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 .
Continuity/reexam data
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18614130 filed 03/22/2024
claims foreign priority to 2023-049055, filed 03/24/2023
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None
Foreign data
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2023-049055
03/24/2023
JP
JAPAN
sdenboba
04/02/2024 09:26:04
Last updated by sdenboba on 04/02/2024 09:26:04
(*) - Request to retrieve electronic copy of foreign priority from participating receiving offices.
1. Claims presented for examination: 1-11
Information Disclosure Statement
2. The information disclosure statement (IDS) submitted on 03/22/2024, 07/15/2025and 06/10/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
2. Notification regarding 35 USC § 112f
The following is a quotation of AIA 35 U.S.C. 112f:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
In claim 1:
Claim limitation "a model information display that displays a plurality of learning models being released on Internet and having a same interface in a selectable manner " has been interpreted under 35 U.S.C. 112(f), because it uses/they use a generic placeholder "a model information display" coupled with functional language " displays a plurality of learning models being released on Internet and having a same interface in a selectable manner" without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Claim limitation " a model selector that displays at least one learning model among the plurality of displayed learning models" has been interpreted under 35 U.S.C. 112(f), because it uses/they use a generic placeholder “a model selector" coupled with functional language " displays at least one learning model among the plurality of displayed learning models” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Claim limitation " a trainer that subjects the selected learning model to machine learning such that the selected learning model learns training data, and generates a trained model that has been trained" has been interpreted under 35 U.S.C. 112(f), because it uses/they use a generic placeholder "a trainer" coupled with functional language" subjects the selected learning model to machine learning such that the selected learning model learns training data, and generates a trained model that has been trained" without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f), claim 1 has been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the present specification shows that the limitations “a model information display that displays a plurality of learning models being released on Internet and having a same interface in a selectable manner; a model selector that displays at least one learning model among the plurality of displayed learning models; and a trainer that subjects the selected learning model to machine learning such that the selected learning model learns training data, and generates a trained model that has been trained” are not clear, however the limitations may be used or performed by a computer, a processor and/or programing…
If applicant wishes to provide further explanation or dispute the examiner's interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f), applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f), or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f).
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
3. Claims 1-6 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
In claim 1: in light of Notification of 112f above, the limitations: “a model information display that displays a plurality of learning models being released on Internet and having a same interface in a selectable manner; a model selector that displays at least one learning model among the plurality of displayed learning models; and a trainer that subjects the selected learning model to machine learning such that the selected learning model learns training data, and generates a trained model that has been trained .” are indefinite because it is unclear what they are. Although the limitations may be used or performed by a processor, a computer, or a programing, but the present disclosure does not describe an algorithm for performing the claimed specific computer function, such that the claim also indefinite. Correction is required. See Section 2181.II.B.
See Section 2181.II.B: “…For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b). See Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367, 88 USPQ2d 1751, 1757 (Fed. Cir. 2008). See also In re Aoyama, 656 F.3d 1293, 1297, 99 USPQ2d 1936, 1939 (Fed. Cir. 2011) ("[W]hen the disclosed structure is a computer programmed to carry out an algorithm, ‘the disclosed structure is not the general purpose computer, but rather that special purpose computer programmed to perform the disclosed algorithm.’") (quoting WMS Gaming, Inc. v. Int’l Game Tech., 184 F.3d 1339, 1349, 51 USPQ2d 1385, 1391 (Fed. Cir. 1999))…”
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.
4. Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1 (See MPEP 2106)
Claims 1-20 are directed to a method, a system and a tangible , non-transitory computer readable medium which belongs to a statutory class.
Step 2A, Prong One:
Claims recite “
“A trainer that subjects the selected learning model to machine learning such that the selected learning model learns training data, and generates a trained model that has been trained” which is a process that, under its broadest reasonable interpretation, covers performance of the limitation by Mental Process, but for the recitation of generic computer components. Nothing in the claim element precludes the steps from practically being performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mental process, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two:
Claims recite instructions stored in memory and computer to perform the method. These are generic computer components and program which use to perform abstract ideas.
