera at p. 6, lines 3-5. In addition, Financiera discloses that a neural network model “the fibre production manufacturing process from debarking to the last step of drying [(“plurality of steps”)] … from collected data [(“plurality of properties”)], and later, this model can be used to control and adjust the fibre drying process. Following this approach, the drying process can be adjusted taking into account any manufacturing process parameter variation. See, e.g., Financiera at p. 4, lines 27-30. Thus, Financiera discloses “controlling processing of wood particles into engineered wood products by sensing … interaction between a plurality of properties associated with materials used to make the engineered wood products”). ),
or [by sensing] interaction between said plurality of steps or said plurality of properties and an additional external factor which is external to said plurality of steps or said plurality of properties (See above for an analysis of the features “said plurality of steps or said plurality of properties.” Financiera also discloses that “the moisture control unit may consider an additional parameter, such a pre-established energy distribution between the drying units [(“an additional external factor which is external to said plurality of steps or said plurality of properties”)], and based on said additional parameter the moisture control unit may select the appropriate combinations of modified input drying temperatures for the drying units.” See, e.g., Financiera at p. 13, line 35 to p. 14, line 2. Thus, Financiera discloses “controlling processing of wood particles into engineered wood products by sensing … interaction between said plurality of steps or said plurality of properties and an additional external factor which is external to said plurality of steps or said plurality of properties”).),
processing the interaction information with machine learning and deriving from the machine learning improvement information associated with improving properties or yields or profitability of the engineered wood products (Financiera discloses that a “moisture control unit 403 receives a plurality of inputs corresponding to parameters related to the fibreboard manufacturing process… [and that] the moisture control unit 403, us[es] a neural network 411 [(“processing the interaction information with machine learning”)] that comprises at least one neural network layer, generates 408 [(“deriving from the machine learning”)] a prediction of the fibre moisture content at the output of the drying stage that is based on the received inputs” [(“improvement information associated with improving properties or yields or profitability of the engineered wood products”)]. See, e.g., Financiera at p. 17, lines 10-24. Financiera also discloses that “all the above-mentioned problems can be addressed using a neural network approach, where the fibre production manufacturing process from debarking to the last step of drying can be modelled from collected data [(“processing the interaction information with machine learning”)], and later, this model can be used to control and adjust the fibre drying process” (“improvement information associated with improving properties or yields or profitability of the engineered wood products”). See, e.g., Financiera at p. 4, lines 27-32. Financiera further discloses that the “moisture control unit may determine several combinations [(“processing the interaction information with machine learning”)] of modified input drying temperatures and select the combination of modified input drying temperatures [(“deriving from the machine learning”)] that, being within the pre-defined ranges of the respective input drying temperatures, optimizes the result” (“improving properties or yields or profitability of the engineered wood products”). See, e.g., Financiera at p. 13, lines 32-35. Thus, Financiera discloses the claimed “processing the interaction information with machine learning and deriving from the machine learning improvement information associated with improving properties or yields or profitability of the engineered wood products.”), and
implementing the improvement information back in the processing of the wood particles to achieve engineered wood products with improved properties or yields or profitability (“The moisture control unit 403 further compares 409 the prediction generated with the pre defined setpoint of the fibre moisture content at the output of the drying stage, and modifies 410 at least one setpoint [(“implementing the improvement information back in the processing of the wood particles”)] associated to a corresponding input drying temperature of a respective drying unit of the fibreboard manufacturing process based on the result of the comparison.” See, e.g., Financiera at p. 17, lines 10-24. Financiera also discloses that the “moisture control unit may determine several combinations of modified input drying temperatures and select the combination of modified input drying temperatures [(“implementing the improvement information back in the processing of the wood particles”)] that, being within the pre-defined ranges of the respective input drying temperatures, optimizes the result” (“to achieve engineered wood products with improved properties or yields or profitability”). See, e.g., Financiera at p. 13, lines 32-35. Financiera further discloses that “the system executes in real time an optimization of some of the parameters of the fibreboard manufacturing process to adjust the current values of said parameters [(“implementing the improvement information back in the processing of the wood particles”)], for example, the input temperatures of the dryers, in order to get an output fibre moisture content close to the reference value.” See, e.g., Financiera at p. 5, lines 6-9. Financiera further discloses that “any deviation of the percentage of moisture content in the fibre with respect to the setpoint can be anticipated and consequently, the input temperatures of the dryers can be automatically optimized and modified” (“to achieve engineered wood products with improved properties or yields or profitability”). See, e.g., Financiera at p. 14, lines 3-5.).
