DETAILED ACTION
Notice of Pre-AIA or AIA Status.
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-20 filed on 12/19/2024 are pending and being examined. Claims 1, 9, and 17 are independent form.
Claim Rejections - 35 USC § 101
3. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed inventions are directed to non-statutory subject matter (an abstract idea without significantly more).
4-1. Regarding independent claim 1, the claim recites a method for data augmentation, comprising:
[1] obtaining one or more candidate descriptions of an image with respect to a question associated with the image;
[2] determining a target description from the one or more candidate descriptions based on respective effectiveness metrics of the one or more candidate descriptions, an effectiveness metric of a candidate description indicating whether the candidate description is useful in answering the question; and
[3] constructing a training sample for a machine learning model, the training sample comprising the image, the question and the target description.
Step 1:
With regard to step (1), claim 1, is directed to a method for data augmentation. Claim 1 therefore is one of statutory categories of invention, i.e., a process.
Step 2A-1:
With regard to 2A-1, The elements recited in claim 1, as drafted, under their broadest reasonable interpretation, encompass a process(es) which can be practically performed in human mind. For example, “determining a target description from the one or more candidate descriptions based on respective effectiveness metrics of the one or more candidate descriptions, an effectiveness metric of a candidate description indicating whether the candidate description is useful in answering the question” in step [2] in the context of this claim, encompasses mental observation, evaluations, judgments, opinions, and/or activities that “can be performed in human mind, or by a human using a pen and paper”, therefore the limitation falls within the “mental processes” grouping of abstract ideas. Claim 1 therefore recites an abstract idea. If a claim limitation is directed to organizing human activity, can be practically performed in human mind, or falls within mathematical concepts, then the claim recites an abstract idea. See MPEP 2106.04(a)(2).
Step 2A-2:
The 2019 PEG defines the phrase "integration into a practical application" to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception. In the instant case, the additional elements of “obtaining one or more candidate descriptions of an image with respect to a question associated with the image” in steps [1] and “constructing a training sample for a machine learning model, the training sample comprising the image, the question and the target description” in step [3] under their broadest reasonable interpretation, are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. Therefore, the claim as a whole does not integrate the judicial exception into a practical application.
Step 2B:
As explained above, the electronic device comprising an image sensor, a depth sensor, and a controller, is at best the equivalent of merely adding the words “apply it” to the judicial exception. The “obtaining in step [1] and “constructing” in step [3] were considered insignificant extra-solution activity. These conclusions should be reevaluated in Step 2B. The limitations are mere data gathering and/or output recited at high level of generality and amount to receiving (i.e., acquiring), accessing, or transmitting data over a network, which is well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The limitations remain insignificant extra-solution activity even upon reconsideration. Even when considered in combination, the additional elements present mere instructions to apply an exception and insignificant extra-solution activity, which cannot provide an inventive concept. The claim therefore is ineligible.
4-2. Regarding dependent claims 2-8, they are viewed individually, these additional elements are under its broadest reasonable interpretation, either covers performance of the limitation in the mind, performing a mathematical algorithm or extra solution activity for data gathering and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. And, when the claims are viewed as a whole, they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment (i.e., computer-based analysis of generic data). Hence, the claimed invention does not constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
4-3. Regarding independent claims 9 and 17, the claims recite an electronic device comprising processing units and storage memories (claim 9) and a non-transitory storage medium (claim 17) and each of which is analogous to apparatus claim 1, grounds of rejection analogous to those applied to claim 1 are applicable to claims 9 and 17. Furthermore, the claim is a method that does not recite any additional elements, and according to step 2A-2 does not integrate the abstract idea into a practical application because it does not recite any additional elements that impose any meaningful limits on practicing the abstract idea. The claim recites an abstract idea.
Because the claim fails under (2A), the claim is further evaluated under (2B). The claim herein does not include any additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
4-4. Regarding dependent claims 10-16, and 18-20 they are dependent from claims 9 and 17, respectively, and viewed individually, these additional elements are under its broadest reasonable interpretation, either covers performance of the limitation in the mind, performing a mathematical algorithm or extra solution activity for data gathering and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. And, when the claims are viewed as a whole, they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment (i.e., computer-based analysis of generic data). Hence, the claimed invention does not constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. Claim 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Baikampady Gopalkrishna et al (US2024/0152767, hereinafter “Baikampady Gopalkrishna”).
Regarding claim 1, Baikampady Gopalkrishna discloses a method for data augmentation (the systems and methods for training a visual question answer (VQA) model; see abstract and figs.1-3), comprising:
obtaining one or more candidate descriptions of an image with respect to a question associated with the image (the vision language model (VLM) of the method may generate the description from each of the training images and form the images and question answer pairs (I, Q, A); see images 102, VLM 104, pairs (I, Q, A) 106 of fig.1; see the images 224, and their corresponding descriptions 226, questions (Q) 222 and answers A in fig.2; see para.31-para.32);
determining a target description from the one or more candidate descriptions based on respective effectiveness metrics of the one or more candidate descriptions (see para.33, lines 3-7: wherein the “teacher model VQGic 308 is trained using images and question answer [(I, Q, A)] pairs from a target dataset 305 and VLM 302. The teacher model 308 is image conditioned to associate question answer pairs with images (I) by optimizing a loss function (LVQG).”), an effectiveness metric of a candidate description indicating whether the candidate description is useful in answering the question (wherein the loss function (LVQG) indicates how good the VQGic agrees with the training pairs (I, Q, A) including the result of answering the question. In other words, the VQGic is trained based on the selected training pairs (I, Q, A), i.e., whose answers (i.e., results) agree with the the VQGic's—namely, which are correct to minimize the loss function (LVQG).); and
constructing a training sample for a machine learning model, the training sample comprising the image, the question and the target description (see para.33, lines 3-7: wherein the “teacher model VQGic 308 is trained using images and question answer [(I, Q, A)] pairs from a target dataset 305 and VLM 302. The teacher model 308 is image conditioned to associate question answer pairs with images (I) by optimizing a loss function (LVQG).”)
