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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/19/2025 has been entered.
Status of Claims
This office action is in response to Applicant Amendments and Remarks filed on 12/19/2025, for application number 18/458,906 filed on 08/30/2023, in which claims 1-15 were previously presented for examination.
Claims 1, 4, and 14 are amended.
Claims 9-13 and 15 are canceled.
Claims 1-8 and 14 are currently pending.
Response to Arguments
Applicant Amendments and Remarks filed on 08/15/2025 in response to the Non-Final office action mailed on 05/19/2025 have been fully considered and are addressed as follows:
Regarding the Claim Objections: The objections are withdrawn, as the amendment to claim 13 has properly addressed the informalities recited in the Non-Final office action.
Regarding the Claim Rejections under 35 USC § 101: The rejections of claims 9-13 and 15 for being patent ineligible are withdrawn, as claims 9-13 and 15 are canceled.
Regarding the Claim Rejections under 35 USC § 103: With respect to the previous claim rejections under 35 U.S.C. §103, Applicant has amended the independent claims and these amendments have changed the scope of the original application. Therefore, the Office has supplied new grounds for rejection attached below in the NON-FINAL office action and therefore the prior arguments are considered moot.
Regarding claim 1, Applicant alleges that “the Kaarnametsä reference discloses controlling a forestry machine by using various data sources (worksite preparation data, navigation data, worksite situation data) to set or adjust machine parameters. Applicant considers it essentially a direct input-to-control system where data is used to influence settings like engine power or saw speed. In contrast, Applicant understands the Leppänen reference to disclose measuring and reporting work production after the fact, using its calibrated data for reporting or planning, not for the immediate control of a harvester head or bucking instructions based on a dynamically updated yield model. Applicant also understands that it calibrates a model to improve the accuracy of future reports on work completed. It does not teach or suggest using this calibrated or compensated data for real-time control of harvesting operations. The Leppänen reference explicitly states at [0028] its purpose is to "measure and report work performed, not to predict work production." As such, the purposes of the references are fundamentally different. One is for accounting, the other for direct machine setting. Applicant submits that a person of ordinary skill would not have been motivated to combine Leppänen's post-hoc reporting tool with Kaarnametsä's proactive control system to arrive at the claimed invention. The proposed combination would not yield the claimed invention without the benefit of hindsight” (Applicant Amendments and Remarks filed on 08/15/2025 at pg. 8-9). Examiner disagrees.
Leppänen may not intend to predict a production quantity. However, Leppänen does not state the modeling method of Leppänen cannot be used for predicting a production quantity. Considering Leppänen in its entirety, the modeling method is for improving accuracy of a production quantity determination regardless of whether the production quantity is measured or predicted. Moreover, nothing in Leppänen criticizes, discredits, or otherwise discourages applying the modeling method to improve accuracy of a production quantity that is predicted.
Therefore, it is obvious to one skilled in the art to modify the proactive method of Kaarnametsä disclosing the predicted product yield by adding the modeling method for improving accuracy of a production quantity determination of Leppänen to improve accuracy of predicted product yield.
Regarding claim 7, Applicant alleges that “Further, with regard to dependent claim 7, the Office alleges that the Laakkonen reference teaches the use of a value matrix and associated optimization steps, and that it would have been obvious to add this to the Kaarnametsä and Leppänen combination. Dependent claim 7 specifies adapting a value matrix based on the compensated product yields. These compensated yields are a specific, technically derived dataset created by the feedback loop of claim 1. Applicant understands Laakkonen's disclosure, the Office citing page 5 thereof at page 24 of the Office Action, to describe adapting fitting instructions based on general initial data, such as concluding from the type of stand or soil composition that the density or fiber length of the timber is not good enough, and therefore changing the proportions of pulpwood versus energy wood. Applicant considers this a high-level adjustment based on static, initial conditions. It is not a dynamic, measurement-driven compensation, where the value matrix is adapted based on a continuously updated model that compares actual harvested yield to predicted yield. Applicant again believes that without the benefit of hindsight one of skill in the art would not have been motivated to replace Laakkonen's general adaptation logic with the specific, complex compensation method derived from combining the Kaarnametsä and Leppänen references to arrive at the claimed invention” (Applicant Amendments and Remarks filed on 08/15/2025 at pg. 9). Examiner disagrees.
