DETAILED ACTION
This non-final Office action is in response to the claims filed 10/8/2025.
Claims 1-20 are pending.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/8/2025 has been considered by the examiner.
Key to Interpreting this Office Action
To enhance clarity, claim language is underlined throughout this Office action, except within the 35 U.S.C. 101 rejections which follow specific formatting guidelines detailed therein.
Claim Objections
Claims 1, 10, and 11 are objected to because of the following informalities:
Claims 1, 10, and 11 recite the limitation of inferior to. The phrase “inferior to” is commonly used to represent a deficiency, not a numerical order for comparison. It is recommended to amend this limitation to recite “less than” or “lower than” to clearly represent the claimed invention.
Claim 10 lacks a conjunction (i.e. “and”) between the last two limitations.
Appropriate correction is required.
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 1-6, 8-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis of Claim 1
Claim 1. A method of controlling operation of a self-driving car (SDC), the method including:
receiving sensor data about an environment of the SDC;
generating, using a Neural Network (NN), a map of the environment using the sensor data;
generating, using the NN, a grid structure with a plurality of cells corresponding to respective portions of the map,
a given cell from the plurality of cells being associated with a probability value indicative of a probability that an object is present in the respective portion of the map;
in response to the probability value being above a detection threshold:
generating a bounding shape covering the given cell, the bounding shape being indicative of that a detected object is present in the respective portion of the map;
in response to the probability value being between the detection threshold and a second threshold, the second threshold being inferior to the detection threshold:
determining that an undetected object is potentially present in the respective portion of the map; and
in response to the determining that the undetected object is potentially present in the respective portion of the map:
triggering the SDC to perform a remedial action.
101 Analysis - Step 1: Statutory category - Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. MPEP 2106.03
101 Analysis - Step 2A Prong one evaluation: Judicial Exception - Yes - Mathematical concepts and Mental processes
The claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitations constitute judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental processes.
The claim recites the limitation of generating, using a Neural Network (NN), a map of the environment using the sensor data.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of a “map of the environment” is spatial data associated with the environment, and the limitation of “sensor data” is data generated from generic sensors. No particular sensors are claimed. The “neural network” is recited as a generic neural network, which consists of known matrices operations, weight calculations, activation functions, and added biases. Thus, this limitation encompasses inputting generally recited data (i.e. sensor data) into a common neural network in order to output generally recited spatial data (i.e. map of the environment).
Because the limitation recites explicitly performing mathematical calculations, the limitation, as drafted, falls within the mathematical concepts grouping of abstract ideas.
The “neural network” is described at a high level of generality that it amounts to using a computer with a generic neural network to apply the abstract idea, which does not preclude the claims from reciting the abstract process when tested per MPEP 2106.04(a)(2)(III)(C)#3.
The claim recites the limitation of generating, using the NN, a grid structure with a plurality of cells corresponding to respective portions of the map. Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of “grid structure with a plurality of cells” is spatial data representative of discretized cells. The “NN” is recited as a generic neural network, which consists of known matrices operations, weight calculations, activation functions, and added biases. Thus, this limitation encompasses inputting generally recited data (i.e. map) into a common neural network in order to output generally recited spatial data (i.e. grid structure).
Because the limitation recites explicitly performing mathematical calculations, the limitation, as drafted, falls within the mathematical concepts grouping of abstract ideas.
The “neural network” is described at a high level of generality that it amounts to using a computer with a generic neural network to apply the abstract idea, which does not preclude the claims from reciting the abstract process when tested per MPEP 2106.04(a)(2)(III)(C)#3.
The claim recites the limitation of a given cell from the plurality of cells being associated with a probability value indicative of a probability that an object is present in the respective portion of the map.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of a “given cell” is spatial data representative of a discretized area, and the limitation of “probability value” is numeric data representative of a probability that an object is present in a particular area.
Therefore, this limitation, as drafted, is a simple cognitive process that, under its broadest reasonable interpretation, can be practically covered in the human mind, or by a human using a pen and paper. For example, the claim encompasses a person looking at data collected (i.e. a given cell from the plurality of cells and a probability value) and forming a simple observation and evaluation (i.e. associate the given cell with the probability value). Such observations and evaluations are listed as abstract by MPEP 2106.04(a)(2)(III).