Regarding claim 2, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a log yard (The background section of Financiera discloses that a “fibreboard manufacturing process may comprise a first stage (chipping stage) [(“one of the steps in manufacturing the engineered wood products”)] in which the trunks are debarked and stripped in a chipper drum which reduces the logs into evenly shaped chips” (“processing at a log yard”). See, e.g., Financiera at p. 1, lines 27-29.).
Regarding claim 3, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a cutting station (The background section of Financiera discloses that a “fibreboard manufacturing process may comprise a first stage (chipping stage) [(“one of the steps in manufacturing the engineered wood products”)] in which the trunks are debarked and stripped in a chipper drum which reduces the logs into evenly shaped chips” (“processing at a cutting station”). See, e.g., Financiera at p. 1, lines 27-29.).
Regarding claim 4, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a dryer (Financiera discloses that at “a fourth stage (drying stage) of the fibreboard manufacturing process, the glued fibre is dried” (“dryer”). See, e.g., Financiera at p. 2, lines 19-33.).
Regarding claim 5, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a blender (Financiera discloses that at “a third stage (mixing or gluing stage) of the fibreboard manufacturing process, the resulting fibre may pass at very high speed and very high temperature through the blowline where it is mixed [(“blender”)] with resin or glue, among other products, that is introduced in a nebulized form.” See, e.g., Financiera at p. 2, lines 13-18.).
Regarding claim 6, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a forming or pressing station (Financiera discloses that at “ a sixth stage (forming stage) of the fibreboard manufacturing process, the fibre may be conveyed to the fibreboard forming system. The fibreboard forming system may transform the fibre pulp into a fibre mat [(“forming or pressing station”)] that is rolled through a series of equipment which produces a fibre mat with controlled weight.” See, e.g., Financiera at p. 3, lines 1-4.).
Regarding claim 7, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a sawing station (Financiera discloses that a “set of edge trimming saws [(“sawing station”)] may trim the edges of the fibre mat to give the desired width to the board.” See, e.g., Financiera at p. 3, lines 4-8.).
Regarding claim 8, Financiera discloses:
wherein the plurality of properties comprises at least two of temperature, torque, force, pressure, flow, moisture content, rotating speed, energy consumption, strand size or geometry, density, material physical properties, material chemical properties and constituent content (Financiera discloses that “the sensors may be volumetric sensors, temperature sensors [(“temperature”)], pressure sensors [(“pressure”)], speed sensors [(“speed”)]… [and that the] parameters collected by the plurality of sensors may be humidity [(“moisture content”)]of the chips; the position of the rotating disc, power of the motors involved in the process, and opening of the valve of the defibrator; speed [(“speed”)] of the endless screws, level of fibre, residence time and pressure [(“pressure”)] inside the digestor; amount of glue and other additives added at and vapour pressure inside the blowline; inlet and outlet temperatures [(“temperature”)], gas and air flows [(“flow”)] in the driers; external air humidity and temperature, etc.” See, e.g., Financiera at p. 12, line 30, to p. 13, line 5.).
Regarding claim 9, Financiera discloses:
wherein the external factor comprises at least one of season, time of day, ambient temperature, parameters from tree growing locations, machine wellness parameters, energy consumption, vibration, sound, reflection, particular labor or labor shift that performs an activity, worker behavior, data related to workers material prices, markets conditions, currency rates, storage capacity or supply chain data (Financiera discloses that the “parameters collected by the plurality of sensors may be … external air humidity and temperature [(“ambient temperature”)], etc.” See, e.g., Financiera at p. 12, line 30, to p. 13, line 5. Financiera also discloses that “the moisture control unit may consider an additional parameter, such a pre-established energy distribution [(“energy consumption”)] between the drying units, and based on said additional parameter the moisture control unit may select the appropriate combinations of modified input drying temperatures for the drying units.” See, e.g., Financiera at p. 13, line 35 to p. 14, line 2.).
