DETAILED ACTION
This communication is in response to the amendment filed 2/28/26. Claims 1-33 are currently 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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/26/25 was filed after the mailing date of the non-final action on 10/1/25. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant’s arguments, see Remarks, filed Feb. 2, 2026, with respect to claims 1, 8, and 27 have been fully considered and are persuasive. The obviousness rejection of claims 1-14 and 27-44 has been withdrawn.
Applicant’s arguments with respect to claims 15 and 21 have been considered but are unpersuasive, at least because claims 15 and 21 are of broader scope than claims 15 and 21 and still read on the prior art.
Applicant’s arguments with respect to the 101 rejection of claims 1-33 have been considered but are unpersuasive. Unlike the limitation at issue in Ex Parte Desjardins, the claims do not reflect the improvement in performance.
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—33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-7
Step 1: YES. Claims 1-7 are directed to a processor and, therefore, belong to a statutory category.
Step 2A Prong 1: YES. Claim 1 describes “determining” respective amounts of unlabeled training data to select from different categories of training data according to a predefined distribution of training data across the categories when the selected unlabeled training data is combined with a set of labeled training data. As drafted, and under a broadest reasonable interpretation, “determining” encompasses a user mentally calculating the number of unlabeled training samples (e.g., images) of each category (e.g., sunny, cloudy) to achieve a target distribution (e.g., 50/50) of training data images. For instance, if there is a single labeled image of a sunny day, then the user may determine that a single unlabeled image of a cloudy day would result in a 50/50 distribution. Such a determination may be made mentally or with the aid of pen and paper and, therefore, falls under the Mental Processes grouping of abstract ideas.
Further, claim 1 describes “selecting” the determined respective amounts of unlabeled training data based on metadata associated with the unlabeled training data indicative of different categories. Under a broadest reasonable interpretation, “selecting” encompasses a user evaluating multiple unlabeled images belonging to a desired category and choosing one based on judgment. Thus, this step falls under the Mental Processes grouping of abstract ideas.
Step 2A Prong 2/Step 2B: NO.
The additional elements of the claim fail to integrate the exception into a practical application or provide an inventive concept.
Recitation of generic computer components (e.g., a processor, one or more circuits) is considered mere instruction to “apply” the exception and, therefore, does not integrate the exception into a practical application or provide an inventive concept.
The claim further recites obtaining labels for the automatically selected unlabeled training data. Data gathering is considered insignificant extra-solution activity and, therefore, does not integrate the exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.”
The final limitation describes the processor causing a neural network to be trained based on the automatically selected labeled training data. As drafted, recitation of a generic computer component to perform a neural network training on labeled training data is mere instruction to “apply” the exception and, therefore, does not integrate the exception or provide an inventive concept.
Accordingly, claim 1 is ineligible.
A summary of the analysis with respect to claim 1 and its dependent claims follows.
[Claim 1] A processor, [Step 1: processor is a machine and therefore constitutes a statutory category] comprising:
one or more circuits to: [Step 2A, prong 2/Step 2B: generic computer component recited at a high level of generality is a mere instruction to apply the exception under MPEP 2106.05(f)]
determine respective amounts of unlabeled training data to select from different categories of training data according to a predefined target distribution of training data across the different categories when the selected unlabeled training data is combined with a set of already-labeled training data, wherein the different categories are determined from the predefined target distribution; [Step 2A, prong 1: determining amounts of data according to a target distribution may be performed manually by human judgement and, therefore, is a mental process]
automatically select the determined respective amounts of unlabeled training data based, at least in part, on metadata associated with the unlabeled training data indicative of the different categories; [Step 2A, prong 1: selecting amounts of training data based on metadata may be performed manually by human judgment and, therefore, is a mental process; Step 2A, prong 2: “automatically” is mere instruction to apply the exception under MPEP 2106.05(f)]
obtain labels for the automatically selected unlabeled training data [Step 2A, prong 2: obtaining labels is data gathering/insignificant extra-solution activity under MPEP 2106.05(g); Step 2B: receiving data is well understood routine and conventional under MPEP 2106.05(d)] to produce automatically selected labeled training data; and [Step 2A, prong 1: producing selected labeled training may be performed manually and, therefore, is a mental process; Step 2A prong 2: “automatically” is mere instruction to apply the exception under MPEP 2106.05(f)]
cause one or more neural networks to be trained based, at least in part, on the automatically selected labeled training data [Step 2A prong 2: using training data to train a neural network recited at a high level of generality is mere instruction to apply the exception under MPEP 2106.05(f)].
[Claim 2] The processor of claim 1, [Step 1: processor is a machine and therefore constitutes a statutory category] wherein the one or more circuits are further to: [Step 2A, prong 2/Step 2B: generic computer component recited at a high level of generality is a mere instruction to apply the exception under MPEP 2106.05(f)]
obtain a plurality of unlabeled training data; [Step 2A prong 2: data gathering is insignificant extra-solution activity under MPEP 2106.05(g)]
process the plurality of unlabeled training data into a set of groups based at least in part on the metadata, wherein each group of the set of groups corresponds to a different category of the different categories; and [Step 2A prong 1: processing unlabeled training data into a set of groups based on metadata may be performed manually and constitutes a mental process]
wherein to automatically select the determined respective amounts of the unlabeled training data the one or more circuits are further to automatically select the determined respective amounts based at least in part on the set of groups [Step 2A prong 2/Step 2B: recitation of “the one or more circuits are further to automatically” is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)].
