DETAILED ACTION
Remarks
The instant application having Application Number 18/700,382 filed on April 11, 2024 has a total of 10 claims pending in the application; there are 3 independent claims and 7 dependent claims, all of which are presented for examination by the examiner.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
The examiner requests, in response to this Office action, supports are shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Information Disclosure Statement
As required by M.P.E.P. 609(C), the applicant’s submissions of the Information Disclosure Statements dated 04/11/2024 and 02/19/2025 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C (2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Claim Rejections - 35 USC § 112
The following is a quotation of the second paragraph of 35 U.S.C. 112:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. Claims 1, 9 and 10 are vague and indefinite because the phrases "artificial case" and "actual case" has not been clearly defined in the claims. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir.1993). Therefore, one of ordinary skill in the art would not be able to ascertain the metes and bounds of the claimed invention. Applicants need not confine themselves to the terminology used in the prior art, but are required to make clear and precise the terms that are used to define the invention whereby the metes and bounds of the claimed invention can be ascertained. See MPEP 2173.05(a)(I). Appropriate correction is required.
The Examiner has given the phrase "artificial case" and "actual case" its broadest reasonable interpretation. For examination purposes, all claim interpretation is predicated upon the broadest reasonable interpretation of the claim terms which would be fairly conveyed to one of ordinary skill in the pertinent art.
Regarding claims 1, 9 and 10, the phrase "to be" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding independent Claims 1, 9, and 10:
Step 1 Analysis:
Claim 1 recites “An information processing device …”; therefore, the claim is a machine.
Claim 9 recites “An information processing method…”, the claim recites a series of steps and therefore is process.
Claim 10 recites “A computer readable recording medium”, therefore the claim is a manufacture.
Step 2A Prong One Analysis: The claim, under the broadest reasonable interpretation, recites limitations directed to an abstract idea, including mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion), but for the recitation of mere instructions to apply an exception language. In particular, the following limitations are directed to an abstract idea:
An information processing device comprising:
at least one memory configured to store instructions; and
at least one processor configured to execute the instructions to:
acquire each actual case formed by features;
generate a plurality of artificial cases based on each acquired actual case;
select each artificial case in which a prediction of a machine learning model is to be uncertain, from the plurality of artificial cases; and
output each selected artificial case.
This limitation is a process that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “one memory”, “one processor”, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, the “acquiring”, “generating” “selecting” and “outputting” in the context of this claim encompasses a user mentally, and with the aid of pen and paper writing the changes down on a sheet of paper and examine the list to determine the relevant ones (rationale).
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind 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: Integrated into a Practical Application
The judicial exception is not integrated into a practical application. In particular, the additional steps: the “acquiring”, “generating” “selecting” and “outputting” steps mount to data gathering which are considered to be insignificant extra-solution activity (see MPEP 2106.05(g)), and the “outputting” and “generating” step is considered as a mere instruction to apply an exception to perform an existing process on a generic computer and/or no more than an idea of a solution or outcome on a generic computer (see MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea, thus fail to integrate the abstract idea into a practical application. See MPEP 2106.05(g).
Step 2B: Claim provides an Inventive Concept
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The insignificant extra-solution activities identified above, which include the data-gathering and the step of “acquiring”, “generating” “selecting” and “outputting” are recognized by the courts as well-understood, routine, and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application.
Accordingly, claim 1 is directed to an abstract idea.
Independent claims 9 and 10 have the similar limitations as claim 1 and are rejected for at least the same reasons as claim 1.
Regarding claim 2. The information processing device according to claim 1, wherein the processor selects the plurality of artificial cases so that each selected artificial case is different.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 3. The information processing device according to claim 1, wherein the processor selects the plurality of artificial cases so that actual cases existing in a vicinity are different in a feature space.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 4. The information processing device according to claim 1, wherein the processor selects the plurality of artificial cases so that actual cases to be generation sources for respective artificial cases are different from each other.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 5. The information processing device according to claim 1, wherein the processor generates the artificial cases using all input actual cases.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 6. The information processing device according to claim 1, wherein the processor generates the artificial cases using a plurality of actual cases randomly selected from among the input actual cases.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 7. The information processing device according to claim 1, wherein the processor selects each actual case in which a prediction of a machine learning model is uncertain among a plurality of the input actual cases, and generates the plurality of artificial cases using each selected actual case.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
Regarding claim 8. The information processing device according to claim 1, wherein the processor assigns a label to each selected artificial case and outputs each labeled actual case.
