DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to amendments filed October 10th, 2025, in which no claims have been amended and claim 15 has been added. No claims have been cancelled. The amendments have been entered, and claims 1-15 are currently pending in the case. Claims 1, 11, 13 and 14 are independent claims.
Allowable Subject Matter
Claims 1-15 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 101 set forth in this Office action.
Prophet et al. (“Semantic Segmentation on 3D Occupancy Grids for Automotive Radar”, Prophet et al., 11 November 2020) hereinafter Prophet teaches a semantic segmentation method to distinguish between frequently occurring infrastructure objects. The resulting semantic grids provide a location-based classification of a vehicle environment.
Prophet et al. (“Semantic Segmentation on Automotive Radar Maps”, Prophet et al., 2019) hereinafter Prophet 2 teaches semantic segmentation networks in order to avoid the clustering step being a bottleneck for the algorithm's performance.
Hoermann et al. (“Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation”, Hoermann et al., 30 January 2018) hereinafter Hoermann teaches a loss function counteracting the high imbalance between mostly static background and extremely rare dynamic grid cells
After detailed search, the cited arts, neither alone nor in combination, teach the claimed subject matter of claims 1, 11, 13 and 14, “aggregating, for each cell of the adjoining cells, the values of the measured variables contained in the training measured data of the training example for all locations in the cell, to form values of the measured variables which relate to the cell”.
Pertinent art Prophet, Prophet 2 and Hoermann discloses a method for monitored training of a neural network, which maps measured data on one or multiple output variables, the measured data assigning values of one or multiple measured variables to locations in the two-dimensional or three-dimensional space but does not specifically disclose the claimed subject matter of claims 1, 11, 13 and 14.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: Claim 1 is directed to a method, therefore it falls under the statuary category of a process.
Step 2A Prong 1: The claim recites, in part:
“which maps measured data on one or multiple output variables, the measured data assigning values of one or multiple measured variables to locations in the two-dimensional or three- dimensional space” this limitation is a mathematical concept.
“subdividing a spatial region, which contains at least a portion of the locations indicated by the training measured data of a training example, into a grid made up of adjoining cells” this encompasses the mental subdivision of an observed region.
“aggregating, for each cell of the adjoining cells, the values of the measured variables contained in the training measured data of the training example for all locations in the cell, to form values of the measured variables which relate to the cell” this encompasses the mental aggregation of observed cells. Further, this limitation is a mathematical concept.
“mapping the aggregated values of the measured variables, by the neural network, on one or multiple output variables” this encompasses the mental mapping of observed values to an output. Further, this limitation is a mathematical concept.
“assessing deviations of the output variables from the training output variables using a predefined cost function, which is composed in weighted form of contributions of individual cells of the grid, the weight of each contribution being a function of an occupancy of the corresponding cell with locations contained in the training measured data of the training example” this encompasses the mental accessing of deviations of an observed output from known variables. Further, this limitation is a mathematical concept.
“optimizing parameters, which characterize a behavior of the neural network, with a goal that upon further processing of training examples, the assessment by the cost function is expected to improve” this encompasses the mental optimization of observed parameters. Further, this limitation is a mathematical concept.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “providing training examples made up of training measured data and associated training output variables;” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitation “providing training examples made up of training measured data and associated training output variables;” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d
Regarding claim 2, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“is set to a first positive value when the training measured data of the training example do not indicate a location in the at least one cell, and
is set to a second, higher positive value when the training measured data of the training example indicate at least one location in the at least one cell” this encompasses the mental process of setting a value based on observed occupancy.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 3, the rejection of claim 2 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“the second positive value is between eight times and twenty times the first positive value” this encompasses the mental setting of a value and ensuring it is between eight times and twenty times another observed value. Further, this limitation is a mathematical concept.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 4, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“wherein a distribution of the weights within the grid is selected in such a way that cells, within which the training measured data of the training example do not indicate a location, overall supply the same contribution to the cost function as cells, within which the training measured data of the training example indicate at least one location” this encompasses the mental selection of weights within an observed grid so they all contribute equally to a cost function. Further, this limitation is a mathematical concept.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 5, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“at least one weight is also optimized with a goal that upon further processing of training measured data, the assessment by the cost function is expected to improve” this limitation is a mathematical concept.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 6, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“that mesh width, for which the training converges on a best assessment by the cost function, is set as an optimum mesh width for live operation” this encompasses the mental setting of an optimal mesh based on observed operation
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “of the neural network” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). “the training is repeated for multiple subdivisions of the spatial region into grids having different mesh widths” The limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitations “of the neural network” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). “the training is repeated for multiple subdivisions of the spatial region into grids having different mesh widths” The limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Regarding claim 7, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: A continuation of the abstract idea identified in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “wherein training measured data including measured variables, which characterize reflections of radar radiation, laser radiation, and/or ultrasonic waves at locations in the space, are selected” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitations “wherein training measured data including measured variables, which characterize reflections of radar radiation, laser radiation, and/or ultrasonic waves at locations in the space, are selected” the limitation is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h).