[Claim 3] The processor of claim 2, [Step 1: processor is a machine and therefore constitutes a statutory category] wherein a first group of the set of groups corresponds to a first combination of metadata values [Step 2A prong 1: processing unlabeled training data into a set of groups based on metadata may be performed manually and constitutes a mental process].
[Claim 4] The processor of claim 2, [Step 1: processor is a machine and therefore constitutes a statutory category] wherein the one or more circuits to [Step 2A prong 2/Step 2B: mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)] determine the respective amounts of unlabeled training data to select from different categories of training data are further to use one or more equation solvers to automatically determine respective amounts [Step 2A prong 1: determining an amount of training data to select to use equation solvers to determine the amounts recites a mental process; Step 2A prong 2/Step 2B: using equation solvers to automatically determine the amounts recites a mere instruction to apply the exception at a high level using a generic computer component].
[Claim 5] The processor of claim 1, [Step 1: processor is a machine and therefore constitutes a statutory category] wherein the one or more circuits are further to: [Step 2A prong 2/Step 2B: mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)]
train the one or more neural networks using at least the set of already-labeled training data and the automatically selected labeled training data [Step 2A prong 2/Step 2B: mere instruction to apply the exception in training step recited at a high level of generality].
[Claim 6] The processor of claim 1, [Step 1: processor is a machine and therefore constitutes a statutory category] wherein the unlabeled training data comprises one or more images captured from one or more vehicles [Step 2A prong 2/Step 2B: the type/source of data does not cause the data gathering activity to integrate the exception into a practical application under MPEP 2106.05(g)].
[Claim 7] The processor of claim 1, [Step 1: processor is a machine and therefore constitutes a statutory category] wherein the metadata indicates one or more operational design domain (ODD) values [Step 2A prong 2/Step 2B: generally linking the exception to a particular technological environment (operational design)].
Claims 8-14
Step 1: YES. Claims 8-14 are directed to a system and, therefore, fall under a statutory category.
Step 2A Prong 1: YES. Claim 8 is substantially similar to claim 1 and, therefore, is similarly analyzed.
Step 2A Prong 2/Step 2B: NO. Claim 8 is substantially similar to claim 1 and, therefore, is similarly analyzed.
A summary of the analysis for claims 8-14 follows.
[Claim 8] A system, comprising: [Step 1: system is a machine and therefore constitutes a statutory category]
one or more computers having one or more processors to: [Step 2A prong 2/Step 2B: mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)]
determine respective amounts of unlabeled training data to select from different categories of training data according to a predefined target distribution of training data across the different categories when the selected unlabeled training data is combined with a set of already-labeled training data, wherein the different categories are determined from the predefined target distribution; [Step 2A, prong 1: determining amounts of data according to a target distribution may be performed manually by human judgement and, therefore, is a mental process]
automatically select the determined respective amounts of unlabeled training data based, at least in part, on metadata associated with the unlabeled training data indicative of the different categories; [Step 2A, prong 1: selecting amounts of training data based on metadata may be performed manually by human judgment and, therefore, is a mental process; Step 2A, prong 2: “automatically” is mere instruction to apply the exception under MPEP 2106.05(f)]
obtain labels for the automatically selected unlabeled training data [Step 2A, prong 2: obtaining labels is data gathering/insignificant extra-solution activity under MPEP 2106.05(g); Step 2B: receiving data is well understood routine and conventional under MPEP 2106.05(d)] to produce automatically selected labeled training data; and [Step 2A, prong 1: producing selected labeled training may be performed manually and, therefore, is a mental process; Step 2A prong 2: “automatically” is mere instruction to apply the exception under MPEP 2106.05(f)]
cause one or more neural networks to be trained based, at least in part, on the automatically selected labeled training data [Step 2A prong 2: using training data to train a neural network recited at a high level of generality is mere instruction to apply the exception under MPEP 2106.05(f)].
[Claim 9] The system of claim 8, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the one or more processors are further to: [Step 2A prong 2/Step 2B: mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)]
obtain a set of unlabeled training data and associated metadata; [Step 2A prong 2: data gathering is insignificant extra-solution activity under MPEP 2106.05(g)]
parse the set of unlabeled training data to calculate subsets of unlabeled training data based at least in part on the associated metadata, wherein each subset of the set of groups corresponds to a different category of the different categories; and [Step 2A prong 1: processing unlabeled training data into a set of groups based on metadata may be performed manually and constitutes a mental process]
wherein to determine the respective amounts of unlabeled training data to select from different categories the one or more processors are to use one or more equation solvers to calculate numbers of assets for the subsets of unlabeled training data [Step 2A prong 1: determining an amount of training data to select to use equation solvers to determine the amounts recites a mental process; Step 2A prong 2/Step 2B: using equation solvers to automatically determine the amounts recites a mere instruction to apply the exception at a high level using a generic computer component].
[Claim 10] The system of claim 9, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the one or more processors are further to automatically [Step 2A prong 2/Step 2B: mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)] select the unlabeled training data from the subsets of unlabeled training data based at least in part on the calculated numbers of assets [Step 2A prong 1: mental process involving evaluation, opinion, judgment].
[Claim 11] The system of claim 9, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the numbers of assets are calculated based at least in part on one or more target proportions of the target distribution [Step 2A prong 1: mathematical calculation].