The judicial exception is not integrated into a practical application. In particular, this additional limitation mounts to data gathering which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)), and does not amount to significantly more than the above-identified judicial exception.
With respect to claims 9 and 10, although claims 9 and 10 directed to a method and medium, they are similar in scope to claim 1. Similar to claim 1, the claims 9 and 10 do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified.
Claim Rejections - 35 USC § 103
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Ishihara et al. (Japanese Patent Publication No. JP 2021019677 A, ‘Ishihara’, hereafter) in view of Comaniciu et al. (Chinese Patent Publication No. CN 106037710 B, ‘Comaniciu’, hereafter).
Regarding claim 1. Ishihara teaches an information processing device (Ishihara [0060-0065], [0087]) comprising:
at least one memory configured to store instructions (Ishihara [0060-0065]); and
at least one processor (Ishihara [0060-0065], [0261]) configured to execute the instructions to:
acquire each actual case formed by features (The acquisition unit 801 stores various acquired information in the storage unit … The acquisition unit 801 acquires first image, second image, fourth image and sixth image (i.e., actual case image), Ishihara [0102-0104]);
generate a plurality of artificial cases based on each acquired actual case (a third image obtained by synthesizing the first image and the second image, which is an artificial case image. … a fifth image obtained by synthesizing a fourth image and a second image, which is an artificial case image (i.e., generate a plurality of artificial cases based on each acquired actual case), Ishihara [0093-0095], [0261]); and
output each selected artificial case (A plurality of image groups including one or more of the third images and one or more of the sixth images obtained by imaging a cross section of the lung (i.e., selected artificial case) in which the shadow of the tumor is reflected are generated, and the images are generated for each of the generated image groups. Based on the group, a model that outputs the result of detecting the shadow of the tumor (i.e., outputting selected artificial case) reflected in the image of the input subject's lung cross section is generated, and the input subjects about the model generated for each image group is generated. The teacher according to any one of Supplementary note 1 to 10, which outputs a result of evaluating the detection accuracy of detecting the shadow of the tumor reflected in the image of the cross section of the lung, and causes the computer to execute the process, Ishihara [0261]).
Ishihara does not teach
select each artificial case in which a prediction of a machine learning model is to be uncertain, from the plurality of artificial cases;
However, Comaniciu teaches
select each artificial case in which a prediction of a machine learning model is to be uncertain, from the plurality of artificial cases (Comaniciu, page 2, lines 44-49 and page 22, lines 39-47);
Therefore, it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Ishihara and Comaniciu before him/her, to modify Ishihara with the teaching of Comaniciu’s blood dynamic measurement of synthesis data in medical imaging. One would have been motivated to do so for the benefit of extracting training data from medical imaging to provide to machine learning in order to reduce cost and time to synthesize imaging data (Comaniciu, Abstract, Page 2, lines 19-35).
Regarding claim 2. Ishihara as modified teaches the information processing device according to claim 1, wherein the processor selects the plurality of artificial cases so that each selected artificial case is different (Ishihara [0091-0095]).
Regarding claim 3. Ishihara as modified teaches the information processing device according to claim 1, wherein the processor selects the plurality of artificial cases so that actual cases existing in a vicinity are different in a feature space (Ishihara [0091-0096]).
Regarding claim 4. Ishihara as modified teaches the information processing device according to claim 1, wherein the processor selects the plurality of artificial cases so that actual cases to be generation sources for respective artificial cases are different from each other (Ishihara [0091-0095]).
Regarding claim 5. Ishihara as modified teaches the information processing device according to claim 1, wherein the processor generates the artificial cases using all input actual cases (Ishihara [0091-0096]).
Regarding claim 6. Ishihara as modified teaches the information processing device according to claim 1, wherein the processor generates the artificial cases using a plurality of actual cases randomly selected from among the input actual cases (Comaniciu, page 16, lines 35-39).
Regarding claim 7. Ishihara as modified teaches, wherein the processor selects each actual case in which a prediction of a machine learning model is uncertain among a plurality of the input actual cases, and generates the plurality of artificial cases using each selected actual case (Comaniciu, page 2, lines 44-49 and page 22, lines 39-47).
Regarding claim 8. Ishihara as modified teaches the information processing device according to claim 1, wherein the processor assigns a label to each selected artificial case and outputs each labeled actual case (Comaniciu, page 17, lines 15-18 and page 27, lines 18-25).