The claim recites the limitation of in response to the probability value being above a detection threshold: generating a bounding shape covering the given cell, the bounding shape being indicative of that a detected object is present in the respective portion of the map.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of a “bounding shape” is spatial data representative of a shape that includes a cell with a detected object, and the limitation of the “detection threshold” is numeric data used for comparison with the probability value.
Therefore, this limitation, as drafted, is a simple cognitive process that, under its broadest reasonable interpretation, can be practically covered in the human mind, or by a human using a pen and paper. For example, the claim encompasses a person looking at data collected (i.e. probability value, detection threshold, and given cell) and forming a simple observation and evaluation (i.e. generate a bounding shape covering the given cell in response to the probability value being above the detection threshold). Such observations and evaluations are listed as abstract by MPEP 2106.04(a)(2)(III).
The claim recites the limitation of in response to the probability value being between the detection threshold and a second threshold, the second threshold being inferior to the detection threshold: determining that an undetected object is potentially present in the respective portion of the map.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of an “undetected object” is data representative of whether an object could be present in a particular area, and the limitation of a “second threshold” is numeric data used for comparison with the probability value.
Therefore, this limitation, as drafted, is a simple cognitive process that, under its broadest reasonable interpretation, can be practically covered in the human mind, or by a human using a pen and paper. For example, the claim encompasses a person looking at data collected (i.e. probability value, detection threshold, and second threshold) and forming a simple observation and evaluation (i.e. determining that an undetected object is potentially present in the respective portion of the map in response to the probability value being between the detection threshold and second threshold). Such observations and evaluations are listed as abstract by MPEP 2106.04(a)(2)(III).
Thus, the claim recites, describes, or sets forth a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
The claim is evaluated for whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined potions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
The claim recites additional element of receiving sensor data about an environment of the SDC.
This step is recited at a high level of generality (i.e. as a general receiving of sensor data about an environment of the SDC) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
The claim recites additional element of in response to the determining that the undetected object is potentially present in the respective portion of the map: triggering the SDC to perform a remedial action.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of “remedial action” is broad and may reasonably encompass outputting an alert on a display of the SDC or indicating a transition to manual operation of the SDC. Thus, this step amounts to post-solution activity, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g). Dependent claim 7 has not been rejected under 35 U.S.C. 101, because claim 7 further limits a “remedial action” to physically controlling the SDC, and thus “effects a transformation or reduction of a particular article to a different state or thing.” See MPEP 2106.04(d).
The “remedial action” attempts to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and thus provides only a result-oriented solution, which does not integrate the abstract idea into a practical application or provide significantly more. See MPEP 2016.05(f)(1).
The “SDC” generally links the judicial exception to a particular technological environment or field of use (e.g., self-driving car), which does not integrate the abstract idea into a practical application or provide significantly more. See MPEP 2016.05(h).
No technological details are recited with respect to the “neural network” itself. Specifically, when tested per MPEP 2106.05(f)(1), such limitation is interpreted as a result-oriented solution rather than an actual technological improvement. Thus, the neural network is found not to integrate the abstract idea into a practical application or provide significantly more.
101 Analysis - Step 2B evaluation: Inventive concept - No
The claim is evaluated for whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, “receiving” and “triggering” steps were considered to be insignificant extra-solution activity in Step 2A, and thus, they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background recites the self-driving car and its associated sensors as conventional and known in the field of autonomous driving technology, and the specification does not provide any indication that the neural network is hosted on anything other than a generic computing device. MPEP 2106.05(d)(II), and the cases cited therein, including TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, indicate that receiving or transmitting data over a network, and storing and retrieving information in memory are well-understood, routine, and conventional functions when claimed in a merely generic manner, as it is here. Thus, the claim is ineligible.
101 Analysis of Dependent Claims 2-6, 8, and 9
Dependent claims 2-6, 8, and 9 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application.
Claim 2 recites the additional element of: the sensor data comprises first sensor data from a first sensor, and second sensor data from a second sensor.
Further limiting the “sensor data” to include first sensor data from a first sensor and second sensor data from a second sensor represents a mere narrowing of the abstract idea (step 2A prong one) and does not impose meaningful limits on the claim beyond what has already been identified as abstract.