Initially, the claimed limitation does not require the alleged “dynamic, measurement-driven compensation, where the value matrix is adapted based on a continuously updated model.” Specifically, the claimed limitation does not require the model to be updated continuously.
Considering Laakkonen in its entirety, the matrix value is for optimizing tree cutting. Laakkonen does not state the matrix value of Laakkonen cannot be used in a dynamic system. Laakkonen does not criticize, discredit, or otherwise discourage applying the value matrix to the alleged “dynamic, measurement-driven compensation, where the value matrix is adapted based on a continuously updated model.”
Therefore, it is obvious to one skilled in the art to modify the method of Kaarnametsä in view of Leppänen by adding the value matrix of Laakkonen to provide optimized tree cutting.
NON-FINAL OFFICE ACTION
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 § 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.
Claims 1-6, 8, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kaarnametsä et al. (US 2019/0213691 A1, hereinafter “Kaarnametsä”) in view of Leppänen et al. (US 2022/0067600 A1, hereinafter “Leppänen”).
Regarding claim 1, Kaarnametsä discloses a method, comprising:
maintaining, at a network service operatively connected to a logging machine, master forest resource information (Kaarnametsä at para. [0027]: “The operation processor 78 may be provided physically on any of the forestry machines 12 as well as in the cloud or a connected server”; para. [0034]: “Control of the forestry machines 12 happens via their electronic control unit 50 located in the cab 54 or elsewhere, whereas the electronic control units 50 of several forestry machines 12 may connect to each other to build a network such that the operation processor 78 is part of the electronic control unit 50”), wherein the master forest resource information comprises prediction cells for product yield over a work area or a set of work areas (Kaarnametsä at FIG. 2 and para. [0019]: “The worksite preparation data 14 are available in the form of a database with information helpful to operate the forestry machines 12, i.e., individually or as a fleet. The data helps making decisions and adjustments of the forestry machines 12 or the composition of the fleet before, but also during, operation”; para. [0020]: “a) Forest data 62 such as the kind of trees (species, size, age, shape, stiffness, weight, amount, diseases, ground wetness, coverage with snow, tree deceases, tree shape, underwood, etc.) at the worksite 10, known from watching the forest like with cameras, drones, LIDAR, human beings, etc.”; para. [0024]: “e) Historical performance data 69 from earlier machines, which may help setting and choosing forestry machines 12, for example, at similar worksites 10”) wherein the prediction cells are structured data for controlling harvesting operations by the logging machine (Kaarnametsä at para. [0027]: “The worksite preparation data 14, navigation data 16 and worksite situation data 8 are the input to an operation processor 78. The operation processor 78 uses this data to run one or more routines to create output signals to valves, switches, controls etc. for adjusting propulsion settings 80 like transmission gear, speed, deceleration, etc., to process power settings 82 like lift capacity of a boom, saw speed, feed wheel speed, etc., to equipment settings 84 like the use of lower knives, use of a top saw, knife pressure, etc., and to production settings 86 like log length”) and each of the one or more prediction cells comprises at least information indicating a predicted product yield of an area of forested soil and geographical positioning information associated with the predicted product yield (Kaarnametsä at para. [0020]: “a) Forest data 62 such as the kind of trees (species, size, age, shape, stiffness, weight, amount, diseases, ground wetness, coverage with snow, tree deceases, tree shape, underwood, etc.) at the worksite 10”), wherein the predicted product yield comprises one or more product yields of products of a product assortment produced by harvesting the work area or the set of work areas by the logging machine (Kaarnametsä at para. [0020]: “a) Forest data 62 such as the kind of trees (species, size, age, shape, stiffness, weight, amount, diseases, ground wetness, coverage with snow, tree deceases, tree shape, underwood, etc.) at the worksite 10; para. [0021]: “b) Business Data 64, like requirements of the purchaser or owner of the trees, sale prices, traffic data, fleet data, etc.”; A tree is a type of forestry product, and the forest data 62 includes an assortment of different tree species, etc.);
receiving, at the network service from the logging machine, logging machine measurements associated with the work area or the set of work areas (Kaarnametsä at para. [0026]: “The worksite situation data 8 may include data which is not collected in advance but is captured during operation. This may include actual tree data 74 and machine performance data 76”), said logging machine measurements comprise product yield information at stump positions for the one or more products of the product assortment (Kaarnametsä at para. [0026]: “Actual tree data 74 may be bends, strong branches, rotten portions, and the like detected by a camera” “During operation at the worksite 10, gathered and processed worksite situation data 8 can be shared among other forestry machines 12; the worksite situation data 8 includes actual tree data 74 (i.e., production yield information) at the worksite 10 (i.e., stump positions));
