DETAILED ACTION
This communication is in response to the after final amendment filed 8/27/26 in which claims 15 and 21 were amended. 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/27/26 has been entered.
Response to Arguments
Applicant’s arguments concerning the 103 rejection are persuasive. Accordingly, the 103 rejection is withdrawn.
With respect to the 101 rejection, applicant argues:
The Office Action alleges that under Step 2A Prong 1, Applicant's claims recite a mental process. However, the Office Action fails to consider the aspect of the claims reciting causing one or more neural networks to be trained based, at least in part, on the automatically selected labeled training data cannot reasonably be performed mentally. Training a neural network is one feature of Applicant's claims that cannot be performed mentally. The Office Action only considers this aspect under Step 2A, Prong 2/Step 2B, alleging that "[a]s 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" (Office Action, p. 4). Applicant respectfully disagrees. The claim as a whole must recite an abstract idea, not merely a portion of the claim. One or more of the features recited by Applicant's claims, including at least the feature of training a neural network based on automatically selected labeled training data, cannot be performed mentally. Thus, Applicant's claim, as a whole, cannot be performed in the human mind.
Applicant’s arguments have been considered but are unpersuasive. In step 2A, the claim as a whole must be directed to, as opposed to must recite, an abstract idea. As explained in the rejection below, the claims recite an abstract idea in prong 1 and the additional elements of the claims fail to integrate the exception into a practical application. Specifically, the recitation of neural networks is mere instruction to apply the exception.
Applicant further argues:
Applicant's claim provides an improvement to computer technology. As illustrated in FIG. 3, the claimed technique based on predefined categories (e.g., buckets) provides for obtaining a desired distribution of training data, as opposed to sampling based on the data's existing distribution, with just as accurate results as other techniques but is a small fraction of the amount of time. As shown in the bottom row in FIG. 3, for a large data set, the technique obtains an equally accurate distribution in 14s total compared to over 2 days for another technique. Thus, the claimed approach greatly speeds up processing and requires much fewer resources, which is a significant improvement to computer technology. The recent precedential PTAB Decision Ex Parte Desjardins et al., Appeal 2024-000567 (ARP Sept. 26, 2025) (precedential) is directly on point. In response to Applicant's previous remarks regarding the applicability of Ex Parte Desjardins et al., the Office Action merely states, "[u]nlike the limitation at issue in Ex Parte Desjardins, the [instant] claims do not reflect the improvement in performance" (Office Action, p. 3). Applicant respectfully disagrees. Applicant's specification, at paragraph [0061] lists several technical advantages of the recited invention, including but not limited to: an ability to automatically select scenes for training to approximate a target distribution, an ability to efficiently select scenes for training by at least grouping scenes by metadata, and an ability to select diverse scenes to train one or more neural networks. Moreover, as noted above, Applicant's specification clearly describes a performance improvement over conventional methods. Thus, Applicant's claim recites a clear improvement to computer technology.
Additionally, in accordance with this Supreme Court and Federal Circuit authority, the USPTO includes in its eligibility guidelines that claims directed to improving the functioning of a computer are patent eligible. See, e.g., MPEP 2106.04(d), MPEP 2106.04(d)(1), 2106.05(a); 2106.06(b). This has recently been affirmed in a precedential Appeals Review Panel decision authored by USPTO Director Squires. As stated by Director Squires in Ex parte Desjardins, "claims directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field are patent eligible." Ex parte Desjardins, Appeal No. 2024-000567 (ARP Sept. 26, 2025) (precedential) at *8 (citing MPEP 2106.04(d)(1) and 2106.05(a)). Because Applicant's claims reflect an improvement in functioning of a computer, Applicant's claims are patent eligible.
Applicant’s arguments have been considered but are unpersuasive. In Desjardins, the Appeals Review Panel evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. In the instant case, the additional elements of the claims merely recite using the selected data to train a generic neural network. The elements argued as providing the improvement (i.e., obtaining a desired distribution of training data) are not additional elements but part of the identified judicial exception, which alone cannot provide the improvement to computer technology.
