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
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. The instant application claims priority to U.S. Provisional Application No. 63/349,454, filed June 6, 2022. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Provisional Application No. 63/349,454, hereafter the provisional application, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for claims 9,17, and 23 of this application, which recite “…wherein the computing model includes a large language model”. The prior-filed application fails to mention any application of large language models in its specification. Thus, claims 9, 17, and 23 for this application, which recite “…wherein the computing model includes a large language model.”, are not provided adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph.
Therefore, the effective filing date for claims 9, 17, and 23 of the instant application is the filing date of the instant, non-provisional application, 06/05/2023. Examiner will consider if the provisional application supports each of the other claims if a rejection would need to rely upon an intervening reference between the actual filing date of the instant application, 06/05/2023 and the 06/06/2023 filing of the provisional application.
Each claim will receive benefit of the earliest filing date above for which a continuous chain of support can be established for the entirety of the claim. As discussed above, the effective filing date for at least claims 9, 17, and 23 of the instant application is the filing date of the instant, non-provisional application, 06/05/2023.
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 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidelines (“2019 PEG”).
Step 1: Independent claim 1 (A method for sensor cueing…), and 18 (A method for sensor cueing…) are directed towards a method. Independent claim 10 (A system for sensor cueing…) is directed towards a system. Therefore, these claims, as well as their dependent claims, are directed towards one of the four statutory categories (process, machine(system), manufacture, or composition of matter).
Claim 1
Step 2A, Prong 1: The claim recites, inter alia:
generating a sensor command based at least in part upon the model inference and the target object, the sensor command comprising one or more object parameters associated with the target object and one or more sensor parameters associated with a sensor;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to think of a command based on the model inference and the target object and relating to target object parameters and sensor parameters;
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
A method for sensor cueing, the method comprising:
This limitation is recited at a high level of generality and recites use of a generic algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic algorithm in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
transmitting the sensor command to the sensor via a sensor API;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
causing to adjust the sensor based on the one or more sensor parameters in the sensor command and the one or more object parameters in the sensor command;
This limitation is recited at a high level of generality and recites application of the sensor command to adjust a generic sensor. Mere instructions to apply a judicial exception on generic computer equipment in their ordinary capacity cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f);
wherein the method is performed using one or more processors.
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
A method for sensor cueing, the method comprising:
This limitation is recited at a high level of generality and recites use of a generic algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic algorithm in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object;
MPEP 2106.05(d)(II) indicates merely storing and retrieving information in memory (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
transmitting the sensor command to the sensor via a sensor API;
MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
causing to adjust the sensor based on the one or more sensor parameters in the sensor command and the one or more object parameters in the sensor command;
This limitation is recited at a high level of generality and recites application of the sensor command to adjust a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity is not significantly more than a judicial exception. See MPEP 2106.05(f);
wherein the method is performed using one or more processors.
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Claim 2
Step 2A, Prong 1: The claim recites, inter alia:
wherein the sensor command is generated based at least in part upon the model inference and the user input
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to think of a command based on the model inference and the user input and relating to target object parameters and sensor parameters;
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
transmitting the model inference to a user device; and
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
receiving a user input from the user device;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
transmitting the model inference to a user device; and
MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
receiving a user input from the user device;
MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
Claim 3
Step 2A, Prong 1: The claim recites, inter alia:
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the sensor is configured to change a sensor configuration, wherein the sensor configuration is associated with or is in accordance with the sensor parameter in the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a sensor configuration of a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the sensor is configured to change a sensor configuration, wherein the sensor configuration is associated with or is in accordance with the sensor parameter in the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a sensor configuration of a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity is not significantly more than a judicial exception. See MPEP 2106.05(f);
Claim 4
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
wherein the sensor is configured to change the one or more sensor parameters based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the sensor is configured to change the one or more sensor parameters based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity is not significantly more than a judicial exception. See MPEP 2106.05(f);
Claim 5
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
wherein the one or more sensor parameters include a target area in which the sensor is configured to gather the first set of sensor data, and the sensor is configured to decrease or increase the target area based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a target area of a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity does not integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the one or more sensor parameters include a target area in which the sensor is configured to gather the first set of sensor data, and the sensor is configured to decrease or increase the target area based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a target area of a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity is not significantly more than a judicial exception. See MPEP 2106.05(f);
Claim 6
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
receiving a second set of sensor data collected by the sensor after the sensor changes the one or more sensor parameters based upon the sensor command.
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
receiving a second set of sensor data collected by the sensor after the sensor changes the one or more sensor parameters based upon the sensor command.
MPEP 2106.05(d)(II)(iv) indicates receiving or transmitting data over a network (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
Claim 7
Step 2A, Prong 1: The claim recites, inter alia:
wherein the sensor command includes at least one action command selected from a group consisting of:
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to select an instruction
a command instructing an edge device associated with the sensor to follow one or more movements of the target object;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to give an instruction to follow the movements of an object to an edge device
a command instructing the sensor to follow the one or more movements of the target object; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to give an instruction to follow the movements of an object to a sensor
a command instructing the edge device to move closer to the target object.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to give an instruction to move closer to an object to an edge device
Step 2A, Prong 2: There are no further additional element(s) included in this claim
Step 2B: There are no further additional element(s) included in this claim
Claim 8
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
wherein the sensor is an image sensor, and the one or more sensor parameters include at least one selected from a group consisting of a zooming parameter, a resolution parameter, a frame rate parameter, a gain parameter, a binning parameter, and an image format parameter
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide the sensor has to be an image sensor and select a parameter from a group of choices
Step 2A, Prong 2: There are no further additional element(s) included in this claim
Step 2B: There are no further additional element(s) included in this claim
Claim 9
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
wherein the computing model includes a large language model.