Regarding claim 10, Financiera discloses:
wherein at least one of production yield or capacity, cost, profitability, and product quality is improved to a controlled level, while the rest of production yield or capacity, cost, profitability, and product quality are not affected within a controlled tolerance level or are purposely degraded to a controlled level (Financiera discloses “[t]o optimize the at least one setpoint, the moisture control unit may minimize the distance (or other defined cost function [(“cost is improved to a controlled level”)] which includes at least the distance as one term) between the moisture setpoint and the moisture prediction [(“product quality is improved to a controlled level”)], finding one value associated to the corresponding input drying temperature within the pre-defined range, considering a maximum variation, e.g., +-3°C, of the input temperature with respect to the current measurements of the input drying temperature[(“within a controlled tolerance level”)] , and considering that other current measurements of sensors used in the neural network model do not change [(“while the rest of production yield or capacity, cost, profitability, and product quality are not affected”)], which makes this said distance minimum..” See, e.g., Financiera at p. 6, lines 11-18.).
Regarding claim 11, Financiera discloses:
Apparatus comprising (Financiera discloses “a moisture content control system and method for controlling a fibre moisture content in a fibreboard manufacturing process.” See, e.g., Financiera at Abstract):
a controller in operative communication with sensors (Financiera discloses a moisture control unit 403 (“controller”) that is connected to sensor1 to sensor (“sensors”). See, e.g., Financiera at p. 16, line 25 to p. 17, line 5.),
said controller being configured to control processing of wood particles into engineered wood products by processing interaction information, sensed by said sensors, associated with interaction between a plurality of steps in manufacturing the engineered wood products or interaction between a plurality of properties associated with materials used to make the engineered wood products, or interaction between said plurality of steps and said plurality of properties, or interaction between said plurality of steps or said plurality of properties and an additional external factor which is external to said plurality of steps or said plurality of properties, said controller being configured to process the interaction information with machine learning and deriving from the machine learning improvement information associated with improving properties or yields or profitability of the engineered wood products, and implementing the improvement information back in the processing of the wood particles to achieve engineered wood products with improved properties or yields or profitability (The remaining features of claim 11 are substantially the same as those recited in claim 1 and thus are disclosed by Financiera for the reasons given above with respect to claim 1.).
Regarding claim 12, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a log yard (This feature is the same as that recited in claim 2 and thus is disclosed by Financiera for the reasons given above with respect to claim 2.).
Regarding claim 13, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a cutting station (This feature is the same as that recited in claim 3 and thus is disclosed by Financiera for the reasons given above with respect to claim 3.).
Regarding claim 14, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a dryer (This feature is the same as that recited in claim 4 and thus is disclosed by Financiera for the reasons given above with respect to claim 4.).
Regarding claim 15, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a blender (This feature is the same as that recited in claim 5 and thus is disclosed by Financiera for the reasons given above with respect to claim 5.).
Regarding claim 16, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a forming or pressing station (This feature is the same as that recited in claim 6 and thus is disclosed by Financiera for the reasons given above with respect to claim 6.).
Regarding claim 17, Financiera discloses:
wherein one of the steps in manufacturing the engineered wood products comprises processing at a sawing station (This feature is the same as that recited in claim 7 and thus is disclosed by Financiera for the reasons given above with respect to claim 7.).
Regarding claim 18, Financiera discloses:
wherein the plurality of properties comprises at least two of temperature, torque, force, pressure, flow, moisture content, rotating speed, energy consumption, strand size or geometry, density, material physical properties, material chemical properties and constituent content (This feature is the same as that recited in claim 8 and thus is disclosed by Financiera for the reasons given above with respect to claim 8.).
Regarding claim 19, Financiera discloses:
wherein the external factor comprises at least one of season, time of day, ambient temperature, parameters from tree growing locations, machine wellness parameters, energy consumption, vibration, sound, reflection, particular labor or labor shift that performs an activity, worker behavior, data related to workers material prices, markets conditions, currency rates, storage capacity or supply chain data (This feature is the same as that recited in claim 9 and thus is disclosed by Financiera for the reasons given above with respect to claim 9.).
Regarding claim 20, Financiera discloses:
wherein at least one of production yield or capacity, cost, profitability, and product quality is improved to a controlled level, while the rest of production yield or capacity, cost, profitability, and product quality are not affected within a controlled tolerance level or are purposely degraded to a controlled level (This feature is the same as that recited in claim 10 and thus is disclosed by Financiera for the reasons given above with respect to claim 10.).