[Claim 12] The system of claim 8, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the unlabeled training data comprises one or more images captured from one or more medical devices [Step 2A prong 2/Step 2B: the type/source of data does not cause the data gathering activity to integrate the exception into a practical application under MPEP 2106.05(g)].
[Claim 13] The system of claim 8, [Step 1: system is a machine and therefore constitutes a statutory category] wherein output data of the one or more neural networks comprises one or more classifications of one or more objects depicted in one or more images [Step 2A prong 1: type of outputted data does not cause judicial exception to be integrated into a practical application; Step 2B: data outputting is well understood routine and conventional under MPEP 2106.05(g)].
[Claim 14] The system of claim 13, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the automatically selected labeled training data is automatically selected to cause the one or more neural networks to [Step 2A prong 2/Step 2B: mere instruction to apply exception using generic computer components] generate output data having one or more attributes comprising one or more accuracy values corresponding to the one or more classifications [Step 2A prong 2: data outputting/transmission is insignificant extra-solution activity; type of data does not cause the outputting step to integrate the judicial exception into a practical application; Step 2B: data outputting/transmission is well understood routine and conventional under MPEP 2106.05(d)].
Claims 15-20
Step 1: YES. Claims 15-20 are directed to a processor and, therefore, falls within a statutory category.
Step 2A Prong 1: YES.
Claim 15 describes selecting training data based on metadata to have a distribution satisfying a predefined target distribution across different categories when the selected training data is combined with training data previously used to train the neural networks, wherein the different categories are determined from the predefined target distribution. As discussed above in relation to claim 1, this encompasses a user manually selecting training images based on a desired target distribution of categories used to train the neural network and may be performed by inspection, evaluation, and judgment and, therefore, falls under the Mental Processes grouping of abstract ideas.
Step 2A Prong 2/Step 2B: NO.
The recitation of generic computer components (e.g., processor, circuits, neural networks) is mere instruction to apply the exception.
Generating output data describes data outputting and, therefore, is insignificant extra-solution activity. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.”
Accordingly, claim 15 is ineligible.
The following is a summary analysis of claim set 15-20.
[Claim 15] A processor, comprising: [Step 1: processor is a machine and therefore constitutes a statutory category]
one or more circuits to use one or more neural networks to [Step 2A prong 2/Step 2B: mere instruction to apply exception using generic computer components] generate output data [Step 2A prong 1: data outputting constitutes insignificant extra-solution activity; Step 2B: data outputting is well understood routine and conventional under MPEP 2106.05(g)] based, at least in part, on training data [Step 2A prong 2: data inputting is insignificant extra solution activity; Step 2B: receiving data is well understood routine and conventional under MPEP 2106.05(d)] automatically selected, based at least in part, on metadata associated with the training data to have a distribution satisfying a predefined target distribution of training data across different categories when the automatically selected training data is combined with training data previously used to train the one or more neural networks, wherein the different categories are determined from the predefined target distribution [Step 2A, prong 1: selecting amounts of training data based on metadata may be performed manually by human judgment and, therefore, is a mental process; Step 2A, prong 2/Step 2B: “automatically” is mere instruction to apply the exception under MPEP 2106.05(f)].
[Claim 16] The processor of claim 15, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the one or more circuits are further to:
obtain one or more images depicting one or more objects; and [Step 2A prong 2: data gathering is insignificant extra-solution activity under MPEP 2106.05(g); Step 2B: receiving data is well understood routine and conventional under MPEP 2106.05(d)]
use the one or more neural networks to generate the output data based on the one or more images [Step 2A prong 2/Step 2B: recitation of using one or more neural networks constitutes mere instruction to apply the exception under MPEP 2106.05 (f)].
[Claim 17] The processor of claim 16, [Step 1: system is a machine and therefore constitutes a statutory category] wherein:
the one or more neural networks include one or more object detection neural networks; and [Step 2A prong 2/Step 2B: recitation of “object detection” refers to field of use and technological environment (object detection) and does not cause integration into a practical application under MPEP 2106.05(h)]
the output data comprises data indicating one or more locations of the one or more objects [Step 2A prong 2: type of data outputted does not cause the data outputting to integrate the exception into a practical application under MPEP 2106.05(g); Step 2B: data transmitting/outputting is well understood routine and conventional under MPEP 2106.05(d)].
[Claim 18] The processor of claim 15, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the metadata indicates one or more conditions of the training data [Step 2A prong 2/Step 2B: the type of metadata does not cause the mental process to be practically integrated].
[Claim 19] The processor of claim 15, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the one or more circuits are further to [Step 2A prong 2/Step 2B: recitation of circuits is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)] use one or more equation solvers to automatically select the training data, wherein the one or more equation solvers are based at least in part on a linear formulation [Step 2A prong 2/Step 2B: mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)].
[Claim 20] The processor of claim 15, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the training data comprises sensor data [Step 2A prong 2: type of training data does not cause the data inputting to be practically integrated under MPEP 2106.05(g); Step 2B: data inputting/receiving is well understood routine and conventional under MPEP 2106.05(d)].
Claims 21-26
Claim 21 is substantially similar to claim 15 and, therefore, is similarly analyzed as ineligible. Claims 21-26 are analyzed as follows.