The “first sensor” and “second sensor” are generic sensors that are merely claimed as a source of the generally recited data; therefore, the first and second sensors do not impose meaningful limits on the claim. See MPEP 2106.05(b).
Claim 3 recites the additional element of: the first sensor data is a point cloud and the first sensor is a LIDAR sensor.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of a “point cloud” is a set of data points defined by spatial coordinates.
Further limiting the “first sensor data” to be a point cloud and the “first sensor” to be a LIDAR sensor represents a mere narrowing of the abstract idea (step 2A prong one) and does not impose meaningful limits on the claim beyond what has already been identified as abstract.
The “LIDAR sensor” is a generic sensor that is merely claimed as a source of the generally recited data (i.e. “point cloud”); therefore, the LIDAR sensor does not impose meaningful limits on the claim. See MPEP 2106.05(b).
Claim 4 recites the additional element of: the method further comprises generating fused sensor data by combining the first sensor data and the second sensor data, and wherein the generating the map of the environment comprises generating the map of the environment using the fused sensor data.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of “fused sensor data” is data comprised of a combination of sensor data.
Therefore, this limitation, as drafted, is a simple cognitive process that, under its broadest reasonable interpretation, can be practically covered in the human mind, or by a human using a pen and paper. For example, the claim encompasses a person looking at data collected (i.e. first sensor data and second sensor data) and forming a simple observation and evaluation (i.e. generating fused sensor data), so as to form a subsequent observation and evaluation (i.e. generate a map of the environment). Such observations and evaluations are listed as abstract by MPEP 2106.04(a)(2)(III).
Claim 5 recites the additional element of: the map of the environment is a Bird Eye View (BEV) map of the environment.
Further limiting the “map of the environment” to be a Bird Eye View (BEV) map of the environment represents a mere narrowing of the abstract idea (step 2A prong one) and does not impose meaningful limits on the claim beyond what has already been identified as abstract.
Claim 6 recites the additional element of: the bounding shape is a bounding box.
Further limiting the “bounding shape” to be a bounding box represents a mere narrowing of the abstract idea (step 2A prong one) and does not impose meaningful limits on the claim beyond what has already been identified as abstract.
Claim 8 recites the additional element of: the triggering the SDC to perform the remedial action is executed independently from one or more path planning operations.
Further limiting the “triggering” step to be executed independently from one or more path planning operations represents a mere narrowing of the abstract idea (step 2A prong one) and does not impose meaningful limits on the claim beyond what has already been identified as abstract.
Claim 9 recites the additional element of: the generating the bounding shape comprises executing a Non-Maximum Suppression (NMS) algorithm onto a plurality of candidate bounding shapes.
The limitation of a “non-maximum suppression algorithm” is a well-known mathematical calculation. Because the limitation recites explicitly performing a mathematical calculation on generally recited data (i.e. candidate bounding shapes), the limitation falls within the mathematical concepts grouping of abstract ideas.
Based on the tests above, the Examiner finds that the additional elements do not integrate the abstract idea into a practical application (Step 2A prong two) or provide significantly more (Step 2B). Therefore, dependent claims 2-6, 8, and 9 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
101 Analysis of Claim 10
Claim 10. A method of controlling operation of a self-driving car (SDC), the method including:
receiving sensor data about an environment of the SDC;
generating, using a Neural Network, a map of the environment using the sensor data;
generating, using the NN, a grid structure with a plurality of cells corresponding to respective portions of the map, the plurality of cells being associated with respective probability values indicative of a probability that an object is present in the respective portions of the map;
executing a two-stage object detection process onto the grid structure, including:
during a first stage:
generating a bounding shape covering a first cell from the plurality of cells based on a first probability value of the first cell, the bounding shape being indicative of that a detected object is present in a first portion of the map corresponding to the first cell, the first cell being a bounded cell;
during a second stage:
determining that an undetected object is potentially present in a second portion of the map corresponding to a non-bounded cell based on a second probability value of the non-bounded cell;
triggering control of the SDC based on the presence of the detected object in the first portion and the potential presence of the undetected object in the second portion.
