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 .
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 1-2, 4, and 6-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite mental processes or judgment and evaluation. This judicial exception is not integrated into a practical application because nor is it sufficient to amount to significantly more than the judicial exception because the additional limitations are extra-solution activity in combination with generic computer hardware. See the explanation below for further details.
Claims 1, 7 and 13
Step 1: The claim recites a method, apparatus and non-transitory computer-readable medium, therefore, they falls into the statutory categories.
Step 2A Prong 1: The claim recites, inter alia:
Selecting, a transfer-source model from a plurality of registered models stored in a model storage unit, wherein the selecting is based on a correct answer labels attached to pieces of learning data and output labels of output results obtained by inputting the pieces of learning data to a plurality of registered models, and wherein the selecting comprises: (This is a mental process of observation, judgement and evaluation wherein a user chooses the model that performs the best based on results matching correct labels.)
(b) evaluating the plurality of registered models based on a relationship between the correct answer labels and the output labels, by for each registered model: (This amounts to a mental step of observation, judgement and evaluation wherein a user evaluates the performance of a model by comparing its output results the correct known answers.)
(i) determining a degree to which pieces of the learning data having a same correct answer label are aggregated into an output result having a same output label. (This amounts to a mental process of judgment and evaluation wherein a user compares the labels in training data to labels on output of a model and picks the model with the same labels.)
(ii) determining a number of distinct output labels generated in response to the pieces of learning data; and (This amount to a mental process of observation, judgment and evaluation wherein a user counts the number of different or distinct output labels, can do with the aid of pen and paper.)
(c) selecting, as the transfer-source model, one of the registered models based on the evaluation, wherein a registered model that generates a smaller number of the distinct output labels evaluated more favorable. (This amounts to a mental process of judgment and evaluation wherein a user compares the labels in training data to the labels on the output of a model and picks the model with smaller number results.)
Representing the new model in the association with data representing the selected transfer-source model to establish a directed parent child relationship, (This amounts to a mental step that can be accomplished with the aid of pen and paper, wherein a user records a new model name wherein the model name associated it when the parent names. For example, the transfer source model name is Model A, then new model name would be Model A.1 as it would associate the model A.1 with being a child of Model. This can don with aid of pen and paper. This is also shown in figure 5 wherein model 2 has child model 2a and 2a has child models of 2aa, 2ab, and 2ac. It’s a naming convention.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
(a) inputting the pieces of learning data to the plurality of registered models to obtain the output results; (This is extra solution activity of sending data, see MPEP 2106.05(g), as the data is sent to model, and then using the model as a tool, see MPEP 2106.05(f).)
creating a new model by performing transfer learning that uses the selected transfer-source model and the pieces of learning data; and (This claim amounts to inputting data, as it is input the learning data to a model, (transfer-source model) which is sending or transmitting data, which are extra-solution activity. See MPEP 2106.05(g) for transmitting data. Also performing transfer learning is using machine learning and is cited at a high level of generality thus results in using a tool to execute the abstract idea, see MPEP 2106.05(f).)
registering the created new model in the model storage unit by storing data representing the new model in associated with data representing the selected transfer-source model to establish a direct parent child relationship, wherein the established parent-child relationship is configured to improve efficiency of a future selection of a transfer-source model by enabling a hierarchical search through generations of model. (This amount to saving the model to model storage unit, which is saving data, and extra-solution activity, see MPEP 2106.05(g).)
using one or more process; model storage unit; and memory storing processing instructions (The memory, processor and storage unit are generic computer components performing generic functions used to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “(a) inputting the pieces of learning data to the plurality of registered models to obtain the output results; creating a new model by performing transfer learning that uses the selected transfer-source model and the pieces of learning data;” amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The using of a machine learning is amounts to using machine learning as tool to apply an abstract idea, see MPEP 2106.05(f). The use of the processor and memory both amount to using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The limitation of “registering the created new model in the model storage unit by storing data representing the new model in associated with data representing the selected transfer-source model to establish a direct parent child relationship, wherein the established parent-child relationship is configured to improve efficiency of a future selection of a transfer-source model by enabling a hierarchical search through generations of model.” amount to saving data to memory which is well-understood, routine and conventional, See MPEP 2106.05(d)(II)(iv) where it states “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;”.
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claims 2 and 8
Step 1: The claim recites a method and apparatus, therefore, they falls into the statutory categories of a method and apparatus.
Step 2A Prong 1: The claim recites, inter alia:
if there are models registered so as to be associated with the selected model, selecting a new model based on output results obtained by inputting the pieces of learning data to the models registered so as to be associated with the selected model; (This is a mental process of judgment and evaluation wherein a user checks if there are models associated with the current model, can be done checking a list and model details. Then the user chooses a model amount any associated models that performs the best based on results.)
registering the created other new model such that the other new model is associated with the selected new model. (This amounts to a mental step that can be accomplished with the aid of pen and paper, wherein a user records a new model name and what model was used to create it.)