As explained above, although Baikampady Gopalkrishna does not explicitly disclose “determining a target description from the one or more candidate descriptions based on respective effectiveness metrics of the one or more candidate descriptions, an effectiveness metric of a candidate description indicating whether the candidate description is useful in answering the question” and “constructing a training sample for a machine learning model, the training sample comprising the image, the question and the target description” as recited by claim 1, Baikampady Gopalkrishna teaches a method which trains a visual question answer (VQA) model only by the selected training datasets (I, Q, A), i.e., whose answers (A') agree with the the VQGic's answers—namely, which are useful to minimize the loss function (LVQG). It would be obvious for one of ordinary skill in the art to know that the answer (A) determined by the description of an image in the method in Baikampady Gopalkrishna is a respective effectiveness metrics of the description of the image. When the answer (A) determined by the description of an image is correct to minimize the loss function (LVQG), then the effectiveness metric of description of the image is useful to minimize the loss function (LVQG). Claim 1 therefore is unpatentable over Baikampady Gopalkrishna.
Regarding claim 2, 10, 18, Baikampady Gopalkrishna discloses, wherein determining the target description from the one or more candidate descriptions comprises: for a given candidate description of the one or more candidate descriptions, obtaining, using the machine learning model, an answer for the question based on the question and the given candidate description; determining whether the obtained answer is correct; and in accordance with a determination that the obtained answer is correct, determining the given candidate description as the target description (wherein the loss function (LVQG) indicates how good the VQGic agrees with the training pairs (I, Q, A) including the result of answering the question (Q, A). In other words, the VQGic is trained based on the selected training dataset (I, Q, A), i.e., whose answers (i.e., results) agree with the VQGic's answers—namely, which are correct to minimize the loss function (LVQG); see para.33, lines 3-7).
Regarding claim 3, 11, 19, Baikampady Gopalkrishna discloses, wherein the answer for the question is obtained by: providing, to the machine learning model, the question and a plurality of candidate answers to the question (see answers A=no or yes; see 226 of fig.2); and determining, as the obtained answer, a candidate answer selected by the machine learning model (ibid.).
Regarding claim 4, 12, 20, Baikampady Gopalkrishna discloses. wherein at least one candidate answer of the plurality of candidate answers is a correct answer to the question, and determining whether the obtained answer is correct comprises: in accordance with a determination that the obtained answer is one of the at least one candidate answer, determining that the obtained answer is correct (ibid.).
Regarding claim 5, 13, Baikampady Gopalkrishna discloses, wherein the answer is generated without providing the image to the machine learning model (wherein the images and question answer (I, Q, A) dataset is provided by the VLM instead of the VQA; see 102[Wingdings font/0xE0]104[Wingdings font/0xE0]106 in fig.1).
Regarding claim 6, 14, Baikampady Gopalkrishna discloses, wherein obtaining the one or more candidate descriptions comprises: generating, using the machine learning model, the one or more candidate descriptions based on the question and the image (see 116->118 synthetic pairs (I, Q’, A’) in fig.1).
Regarding claim 7, 15, Baikampady Gopalkrishna discloses, wherein generating the one or more candidate descriptions comprises: generating, based on the question, a prompt instructing the machine learning model to provide a description in assistance of answering the question; providing the prompt to the machine learning mode to obtain a model output from the machine learning model; and determining the one or more candidate descriptions based on the model output (see the small scale VQA task pairs 110 (I, Q, A) in fig.1).
Regarding claim 8, 16, Baikampady Gopalkrishna discloses, wherein the machine learning model is trained by: generating, using the machine learning model, a predicted description based on the image and the question (see VQA 122 of fig.1 which outputs the prediction P(A|Q,I)); determining a difference between the predicted description and the target description; and updating the machine learning model based on the difference (see para.33, wherein the loss function (LVQG) indicates how good the VQGic (i.e., the VQA 220 of fig.2) agrees with the training pairs (I, Q, A) including the result of answering the question (Q, A). In other words, the VQGic (i.e., the VQA 220 of fig.2) is trained based on the selected training dataset (I, Q, A), i.e., whose answers (i.e., results) agree with the VQGic's answers—namely, which are correct to minimize the loss function (LVQG);).
Regarding claims 9, 17, each of them is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li et al, US 20230419652, discloses a zero-shot visual question answering (VQA) framework, which conjoins foundation network models with zero additional training. A first image and a question relating to the first image are received. The first image is divided into a plurality of image patches. A plurality of relevant image patches that are relevant to the question are determined, using a first neural network model, from the plurality of image patches. A plurality of image captions are generated, using a second neural network model, based on the plurality of relevant image patches. An answer to the question is generated based on the plurality of image captions.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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