101 Analysis - Step 1: Statutory category - Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. MPEP 2106.03
101 Analysis - Step 2A Prong one evaluation: Judicial Exception - Yes - Mathematical concepts and Mental processes
The claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitations constitute judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the claim covers performance using mental processes.
The claim recites the limitation of generating, using a Neural Network, a map of the environment using the sensor data.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of a “map of the environment” is spatial data associated with the environment, and the limitation of “sensor data” is data generated from generic sensors. No particular sensors are claimed. The “neural network” is recited as a generic neural network, which consists of known matrices operations, weight calculations, activation functions, and added biases. Thus, this limitation encompasses inputting generally recited data (i.e. sensor data) into a common neural network in order to output generally recited spatial data (i.e. map of the environment).
Because the limitation recites explicitly performing mathematical calculations, the limitation, as drafted, falls within the mathematical concepts grouping of abstract ideas.
The “neural network” is described at a high level of generality that it amounts to using a computer with a generic neural network to apply the abstract idea, which does not preclude the claims from reciting the abstract process when tested per MPEP 2106.04(a)(2)(III)(C)#3.
The claim recites the limitation of generating, using the NN, a grid structure with a plurality of cells corresponding to respective portions of the map.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of “grid structure with a plurality of cells” is spatial data representative of discretized cells. The “NN” is recited as a generic neural network, which consists of known matrices operations, weight calculations, activation functions, and added biases. Thus, this limitation encompasses inputting generally recited data (i.e. map) into a common neural network in order to output generally recited spatial data (i.e. grid structure).
Because the limitation recites explicitly performing mathematical calculations, the limitation, as drafted, falls within the mathematical concepts grouping of abstract ideas.
The “neural network” is described at a high level of generality that it amounts to using a computer with a generic neural network to apply the abstract idea, which does not preclude the claims from reciting the abstract process when tested per MPEP 2106.04(a)(2)(III)(C)#3.
The claim recites the limitation of the plurality of cells being associated with respective probability values indicative of a probability that an object is present in the respective portions of the map.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of “probability values” is numeric data representative of probabilities that an object is present in particular areas.
Therefore, this limitation, as drafted, is a simple cognitive process that, under its broadest reasonable interpretation, can be practically covered in the human mind, or by a human using a pen and paper. For example, the claim encompasses a person looking at data collected (i.e. probability values) and forming a simple observation and evaluation (i.e. associate the plurality of cells with respective probability values). Such observations and evaluations are listed as abstract by MPEP 2106.04(a)(2)(III).
The claim recites the limitation of during a first stage: generating a bounding shape covering a first cell from the plurality of cells based on a first probability value of the first cell, the bounding shape being indicative of that a detected object is present in a first portion of the map corresponding to the first cell, the first cell being a bounded cell.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of a “bounding shape” is spatial data representative of a shape that includes a first cell with a detected object, and the limitation of the “first probability value” is numeric data representative of a probability of a detected object being present in the first cell.
Therefore, this limitation, as drafted, is a simple cognitive process that, under its broadest reasonable interpretation, can be practically covered in the human mind, or by a human using a pen and paper. For example, the claim encompasses a person looking at data collected (i.e. first cell and first probability value) and forming a simple observation and evaluation (i.e. generate a bounding shape covering the first cell based on the first probability value). Such observations and evaluations are listed as abstract by MPEP 2106.04(a)(2)(III).
The claim recites the limitation of during a second stage: determining that an undetected object is potentially present in a second portion of the map corresponding to a non-bounded cell based on a second probability value of the non-bounded cell.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of an “undetected object” is data representative of whether an object could be present in a particular area, and the limitation of a “second probability value” is numeric data representative of a probability of a detected object being present in a non-bounded cell.
Therefore, this limitation, as drafted, is a simple cognitive process that, under its broadest reasonable interpretation, can be practically covered in the human mind, or by a human using a pen and paper. For example, the claim encompasses a person looking at data collected (i.e. second probability value) and forming a simple observation and evaluation (i.e. determining that an undetected object is potentially present in a non-bounded cell). Such observations and evaluations are listed as abstract by MPEP 2106.04(a)(2)(III).