causing, by the network service, to control at least one harvesting operation of the logging machine based on(Kaarnametsä at para. [0019]: “The information may be provided to the forestry machines 12 online or by a transferable data source, like a USB stick or the like”; para. [0026]: “All data may be used to influence the operation of the forest machine 12 in order to reduce fuel consumption, achieve a higher output of log, prevent damages at the knives, etc.”; para. [0027]: “The worksite preparation data 14, navigation data 16 and worksite situation data 8 are the input to an operation processor 78. The operation processor 78 uses this data to run one or more routines to create output signals to valves, switches, controls etc. for adjusting propulsion settings 80 like transmission gear, speed, deceleration, etc., to process power settings 82 like lift capacity of a boom, saw speed, feed wheel speed, etc., to equipment settings 84 like the use of lower knives, use of a top saw, knife pressure, etc., and to production settings 86 like log length”).
However, Kaarnametsä does not explicitly state
associating, at the network service, the received logging machine measurements to at least one prediction cell of the master forest resource information;
determining, at the network service, based on the received logging machine measurements, a logging machine product yield for the work area or the set of work areas;
modeling, at the network service, a difference between the logging machine product yield and a product yield of the at least one prediction cell;
determining, at the network service, one or more other prediction cells that are associated with the at least one prediction cell;
compensating, at the network service, product yields of the associated one or more other prediction cells based on the modeled difference; and
the compensated product yields of prediction cells.
Nevertheless, Kaarnametsä at least suggests the idea of constantly updating the worksite preparation data 14 with new information (see Kaarnametsä at para. [0019]).
In the same field of endeavor, Leppänen teaches associating, at the network service, the received logging machine measurements to at least one prediction cell of the master forest resource information (Leppänen at para. [0030]: “The work area may be defined as a geometric area that can be represented for example by a geometric polygon, surface, selection of cells in a grid or selection of pixels from a rater or any other way to represent areas in geographic setting”; para. [0084]: “Identify the cells of work quantification that have been processed the first time within your desired time period”);
determining, at the network service, based on the received logging machine measurements, a logging machine product yield for the work area or the set of work areas (Leppänen at para. [0088]: “Optionally, Receiving Reference Measurement 1.1.8; para. [0089]: “timber volume measurements may be done using different definition of minimum top diameter of the usable stem. If such information is used for work quantification”);
modeling, at the network service, a difference between the logging machine product yield and a product yield of the at least one prediction cell (Leppänen at para. [0090]: “Reference measurement of the actual work performed (for example, the amount of timber and wood processed; measured from the wood and timber products that are finished), when linked to the time period when the said work was done, can be used to calibrate a correction model”);
compensating, at the network service, product yields (Leppänen at para. [0089]: “there may be a deviation between the work quantification information and the actual work performance experienced”; para. [0090]: “This deviation may be corrected, assuming the amount of work left non-finished or performed in excess when compared to the work quantification Information is somewhat systematic proportion of the total work quantified, by optionally, performing model calibration for Improved Precision 1.1.9”; para. [0092]: “A model is calibrated to predict the reference measurement values with the work production values. The resulting output is a calibration model for work production results”); and
the compensated product yields of prediction cells (Leppänen at para. [0089]: “there may be a deviation between the work quantification information and the actual work performance experienced”; para. [0090]: “This deviation may be corrected, assuming the amount of work left non-finished or performed in excess when compared to the work quantification Information is somewhat systematic proportion of the total work quantified, by optionally, performing model calibration for Improved Precision 1.1.9”; para. [0092]: “A model is calibrated to predict the reference measurement values with the work production values. The resulting output is a calibration model for work production results”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Kaarnametsä by adding the modeling and compensating as taught by Leppänen with a reasonable expectation of success. The motivation to modify the method of Kaarnametsä in view of Leppänen is to provide improved accuracy of production quantity determination for forest operations.