[Claim 21] A system, comprising: [Step 1: system is a machine and therefore constitutes a statutory category]
one or more computers having one or more processors to [Step 2A prong 2/Step 2B: recitation of computers/processors is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)] use one or more neural networks to [Step 2A prong 2/Step 2B: recitation of neural networks is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)] generate output data [Step 2A prong 2: data outputting constitutes insignificant extra-solution activity; Step 2B: data outputting is well understood routine and conventional under MPEP 2106.05(g)] based, at least in part, on training data [Step 2A prong 2: data inputting is insignificant extra solution activity; Step 2B: receiving data is well understood routine and conventional under MPEP 2106.05(d)] automatically selected to have a distribution satisfying a predefined target distribution of training data across different categories when the automatically selected training data is combined with training data previously used to train the one or more neural networks, wherein the different categories are determined from the predefined target distribution [Step 2A, prong 1: selecting training data to cause output data to have one or more attributes may be performed manually by human judgment and, therefore, is a mental process; Step 2A, prong 2/Step 2B: “automatically” is mere instruction to apply the exception under MPEP 2106.05(f)].
[Claim 22] The system of claim 21, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the one or more processors are further to [Step 2A prong 2/Step 2B: recitation of computers/processors is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)] use one or more solvers to automatically [Step 2A prong 2/Step 2B: recitation of solvers is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)] select the training data, [Step 2A prong 1: Mental process] wherein the one or more solvers are based at least in part on a quadratic formulation [Step 2A prong 1: quadratic formulation refers to a numerical algorithm and therefore constitutes a mathematical concept].
[Claim 23] The system of claim 21, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the one or more processors are further to [Step 2A prong 2/Step 2B: recitation of computers/processors is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)] use the one or more neural networks to [Step 2A prong 2/Step 2B: recitation of neural networks is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)] generate the output data based at least in part on a set of images [Step 2A prong 2: data outputting is insignificant extra-solution activity under MPEP 2106.05(g); Step 2B: data transmitting is well understood routine and conventional under MPEP 2106.05(d)].
[Claim 24] The system of claim 21, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the output data comprises one or more results of the one or more neural networks [Step 2A prong 2: source of data does not cause data outputting to be practically integrated under MPEP 2106.05(g); transmitting data is well understood routine and conventional under MPEP 2106.05(d)].
[Claim 25] The system of claim 24, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the one or more attributes indicate one or more confidence values for the one or more results [Step 2A prong 2: type of data outputted does not cause the data outputting to practically integrate the exception under MPEP 2106.05(g); Step 2B: data transmitting is well understood routine and conventional under MPEP 2106.05(d)].
[Claim 26] The system of claim 21, [Step 1: system is a machine and therefore constitutes a statutory category] wherein the training data comprises one or more frames of one or more videos [Step 2A prong 2: type of data inputted does not cause the data inputting to practically integrate the exception under MPEP 2106.05(g); Step 2B: data receiving is well understood routine and conventional under MPEP 2106.05(d)].
Claims 27-33
Step 1: YES. Claims 27-33 are directed to a machine readable medium and, therefore, fall under a statutory category.
Step 2A Prong 1: YES. Claim 27 is substantially similar to claim 1 and, therefore, is similarly ineligible.
Step 2A Prong 2/Step 2B: NO. Recitation of generic computer components (e.g., machine-readable medium, instructions, processors) is mere instruction to apply the exception.
The following is a summary analysis of claims 27-33.
[Claim 27] A machine-readable medium having stored thereon a set of instructions, [Step 1: machine-readable medium comprising instructions is a composition of matter and therefore constitutes a statutory category] which if performed by one or more processors, cause the one or more processors to at least: [Step 2A prong 2/Step 2B: recitation of computers/processors is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)]
determine respective amounts of unlabeled training data to select from different categories of training data according to a predefined target distribution of training data across the different categories when the selected unlabeled training data is combined with a set of already-labeled training data, wherein the different categories are determined from the predefined target distribution; [Step 2A, prong 1: determining amounts of data according to a target distribution may be performed manually by human judgement and, therefore, is a mental process]
automatically select the determined respective amounts of unlabeled training data based, at least in part, on metadata associated with the unlabeled training data indicative of the different categories; [Step 2A, prong 1: selecting unlabeled training data based on metadata may be performed manually by human judgement and, therefore, is a mental process]
obtain labels for the automatically selected unlabeled training data; and [Step 2A prong 2: data gathering is insignificant extra-solution activity under MPEP 2106.05(g); Step 2B: receiving data is well understood routine and conventional under MPEP 2106.05(d)]
cause one or more neural networks to be trained using training data [Step 2A prong 2/Step 2B: recitation of neural networks to be trained using training data constitutes instruction to apply the exception using generic computer components recited at a high level of generality under MPEP 2106.05(f)] automatically selected based, at least in part, on metadata associated with the training data [Step 2A, prong 1: may be performed manually by human judgment and, therefore, is a mental process; Step 2A, prong 2/Step 2B: “automatically” is mere instruction to apply the exception under MPEP 2106.05(f)].
[Claim 28]. The machine-readable medium of claim 27, wherein the set of instructions further comprise instructions, [Step 1: machine-readable medium comprising instructions is a composition of matter and therefore constitutes a statutory category] which if performed by the one or more processors, cause the one or more processors to: [Step 2A prong 2/Step 2B: recitation of computers/processors is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)]
obtain a distribution indicating one or more clauses; and [Step 2A prong 2: data inputting/receiving is insignificant extra-solution activity under MPEP 2106.05(g); type of data does not cause the data inputting to integrate the exception into a practical application under MPEP 2106.05(g); receiving data is well understood routine and conventional under MPEP 2106.05(d)]
automatically select the training data based at least in part on one or more proportions indicated by the one or more clauses [Step 2A prong 1: mental process; Step 2A prong 2/Step 2B: “automatically” is mere instruction to apply the exception under MPEP 2106.05(f)].