Thus, the claim recites, describes, or sets forth a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
The claim is evaluated for whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined potions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”).
The claim recites additional element of receiving sensor data about an environment of the SDC.
This step is recited at a high level of generality (i.e. as a general receiving of sensor data about an environment of the SDC) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
The claim recites additional element of triggering control of the SDC based on the presence of the detected object in the first portion and the potential presence of the undetected object in the second portion.
Based on the plain meaning of the terms in light of the Applicant's disclosure, the limitation of “triggering control” is broad and may reasonably encompass outputting an alert on a display of the SDC or indicating a transition to manual operation of the SDC. Thus, this step amounts to post-solution activity based upon generally recited data (i.e. presence of the detected object and potential presence of the undetected object), which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
The “control” attempts to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and thus provides only a result-oriented solution, which does not integrate the abstract idea into a practical application or provide significantly more. See MPEP 2016.05(f)(1).
The “SDC” generally links the judicial exception to a particular technological environment or field of use (e.g., self-driving car), which does not integrate the abstract idea into a practical application or provide significantly more. See MPEP 2016.05(h).
No technological details are recited with respect to the “neural network” itself. Specifically, when tested per MPEP 2106.05(f)(1), such limitation is interpreted as a result-oriented solution rather than an actual technological improvement. Thus, the neural network is found not to integrate the abstract idea into a practical application or provide significantly more.
101 Analysis - Step 2B evaluation: Inventive concept - No
The claim is evaluated for whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, “receiving” and “triggering” steps were considered to be insignificant extra-solution activity in Step 2A, and thus, they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The background recites the self-driving car and its associated sensors as conventional and known in the field of autonomous driving technology, and the specification does not provide any indication that the neural network is hosted on anything other than a generic computing device. MPEP 2106.05(d)(II), and the cases cited therein, including TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016), OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, indicate that receiving or transmitting data over a network, and storing and retrieving information in memory are well-understood, routine, and conventional functions when claimed in a merely generic manner, as it is here. Thus, the claim is ineligible.
101 Analysis of Claim 11
Claim 11. An electronic device for controlling operation of a self-driving car (SDC), the electronic device being configured to:
receive sensor data about an environment of the SDC;
generate, using a Neural Network (NN), a map of the environment using the sensor data;
generate, using the NN, a grid structure with a plurality of cells corresponding to respective portions of the map, a given cell from the plurality of cells being associated with a probability value indicative of a probability that an object is present in the respective portion of the map;
in response to the probability value being above a detection threshold:
generate a bounding shape covering the given cell, the bounding shape being indicative of that a detected object is present in the respective portion of the map;
in response to the probability value being between the detection threshold and a second threshold, the second threshold being inferior to the detection threshold:
determine that an undetected object is potentially present in the respective portion of the map; and
in response to determining that the undetected object is potentially present in the respective portion of the map: trigger the SDC to perform a remedial action.
101 Analysis - Step 1: Statutory category - Yes
The claim recites an apparatus. The claim falls within one of the four statutory categories. MPEP 2106.03
101 Analysis - Step 2A Prong one evaluation: Judicial Exception - Yes - Mathematical concepts and Mental processes
An analysis similar to that of independent claim 1 is made for independent claim 11 in step 2A prong one.
101 Analysis - Step 2A Prong two evaluation: Practical Application - No
An analysis similar to that of independent claim 1 is made for independent claim 11 in step 2A prong two.
101 Analysis - Step 2B evaluation: Inventive concept - No
An analysis similar to that of independent claim 1 is made for independent claim 11 in step 2B.
101 Analysis of Dependent Claims 12-16 and 18-20
Dependent claims 12-16 and 18-20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application.
Specifically, claims 12-16, 18, and 19 correspond in scope to claims 2-6, 8, and 9, respectively, and therefore, the limitations of claims 12-16, 18, and 19 represent a mere narrowing of the abstract idea (step 2A prong one) with no additional computer-based elements integrating the abstract idea into a practical application (step 2A prong two) or providing significantly more (step 2B) using analyses similar to those discussed in the rejections of dependent claims 2-6, 8, and 9 above.
Claim 20 recites the additional element of: the electronic device is a local electronic device of the SDC.