However, Kaarnametsä in view of Leppänen does not explicitly state:
determining, at the network service, one or more other prediction cells that are associated with the at least one prediction cell;
compensating, at the network service, product yields of the associated one or more other prediction cells based on the modeled difference.
In the same field of endeavor, Meier teaches:
determining, at the network service, one or more other prediction cells that are associated with the at least one prediction cell (Meier at para. [0061]: “At block 604, at least a first measurement of actual yield obtained from at least a first field region during harvesting of the field, the at least the first field region being less than entirety of the field”; para. [0062]: “At block 606, a yield prediction model is generated based on the set of spatial yield data obtained at block 602 and the at least the first measurement received at block 604. The yield prediction model is for predicting actual yield in a second field region during harvesting of the field, the second field region being different from the first field region”);
compensating, at the network service, product yields of the associated one or more other prediction cells based on the modeled difference (Meier at para. [0062]: “At block 606, a yield prediction model is generated based on the set of spatial yield data obtained at block 602 and the at least the first measurement received at block 604. The yield prediction model is for predicting actual yield in a second field region during harvesting of the field, the second field region being different from the first field region”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Kaarnametsä in view of Leppänen by adding determining one or more other prediction cells of Meier with a reasonable expectation of success. The motivation to modify the method of Kaarnametsä in view of Leppänen further in view of Meier is to provide accurate yield prediction.
Office Note: The Office interprets the term “compensating” as “offsetting an error, defect, or undesired effect” (see “compensate,” Merriam-Webster.com Dictionary, https://www.merriam-webster.com/dictionary/compensate. Accessed 5/7/2025.).
Regarding claim 2, Kaarnametsä in view of Leppänen further in view of Meier teaches the method of claim 1.
Kaarnametsä further discloses comprising:
sending, by the network service, information indicating (Kaarnametsä at para. [0027]: “The worksite preparation data 14, navigation data 16 and worksite situation data 8 are the input to an operation processor 78. The operation processor 78 uses this data to run one or more routines to create output signals to valves, switches, controls etc. for adjusting propulsion settings 80 like transmission gear, speed, deceleration, etc., to process power settings 82 like lift capacity of a boom, saw speed, feed wheel speed, etc., to equipment settings 84 like the use of lower knives, use of a top saw, knife pressure, etc., and to production settings 86 like log length”).
Leppänen further teaches the compensated product yields (Leppänen at para. [0089]: “there may be a deviation between the work quantification information and the actual work performance experienced”; para. [0090]: “This deviation may be corrected, assuming the amount of work left non-finished or performed in excess when compared to the work quantification Information is somewhat systematic proportion of the total work quantified, by optionally, performing model calibration for Improved Precision 1.1.9”; para. [0092]: “A model is calibrated to predict the reference measurement values with the work production values. The resulting output is a calibration model for work production results”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Kaarnametsä in view of Leppänen further in view of Meier by replacing the information that is sent to the machine of Kaarnametsä with the compensated product yields as taught by Leppänen with a reasonable expectation of success. The motivation to modify the method of Kaarnametsä in view of Leppänen further in view of Meier is to provide improved accuracy of production quantity determination for forest operations.
Regarding claim 3, Kaarnametsä in view of Leppänen further in view of Meier teaches the method of claim 1.
Leppänen further teaches comprising: generating, by the network service, a product yield estimate for the at least one prediction cell based on the modeled difference between the logging machine product yield and a product yield of the at least one prediction cell (Leppänen at para. [0096]: “the summarized work production from Cells 1.1.7 may be corrected to calibrated work production from cells by optionally, performing calibrated work production measurement calculation 1.1.10. The output of this calculation is the calibrated work production measurement”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Kaarnametsä in view of Leppänen further in view of Meier by adding generating the product yield estimate as taught by Leppänen with a reasonable expectation of success. The motivation to modify the method of Kaarnametsä in view of Leppänen further in view of Meier is to provide improved accuracy of production quantity determination for forest operations.