“displays a plurality of learning models being released on Internet and having a same interface in a selectable manner” is a computer interface for displaying selection information.
“displays at least one learning model among the plurality of displayed learning models” is a generic computer display information for selection.
The limitation is thus insignificant extra-solution activity. Limitations that the courts have found not to be enough to qualify as "significantly more” when recited in a claim with a judicial exception include: i. Adding the words "apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)). 2106.05(g)--Insignificant Extra-Solution Activity.
Step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As to claim 2, the limitations:
“An image data acquirer that acquires image data” is a data capturing process or further processing” is an information capturing of the data.
“An training data generator that generates training data based on the image data acquired by the image data acquirer, wherein the training data generator has a graphical user interface for generating training data” is a processes of providing information from retrieval or analysis.
As to claim 3, the limitation:
“The graphical user interface has an operation screen for receiving an instruction for generating another new image data based on the image data” is the generic interface which allow the user to interact with and make selection to generate or create additional information.
As to claim 4, the limitation:
“The graphical user interface has an operation screen for receiving information to be added to the image data” is a generic software interface for receiving information.
As to claim 5, the limitation:
“The model information display further displays information in regard to characteristics of the plurality of learning models” is a generic display for displaying any information either retrieval, analysis or others.
As to claim 6, the limitation:
“The parameter setter that sets a hyperparameter that is used when the trainer subjects the learning model to machine learning, wherein the parameter setter has a graphical user interface for setting a hyperparameter” is the process setting the parameter to be used in retrieval or analyzing the data.
As to claim 7, the limitation”
“The model selector is configured to select a custom model which is prepared in advance separately from the plurality of learning models released on the Internet, and the custom model has a same interface as that of the plurality of learning models released on the Internet” is the generic software which allow the selection of model to be used.
As to claim 8, the limitations:
“An evaluation data acquirer that acquires evaluation data” is a mental process.
“An evaluator that evaluates the trained model generated by the trainer with use of the evaluation data” is a mental process.
“An evaluation result display that displays an evaluation result in regard to the trained model” is a mental process.
As to claim 9, the limitation:
“In a case in which there are the plurality of trained models, the evaluation result display displays a plurality of evaluation results respectively corresponding to the plurality of trained models side by side” is the generic process of displaying results in such a way which allow to be visualized evaluation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
5. Claim(s) 1-7 and 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Riley et al. (Pub. No. US 2019/0294923 A1) in view CASALLAS SUAREZ et al. (Pub. No. 2022/0058859 A1).
As to claim 1, Ridley discloses a learning model generation device comprising:
a model information display that displays a plurality of learning models (display various option for training the machine learning model if the user initiates training, e.g., by selecting the train button shown in FIG. 2) (paragraph 0090) being released on Internet and having a same interface in a selectable manner (internet provide the communication to allow the user access the application using same interface) (paragraph 0024); and
a trainer that subjects the selected learning model to machine learning such that the selected learning model learns training data, and generates a trained model that has been trained (FIG. 6, includes an example of a synthetic image that may be generated using the embodiment described …) (paragraph 0019)
Riley does not disclose a model selector that displays at least one learning model among the plurality of displayed learning models.
CASALLAS SUAREZ discloses a model selector that displays at least one learning model among the plurality of displayed learning models (… As example, graphical user interface 228 may include graphical controls for selecting a 3D model style form a plurality of 3D model styles. Each 3D model style may correspond to different machine learning models, such as organic man-made, humans, cars, animals, or other styles of 3D models used to train the different machine learning models) (paragraph 0070).
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching Riley to include a model selector that displays at least one learning model among the plurality of displayed learning models as disclosed by CASALLAS SUAREZ in order to select learning model for learning.