Further limiting the “electronic device” to be a local electronic device of the SDC represents a mere narrowing of the abstract idea (step 2A prong one) and does not impose meaningful limits on the claim beyond what has already been identified as abstract.
The “SDC” generally links the judicial exception to a particular technological environment or field of use (e.g., self-driving car), which does not integrate the abstract idea into a practical application or provide significantly more. See MPEP 2016.05(h).
Claims 1-6, 8-16, and 18-20 are thus found ineligible under 35 U.S.C. §101 as directed to an abstract idea, with the additional computer-based elements, as tested above, not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more (Step 2B).
Allowable Subject Matter
Claims 1-6, 8-16, and 18-20 would be allowable if rewritten or amended to overcome the rejections under 35 U.S.C. 101 set forth in this Office action. Claims 7 and 17 are allowed.
Per MPEP 2106.05(I), the novelty of any elements or steps in a process or even the process itself, is of no relevance in determining whether the subject matter of a claim falls within the §101 categories of possibly patentable subject matter. A claim for a new abstract idea is still an abstract idea.
With respect to claims 1 and 11, the closest prior art of record, Philbin et al. (US 2021/0103285 A1), hereinafter Philbin, Porebski et al. (US 2022/0187469 A1), hereinafter Porebski, and Fay et al. (US 2023/0215184 A1), hereinafter Fay, taken alone or in combination, does not teach the claimed electronic device and method of controlling operation of a self-driving car (SDC), the method including:
receiving sensor data about an environment of the SDC;
generating, using a Neural Network (NN), a map of the environment using the sensor data;
generating, using the NN, a grid structure with a plurality of cells corresponding to respective portions of the map, a given cell from the plurality of cells being associated with a probability value indicative of a probability that an object is present in the respective portion of the map;
in response to the probability value being above a detection threshold:
generating a bounding shape covering the given cell, the bounding shape being indicative of that a detected object is present in the respective portion of the map;
in response to the probability value being between the detection threshold and a second threshold, the second threshold being inferior to the detection threshold:
determining that an undetected object is potentially present in the respective portion of the map; and
in response to the determining that the undetected object is potentially present in the respective portion of the map:
triggering the SDC to perform a remedial action.
Specifically, Philbin teaches a method of controlling operation of a self-driving car (SDC) (see Figure 4, where vehicle 102 is defined as an autonomous vehicle in ¶0021), the method including receiving sensor data about an environment of the SDC (see ¶0022-0023, regarding sensor data is received from sensor(s) 104 of vehicle 102), generating, using a Neural Network (NN), a map of the environment using the sensor data (see ¶0029, regarding generating a top-down representation of the environment based on the received sensor data, defined as being generated by an ML model in at least ¶0073, such as a neural network, as described in ¶0048), and generating, using the NN, a grid structure with a plurality of cells corresponding to respective portions of the map, a given cell from the plurality of cells being associated with a probability value indicative of a probability that an object is present in the respective portion of the map (see ¶0053, regarding that the ML models are trained to output a set of occupancy maps associated with a most-recent time at which sensor data was received, where an occupancy map is a data structure that indicates whether a pixel of the environment is occupied or unoccupied and comprises a field of confidence scores indicating a likelihood that the respective pixel of the environment occupied/unoccupied, as described in ¶0054-0055; ¶0068, with respect to Figure 5A, regarding confidence scores are depicted as different shades of gray on a discretized grid). However, Philbin does not teach in response to the probability value being above a detection threshold: generating a bounding shape covering the given cell, the bounding shape being indicative of that a detected object is present in the respective portion of the map, and in response to the probability value being between the detection threshold and a second threshold, the second threshold being inferior to the detection threshold: determining that an undetected object is potentially present in the respective portion of the map, such that in response to the determining that the undetected object is potentially present in the respective portion of the map: triggering the SDC to perform a remedial action.