Applicant further argues:
Applicant's claims are also specific to a practical application. Under step 2B of the Alice framework (see MPEP 2106.05), claims are patent eligible if elements of the claim provide an inventive concept; that is, whether they provide "significantly more" than a judicial exception. Applicant's claims recite a specific "ordered combination" of features to 1) determine respective amounts of unlabeled training data in a specific non- conventional way, 2) automatically select the determined respective amounts based on metadata, 3) obtain labels for the automatically selected unlabeled training data, and 4) cause one or more neural networks to be trained based, at least in part, on the automatically selected labeled training data. The non-conventional specific ordered combination is "significantly more" than any judicial exception. Even if the claims could be considered directed to an abstract idea, which Applicant does not concede, the claims would be patent- eligible based on Pathway C of MPEP 2106(III) because additional elements recited in the claim amount to substantially more than any such abstract idea.
Applicant’s arguments have been considered but are unpersuasive. Determining respective amounts of unlabeled training data to match a target distribution, selecting the determined amounts based on metadata, obtaining labels for the selected unlabeled data, are steps that may be performed manually by judgement, evaluation, and opinion. The additional elements of the claim merely recite instructions to apply the identified exception. In step 2B, the additional elements of using the data to train a neural network may also be characterized as generally linking the abstract idea to a technological environment or a field of use (e.g., AI).
Additionally, similar to the claim of Example 39 of the USPTO's 2019 Revised Patent Subject Matter Eligibility Guidance, Applicant's claims recite a specific way of producing training data for training a neural network as well as training the neural network. As with the claim of Example 39, Applicant's claims do not recite any mathematical relationships, formulas, or calculations. Further, the claims do not recite a mental process because, as shown above, the steps cannot be practically performed in the human mind. Lastly, the claims do not recite any method of organizing human activity. Similar to the claim in Example 39, Applicant's claim organizes sets of training data and transforms the training data into a new set of training data for training the neural network. In Applicant's claim, amounts of unlabeled training data are selected to achieve a particular distribution of training data across different categories when combined with already-labeled training data. As with the claim of Example 39, Applicant's claims are eligible because they do not recite a judicial exception.
Applicant’s arguments have been considered but are unpersuasive. In Example 39, the analysis stops after step 2A prong 1 because the recited steps do not recite any steps that can be mentally performed and, therefore, the claim does not recite a judicial exception. Unlike the claim in Example 39, the steps of the identified abstract idea in the instant case can be performed mentally and, therefore, the analysis proceeds to prong 2 and step 2b.
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 unlabeled 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 unlabeled training data to have a distribution satisfying a predefined target distribution of training data across different categories when the automatically selected unlabeled training data is combined with already-labeled 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 unlabeled 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 unlabeled training data is combined with already-labeled 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)].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sarker, Iqbal H. "Machine learning: Algorithms, real-world applications and research directions." SN computer science 2.3 (2021): 160 (“Semi-supervised: Semi-supervised learning can be defined as a hybridization of the above-mentioned supervised and unsupervised methods, as it operates on both labeled and unlabeled data [41, 105]. Thus, it falls between learning “without supervision” and learning “with supervision”. In the real world, labeled data could be rare in several contexts, and unlabeled data are numerous, where semi-supervised learning is useful [75]. The ultimate goal of a semi-supervised learning model is to provide a better outcome for prediction than that produced using the labeled data alone from the model. Some application areas where semi-supervised learning is used include machine translation, fraud detection, labeling data and text classification.”).
Haeusser et al. (US 2020/0057936 A1; published Feb. 20, 2020) Semi-Supervised Training of Neural Networks.
Qi Tian, Jie Yu, Qing Xue and N. Sebe, "A new analysis of the value of unlabeled data in semi-supervised learning for image retrieval," 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), Taipei, Taiwan, 2004, pp. 1019-1022 Vol.2, doi: 10.1109/ICME.2004.1394376.
Peng, Jizong, et al. "Self-paced contrastive learning for semi-supervised medical image segmentation with meta-labels." Advances in Neural Information Processing Systems 34 (2021): 16686-16699.
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/SHAHID K KHAN/Primary Examiner, Art Unit 2146