This limitation is recited at a high level of generality and indicates limiting the technological environment of a computing model to include large language model in order to perform the abstract idea limitations. Merely indicating a field of use or technological environment that a judicial exception is to be performed using cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(h);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the computing model includes a large language model.
This limitation is recited at a high level of generality and indicates limiting the technological environment of a computing model to include large language model in order to perform the abstract idea limitations. Merely indicating a field of use or technological environment that a judicial exception is to be performed using is not significantly more than a judicial exception. See MPEP 2106.05(h);
Claim 10
Step 2A, Prong 1: The claim recites, inter alia:
generating, by the one or more processors, a sensor command based at least in part upon the model inference and the target object, the sensor command comprising one or more object parameters associated with the target object and one or more sensor parameters associated with a sensor;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to think of a command based on the model inference and the target object and relating to target object parameters and sensor parameters;
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
A system for sensor cueing, the system comprising: one or more memories comprising instructions stored thereon; and
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
one or more processors configured to execute the instructions and perform operations comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
transmitting the sensor command to the sensor via a sensor API;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
causing to adjust the sensor based on the one or more sensor parameters in the sensor command and the one or more object parameters in the sensor command;
This limitation is recited at a high level of generality and recites application of the sensor command to adjust a generic sensor. Mere instructions to apply a judicial exception on generic computer equipment in their ordinary capacity cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
A system for sensor cueing, the system comprising: one or more memories comprising instructions stored thereon; and
This limitation is recited at a high level of generality and recites use of a generic algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic algorithm in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
one or more processors configured to execute the instructions and perform operations comprising:
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
transmitting the sensor command to the sensor via a sensor API;
MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
Claim 11
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
transmitting the model inference to a user device; and
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
receiving a user input from the user device;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
wherein the generating, by the one or more processors, a sensor command based at least in part upon the model inference comprises generating the sensor command based at least in part upon the model inference and the user input.
This limitation represents an insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated performed by a generic computing system. See MPEP 2106.05(g);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
transmitting the model inference to a user device; and
MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
receiving a user input from the user device;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
wherein the generating, by the one or more processors, a sensor command based at least in part upon the model inference comprises generating the sensor command based at least in part upon the model inference and the user input.
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
Claim 12
Step 2A, Prong 1: The claim recites, inter alia:
wherein the sensor is configured to change a sensor configuration,
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to decide a change of parameters for the sensor
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
wherein the sensor configuration is associated with or is in accordance with the sensor parameter in the sensor command
This limitation is recited at a high level of generality and recites use of a generic element to apply the abstract idea to. Mere recitation that a judicial exception is to be performed on a generic element in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the sensor configuration is associated with or is in accordance with the sensor parameter in the sensor command
This limitation is recited at a high level of generality and recites use of a generic element to apply the abstract idea to. Mere recitation that a judicial exception is to be performed on a generic element in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Claim 13
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
wherein the sensor is configured to change the one or more sensor parameters based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the sensor is configured to change the one or more sensor parameters based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity is not significantly more than a judicial exception. See MPEP 2106.05(f);
Claim 14
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
wherein the one or more sensor parameters include a target area in which the sensor is configured to gather the first set of sensor data, and the sensor is configured to decrease or increase the target area based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a target area of a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity does not integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the one or more sensor parameters include a target area in which the sensor is configured to gather the first set of sensor data, and the sensor is configured to decrease or increase the target area based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a target area of a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity is not significantly more than a judicial exception. See MPEP 2106.05(f);
Claim 15
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
receiving a second set of sensor data collected by the sensor after the sensor changes the one or more sensor parameters based upon the sensor command.
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
receiving a second set of sensor data collected by the sensor after the sensor changes the one or more sensor parameters based upon the sensor command.
MPEP 2106.05(d)(II)(iv) indicates receiving or transmitting data over a network (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
Claim 16
Step 2A, Prong 1: The claim recites, inter alia:
wherein the sensor command includes at least one action command selected from a group consisting of:
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to select an instruction
a command instructing an edge device associated with the sensor to follow one or more movements of the target object;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to give an instruction to follow the movements of an object to an edge device
a command instructing the sensor to follow the one or more movements of the target object; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to give an instruction to follow the movements of an object to a sensor
a command instructing the edge device to move closer to the target object.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to give an instruction to move closer to an object to an edge device
Step 2A, Prong 2: There are no further additional element(s) included in this claim
Step 2B: There are no further additional element(s) included in this claim
Claim 17
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
wherein the computing model includes a large language model.
This limitation is recited at a high level of generality and indicates limiting the technological environment of a computing model to include large language model in order to perform the abstract idea limitations. Merely indicating a field of use or technological environment that a judicial exception is to be performed using cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(h);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the computing model includes a large language model.
This limitation is recited at a high level of generality and indicates limiting the technological environment of a computing model to include large language model in order to perform the abstract idea limitations. Merely indicating a field of use or technological environment that a judicial exception is to be performed using is not significantly more than a judicial exception. See MPEP 2106.05(h);
Claim 18
Step 2A, Prong 1: The claim recites, inter alia:
generating a sensor command based at least in part upon the model inference, the target object, and the user input, the sensor command comprising one or more object parameters associated with the target object, and one or more sensor parameters associated with a sensor;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to think of a command based on the model inference, the target object and user input, and relating to target object and sensor parameters;
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the judicial exception into a practical application.