Porebski teaches a detection threshold (see ¶0038, regarding that each cell may be assigned as “occupied” if the occupancy is above an upper threshold), such that in response to the probability value being between the detection threshold and a second threshold, the second threshold being inferior to the detection threshold: determining that an undetected object is potentially present in the respective portion of the map (see ¶0038, regarding that a cell is assigned as “unknown” if the occupancy probability is between the upper and lower threshold). However, Porebski does not teach that in response to the probability value being above a detection threshold, generating a bounding shape covering the given cell, the bounding shape being indicative of that a detected object is present in the respective portion of the map, such that in response to the determining that the undetected object is potentially present in the respective portion of the map: triggering the SDC to perform a remedial action.
Further, while Porebski teaches generating a set of occupancy probabilities for cells in an occupancy grid based on detection data from sensors (see ¶0037-0038), Porebski is silent regarding the use of a neural network and thus does not teach generating, using a Neural Network (NN), a map of the environment using the sensor data, and generating, using the NN, a grid structure with a plurality of cells corresponding to respective portions of the map, a given cell from the plurality of cells being associated with a probability value indicative of a probability that an object is present in the respective portion of the map.
Fay teaches the known technique of in response to the probability value being above a detection threshold, generating a bounding shape covering the given cell, the bounding shape being indicative of that a detected object is present (see ¶0028, regarding that an analysis is performed on each region of the image to output a prediction of whether an object is present and predicted bounding box coordinates within a particular region, such that object predictions associated with a probability below a threshold are discarded). However, the “bounding shape” is generated with respect to the image of Fay, not a “map” generated “using a Neural Network (NN),” as claimed.
Additional prior art considered pertinent to the claimed invention include Murveit et al. (US 2021/0064890 A1) that teaches an occupancy prediction neural network generates an occupancy prediction output, e.g. heat maps, that characterizes a respective likelihood that each of multiple target regions of the environment will be occupied by an agent in the future interval of time, where target regions are arranged in a predefined grid centered on the current location of vehicle (see ¶0043-0044, with respect to Figure 2), Kogut et al. (US 2022/0319188 A1) that teaches assigning cells as “occupied” when the occupancy probability is above an upper threshold, “empty” when the occupancy probability is below a lower threshold, and “unknown” if the occupancy probability is between the upper and lower threshold (see ¶0053), and Jia et al. (US 2019/0294896 A1) that teaches generating bounding boxes that represent detected objects in a projected laser image using a high-recall object detection neural network (see ¶0093, with respect to Figure 4A).
No reasonable combination of prior art can be made to teach the claimed invention. The claimed invention would not have been obvious to one of ordinary skill in the art before the effective filing date.
With respect to claim 10, the closest prior art of record, Philbin, Porebski, and Fay, taken alone or in combination, does not teach the claimed method of controlling operation of a self-driving car (SDC), the method including:
receiving sensor data about an environment of the SDC;
generating, using a Neural Network, a map of the environment using the sensor data;
generating, using the NN, a grid structure with a plurality of cells corresponding to respective portions of the map, the plurality of cells being associated with respective probability values indicative of a probability that an object is present in the respective portions of the map;
executing a two-stage object detection process onto the grid structure, including:
during a first stage:
generating a bounding shape covering a first cell from the plurality of cells based on a first probability value of the first cell, the bounding shape being indicative of that a detected object is present in a first portion of the map corresponding to the first cell, the first cell being a bounded cell;
during a second stage:
determining that an undetected object is potentially present in a second portion of the map corresponding to a non-bounded cell based on a second probability value of the non-bounded cell;
triggering control of the SDC based on the presence of the detected object in the first portion and the potential presence of the undetected object in the second portion.
Claim 10 includes the allowable subject matter of claim 1; therefore, claim 10 is allowable for the same reasons discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Specifically, Gier et al. (US 2020/0278681 A1) teaches representing an occluded area of an environment of a vehicle using an occlusion grid that includes a plurality of occlusion fields that indicate an occupancy state (see ¶0025), Slobodyanyuk et al. (US 2023/0036838 A1) teaches receiving grid information and point data from a LIDAR scanner 250 in order to generate an occupancy grid for vehicle control (see ¶0045-0046, with respect to Figure 3), and Gutmann (US 10,303,178 B1) teaches projecting a trajectory and sensor data onto a static occupancy grid to determine whether an obstacle is present along the trajectory for vehicle control (see abstract).
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/SARA J LEWANDROSKI/Primary Examiner, Art Unit 3661
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