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
creating another new model by inputting the pieces of learning data to the selected new model and performing machine learning; and (This claim amounts to inputting data, which is sending or transmitting data, which are extra-solution activity. See MPEP 2106.05(g) for transmitting data. Also using machine learning is cited at a high level of generality and results in using a tool to execute the abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “creating another new model by inputting the pieces of learning data to the selected new model and performing machine learning;” amount to transmitting data and well-understood, routine and conventional and does not amount to significantly more. See MPEP 2106.06(d)(II) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data”. The using of a machine learning is amounts to using machine learning as tool to apply an abstract idea, see MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well, understood, routine and conventional activity as disclosed in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 4 and 10
Step 1: The claim recites a method and apparatus, therefore, they falls into the statutory categories of a method and apparatus.
Step 2A Prong 1: The claim recites, inter alia:
wherein the selecting the model comprises if the pieces of learning data provided with an the same label are inputted to a registered model and if the pieces of learning data provided with the same label are included in an aggregate of an output result provided with an the same label of the registered model, selecting the registered model. (This amounts to a mental process of judgment and evaluation wherein a user compares the labels in training data to labels on output of a model and picks the model with the same labels.)
Step 2A Prong 2:
The claim does not include any additional elements or limitations.
Step 2B:
The claim does not include any additional elements or limitations.
Claim 11
Step 1: The claim recites an apparatus, therefore, they falls into the statutory categories an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
wherein the selecting the model comprises if the pieces of learning data are inputted to a registered model and if the number of labelled output results of the registered model is smaller, selecting the registered model (This amounts to a mental process of judgment and evaluation wherein a user compares the labels in training data to the labels on the output of a model and picks the model with smaller results.)
Step 2A Prong 2:
The claim does not include any additional elements or limitations.
Step 2B:
The claim does not include any additional elements or limitations.
Claim 6 and 12
Step 1: The claim recites a method and apparatus, therefore, they falls into the statutory categories of a method and apparatus.
Step 2A Prong 1: The claim recites, inter alia:
The claim inherits the abstract ideas of claim 1 but does not have any additional ones.
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
Outputting associations between two models for display. (This claims amount to using a display to display data which is using generic computer hardware performing generic functions to implement the abstract idea, See MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant generic computer functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of “Outputting associations between two models for display;” amount to using a display to display data which is using generic computer hardware performing generic functions to implement the abstract idea, See MPEP 2106.05(f).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they generic computer functions that are implemented to perform the disclosed abstract idea above.
Claim 9
Step 1: The claim recites an apparatus, therefore, they falls into the statutory categories of an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
selecting the model comprises selecting the model based on labels attached to the pieces of learning data and labels of the output results obtained by inputting the pieces of learning data to the registered models. (This amounts to a mental process of judgment and evaluation wherein a user compares the labels in training data to labels on output of a model and picks the model with best or most matching results.)
Step 2A Prong 2:
The claim does not include any additional elements or limitations.
Step 2B:
The claim does not include any additional elements or limitations.
Claim 14
Step 1: The claim recites an apparatus, therefore, they falls into the statutory categories of an apparatus.
Step 2A Prong 1: The claim recites, inter alia:
No mental process
Step 2A Prong 2:
This judicial exception is no integrated into a practical application. Aside from the limitations above, the claim recites:
Wherein the new model created by the transfer learning is a model for automated decision making in a specific domain comprising one of determining whether a product at production site is normal or defective based on images of the product, or classifying a type of part based on images of the part. (this amount to linking the abstract idea to a particular technology or field, see MPEP 2106.05(h.)
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, the additional elements are: Wherein the new model created by the transfer learning is a model for automated decision making in a specific domain comprising one of determining whether a product at production site is normal or defective based on images of the product, or classifying a type of part based on images of the part. (this amount to linking the abstract idea to a particular technology or field, see MPEP 2106.05(h.)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they simply link the abstract idea to a particular technology.
Response to Arguments
Applicant's arguments filed 21 August 2025 have been fully considered but they are not persuasive. The applicant argues the amendment to claims 4 and 10 overcome the rejection under 35 USC 112, to which the examiner agrees and the rejection is withdrawn. The applicant argues that amendment to the independent claims overcome the rejections under 35 USC 103, to which the examiner agrees and the rejection is withdrawn. The applicant further argues that the amendment to the claims integrate the abstract idea into a practical application as it improves the model training method offering increased accuracy, decreased training time, and reduced training data demands, and thus clearly representing an improvement to functioning a computer. The examiner respectfully disagrees. The amendment to claim 1 does not integrate the abstract idea into a practical application as the amendment the claims are mental process. The amendment of selecting a transfer-source model based on the performance of the model is a mental process. The amendment of the registering the new model in a manner that is associated with the parent model is also a mental process wherein in the user saves the model name in hierarchical manner. For example, the model name is model 1. Its child model would be model 1a. If model 1a was further used to train another model, that model would be model 1aa. This is simply a naming convention that allows a user to determine what model comes from another model and can done with the aid of pen and paper. As such the improvement is not an improvement to a computer, but rather a mental process, as such it does not integrate the abstract idea into a practical application. For these reason the examiner maintains the rejection under 35 USC 101.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at 571-270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127