Regarding claim 8, the rejection of claim 7 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“wherein the training measured data are obtained by observation of a scenery using a first measuring setup and/or from a first perspective; and
the training output variables are obtained by observation of the same scenery using a second measuring setup and/or from a second perspective” this encompasses the mental observation of scenery from various locations.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 9, the rejection of claim 7 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“wherein the training measured data and the training output variables are obtained by observation of a scenery using the same measuring setup and/or from the same perspective.” this encompasses the mental observation of scenery from a defined perspective.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 10, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“wherein the training output variables contain classification scores of the training input variables with respect to one or multiple classes of a predefined classification” this encompasses the mental association of classifications with observed data.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Regarding claim 11:
Step 1: Claim 1 is directed to a method, therefore it falls under the statuary category of a process.
“which maps measured data on one or multiple output variables, the measured data assigning values of one or multiple measured variables to locations in the two-dimensional or three-dimensional space” this limitation is a mathematical concept.
“subdividing a spatial region, which contains at least a portion of the locations indicated by the training measured data of a training example, into a grid made up of adjoining cells” this encompasses the mental subdivision of an observed region.
“aggregating, for each cell of the adjoining cells, the values of the measured variables contained in the training measured data of the training example for all locations in the cell, to form values of the measured variables which relate to the cell” this encompasses the mental aggregation of observed cells. Further, this limitation is a mathematical concept.
“mapping the aggregated values of the measured variables, by the neural network, on one or multiple output variables” this encompasses the mental mapping of observed values to an output. Further, this limitation is a mathematical concept.
“assessing deviations of the output variables from the training output variables using a predefined cost function, which is composed in weighted form of contributions of individual cells of the grid, the weight of each contribution being a function of an occupancy of the corresponding cell with locations contained in the training measured data of the training example” this encompasses the mental accessing of deviations of an observed output from known variables. Further, this limitation is a mathematical concept.
“optimizing parameters, which characterize a behavior of the neural network, with a goal that upon further processing of training examples, the assessment by the cost function is expected to improve” this encompasses the mental optimization of observed parameters. Further, this limitation is a mathematical concept.
“ascertaining an activation signal from the output variables supplied by the neural network” this encompasses the mental ascertaining of a signal from observed variables.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “providing training examples made up of training measured data and associated training output variables;”, “supplying measured data” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). “the trained neural network”, “by a vehicle” these limitations are an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). “which are recorded using at least one sensor carried along” The limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitation “providing training examples made up of training measured data and associated training output variables;”, “supplying measured data” these limitations are an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d. See MPEP § 2106.05(d)/(II). “the trained neural network”, “by a vehicle” these limitations are an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §2106.05(h). “which are recorded using at least one sensor carried along” The limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
Regarding claim 12, the rejection of claim 11 is incorporated and further:
Step 2A Prong 1: a continuation of the abstract idea identified in the parent claim.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “the vehicle is additionally activated using the activation signal” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitations “the vehicle is additionally activated using the activation signal.” The limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Further, the limitation is well‐understood, routine, and conventional activity as supported by Murfin (US 20030193241 A1), ¶2 “In recent years, a common modification to the modern automobile has been to install a remote starter system, typically having an electronic component, as means by which a person might start their vehicle from a removed location.”
Regarding claim 13:
Step 1: Claim 1 is directed to a machine-readable medium, therefore it falls under the statuary category of a manufacture.
Step 2A Prong 1: The claim recites, in part:
“which maps measured data on one or multiple output variables, the measured data assigning values of one or multiple measured variables to locations in the two-dimensional or three-dimensional space” this limitation is a mathematical concept.
“subdividing a spatial region, which contains at least a portion of the locations indicated by the training measured data of a training example, into a grid made up of adjoining cells” this encompasses the mental subdivision of an observed region.
“aggregating, for each cell of the adjoining cells, the values of the measured variables contained in the training measured data of the training example for all locations in the cell, to form values of the measured variables which relate to the cell” this encompasses the mental aggregation of observed cells. Further, this limitation is a mathematical concept.
“mapping the aggregated values of the measured variables, by the neural network, on one or multiple output variables” this encompasses the mental mapping of observed values to an output. Further, this limitation is a mathematical concept.
“assessing deviations of the output variables from the training output variables using a predefined cost function, which is composed in weighted form of contributions of individual cells of the grid, the weight of each contribution being a function of an occupancy of the corresponding cell with locations contained in the training measured data of the training example” this encompasses the mental accessing of deviations of an observed output from known variables. Further, this limitation is a mathematical concept.
“optimizing parameters, which characterize a behavior of the neural network, with a goal that upon further processing of training examples, the assessment by the cost function is expected to improve” this encompasses the mental optimization of observed parameters. Further, this limitation is a mathematical concept.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “when executed by one or multiple computers, causing the one or multiple computers to perform” The limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “providing training examples made up of training measured data and associated training output variables;” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitation “when executed by one or multiple computers, causing the one or multiple computers to perform” The limitation is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). “providing training examples made up of training measured data and associated training output variables;” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d.