As to claim 2, Ridley discloses the learning model generation device according to claim 1, further comprising:
an image data acquirer that acquires image data (one or more images) (paragraph 0011); and
a training data generator (training) (paragraph 0090) that generates training data based on the image data acquired by the image data acquirer, wherein the training data generator has a graphical user interface (GUI) (paragraph 0090) for generating training data (paragraph 0090).
As to claim 3, Ridley discloses the learning model generation device according to claim 2, wherein the graphical user interface has an operation screen for receiving an instruction for generating another new image data based on the image data (new image) (paragraph 0070)
As to claim 4, Ridley discloses the learning model generation device according to claim 2, wherein the graphical user interface has an operation screen for receiving information to be added to the image data (adding defect to image) (paragraph 0026).
As to claim 5, Ridley discloses the learning model generation device according to claim 1, wherein the model information display further displays information in regard to characteristics of the plurality of learning models (features) (paragraph 0028)
As to claim 6, Ridley discloses the learning model generation device according to claim 1, further comprising a parameter setter that sets a hyperparameter that is used when the trainer subjects the learning model to machine learning, wherein the parameter setter has a graphical user interface for setting a hyperparameter (parameters) (paragraph 0090).
As to claim 7, Ridley discloses the learning model generation device according to claim 1, wherein the model selector is configured to select a custom model (2D model) (paragraph 0070) which is prepared in advance separately from the plurality of learning models released on the Internet, and the custom model has a same interface as that of the plurality of learning models released on the Internet (the application provisioned the application through Internet) (paragraph 0024).
Claim 10 is rejected under the same reason as to claim 1, Ridley discloses a method (method) (paragraph 0009).
Claim 11is rejected under the same reason as to clam 1, Ridley discloses a non-transitory computer readable medium (memory) (paragraph 0026) storing a learning model generation program (software program) (paragraph 0026) that causes a computer to execute.
6. Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Riley et al. (Pub. No. US 2019/0294923 A1) in view CASALLAS SUAREZ et al. (Pub. No. 2022/0058859 A1) and further in view of ISHIKAWA et al. (Pub. No. US 2023/00004811 A1).
As to claim 8, Ridley and CASALLAS SUAREZ disclose the learning model generation device according to claim 1 excepting for an evaluation data acquirer that acquires evaluation data; an evaluator that evaluates the trained model generated by the trainer with use of the evaluation data; and an evaluation result display that displays an evaluation result in regard to the trained model. However, ISHIKAWA discloses an evaluation data acquirer that acquires evaluation data; an evaluator that evaluates the trained model generated by the trainer with use of the evaluation data; and an evaluation result display that displays an evaluation result in regard to the trained model (the pre-trained model evaluation result display unit 1603 displays evaluation result of the respective pre-trained model…) (paragraph 0158). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching of Ridley and CASALLAS SUAREZ an evaluation data acquirer that acquires evaluation data; an evaluator that evaluates the trained model generated by the trainer with use of the evaluation data; and an evaluation result display that displays an evaluation result in regard to the trained model as disclosed by ISHIKAWA to provide to see which model to be used.
As to claim 9, Ridley and CASALLAS SUAREZ disclose the learning model generation device according to claim 8 excepting for wherein in a case in which there are the plurality of trained models, the evaluation result display displays a plurality of evaluation results respectively corresponding to the plurality of trained models side by side. ISHIKAWA discloses wherein in a case in which there are the plurality of trained models, the evaluation result display displays a plurality of evaluation results respectively corresponding to the plurality of trained models side by side (FIG. 18 IS A diagram illustrating a configuration of a screen for performing the degree-of-importance evaluation…) (paragraph 0155) (the fig.18 discloses different models in table along with evaluations). Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the instant application to modify teaching of Ridley and CASALLAS SUAREZ to include wherein in a case in which there are the plurality of trained models, the evaluation result display displays a plurality of evaluation results respectively corresponding to the plurality of trained models side by side as disclosed by ISHIKAWA to provide to see which model to be used.
Conclusion
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BAOQUOC N. TO
Examiner
Art Unit 2154
/BAOQUOC N TO/Primary Examiner, Art Unit 2154
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