[Claim 29] The machine-readable medium of claim 27, wherein the set of instructions further comprise instructions, [Step 1: machine-readable medium comprising instructions is a composition of matter and therefore constitutes a statutory category] which if performed by the one or more processors, cause the one or more processors to: [Step 2A prong 2/Step 2B: recitation of computers/processors is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)]
obtain one or more collections of assets corresponding to one or more operational design domain (ODD) values; [Step 2A prong 2: data inputting/receiving is insignificant extra-solution activity under MPEP 2106.05(g); type of data does not cause the data inputting to integrate the exception into a practical application under MPEP 2106.05(g); receiving data is well understood routine and conventional under MPEP 2106.05(d)]
select a first asset from a first collection of assets; [Step 2A prong 1: mental process]
compute one or more distances between the first asset and the first collection of assets; and [Step 2A prong 1: mathematical calculation]
select a second asset based at least in part on the one or more distances, wherein the training data comprises the first asset and the second asset [Step 2A prong 1: mental process].
[Claim 30] The machine-readable medium of claim 29, [Step 1: machine-readable medium comprising instructions is a composition of matter and therefore constitutes a statutory category] wherein the one or more distances are based on a temporal distance or a spatial distance [Step 2A prong 1: mathematical calculation].
[Claim 31] The machine-readable medium of claim 27, [Step 1: machine-readable medium comprising instructions is a composition of matter and therefore constitutes a statutory category] wherein the training data is selected based at least in part on labelled training data used to train the one or more neural networks [Step 2A prong 1: mental process].
[Claim 32] The machine-readable medium of claim 27, [Step 1: machine-readable medium comprising instructions is a composition of matter and therefore constitutes a statutory category] wherein the set of instructions further comprise instructions, which if performed by the one or more processors, cause the one or more processors to: [Step 2A prong 2/Step 2B: recitation of computers/processors is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)]
provide the training data to one or more labelling entities [Step 2A prong 2: data outputting is insignificant extra-solution activity under MPEP 2106.05(g); Step 2B: data transmission is well understood routine and conventional under MPEP 2106.05(d)] to obtain one or more labels [Step 2A prong 2: data inputting is insignificant extra-solution activity under MPEP 2106.05(g); Step 2B: receiving data is well understood routine and conventional under MPEP 2106.05(d)];
cause the one or more neural networks to [Step 2A prong 2/Step 2B: recitation of neural networks is mere instruction to apply the exception using generic computer components under MPEP 2106.05(f)] process the training data to calculate one or more results [Step 2A prong 1: mathematical calculations]; and
update the one or more neural networks based at least in part on the one or more results and the one or more labels [Step 2A prong 2/Step 2B: updating neural network based on training data is how neural networks are trained and is mere instruction to apply the exception under MPEP 2106.05(f)].
[Claim 33] The machine-readable medium of claim 27, [Step 1: machine-readable medium comprising instructions is a composition of matter and therefore constitutes a statutory category] wherein the training data comprises one or more images captured from one or more autonomous devices [Step 2A prong 2: type of input data or source of data does not cause the data inputting to practically integrate the exception under MPEP 2106.05(g); Step 2B: receiving data is well understood routine and conventional under MPEP 2106.05(d)].
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 non-obviousness.
Claims 15-25 are rejected under 35 U.S.C. 103 as being unpatentable over Bernard (US 10,140,421 B1; published Nov. 27, 2018) in view of Weider (US 2020/0380309 A1; published Dec. 3, 2020).
Regarding claim 15, Bernard discloses [a] processor, comprising:
one or more circuits (72:30-34 (“The medical scan image analysis system can include a processing system that includes a processor and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations.”)) to use one or more neural networks to generate output data based, at least in part, on training data automatically selected, based at least in part, on metadata associated with the training data (72:52-61 (“A training set of medical scans used to train one more medical scan image analysis functions can be received from one or more client devices via the network and/or can be retrieved from the medical scan database 342, for example, based on training set data 621 corresponding to medical scan image analysis functions. Training set criteria, for example, identified in training parameters 620 of the medical scan image analysis function, can be utilized to automatically identify and select medical scans to be included in the training set from a plurality of available medical scans.”), 80:62-81:5 (“The inferred output vector of the inference data 1370 can include a plurality of abnormality probabilities mapped to a pixel location of each of a plurality of cross-sectional image slices of the new medical scan. For example, the inferred output vector can indicate a set of probability matrices 1371, where each matrix in the set corresponds to one of the plurality of image slices of the medical scan, where each matrix is a size corresponding to the number of pixels in each image slice, where each cell of each matrix corresponds to a pixel of the corresponding image slice, whose value is the abnormality probability of the corresponding pixel.”)).