Additional element(s):
A method for sensor cueing, the method comprising:
This limitation is recited at a high level of generality and recites use of a generic algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic algorithm in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object and a target area associated with the target object;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
transmitting the model inference to a user device;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
receiving, from the user device, a user input comprising an identification of the target object or the target area;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
transmitting the sensor command to the sensor via a sensor API;
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
causing to adjust the sensor based on the one or more sensor parameters in the sensor command and the one or more object parameters in the sensor command;
This limitation is recited at a high level of generality and recites application of the sensor command to adjust a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity is not significantly more than the judicial exception. See MPEP 2106.05(f);
wherein the method is performed using one or more processors.
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
A method for sensor cueing, the method comprising:
This limitation is recited at a high level of generality and recites use of a generic algorithm to apply the abstract idea. Mere recitation that a judicial exception is to be performed using a generic algorithm in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object and a target area associated with the target object;
MPEP 2106.05(d)(II)(iv) indicates that merely storing and retrieving information in memory is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
transmitting the model inference to a user device;
MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
receiving, from the user device, a user input comprising an identification of the target object or the target area;
MPEP 2106.05(d)(II) indicates merely storing and retrieving information in memory (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
transmitting the sensor command to the sensor via a sensor API;
MPEP 2106.05(d)(II) indicates that merely receiving or transmitting data over a network (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
wherein the method is performed using one or more processors.
This limitation is recited at a high level of generality and recites use of generic computer equipment to perform the abstract idea limitations. Mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Claim 19
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
wherein the sensor is configured to change the one or more sensor parameters based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity cannot integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the sensor is configured to change the one or more sensor parameters based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity is not significantly more than a judicial exception. See MPEP 2106.05(f);
Claim 20
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
wherein the one or more sensor parameters include a target area in which the sensor is configured to gather the first set of sensor data, and the sensor is configured to decrease or increase the target area based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a target area of a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity does not integrate the judicial exception into a practical application. See MPEP 2106.05(f);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the one or more sensor parameters include a target area in which the sensor is configured to gather the first set of sensor data, and the sensor is configured to decrease or increase the target area based upon the sensor command
This limitation is recited at a high level of generality and recites application of the sensor command to change a target area of a generic sensor. Mere instruction to apply a judicial exception on generic computer equipment in their ordinary capacity is not significantly more than a judicial exception. See MPEP 2106.05(f);
Claim 21
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
receiving a second set of sensor data collected by the sensor after the sensor changes the one or more sensor parameters based upon the sensor command.
This limitation represents an insignificant extra-solution activity of mere data gathering performed by a generic computing system. See MPEP 2106.05(g);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
receiving a second set of sensor data collected by the sensor after the sensor changes the one or more sensor parameters based upon the sensor command.
MPEP 2106.05(d)(II)(iv) indicates receiving or transmitting data over a network (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is in the present claim);
Claim 22
Step 2A, Prong 1: The claim recites, inter alia:
wherein the sensor command includes at least one action command selected from a group consisting of:
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to select an instruction
a command instructing an edge device associated with the sensor to follow one or more movements of the target object;
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to give an instruction to follow the movements of an object to an edge device
a command instructing the sensor to follow the one or more movements of the target object; and
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to give an instruction to follow the movements of an object to a sensor
a command instructing the edge device to move closer to the target object.
This limitation recites a mental process using evaluation, judgement, and opinion, with aid of pen and paper to give an instruction to move closer to an object to an edge device
Step 2A, Prong 2: There are no further additional element(s) included in this claim
Step 2B: There are no further additional element(s) included in this claim
Claim 23
Step 2A, Prong 1: This claim does not recite any additional abstract ideas
Step 2A, Prong 2: The additional element(s) recited in the claim do not integrate the
judicial exception into a practical application.
Additional element(s):
wherein the computing model includes a large language model.
This limitation is recited at a high level of generality and indicates limiting the technological environment of a computing model to include large language model in order to perform the abstract idea limitations. Merely indicating a field of use or technological environment that a judicial exception is to be performed using cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(h);
Step 2B: The claim does not include any additional element(s) that are sufficient to amount to significantly more than the judicial exception.
Additional element(s):
wherein the computing model includes a large language model.
This limitation is recited at a high level of generality and indicates limiting the technological environment of a computing model to include large language model in order to perform the abstract idea limitations. Merely indicating a field of use or technological environment that a judicial exception is to be performed using is not significantly more than a judicial exception. See MPEP 2106.05(h);
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-8, 10-16, and 18-22 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by US 20190179317 A1 by Englard et al. hereafter Englard.