Regarding claim 4, Kaarnametsä in view of Leppänen further in view of Meier teaches the method of claim 1,
Kaarnametsä further discloses wherein the determining of the one or more prediction cells that are associated with the at least one prediction cell is in terms of one or more of:
products of product assortment (Kaarnametsä at para. [0021]: “b) Business Data 64, like requirements of the purchaser or owner of the trees, sale prices, traffic data, fleet data, etc.”);
tree species (Kaarnametsä at para. [0020]: “a) Forest data 62 such as the kind of trees (species, size, age, shape, stiffness, weight, amount, diseases, ground wetness, coverage with snow, tree deceases, tree shape, underwood, etc.) at the worksite 10, known from watching the forest like with cameras, drones, LIDAR, human beings, etc.”); and
terrain locations (Kaarnametsä at para. [0025]: “The navigation data 16 may include territory data 70, like the borders of the worksite 10, the course of the paths 18, creeks 20 and power lines 32, the location of the rocks 30 and buildings 36, as well as the landing places 28, swampland 38, the tree areas 22 to 26 and soil data”).
Leppänen further teaches performing one or more of:
compensating, at the network service, product yields of the associated one or more prediction cells based on the modeled difference (Leppänen at para. [0089]: “there may be a deviation between the work quantification information and the actual work performance experienced”; para. [0090]: “This deviation may be corrected, assuming the amount of work left non-finished or performed in excess when compared to the work quantification Information is somewhat systematic proportion of the total work quantified, by optionally, performing model calibration for Improved Precision 1.1.9”; para. [0092]: “A model is calibrated to predict the reference measurement values with the work production values. The resulting output is a calibration model for work production results”);
modeling, at the network service, a difference between product yields at stump positions of the logging machine and product yields of the prediction cells, and correcting, at the network service, a position of the logging machine based on the modeled difference; and
generating, by the network service, forest growth estimates for the associated one or more prediction cells based on the modeled difference between the logging machine product yield and the product yield of the at least one prediction cell.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Kaarnametsä in view of Leppänen further in view of Meier by adding compensating the product yields as taught by Leppänen with a reasonable expectation of success. The motivation to modify the method of Kaarnametsä in view of Leppänen further in view of Meier is to provide improved accuracy of production quantity determination for forest operations.
Regarding claim 5, Kaarnametsä in view of Leppänen further in view of Meier teaches the method of claim 1.
Kaarnametsä further discloses wherein the at least one harvesting operation comprises controlling a position of a harvester head, controlling grasping performed by a harvester head, controlling felling performed by a harvester head or controlling processing performed by a harvester head (Kaarnametsä at para. [0027]: “The operation processor 78 uses this data to run one or more routines to create output signals to valves, switches, controls etc. for adjusting propulsion settings 80 like transmission gear, speed, deceleration, etc., to process power settings 82 like lift capacity of a boom, saw speed, feed wheel speed, etc.”).
Regarding claim 6, Kaarnametsä in view of Leppänen further in view of Meier teaches the method of claim 1.
Kaarnametsä further discloses comprising: adapting or generating, at the network service, bucking instructions based on the compensated product yields of prediction cells; and causing, at the network service, to control the at least one harvesting operation of the logging machine based on the adapted or generated bucking instructions (Kaarnametsä at para. [0027]: “The operation processor 78 uses this data to run one or more routines to create output signals to valves, switches, controls etc. for … production settings 86 like log length”).
Regarding claim 8, Kaarnametsä in view of Leppänen further in view of Meier teaches the method according to claim 1.
Kaarnametsä further discloses comprising one or more of: causing to display, by the network service, at least one compensated product yield of the prediction cells on a user interface of the logging machine; and
receiving user input from an operator of the logging machine via a user interface of the logging machine and causing to continue the at least one harvesting operation based on the user input or causing to interrupt the at least one harvesting operation based on the user input (Kaarnametsä at para. [0034]: “The control of the forestry machines 12 may happen remotely from a control station, directly by an operator on the forestry machine 12 itself, or as a combination thereof. Here, an advanced adjustment may happen remotely, whereas fine-tuning may be made by a local operator depending on the circumstances”).