Bernard does not expressly disclose that the unlabeled samples are selected from different categories to have a distribution satisfying a predefined target distribution of training data across different categories when the automatically selected training data is combined with training data previously used to train the one or more neural networks, wherein the different categories are determined from the predefined target distribution (but see Weider ¶ 41 (“FIGS. 3A-3B depict example bar charts for displaying distribution in data to show how bias can be present in a dataset and affect outcome of a model. FIG. 3A displays a bar chart 300A that depicts an ideal distribution of data in a dataset based on a gender attribute of the dataset. This assumes that one of the attributes of a datapoint in the dataset is gender and gender is categorized by three categories: female, male and non-binary. The example also assumes that the dataset is used to train a model for determining loan approvals. For such a dataset, an ideal distribution based on gender may result in a female bar 310 that has an equal distribution to the male bar 320 and the non-binary bar 330. This means the number of datapoints that represent each of the categories of the gender attribute may be equal or be within a predetermined distribution threshold. As a result, the percentage of loans approved for people falling into each category may also be equal. Thus, the model trained by this dataset may generate outcomes that are consistent across the gender spectrum (e.g. 10% of loans submitted by applicants in each category are approved).”), ¶ 42 (“The ideal distribution depicted in FIG. 3A, however, rarely occurs in the real world. Often the dataset is representative of one category more than others. FIG. 3B depicts a bar chart 300B displaying a more realistic real-world distribution of data across the gender spectrum in a dataset. The bar chart 300B shows the female bar 340 represents 35% of the data, while the male bar 350 represents 55% of the data and the non-binary bar chart 360 represents only 10% of data. This shows a clear imbalanced distribution of data across the three categories. When such an imbalanced dataset is used to train a ML mode, the outcome is often severely biased. FIG. 3C depicts a bar chart 300C displaying such an outcome. The female bar 370 of bar chart 300C shows that the ML model rejects 97% of female applicants, while the male bar 380 displays how only 3% of the male applicants are rejected by the ML model. As the non-binary bar 390 shows, the percentage of people falling into the non-binary category that are rejected is even higher than the female applicants, with a 99% rejection rate. As such, imbalanced or biased distribution of input data in a dataset can significantly impact the outcome produced by a ML model trained with the imbalanced dataset.”), ¶ 43 (“To address such imbalanced distributions, the input dataset may be trimmed to select a subset of the dataset that represents a more balanced distribution. For example, the subset may be selected based on the size of the category having the smallest distribution. Referring to the imbalanced distribution of FIG. 3B, this may mean choosing the size of the non-binary category as the measuring point and selecting a dataset that corresponds with data in each of the female and male categories in numbers that are equal to or within a desired distribution of the non-binary category. For example, if the non-binary category includes 1000 datapoints from a total of 10,000 datapoints for the entire dataset, a subset may be selected such that each of the female, male and non-binary categories has 1000 datapoints. This is illustrated in FIG. 4 which depicts a bar chart 400 displaying a distribution of data across the gender spectrum in a corrected subset of data. As shown in FIG. 4, because of trimming of the dataset, the resulting subset shows a balanced distribution of data across the three categories of the spectrum. As a result, each of the categories of the corrected subset has about 33% of the data. In one implementation, after a correction is performed, the bias detection tool may be executed again to ensure that the new subset achieves its purposes and it does not generate new undesired imbalance in data. The process may be repeated iteratively until an acceptable corrected dataset is achieved.”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Bernard to incorporate the teachings of Weider to choose the size of each category of unlabeled data according to a desired target distribution of the overall dataset, at least because doing so would correct datasets of labeled and unlabeled data for which bias is detected. See Weider ¶ 37 (“Datasets for which bias is examined, detected, and corrected by the bias detection and correction service may be used for training ML models by a training mechanism 224. The training mechanism 224 may use training datasets stored in the datastore 212 to provide initial and/or ongoing training for ML models. In one implementation, the training mechanism 224 may use labeled training data from the data store 212 train the ML models. The initial training may be performed in an offline or online stage. In another example, the training mechanism 224 may utilize unlabeled training data from the datastore 212 to train the ML model via an unsupervised learning mechanism. Unsupervised learning may allow the ML model to create and/or output its own labels. In an example, an unsupervised learning mechanism may apply reinforcement learning to maximize a given value function or achieve a desired goal.”).
Regarding claim 16, Bernard, in view of Weider, discloses the invention of claim 15 as discussed above. Bernard further discloses wherein the one or more circuits are further to:
obtain one or more images depicting one or more objects; and use the one or more neural networks to generate the output data based on the one or more images (80:62-81:5 (“The inferred output vector of the inference data 1370 can include a plurality of abnormality probabilities mapped to a pixel location of each of a plurality of cross-sectional image slices of the new medical scan. For example, the inferred output vector can indicate a set of probability matrices 1371, where each matrix in the set corresponds to one of the plurality of image slices of the medical scan, where each matrix is a size corresponding to the number of pixels in each image slice, where each cell of each matrix corresponds to a pixel of the corresponding image slice, whose value is the abnormality probability of the corresponding pixel.”)).
Regarding claim 17, Bernard, in view of Weider, discloses the invention of claim 16 as discussed above. Bernard further discloses wherein:
the one or more neural networks include one or more object detection neural networks; and (74:15-18 (“each of the medical scan image analysis functions associated with each neural network model can correspond to one of the plurality of neural network models generated by the medical scan image analysis system”))
the output data comprises data indicating one or more locations of the one or more objects (80:62-81:5 (“The inferred output vector of the inference data 1370 can include a plurality of abnormality probabilities mapped to a pixel location of each of a plurality of cross-sectional image slices of the new medical scan. For example, the inferred output vector can indicate a set of probability matrices 1371, where each matrix in the set corresponds to one of the plurality of image slices of the medical scan, where each matrix is a size corresponding to the number of pixels in each image slice, where each cell of each matrix corresponds to a pixel of the corresponding image slice, whose value is the abnormality probability of the corresponding pixel.”)).