Regarding claim 1, Englard teaches:
A method for sensor cueing, the method comprising:
receiving a model inference (prediction signal 122) from a computing model (prediction component 120) using a first set of sensor data (perception signals 106), the model inference associated with a target object (“prediction component 120 may analyze the type/class of the object … to predict”); (Fig. 1; Paragraph [0056], “The sensor control architecture 100 also includes a prediction component 120, which processes the perception signals 106 to generate prediction signals 122 descriptive of one or more predicted future states of the vehicle's environment. For a given object, for example, the prediction component 120 may analyze the type/class of the object (as determined by the classification module 112) along with the recent tracked movement of the object (as determined by the tracking module 114) to predict one or more future positions of the object.” Analyzing the type/class of an object means the prediction component and the prediction signal generated by it are associated with the object. Fig. 1 shows the sensor control component receiving the prediction signal.)
generating a sensor command (sensor control signals 132) based at least in part upon the model inference (prediction signals 122) and the target object ( Paragraph [0056], “For a given object, for example, the prediction component 120 may analyze the type/class of the object…”), the sensor command comprising one or more object parameters associated with the target object (“the sensor control component 130 attempts to direct the focus of one or more of the sensors 102 based on the presence, positions, and/or types of “dynamic” objects within the vehicle's environment”) and one or more sensor parameters associated with a sensor (“control one or more parameters of at least one of the sensors 102”); (Paragraph [0058])
transmitting (Fig.1, arrow 132 from 130 to 102) the sensor command (sensor control signals 132) to the sensor (sensors 102) via a sensor API (sensor control component 130); (Fig. 1; Paragraph [0058]) and
causing to adjust the sensor (Paragraph [0058], “the sensor control component 130 attempts to direct the focus of one or more of the sensors 102…”) based on the one or more sensor parameters in the sensor command (Paragraph [0061], “the parameter adjustment module 136 determines values of one or more parameters that set the area of focus of the controlled sensor(s)”) and the one or more object parameters in the sensor command (Paragraph [0058], “…based on the presence, positions, and/or types of “dynamic” objects within the vehicle's environment”); (The parameter adjustment module 136 is a part of sensor control component 130)
wherein the method is performed using one or more processors. (Paragraph [0174], “The computing system 800 includes one or more processors 802, and a memory 804 storing instructions 806”)
Regarding claim 2, Englard teaches the material disclosed in claim 1, and additionally teaches:
transmitting (Fig.1, arrow 132 from 130 to 102) the model inference (Fig.1, prediction signal 122 to sensor control component 130) to a user device (sensor control component); and (Fig. 1; Paragraph [0049], “As seen in FIG. 1, the vehicle includes N different sensors 102” The model inference is the prediction signal 122 and is shown in Fig. 1 being transmitted to the sensor control component)
receiving a user input (Paragraph [0041], labels that correspond to the visual focus of expert human drivers) from the user device (sensor control component); (Paragraph [0041], “Alternatively, an attention model may be trained using other techniques, such as supervised learning with labels that correspond to the visual focus of expert human drivers”) (Paragraph [0129], “Human gaze directions/locations may be tracked using any suitable technology, such as image processing of driver-facing cameras to detect the direction in which the user's pupils are facing over time.”.) (Paragraph [0125], “The sensor control component 630 includes an attention model 634 to determine where to focus one or more of the sensors 602” A sensor control component can include an attention model.)
wherein the sensor command (sensor control signals) is generated based at least in part upon the model inference (prediction signals) and the user input (human visual focus labels used to train an attention model). (Paragraph [0125], “The perception signals 106 and (in some embodiments) prediction signals 122 are input to a sensor control component 130”)
Regarding claim 3, Englard teaches the material disclosed in claim 1, and additionally teaches:
wherein the sensor (sensors 102) is configured to change a sensor configuration (parameter adjustment module 136 determines the setting for parameter(s) of the controlled sensor(s)), wherein the sensor configuration (parameter adjustment module) is associated with or is in accordance with the sensor parameter in the sensor command. (parameter adjustment module 136 determines the setting for parameter(s) of the controlled sensor(s)) (Paragraph [0061], “The parameter adjustment module 136 determines the setting for parameter(s) of the controlled sensor(s) (among sensors 102) based on the dynamic objects detected by the dynamic object detector 134. In particular, the parameter adjustment module 136 determines values of one or more parameters that set the area of focus of the controlled sensor(s).”)
Regarding claim 4, Englard teaches the material disclosed in claim 1, and additionally teaches:
wherein the sensor (sensors 102) is configured to change the one or more sensor parameters based upon the sensor command. (parameter adjustment module 136 determines values of one or more parameters that set the area of focus of the controlled sensor(s)) (Paragraph [0061])
Regarding claim 5, Englard teaches the material disclosed in claim 4, and additionally teaches:
wherein the one or more sensor parameters include a target area ( “field of regard”) in which the sensor is configured to gather the first set of sensor data, and the sensor is configured to decrease or increase the target area (“reducing the horizontal and vertical field of regard”) based upon the sensor command. (Paragraph [0062], “For example, the parameter adjustment module 136 may set lidar device parameters such that the field of regard of the lidar device is centered on the current position of a dynamic object, and possibly also “zoomed in” on that object (e.g., by reducing the horizontal and vertical field of regard without necessarily reducing the number of points in each point cloud frame)”)
Regarding claim 6, Englard teaches the material disclosed in claim 4, and additionally teaches:
receiving a second set of sensor data collected by the sensor after the sensor changes the one or more sensor parameters based upon the sensor command. (Fig. 1, Fig. 1 shows that the sensor command (sensor control signal 132), which cause the sensor to change one or more sensor parameters, loops to the sensors, resulting in a loop that a person having ordinary skill in the art would recognize causes the perception component to receive a second set of sensor data collected by the sensor.)