Regarding claim 14, Kaarnametsä discloses a network service, comprising at least one processor operatively connected to at least one data network interface for communications with one or more logging machines, and a memory comprising instructions that when executed by the processor cause (Kaarnametsä at para. [0027]: “The operation processor 78 may be provided physically on any of the forestry machines 12 as well as in the cloud or a connected server” “The operation processor 78 is part of an electronic control 50 disposed in an onboard computer”):
maintaining, at a network service operatively connected to a logging machine (Kaarnametsä at para. [0027]: “The operation processor 78 may be provided physically on any of the forestry machines 12 as well as in the cloud or a connected server”), master forest resource information (Kaarnametsä at para. [0034]: “Control of the forestry machines 12 happens via their electronic control unit 50 located in the cab 54 or elsewhere, whereas the electronic control units 50 of several forestry machines 12 may connect to each other to build a network such that the operation processor 78 is part of the electronic control unit 50”), wherein the master forest resource information comprises prediction cells for product yield over a work area or a set of work areas (Kaarnametsä at FIG. 2 and para. [0019]: “The worksite preparation data 14 are available in the form of a database with information helpful to operate the forestry machines 12, i.e., individually or as a fleet. The data helps making decisions and adjustments of the forestry machines 12 or the composition of the fleet before, but also during, operation”; para. [0020]: “a) Forest data 62 such as the kind of trees (species, size, age, shape, stiffness, weight, amount, diseases, ground wetness, coverage with snow, tree deceases, tree shape, underwood, etc.) at the worksite 10, known from watching the forest like with cameras, drones, LIDAR, human beings, etc.”; para. [0024]: “e) Historical performance data 69 from earlier machines, which may help setting and choosing forestry machines 12, for example, at similar worksites 10”) wherein the prediction cells are structured data for controlling harvesting operations by the logging machine (Kaarnametsä at para. [0027]: “The worksite preparation data 14, navigation data 16 and worksite situation data 8 are the input to an operation processor 78. The operation processor 78 uses this data to run one or more routines to create output signals to valves, switches, controls etc. for adjusting propulsion settings 80 like transmission gear, speed, deceleration, etc., to process power settings 82 like lift capacity of a boom, saw speed, feed wheel speed, etc., to equipment settings 84 like the use of lower knives, use of a top saw, knife pressure, etc., and to production settings 86 like log length”) and each of the one or more prediction cells comprises at least information indicating a predicted product yield of an area of forested soil and geographical positioning information associated with the predicted product yield (Kaarnametsä at para. [0020]: “a) Forest data 62 such as the kind of trees (species, size, age, shape, stiffness, weight, amount, diseases, ground wetness, coverage with snow, tree deceases, tree shape, underwood, etc.) at the worksite 10”), wherein the predicted product yield comprises one or more product yields of products of a product assortment produced by harvesting the work area or the set of work areas by the logging machine (Kaarnametsä at para. [0020]: “a) Forest data 62 such as the kind of trees (species, size, age, shape, stiffness, weight, amount, diseases, ground wetness, coverage with snow, tree deceases, tree shape, underwood, etc.) at the worksite 10; para. [0021]: “b) Business Data 64, like requirements of the purchaser or owner of the trees, sale prices, traffic data, fleet data, etc.”; A tree is a type of forestry product, and the forest data 62 includes an assortment of different tree species, etc.);
receiving, at the network service from the logging machine, logging machine measurements associated with the work area or the set of work areas (Kaarnametsä at para. [0026]: “The worksite situation data 8 may include data which is not collected in advance but is captured during operation. This may include actual tree data 74 and machine performance data 76” “Machine data may include hydraulic pressure, inclination, speed, temperature, fuel consumption, steering angle, etc.”);
causing, at the network service, control of at least one harvesting operation of the logging machine based on (Kaarnametsä at para. [0019]: “The information may be provided to the forestry machines 12 online or by a transferable data source, like a USB stick or the like”; para. [0026]: “All data may be used to influence the operation of the forest machine 12 in order to reduce fuel consumption, achieve a higher output of log, prevent damages at the knives, etc.”; para. [0027]: “The worksite preparation data 14, navigation data 16 and worksite situation data 8 are the input to an operation processor 78. The operation processor 78 uses this data to run one or more routines to create output signals to valves, switches, controls etc. for adjusting propulsion settings 80 like transmission gear, speed, deceleration, etc., to process power settings 82 like lift capacity of a boom, saw speed, feed wheel speed, etc., to equipment settings 84 like the use of lower knives, use of a top saw, knife pressure, etc., and to production settings 86 like log length”).