Regarding claim 14:
Step 1: Claim 1 is directed to a computer, therefore it falls under the statuary category of a machine.
Step 2A Prong 1: The claim recites, in part:
“which maps measured data on one or multiple output variables, the measured data assigning values of one or multiple measured variables to locations in the two-dimensional or three- dimensional space” this limitation is a mathematical concept.
“subdividing a spatial region, which contains at least a portion of the locations indicated by the training measured data of a training example, into a grid made up of adjoining cells” this encompasses the mental subdivision of an observed region.
“aggregating, for each cell of the adjoining cells, the values of the measured variables contained in the training measured data of the training example for all locations in the cell, to form values of the measured variables which relate to the cell” this encompasses the mental aggregation of observed cells. Further, this limitation is a mathematical concept.
“mapping the aggregated values of the measured variables, by the neural network, on one or multiple output variables” this encompasses the mental mapping of observed values to an output. Further, this limitation is a mathematical concept.
“assessing deviations of the output variables from the training output variables using a predefined cost function, which is composed in weighted form of contributions of individual cells of the grid, the weight of each contribution being a function of an occupancy of the corresponding cell with locations contained in the training measured data of the training example” this encompasses the mental accessing of deviations of an observed output from known variables. Further, this limitation is a mathematical concept.
“optimizing parameters, which characterize a behavior of the neural network, with a goal that upon further processing of training examples, the assessment by the cost function is expected to improve” this encompasses the mental optimization of observed parameters. Further, this limitation is a mathematical concept.
Step 2A Prong 2: The judicial exception is not integrated into a practical application; the remaining limitations of the claim are as follows: “providing training examples made up of training measured data and associated training output variables;” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Step 2B: The claim does not contain significantly more than the judicial exception. The limitation “providing training examples made up of training measured data and associated training output variables;” the limitation is an additional element that amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). Furthermore the additional element is directed to receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d.
Regarding claim 15, the rejection of claim 1 is incorporated and further:
Step 2A Prong 1: The claim recites, in part:
“converting the aggregated values to a matrix or tensor representing the aggregated values of the measured variables” this limitation is a mathematical concept.
“mapping the aggregated values on the one or multiple output variables using the matrix or tensor” this limitation is a mathematical concept.
Step 2A Prong 2: The claim does not recite any additional limitations, thus does not further recite any additional elements that integrates the judicial exception into a practical application or amount to significantly more.
Response to Arguments
Regarding the 35 U.S.C. 101 rejections, applicant’s arguments have been considered, but they are not persuasive.
Applicant first argues “the amended claims integrate the alleged abstract ideas into a practical application that improves a technology/technical field. According to the present invention, the novel training method, which utilizes a cost function composed in weighted form and having weights of each contribution be a function of an occupancy, improves the numerical stability of neural networks during training.” Applicant Remarks filed October 10th, 2025, hereinafter “Remarks”, page 8. The MPEP states “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” See MPEP § 2106.05(a)(II). The improvement to creating training data for a machine learning model is an improvement to the abstract idea, not an improvement to a technology or technical field. The use of that data in training a model amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). Therefore, the claims do not integrate the exception into a practical application. The claims are rejected under 35 U.S.C. § 101.
Applicant next argues “It is the very construction of the cost function in weighted form (and in which weights of each contribution are a function of an occupancy) that the numerical stability of the training of the neural network can be advantageously improved. Therefore, the specific combination of claim 1 integrates the alleged abstract idea into a practical application that improves the numerical stability of neural network training.” Remarks, page 9. However, this argument is unpersuasive — the applicant merely uses a computer to perform processes which can be performed by a mental process as well as mathematical concepts. An improvement to the numerical stability of neural network training may be an improvement in an abstract idea, but not an improvement in the functioning of a computer, as a computer, or an improvement in a technology or technical field.
Applicant next argues “claim 12 recites “wherein the vehicle is additionally activated using the activation signal.” The Examiner amounts this to adding the words “apply it” (or an equivalent)…. Applicant respectfully disagrees…Thus, claim 12 further recites elements that amount to a practical application of the alleged abstract ideas under Step 2A, Prong II of the 2-step inquiry as per MPEP 2106.04(d)(1).” Remarks, pages 9-10. Applicant’s arguments have been fully considered and are, in part, persuasive. The use of a signal to activate a vehicle does not amount to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2). Therefore, the rejection has been withdrawn. However, upon further consideration, the use of a signal to activate a vehicle amounts to adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g). The MPEP states “If the additional element (or combination of elements) is a specific limitation other than what is well-understood, routine and conventional in the field, for instance because it is an unconventional step that confines the claim to a particular useful application of the judicial exception, then this consideration favors eligibility. If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.” See MPEP § 2106.05(d). Therefore, the additional element has been identified as well‐understood, routine, and conventional, as supported by Murfin (see the rejection of claim 12 above).
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB Z SUSSMAN MOSS whose telephone number is (571) 272-1579. The examiner can normally be reached Monday - Friday, 9 a.m. - 5 p.m. ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.S.M./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122