Regarding claim 7, Englard teaches the material disclosed in claim 1, and additionally teaches:
wherein the sensor command includes at least one action command selected from a group consisting of:
a command instructing an edge device associated with the sensor to follow one or more movements of the target object;
a command instructing the sensor (sensors 102) to follow the one or more movements of the target object; and (“track distinct objects”)
a command instructing the edge device to move closer to the target object. (Fig. 1; Paragraph [0054], “The tracking module 114 is generally configured to track distinct objects over time”; Paragraph [0060], “For example, the dynamic object detector 134 may apply one or more rules or algorithms, or use a machine learning model, to directly identify dynamic objects within point cloud frames from one of the sensors 102. In effect, in some embodiments, this may be viewed as the dynamic object detector 134 implementing a more simplistic version of the functionality of segmentation module 110, classification module 112, and/or tracking module 114” Dynamic object detector 134 is a part of the sensor control component which outputs commands. Thus, the sensor control component includes an action command of a command instructing the sensor to follow the one or more movements of the target object.)
Regarding claim 8, Englard teaches the material disclosed in claim 1, and additionally teaches:
wherein the sensor is an image sensor (Paragraph [0035], ““The sensors may be any type or types of sensors capable of sensing an environment through which the vehicle is moving, such as lidar, radar, cameras”), and the one or more sensor parameters include at least one selected from a group consisting of a zooming parameter (Paragraph [0062], “zoomed in”), a resolution parameter (Paragraph [0061], “parameter adjustment module 136 may control … the resolution”), a frame rate parameter (Paragraph [0061], “parameter adjustment module 136 may control the frame/refresh rate of the sensor”), a gain parameter, a binning parameter, and an image format parameter.
Regarding claim 10, Englard teaches:
A system for sensor cueing, the system comprising:
one or more memories (memory 804) comprising instructions (instruction 806) stored thereon; and
one or more processors (processors 802) configured to execute the instructions and perform operations comprising: (Paragraph [0174], “The computing system 800 includes one or more processors 802, and a memory 804 storing instructions 806”)
receiving a model inference (prediction signal 122) from a computing model (prediction component 120) using a first set of sensor data (perception signals 106), the model inference associated with a target object (“prediction component 120 may analyze the type/class of the object … to predict”); (Fig. 1; Paragraph [0056], “The sensor control architecture 100 also includes a prediction component 120, which processes the perception signals 106 to generate prediction signals 122 descriptive of one or more predicted future states of the vehicle's environment. For a given object, for example, the prediction component 120 may analyze the type/class of the object (as determined by the classification module 112) along with the recent tracked movement of the object (as determined by the tracking module 114) to predict one or more future positions of the object.” Analyzing the type/class of an object means the prediction component and the prediction signal generated by it are associated with the object. Fig. 1 shows the sensor control component receiving the prediction signal.)
generating, by the one or more processors, a sensor command (sensor control signals 132) based at least in part upon the model inference (prediction signals 122) and the target object ( Paragraph [0056], “For a given object, for example, the prediction component 120 may analyze the type/class of the object…”), the sensor command comprising one or more object parameters associated with the target object (“the sensor control component 130 attempts to direct the focus of one or more of the sensors 102 based on the presence, positions, and/or types of “dynamic” objects within the vehicle's environment”) and one or more sensor parameters associated with a sensor (“control one or more parameters of at least one of the sensors 102”); (Paragraph [0058])
transmitting (Fig.1, arrow 132 from 130 to 102) the sensor command (sensor control signals 132) to the sensor (sensors 102) via a sensor API (sensor control component 130); (Fig. 1; Paragraph [0058]) and
causing to adjust the sensor (Paragraph [0058], “the sensor control component 130 attempts to direct the focus of one or more of the sensors 102…”) based on the one or more sensor parameters in the sensor command (Paragraph [0061], “the parameter adjustment module 136 determines values of one or more parameters that set the area of focus of the controlled sensor(s)”) and the one or more object parameters in the sensor command (Paragraph [0058], “…based on the presence, positions, and/or types of “dynamic” objects within the vehicle's environment”); (The parameter adjustment module 136 is a part of sensor control component 130)
Regarding claim 11, Englard teaches the material disclosed in claim 10, and additionally teaches:
transmitting (Fig.1, arrow 132 from 130 to 102) the model inference (Fig.1, prediction signal 122 to sensor control component 130 to 102 by 132) to a user device (sensor control component); and (Fig. 1; Paragraph [0049], “As seen in FIG. 1, the vehicle includes N different sensors 102” The model inference is the prediction signal 122 and is shown in Fig. 1 being transmitted to the sensor control component.)
receiving a user input (Paragraph [0041], labels that correspond to the visual focus of expert human drivers) from the user device (sensor control component); (Paragraph [0041], “Alternatively, an attention model may be trained using other techniques, such as supervised learning with labels that correspond to the visual focus of expert human drivers”) (Paragraph [0129], “Human gaze directions/locations may be tracked using any suitable technology, such as image processing of driver-facing cameras to detect the direction in which the user's pupils are facing over time.”) (Paragraph [0125], “The sensor control component 630 includes an attention model 634 to determine where to focus one or more of the sensors 602” A sensor control component can include an attention model.)
wherein the generating, by the one or more processors, a sensor command (sensor control signals) based at least in part upon the model inference (prediction signals) comprises generating the sensor command (sensor control signals) based at least in part upon the model inference (prediction signals) and the user input (human visual focus labels used to train an attention model). (Paragraph [0125], “The perception signals 106 and (in some embodiments) prediction signals 122 are input to a sensor control component 130”)
Regarding claim 12, Englard teaches the material disclosed in claim 10, and additionally teaches:
wherein the sensor (sensors 102) is configured to change a sensor configuration (parameter adjustment module 136 determines the setting for parameter(s) of the controlled sensor(s)), wherein the sensor configuration (parameter adjustment module) is associated with or is in accordance with the sensor parameter in the sensor command. (parameter adjustment module 136 determines the setting for parameter(s) of the controlled sensor(s)) (Paragraph [0061], “The parameter adjustment module 136 determines the setting for parameter(s) of the controlled sensor(s) (among sensors 102) based on the dynamic objects detected by the dynamic object detector 134. In particular, the parameter adjustment module 136 determines values of one or more parameters that set the area of focus of the controlled sensor(s).”)