However, Kaarnametsä does not explicitly state associating, at the network service, the received logging machine measurements to at least one prediction cell of the master forest resource information;
determining, at the network service, based on the received logging machine measurements, a logging machine product yield for the work area or the set of work areas;
modeling, at the network service, a difference between the logging machine product yield and a product yield of the at least one prediction cell;
determining, at the network service, one or more other prediction cells that are associated with the at least one prediction cell;
compensating, at the network service, product yields of the associated one or more other prediction cells based on the modeled difference; and
the compensated product yields of prediction cells.
Nevertheless, Kaarnametsä at least suggests the idea of constantly updating the worksite preparation data 14 with new information (see Kaarnametsä at para. [0019]).
In the same field of endeavor, Leppänen teaches associating, at the network service, the received logging machine measurements to at least one prediction cell of the master forest resource information (Leppänen at para. [0030]: “The work area may be defined as a geometric area that can be represented for example by a geometric polygon, surface, selection of cells in a grid or selection of pixels from a rater or any other way to represent areas in geographic setting”; para. [0084]: “Identify the cells of work quantification that have been processed the first time within your desired time period”);
determining, at the network service, based on the received logging machine measurements, a logging machine product yield for the work area or the set of work areas (Leppänen at para. [0088]: “Optionally, Receiving Reference Measurement 1.1.8; para. [0089]: “timber volume measurements may be done using different definition of minimum top diameter of the usable stem. If such information is used for work quantification”);
modeling, at the network service, a difference between the logging machine product yield and a product yield of the at least one prediction cell (Leppänen at para. [0090]: “Reference measurement of the actual work performed (for example, the amount of timber and wood processed; measured from the wood and timber products that are finished), when linked to the time period when the said work was done, can be used to calibrate a correction model”); and
compensating, at the network service, product yields (Leppänen at para. [0089]: “there may be a deviation between the work quantification information and the actual work performance experienced”; para. [0090]: “This deviation may be corrected, assuming the amount of work left non-finished or performed in excess when compared to the work quantification Information is somewhat systematic proportion of the total work quantified, by optionally, performing model calibration for Improved Precision 1.1.9”; para. [0092]: “A model is calibrated to predict the reference measurement values with the work production values. The resulting output is a calibration model for work production results”);
the compensated product yields of prediction cells (Leppänen at para. [0089]: “there may be a deviation between the work quantification information and the actual work performance experienced”; para. [0090]: “This deviation may be corrected, assuming the amount of work left non-finished or performed in excess when compared to the work quantification Information is somewhat systematic proportion of the total work quantified, by optionally, performing model calibration for Improved Precision 1.1.9”; para. [0092]: “A model is calibrated to predict the reference measurement values with the work production values. The resulting output is a calibration model for work production results”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the network service of Kaarnametsä by adding the modeling and compensating as taught by Leppänen with a reasonable expectation of success. The motivation to modify the network service of Kaarnametsä in view of Leppänen is to provide improved accuracy of production quantity determination for forest operations (see Leppänen at para. [0027]).
However, Kaarnametsä in view of Leppänen does not explicitly state:
determining, at the network service, one or more other prediction cells that are associated with the at least one prediction cell;
compensating, at the network service, product yields of the associated one or more other prediction cells based on the modeled difference.
In the same field of endeavor, Meier teaches:
determining, at the network service, one or more other prediction cells that are associated with the at least one prediction cell (Meier at para. [0061]: “At block 604, at least a first measurement of actual yield obtained from at least a first field region during harvesting of the field, the at least the first field region being less than entirety of the field”; para. [0062]: “At block 606, a yield prediction model is generated based on the set of spatial yield data obtained at block 602 and the at least the first measurement received at block 604. The yield prediction model is for predicting actual yield in a second field region during harvesting of the field, the second field region being different from the first field region”);
compensating, at the network service, product yields of the associated one or more other prediction cells based on the modeled difference (Meier at para. [0062]: “At block 606, a yield prediction model is generated based on the set of spatial yield data obtained at block 602 and the at least the first measurement received at block 604. The yield prediction model is for predicting actual yield in a second field region during harvesting of the field, the second field region being different from the first field region”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the network service of Kaarnametsä in view of Leppänen by adding determining one or more other prediction cells of Meier with a reasonable expectation of success. The motivation to modify the network service of Kaarnametsä in view of Leppänen further in view of Meier is to provide accurate yield prediction.