Regarding claim 18, Bernard, in view of Weider, discloses the invention of claim 15 as discussed above. Bernard further discloses wherein the metadata indicates one or more conditions of the training data (73:1-2 (“requirements for medical scans in the training set such as requiring that the medical scan is assigned a truth flag”)).
Regarding claim 19, Bernard, in view of Weider, discloses the invention of claim 15 as discussed above. Bernard further discloses wherein the one or more circuits are further to use one or more equation solvers to automatically select the training data, wherein the one or more equation solvers are based at least in part on a linear formulation (73:17-32 (training set criteria can include quota or proportion requirements for one or more medical scan classification data)).
Regarding claim 20, Bernard, in view of Weider, discloses the invention of claim 15 as discussed above. Bernard further discloses wherein the training data comprises sensor data (5:25-33 (“FIG. 4A presents an embodiment of a medical scan entry 352, stored in medical scan database 342, included in metadata of a medical scan, and/or otherwise associated with a medical scan. A medical scan can include imaging data corresponding to a CT scan, x-ray, MRI, PET scan, Ultrasound, EEG, mammogram, or other type of radiological scan or medical scan taken of an anatomical region of a human body, animal, organism, or object and further can include metadata corresponding to the imaging data.”)).
Regarding claim 21, Bernard discloses [a] system, comprising:
one or more computers having one or more processors (72:30-34 (“The medical scan image analysis system can include a processing system that includes a processor and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations.”)) to use one or more neural networks to generate output data based, at least in part, on training data automatically selected (72:52-61 (“A training set of medical scans used to train one more medical scan image analysis functions can be received from one or more client devices via the network and/or can be retrieved from the medical scan database 342, for example, based on training set data 621 corresponding to medical scan image analysis functions. Training set criteria, for example, identified in training parameters 620 of the medical scan image analysis function, can be utilized to automatically identify and select medical scans to be included in the training set from a plurality of available medical scans.”), 80:62-81:5 (“The inferred output vector of the inference data 1370 can include a plurality of abnormality probabilities mapped to a pixel location of each of a plurality of cross-sectional image slices of the new medical scan. For example, the inferred output vector can indicate a set of probability matrices 1371, where each matrix in the set corresponds to one of the plurality of image slices of the medical scan, where each matrix is a size corresponding to the number of pixels in each image slice, where each cell of each matrix corresponds to a pixel of the corresponding image slice, whose value is the abnormality probability of the corresponding pixel.”)).
Bernard does not expressly disclose that the unlabeled samples are selected from different categories to have a distribution satisfying a predefined target distribution of training data across different categories when the automatically selected training data is combined with training data previously used to train the one or more neural networks, wherein the different categories are determined from the predefined target distribution (but see Weider ¶ 41 (“FIGS. 3A-3B depict example bar charts for displaying distribution in data to show how bias can be present in a dataset and affect outcome of a model. FIG. 3A displays a bar chart 300A that depicts an ideal distribution of data in a dataset based on a gender attribute of the dataset. This assumes that one of the attributes of a datapoint in the dataset is gender and gender is categorized by three categories: female, male and non-binary. The example also assumes that the dataset is used to train a model for determining loan approvals. For such a dataset, an ideal distribution based on gender may result in a female bar 310 that has an equal distribution to the male bar 320 and the non-binary bar 330. This means the number of datapoints that represent each of the categories of the gender attribute may be equal or be within a predetermined distribution threshold. As a result, the percentage of loans approved for people falling into each category may also be equal. Thus, the model trained by this dataset may generate outcomes that are consistent across the gender spectrum (e.g. 10% of loans submitted by applicants in each category are approved).”), ¶ 42 (“The ideal distribution depicted in FIG. 3A, however, rarely occurs in the real world. Often the dataset is representative of one category more than others. FIG. 3B depicts a bar chart 300B displaying a more realistic real-world distribution of data across the gender spectrum in a dataset. The bar chart 300B shows the female bar 340 represents 35% of the data, while the male bar 350 represents 55% of the data and the non-binary bar chart 360 represents only 10% of data. This shows a clear imbalanced distribution of data across the three categories. When such an imbalanced dataset is used to train a ML mode, the outcome is often severely biased. FIG. 3C depicts a bar chart 300C displaying such an outcome. The female bar 370 of bar chart 300C shows that the ML model rejects 97% of female applicants, while the male bar 380 displays how only 3% of the male applicants are rejected by the ML model. As the non-binary bar 390 shows, the percentage of people falling into the non-binary category that are rejected is even higher than the female applicants, with a 99% rejection rate. As such, imbalanced or biased distribution of input data in a dataset can significantly impact the outcome produced by a ML model trained with the imbalanced dataset.”), ¶ 43 (“To address such imbalanced distributions, the input dataset may be trimmed to select a subset of the dataset that represents a more balanced distribution. For example, the subset may be selected based on the size of the category having the smallest distribution. Referring to the imbalanced distribution of FIG. 3B, this may mean choosing the size of the non-binary category as the measuring point and selecting a dataset that corresponds with data in each of the female and male categories in numbers that are equal to or within a desired distribution of the non-binary category. For example, if the non-binary category includes 1000 datapoints from a total of 10,000 datapoints for the entire dataset, a subset may be selected such that each of the female, male and non-binary categories has 1000 datapoints. This is illustrated in FIG. 4 which depicts a bar chart 400 displaying a distribution of data across the gender spectrum in a corrected subset of data. As shown in FIG. 4, because of trimming of the dataset, the resulting subset shows a balanced distribution of data across the three categories of the spectrum. As a result, each of the categories of the corrected subset has about 33% of the data. In one implementation, after a correction is performed, the bias detection tool may be executed again to ensure that the new subset achieves its purposes and it does not generate new undesired imbalance in data. The process may be repeated iteratively until an acceptable corrected dataset is achieved.”)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Bernard to incorporate the teachings of Weider to choose the size of each category of unlabeled data according to a desired target distribution of the overall dataset, at least because doing so would correct datasets of labeled and unlabeled data for which bias is detected. See Weider ¶ 37 (“Datasets for which bias is examined, detected, and corrected by the bias detection and correction service may be used for training ML models by a training mechanism 224. The training mechanism 224 may use training datasets stored in the datastore 212 to provide initial and/or ongoing training for ML models. In one implementation, the training mechanism 224 may use labeled training data from the data store 212 train the ML models. The initial training may be performed in an offline or online stage. In another example, the training mechanism 224 may utilize unlabeled training data from the datastore 212 to train the ML model via an unsupervised learning mechanism. Unsupervised learning may allow the ML model to create and/or output its own labels. In an example, an unsupervised learning mechanism may apply reinforcement learning to maximize a given value function or achieve a desired goal.”).