Regarding claim 13, Englard teaches the material disclosed in claim 10, and additionally teaches:
wherein the sensor (sensors 102) is configured to change the one or more sensor parameters based upon the sensor command. (parameter adjustment module 136 determines values of one or more parameters that set the area of focus of the controlled sensor(s)) (Paragraph [0061])
Regarding claim 14, Englard teaches the material disclosed in claim 13, and additionally teaches:
wherein the one or more sensor parameters include a target area ( “field of regard”) in which the sensor is configured to gather the first set of sensor data, and the sensor is configured to decrease or increase the target area (“reducing the horizontal and vertical field of regard”) based upon the sensor command. (Paragraph [0062], “For example, the parameter adjustment module 136 may set lidar device parameters such that the field of regard of the lidar device is centered on the current position of a dynamic object, and possibly also “zoomed in” on that object (e.g., by reducing the horizontal and vertical field of regard without necessarily reducing the number of points in each point cloud frame)”)
Regarding claim 15, Englard teaches the material disclosed in claim 13, and additionally teaches:
receiving a second set of sensor data collected by the sensor after the sensor changes the one or more sensor parameters based upon the sensor command. (Fig. 1, Fig. 1 shows that the sensor command (sensor control signal 132), which cause the sensor to change one or more sensor parameters, loops to the sensors, resulting in a loop that a person having ordinary skill in the art would recognize causes the perception component to receive a second set of sensor data collected by the sensor.)
Regarding claim 16, Englard teaches the material disclosed in claim 10, and additionally teaches:
wherein the sensor command includes at least one action command selected from a group consisting of:
a command instructing an edge device associated with the sensor to follow one or more movements of the target object;
a command instructing the sensor (sensors 102) to follow the one or more movements of the target object; and (“track distinct objects”)
a command instructing the edge device to move closer to the target object. (Fig. 1; Paragraph [0054], “The tracking module 114 is generally configured to track distinct objects over time”; Paragraph [0060], “For example, the dynamic object detector 134 may apply one or more rules or algorithms, or use a machine learning model, to directly identify dynamic objects within point cloud frames from one of the sensors 102. In effect, in some embodiments, this may be viewed as the dynamic object detector 134 implementing a more simplistic version of the functionality of segmentation module 110, classification module 112, and/or tracking module 114” Dynamic object detector 134 is a part of the sensor control component which outputs commands. Thus, the sensor control component includes an action command of a command instructing the sensor to follow the one or more movements of the target object.)
A method for sensor cueing, the method comprising:
receiving a model inference (prediction signal 122) from a computing model (prediction component 120) using a first set of sensor data (perception signals 106), the model inference associated with a target object (“prediction component 120 may analyze the type/class of the object … to predict”) and a target area associated with the target object (“future positions of the object”); (Fig. 1; Paragraph [0056], “The sensor control architecture 100 also includes a prediction component 120, which processes the perception signals 106 to generate prediction signals 122 descriptive of one or more predicted future states of the vehicle's environment. For a given object, for example, the prediction component 120 may analyze the type/class of the object (as determined by the classification module 112) along with the recent tracked movement of the object (as determined by the tracking module 114) to predict one or more future positions of the object.” Analyzing the type/class of an object means the prediction component and the prediction signal generated by it are associated with the object. Fig. 1 shows the sensor control component receiving the prediction signal.)
transmitting (Fig.1, arrow 132 from 130 to 102) the model inference (Fig.1, prediction signal 122 to sensor control component 130 to 102 by 132) to a user device (sensor control component); (Fig. 1; Paragraph [0049], “As seen in FIG. 1, the vehicle includes N different sensors 102” The model inference is the prediction signal 122 and is shown in Fig. 1 being transmitted to the sensor control component.)
receiving, from the user device (sensor control component), a user input comprising an identification of the target object or the target area (labels that correspond to the visual focus of expert human drivers); (Paragraph [0041], “Alternatively, an attention model may be trained using other techniques, such as supervised learning with labels that correspond to the visual focus of expert human drivers”) (Paragraph [0129], “Human gaze directions/locations may be tracked using any suitable technology, such as image processing of driver-facing cameras to detect the direction in which the user's pupils are facing over time.” Visual focus of expert human drivers is received from cameras on a vehicle.) (Paragraph [0125], “The sensor control component 630 includes an attention model 634 to determine where to focus one or more of the sensors 602” A sensor control component can include an attention model.)