Regarding claim 9, although claim 10 directed to a method, it is similar in scope to claim 1. The device steps of claim 1 substantially encompass the method recited in claim 9. Therefore; claim 10 is rejected for at least the same reason as claim 1 above.
Regarding claim 10, although claim 10 directed to a medium, it is similar in scope to claim 1. The device steps of claim 1 substantially encompass the medium recited in claim 10. Therefore; claim 10 is rejected for at least the same reason as claim 1 above.
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant’s disclosure.
Hatakeyama et al. (WIPO Patent Publication No. WO 2022249373 A1) discloses generating an artificial example that more efficiently improves the prediction accuracy of a machine learning model, an information processing device (10) comprises: an acquisition unit (11) that acquires a plurality of training examples; a selection unit (12) that selects two or more training examples in which at least one inaccurate prediction result is obtained by using at least one machine learning model, from among the plurality of training examples, the at least one machine learning model receiving the examples as inputs and outputting the prediction result; and a generation unit (13) that generates an artificial example by combining the two or more training examples selected by the selection unit (12).
Furumoto et al. (European Patent Publication No. EP 0952501 A2) discloses process control method uses detection of process parameters for the technical process, used to provide a data set which is used for optimizing the operating point for the process control. An instance- or dimensional compression is carried out, whereby an individual data set is written as a matrix. The data of an instance is written as a matrix row, and the measured value as the matrix column, so that the height and width of the acquired matrix is reduced. The data set is obtained via data compression by combining data, e.g. using an offline-, online, or batch compression method.
Kong et al. (US Patent Publication No. 2020/0311575 A1) discloses online recognition apparatus includes a feature amount extraction unit that extracts a feature amount of input data, an identification result prediction unit that predicts an identification result based on the extracted feature amount, a prediction result evaluation unit that determines necessity of labeling from the predicted identification result unit, a correct answer assigning unit that assigns a correct answer to input data online from the determination result, a generator update unit that updates a parameter of a generator based on the input data with the correct answer, a pseudo-learning data generation unit that establishes a generator based on the parameter of the updated generator and generates pseudo-learning data, and an identifier update unit that online updates a parameter of an identifier prepared in advance based on the input data with the correct answer and the pseudo-learning data. The updated identifier is updated as a new identification result prediction unit.
Yoo et al. (WIPO Patent Publication No. WO 2021060899 A1) discloses a training method for specializing an artificial intelligence model in an institution for deployment, and an apparatus for training the artificial intelligence model. An operation method for a training apparatus operated by at least one processor comprises the steps of: extracting, from among data owned by a specific institution, a dataset to be used for specialized training; selecting, from the dataset, an annotation object required to be annotated, by using a pre-trained artificial intelligence model; and supervised-training the pre-trained artificial intelligence model by using data on which labels are annotated with respect to the annotation object.
Zhang et al. (US Patent Publication No. 2021/0056417 A1) discloses a method for active learning includes obtaining a set of unlabeled training samples and for each unlabeled training sample, perturbing the unlabeled training sample to generate an augmented training sample. The method includes generating, using a machine learning model, a predicted label for both samples and determining an inconsistency value for the unlabeled training sample that represents variance between the predicted labels for the unlabeled and augmented training samples. The method includes sorting the unlabeled training samples based on the inconsistency values and obtaining, for a threshold number of samples selected from the sorted unlabeled training samples, a ground truth label. The method includes selecting a current set of labeled training samples including each selected unlabeled training samples paired with the corresponding ground truth label. The method includes training, using the current set and a proper subset of unlabeled training samples, the machine learning model.
Kong et al. (US Patent Publication No. 2020/0311575 A1) discloses online recognition apparatus includes a feature amount extraction unit that extracts a feature amount of input data, an identification result prediction unit that predicts an identification result based on the extracted feature amount, a prediction result evaluation unit that determines necessity of labeling from the predicted identification result unit, a correct answer assigning unit that assigns a correct answer to input data online from the determination result, a generator update unit that updates a parameter of a generator based on the input data with the correct answer, a pseudo-learning data generation unit that establishes a generator based on the parameter of the updated generator and generates pseudo-learning data, and an identifier update unit that online updates a parameter of an identifier prepared in advance based on the input data with the correct answer and the pseudo-learning data. The updated identifier is updated as a new identification result prediction unit.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASANUL MOBIN whose telephone number is (571)270-1289. The examiner can normally be reached on 9AM to 6:00PM EST M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at 571-272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/HASANUL MOBIN/
Primary Examiner, Art Unit 2168
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