Regarding claim 22, Bernard, in view of Weider, discloses the invention of claim 21 as discussed above. Bernard further discloses wherein the one or more processors are further to use one or more solvers to automatically select the training data, wherein the one or more solvers are based at least in part on a quadratic formulation (74:6-12 (“The medical scan classifications selected to segregate the medical scans can be automatically determined by the medical scan image analysis system, for example, where an unsupervised clustering algorithm is applied to the original training set to determine appropriate medical scan classifications based on the output of the unsupervised clustering algorithm.”) (An unsupervised clustering algorithm such as k-means employs a quadratic formulation of distance clustering.).
Regarding claim 23, Bernard, in view of Weider, discloses the invention of claim 21 as discussed above. Bernard further discloses wherein the one or more processors are further to use the one or more neural networks to generate the output data based at least in part on a set of images (80:62-81:5 (“The inferred output vector of the inference data 1370 can include a plurality of abnormality probabilities mapped to a pixel location of each of a plurality of cross-sectional image slices of the new medical scan. For example, the inferred output vector can indicate a set of probability matrices 1371, where each matrix in the set corresponds to one of the plurality of image slices of the medical scan, where each matrix is a size corresponding to the number of pixels in each image slice, where each cell of each matrix corresponds to a pixel of the corresponding image slice, whose value is the abnormality probability of the corresponding pixel.”)).
Regarding claim 24, Bernard, in view of Weider, discloses the invention of claim 21 as discussed above. Bernard further discloses wherein the output data comprises one or more results of the one or more neural networks (80:62-81:5 (“The inferred output vector of the inference data 1370 can include a plurality of abnormality probabilities mapped to a pixel location of each of a plurality of cross-sectional image slices of the new medical scan. For example, the inferred output vector can indicate a set of probability matrices 1371, where each matrix in the set corresponds to one of the plurality of image slices of the medical scan, where each matrix is a size corresponding to the number of pixels in each image slice, where each cell of each matrix corresponds to a pixel of the corresponding image slice, whose value is the abnormality probability of the corresponding pixel.”)).
Regarding claim 25, Bernard, in view of Weider, discloses the invention of claim 24 as discussed above. Bernard further discloses wherein the automatically selected training data is automatically selected to have one or more attributes that indicate one or more confidence values for the one or more results (80:62-81:5 (“The inferred output vector of the inference data 1370 can include a plurality of abnormality probabilities mapped to a pixel location of each of a plurality of cross-sectional image slices of the new medical scan. For example, the inferred output vector can indicate a set of probability matrices 1371, where each matrix in the set corresponds to one of the plurality of image slices of the medical scan, where each matrix is a size corresponding to the number of pixels in each image slice, where each cell of each matrix corresponds to a pixel of the corresponding image slice, whose value is the abnormality probability of the corresponding pixel.”)).
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Bernard and Weider as applied to claim 21 above, and further in view of Gaidon (US 2020/0134379 A1; published Apr. 30, 2020).
Regarding claim 26, Bernard, in view of Weider, discloses the invention of claim 21 as discussed above. Bernard does not expressly disclose wherein the training data comprises one or more frames of one or more videos. However, Gaidon teaches automated labeling of video obtained by vehicle sensors in order to develop machine learning algorithms. ¶ 14.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bernard to incorporate the teachings of Gaidon to automatically select training images captured by vehicle sensors for use in developing machine learning algorithms at least because manually labeling vehicle data is inefficient and not scalable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Halford, Max, Subsampling a training set to match a test set – Part 1 (Jun. 19, 2017) (available at https://maxhalford.github.io/blog/subsampling-1/).
Sener, Ozan, and Silvio Savarese. "Active learning for convolutional neural networks: A core-set approach." arXiv preprint arXiv:1708.00489 (2017).
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/SHAHID K KHAN/Primary Examiner, Art Unit 2146
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