generating a sensor command (sensor control signals 132) based at least in part upon the model inference (prediction signals 122), the target object ( Paragraph [0056], “For a given object, for example, the prediction component 120 may analyze the type/class of the object…”), and the user input (visual focus of expert human drivers used to train an attention model used in the sensor control component) the sensor command comprising one or more object parameters associated with the target object (“the sensor control component 130 attempts to direct the focus of one or more of the sensors 102 based on the presence, positions, and/or types of “dynamic” objects within the vehicle's environment”) and one or more sensor parameters associated with a sensor (“control one or more parameters of at least one of the sensors 102”); (Paragraph [0058])
transmitting (Fig.1, arrow 132 from 130 to 102) the sensor command (sensor control signals 132) to the sensor (sensors 102) via a sensor API (sensor control component 130); (Fig. 1; Paragraph [0058]) and
causing to adjust the sensor (Paragraph [0058], “the sensor control component 130 attempts to direct the focus of one or more of the sensors 102…”) based on the one or more sensor parameters in the sensor command (Paragraph [0061], “the parameter adjustment module 136 determines values of one or more parameters that set the area of focus of the controlled sensor(s)”) and the one or more object parameters in the sensor command (Paragraph [0058], “…based on the presence, positions, and/or types of “dynamic” objects within the vehicle's environment”); (The parameter adjustment module 136 is a part of sensor control component 130)
wherein the method is performed using one or more processors. (Paragraph [0174], “The computing system 800 includes one or more processors 802, and a memory 804 storing instructions 806”)
Regarding claim 19, Englard teaches the material disclosed in claim 18, and additionally teaches:
wherein the sensor (sensors 102) is configured to change the one or more sensor parameters based upon the sensor command. (parameter adjustment module 136 determines values of one or more parameters that set the area of focus of the controlled sensor(s)) (Paragraph [0061])
Regarding claim 20, Englard teaches the material disclosed in claim 19, and additionally teaches:
wherein the one or more sensor parameters include the target area received in the user input (Paragraph [0041] “visual focus of expert human drivers”), and the sensor is configured to decrease or increase the target area (“reducing the horizontal and vertical field of regard”) based upon the sensor command. (Paragraph [0062], “For example, the parameter adjustment module 136 may set lidar device parameters such that the field of regard of the lidar device is centered on the current position of a dynamic object, and possibly also “zoomed in” on that object (e.g., by reducing the horizontal and vertical field of regard without necessarily reducing the number of points in each point cloud frame)”)
Regarding claim 21, Englard teaches the material disclosed in claim 19, and additionally teaches:
receiving a second set of sensor data collected by the sensor after the sensor changes the one or more sensor parameters based upon the sensor command. (Fig. 1, Fig. 1 shows that the sensor command (sensor control signal 132), which cause the sensor to change one or more sensor parameters, loops to the sensors, resulting in a loop that a person having ordinary skill in the art would recognize causes the perception component to receive a second set of sensor data collected by the sensor.)
Regarding claim 22, Englard teaches the material disclosed in claim 18, and additionally teaches:
wherein the sensor command includes at least one action command selected from a group consisting of:
a command instructing an edge device associated with the sensor to follow one or more movements of the target object;
a command instructing the sensor (sensors 102) to follow the one or more movements of the target object; and (“track distinct objects”)
a command instructing the edge device to move closer to the target object. (Fig. 1; Paragraph [0054], “The tracking module 114 is generally configured to track distinct objects over time”; Paragraph [0060], “For example, the dynamic object detector 134 may apply one or more rules or algorithms, or use a machine learning model, to directly identify dynamic objects within point cloud frames from one of the sensors 102. In effect, in some embodiments, this may be viewed as the dynamic object detector 134 implementing a more simplistic version of the functionality of segmentation module 110, classification module 112, and/or tracking module 114” Dynamic object detector 134 is a part of the sensor control component which outputs commands. Thus, the sensor control component includes an action command of a command instructing the sensor to follow the one or more movements of the target object.)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 9, 17, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Englard, in view of US 20230344705 A1 by Amini et al., hereafter Amini.
Regarding claim 9, Englard teaches the material disclosed in claim 1.
Englard does not explicitly disclose:
wherein the computing model includes a large language model.
Amini teaches:
wherein the computing model includes a large language model. ((Amini) Paragraph [0261], “In some embodiments, ML system 1900 is a generative artificial intelligence or generative AI system capable of generating text, images, or other media in response to prompts. Generative AI systems use generative models such as large language models to produce data based on the training data set that was used to create them.”)
Amini and Englard are in the same area of invention, that being adjustment of parameters including of sensor parameters.
Thus, it would have been obvious to a person having ordinary skill in the art to have combined the large language model, as disclosed in Amini, with the computing model as disclosed in Englard in order to generate captions for images to make it easier for manual control users to recognize objects. This combination of a large language model with a computing model does not change the functionality of either prior art element and would produce the predictable result of a computing model including a large language model which is the same as claimed in claim 9 of the instant application.
Regarding claim 17, Englard teaches the material disclosed in claim 10.
Englard does not explicitly disclose:
wherein the computing model includes a large language model.
Amini teaches:
wherein the computing model includes a large language model. ((Amini) Paragraph [0261], “In some embodiments, ML system 1900 is a generative artificial intelligence or generative AI system capable of generating text, images, or other media in response to prompts. Generative AI systems use generative models such as large language models to produce data based on the training data set that was used to create them.”)
Amini and Englard are in the same area of invention, that being adjustment of parameters including of sensor parameters.