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.
Claims 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Financiera in view of U.S. Patent Application Publication No. 2022/0188775 to Zhou et al. (“Zhou”).
Regarding claim 21: The method according to claim 1,
wherein processing the interaction information with machine learning is conducted by a plurality of individual machine learning units and a central machine learning unit, and each individual machine learning unit cooperates with the central machine learning unit (Financiera discloses machine learning as discussed above but does not explicitly disclose a “a plurality of individual machine learning units and a central machine learning unit, and each individual machine learning unit cooperates with the central machine learning unit.” However, Zhou discloses a machine learning model for an industrial asset management that includes a centralized management (“central machine learning unit”) of cohort and prediction models that are dispatched to local sites (“a plurality of individual machine learning units”). Zhou at Abstract and pars. [0001] and [0034]. The centralized management helps “ensure learning from each site will be aligned to a common or similar scenario group” (“each individual machine learning unit cooperates with the central machine learning unit”). Zhou at par. [0034]. Zhou is analogous art because it reasonable pertinent to the problem facing the inventor which is to implement a vertical federated learning type of machine learning structure. See MPEP 2141.01(a). It would have been obvious and one skilled in the art would have been motivated to incorporate a vertical federated learning structure in the system of Financiera in order to “provide a significant improvement in asset failure prediction management models by sharing models across multiple sites in efforts to use information learned from one site at other sites ….” Zhou at par. [0034]. Because both Financiera and Zhou relate to machine learning, there would have been a reasonable chance of success. See MPEP 2143.I.G.).
Regarding claim 22: The apparatus according to claim 11,
wherein processing the interaction information with machine learning is conducted by a plurality of individual machine learning units and a central machine learning unit, and each individual machine learning unit cooperates with the central machine learning unit (Please see analysis in claim 21.).
Response to Arguments
Applicant's arguments filed on May 11, 2026, have been fully considered but they are not persuasive. On pages 5-6, Applicant argues:
Applicant respectfully traverses the rejection at least because Financiera fails to disclose the claimed "sensing interaction information" step. Claim I explicitly requires "sensing interaction information associated with interaction between ….” Accordingly, claim 1 is not directed merely to sensing multiple parameters. Rather, claim 1 requires sensing interaction information associated with interaction among a plurality of manufacturing steps, among a plurality of material properties, between the steps and properties, or between the steps/properties and an additional external factor-i.e., the sensed information must be interaction information. As detailed in the subject application, the interaction information may include: interactions between the properties and the properties relevant to any interactions may change with time (Specification ¶[0007]); interaction between different steps of the manufacturing process, such as but not limited to, the interaction between properties of the wood at different stations, the interaction between the activities at different stations, and/or interaction between properties of the wood and the activities at different stations (Specification ¶[0008]); and interaction between properties of the wood and/or activities at the different stations and external factors (Specification ¶[0009]). The subject application thereafter details that the method includes sensing such interaction information, followed by processing the interaction information with machine learning. See Specification ¶[0010].
Financiera, by contrast, discloses a moisture-control system in which a plurality of sensors monitor respective process parameters, and those monitored parameters are provided to a neural network for predicting fibre moisture content and controlling dryer-related setpoints. Financiera therefore discloses collecting monitored parameters for moisture prediction/control, but does not disclose sensing interaction information as claimed. Specifically, Financiera states that its moisture control system includes "a plurality of sensors where each sensor is configured to monitor a respective parameter of the fibreboard manufacturing process," and that the moisture control unit generates, using a neural network, an estimate or prediction of fibre moisture content at the output of the drying stage using monitored parameters as inputs. See Financiera, Abstract; p. 5, lines 10-15; p. 5, line 35 to p. 6, line 2. Financiera further describes comparing that moisture prediction with a predefined moisture setpoint and modifying at least one setpoint associated with an input drying temperature. See Financiera, p. 17, lines 10-24.
(Bold Emphasis added.)