Office Note: The Office interprets the term “compensating” as “offsetting an error, defect, or undesired effect” (see “compensate,” Merriam-Webster.com Dictionary, https://www.merriam-webster.com/dictionary/compensate. Accessed 5/7/2025.).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Kaarnametsä in view of Leppänen further in view of Meier and Laakkonen et al. (FI 20185171 A1, which is found in the IDS submission on 08/30/2023, hereinafter “Laakkonen”). The rejections below are based on the machine translation of Laakkonen.
Regarding claim 7, Kaarnametsä in view of Leppänen further in view of Meier teaches the method of claim 6.
Kaarnametsä further discloses wherein
determining, at the network service, one or more product fittings to a stem of a tree at a position of the work area or the set of work areas (Kaarnametsä at para. [0039]: “The bucking instructions, combined with the estimated stem profile, predicts that three saw logs may be received from that stem with a length of 5.2 m each, and this will be indicated to the operator through an operation user interface. However, image data processing recognizes that there is a bad bend at the stem at the height of 9 m and stem part between 9.0 and 9.4 m is not valid for saw log quality requirements. Based on this information, the bucking is changed and two saw logs with lengths of 4.6 and 4.3 mare proposed to be cut before the bent part of the stem”).
However, Kaarnametsä in view of Leppänen further in view of Meier does not explicitly state the bucking instructions may comprise a value matrix and
adapting, at the network service, one or more values of the value matrix based on the compensated product yields;
determining, at the network service, total values of the products for the determined one or more product fittings based on the value matrix; and
selecting, at the network service, a product fitting that has the highest value from the determined one or more product fittings, for controlling the at least one harvesting operation.
In the same field of endeavor, Laakkonen teaches the bucking instructions may comprise a value matrix (Laakkonen at pg. 3, ln. 15-18: “the fitting instructions include, for example, fitting dimensions and a price matrix, also called a value matrix”) and
adapting, at the network service, one or more values of the value matrix based on the compensated product yields (Laakkonen at pg. 5, ln. 25-28: “on the basis of the initial data on wood procurement, in particular the type of stand or soil composition of the stand, it can be concluded that the density or fiber length of the timber in the stand is not good enough, which also affects the yield. In this case, in connection with the optimization, the fitting instructions have been proposed so that during this time the proportion of pulpwood is reduced and the proportion of energy wood is increased compared to a situation where the quality class would be good enough”);
determining, at the network service, total values of the products for the determined one or more product fittings based on the value matrix (Laakkonen at pg. 2, ln. 10-11: “In trunk pricing, the financial compensation received from the trees in the stand, in turn, depends on the sum of the values of the unequal parts of the trunk. Based on the diameter, the tree trunk is divided into trunk parts, the so-called diameter receipts, each priced from the trunk cutting method of the tree trunks”; pg. 3, ln. 16: “the fitting instructions include, for example, fitting dimensions and a price matrix, also called a value matrix”); and
selecting, at the network service, a product fitting that has the highest value from the determined one or more product fittings, for controlling the at least one harvesting operation (Laakkonen at pg. 4, ln. 5-6: “said optimization system performs the optimization described above so that the cutting of the tree trunks 25 by the harvester produces the desired types of goods, for example taking into account the yield from said one or more types of goods, for example to maximize that yield”; pg. 6, ln. 18-19: “selected fitting instructions 12, 14, for example your values, are generated by the optimization system 10 to achieve the desired optimized result”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Kaarnametsä in view of Leppänen further in view of Meier by adding the value matrix of Laakkonen with a reasonable expectation of success. The motivation to modify the method of Kaarnametsä in view of Leppänen further in view of Meier and Laakkonen is to provide optimized tree cutting.
Conclusion
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/JISUN CHOI/Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666