Thus, it would have been obvious to a person having ordinary skill in the art to have combined the large language model, as disclosed in Amini, with the computing model as disclosed in Englard in order to generate captions for images to make it easier for manual control users to recognize objects. This combination of a large language model with a computing model does not change the functionality of either prior art element and would produce the predictable result of a computing model including a large language model which is the same as claimed in claim 17 of the instant application.
Regarding claim 23, Englard teaches the material disclosed in claim 18.
Englard does not explicitly disclose:
wherein the computing model includes a large language model.
Amini teaches:
wherein the computing model includes a large language model. ((Amini) Paragraph [0261], “In some embodiments, ML system 1900 is a generative artificial intelligence or generative AI system capable of generating text, images, or other media in response to prompts. Generative AI systems use generative models such as large language models to produce data based on the training data set that was used to create them.”)
Amini and Englard are in the same area of invention, that being adjustment of parameters including of sensor parameters.
Thus, it would have been obvious to a person having ordinary skill in the art to have combined the large language model, as disclosed in Amini, with the computing model as disclosed in Englard in order to generate captions for images to make it easier for manual control users to recognize objects. This combination of a large language model with a computing model does not change the functionality of either prior art element and would produce the predictable result of a computing model including a large language model which is the same as claimed in claim 23 of the instant application.
Response to Arguments
Applicant's arguments filed 08/05/2026 have been fully considered but they are not persuasive.
In response to applicant’s argument, on page 9 lines 9-18, that Englard fails to disclose “receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object” because the prediction signals are not associated with a target object, paragraph [0056] of Englard recites that “the prediction component 120 may analyze the type/class of the object” to predict future positions of the object. The type/class of the object is an object parameter associated with the object and is used by the prediction component to produce the prediction signals, so the prediction signals (model inference) are associated with the object.
In response to applicant’s argument, on page 10 lines 7-24 concerning 102 and 103 rejections, that Englard does not disclose including any “object parameters” in the sensor control signals, paragraph [0058] recites that “the sensor control component 130 attempts to direct the focus of one or more of the sensors 102 based on the presence, positions, and/or types of “dynamic” objects within the vehicle’s environment” and a sensor control component 130 generates sensor control signals 132 that control one or more parameters of at least one of the sensors 102. The presence, positions, and/or types of “dynamic” objects are object parameters used to generate the sensor control signal (sensor command).
In response to applicant’s argument, on page 10 lines 24-28 concerning 102 and 103 rejections, that Englard does not disclose the amended limitation, “causing to adjust the sensor based on the one or more sensor parameters in the sensor command and the one or more object parameters in the sensor command”, paragraph [0058] recites that “the sensor control component 130 attempts to direct the focus of one or more of the sensors 102 based on the presence, positions, and/or types of “dynamic” objects within the vehicle’s environment” and paragraph [0061] recites that “The parameter adjustment module 136 determines the setting for parameter(s) of the controlled sensor(s) (among sensors 102) based on the dynamic objects detected by the dynamic object detector 134.” The parameter adjustment module 136 is a part of the sensor control component 130. Attempting to direct the focus of one or more of the sensors is causing to adjust the one or more of the sensors based on one or more sensor parameters, and based on the presence, positions, and/or type of “dynamic” objects indicates that the adjusting is based on object parameters in the senor control signal (sensor command).
In response to applicant’s argument, on pages 12 and 13 concerning Step 2A, Prong One for a 35 U.S.C. 101 rejection, “receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object” is rejected under Step 2A, Prong Two and Step 2B rather than Step 2A, Prong One, and as such does not necessarily need to be practically performed in the human mind. “receiving a model inference from a computing model using a first set of sensor data, the model inference associated with a target object” is instead insignificant extra-solution activity of data gathering, involving storing and retrieving information in memory, which MPEP 2106.05(d) indicates (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) is well-understood, routine and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity. In addition, “generating a sensor command based at least in part upon the model inference and the target object, the sensor command comprising one or more object parameters associated with the target object and one or more sensor parameters associated with a sensor,” and “causing to adjust the sensor based on the one or more sensor parameters in the sensor command and the one or more object parameters in the sensor command” can be done by observing a received model inference and data concerning a target object, determining instructions concerning changing parameters of a target object and parameters of a sensor using those observations, and applying a change to the sensor based on the instructions. This amounts to actions that are performed mentally with aid of pen and paper then actions amounting to no more than mere instructions to apply those mental actions on a generic sensor, see MPEP 2106.05(f) and MPEP 2106.05(a)(2)(III)(C). Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94 (Fed. Cir. 2015).
In response to applicant’s argument, on pages 13 to 15, concerning Step 2A, Prong Two and Step 2B for a 35 U.S.C. 101 rejection, the Desjardins Memo page 2, paragraph 2, states, “Conversely, if the specification explicitly sets forth an improvement but only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field. Second, if the specification sets forth an improvement in technology or a technical field, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement, i.e., that the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”).” The processes described in claim 1, do not provide any indication of the ability of “correcting sensor biases” or “removing erroneous information” and as such claim 1 does not reflect the disclosed improvement. Suggestions of the technical benefit of improving the performance of the sensor using sensor cueing are also not reflected for the same reason. “Collect more relevant data from the sensor” sets forth an improvement but only in a conclusory manner and thus does not improve the technology.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Patents and/or related publications are cited in the Notice of References Cited (Form PTO-892) attached to this action to further show the state of the art.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 DYLAN H LAI whose telephone number is (571)272-8628. The examiner can normally be reached Monday - Friday 7:30am-5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 5712524241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/D.H.L./Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
Prosecution Insights