Claim 1 recites a method comprising, inter alia, “controlling processing of wood particles into engineered wood products by sensing interaction information associated with interaction between a plurality of steps in manufacturing the engineered wood products or interaction between a plurality of properties associated with materials used to make the engineered wood products, or interaction between said plurality of steps and said plurality of properties, or interaction between said plurality of steps or said plurality of properties and an additional external factor ….” The claim language does not require that the “sens[ed] interaction information” “must be interaction information” or that the “plurality of properties” be “material properties,” as argued. The claims only require that the “sens[ed] interaction information” be associated with the “interaction between a plurality of steps … or interaction between a plurality of properties associated with materials …” and the “plurality of properties be associated with materials used to make the engineered wood.” As discussed above, Financiera discloses sensing at least fibre moisture content and input drying temperature, and this information is associated with the “steps in manufacturing the engineered wood products” and/or “properties associated with materials used to make the engineered wood products.” Accordingly, Financiera discloses “sensing interaction information,” as claimed.
On page 6, Applicant further argues:
Under MPEP § 2131, anticipation requires that each and every claim element be found in a single prior-art reference, arranged as in the claim. See also, Net MoneyIN, Inc. V. VeriSign, Inc., 545 F.3d 1359 (2008), Regeneron Pharms., Inc. V. Mylan Pharms. Inc., 714 F. Supp. 3d 652 (2023). Anticipation of claims 1 and 11 is not satisfied by merely sensing multiple individual process parameters and later providing those individual values to a model. The claim requires sensing "interaction information," i.e., information associated with an interaction or relationship among the claimed steps, properties, steps/properties, or external factors. The subject invention contrasts monitoring an individual property with sensing and monitoring a plurality of properties and/or activities, learning the interaction between properties and/or activities, and using information learned from the interaction to improve engineered wood manufacture. Because this missing limitation is present in independent claims 1 and 11, Financiera cannot anticipate those claims.
The examiner respectfully disagrees that Financiera does not anticipate claims 1-20. As discussed above, the claims merely require “sensing interaction information” to be associated with the claimed “interactions.” Applicant concedes that Financiera “generates, using a neural network, an estimate or prediction of fibre moisture content at the output of the drying stage using monitored parameters as inputs.” See pages 5-6 of Applicant’s Response (emphasis added). The passage cited by Applicant describes “sensing interaction information” (e.g., monitored parameters) associated with “steps in manufacturing the engineered wood products” (e.g., drying stage) and/or “properties associated with materials used to make the engineered wood products” (e.g., fibre moisture content). Indeed, the cited passage is consistent with Applicant’s specification which discloses that sensors “monitor [inter alia] moisture content” and that a “controller [is] configured to control processing of wood particles into engineered wood products by processing interaction information, sensed by said sensors ….” See Specification (published application) at pars. [0006] and [0015]. Thus, per Applicant’s specification, “interaction information” includes information from sensors. If Applicant wishes to limit the meaning of ”sensing interaction information” to not include “merely sensing multiple individual process parameters,” then Applicant should amend the claims to more precisely recite how the “interaction information” is being sensed. As recited, the examiner’s broad but reasonable interpretation of “sensing interaction information” is consistent with Applicant’s specification and the claim language. Accordingly, for at least the above reasons and for the reasons given in the 102 rejection above, the examiner respectfully maintains the 102 rejection of claims 1-20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Publication No. 11,836,583B2 to Chu et al. vertical federated learning environment.
U.S. Patent Application Publication No. 2023/0316131 to Vandikas et al. discloses vertical federated learning with machine learning agents.
U.S. Patent Application Publication No. 2021/0334704 to Schall et al. discloses vertical federated learning in a technical industry.
Sun, Wen, Jiajia Liu, and Yanlin Yue. "AI-enhanced offloading in edge computing: When machine learning meets industrial IoT." IEEE Network 33.5 (2019): 68-74. Sun discloses machine learning in a vertical industrial structure.
Hao, Meng, et al. "Efficient and privacy-enhanced federated learning for industrial artificial intelligence." IEEE Transactions on Industrial Informatics 16.10 (2019): 6532-6542. Hao discloses federated learning environment.
Sun, Wen, et al. "Adaptive federated learning and digital twin for industrial internet of things." IEEE Transactions on Industrial Informatics 17.8 (2020): 5605-5614. Sun disclose federated learning in an industrial application.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.K./Examiner, Art Unit 2116
/KENNETH M LO/Supervisory Patent Examiner, Art Unit 2116
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