DETAILED ACTION
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 .
Priority
Receipt is acknowledged that application claims priority to foreign application with application number KR10-2024-0147826 dated 25 October 2024. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDS dated 26 January 2025 has been considered and placed in the application file.
Specification - Title
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: Using Importance Probabilities to tie images to text.
Specification - Drawings
The drawings are objected to because the blocks pertaining to all elements in FIG. 5 do not have descriptive labels in conformance with 37 CFR 1.84(n) and 1.84(o), or numbering that is further described in the specification. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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- 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The limitations, under their broadest reasonable interpretation, cover mental process using images/ drawings (concept performed in a human mind, including as observation, evaluation, judgment, opinion, prediction, etc.), and mathematical calculations for likelihood/ probability (e.g., - P(A) = f / N Where P(A) = Probability of an event (event A) occurring; f = Number of ways an event can occur (frequency); N = Total number of outcomes possible).
This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally and no additional features in the claims would preclude them from being performed as such.
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 1 is directed to an abstract idea as shown below:
STEP 1: Do the claims fall within one of the statutory categories?
YES. Claim 1 is directed to a device, i.e., a machine.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES, the claims are directed toward a mental process (i.e., abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The machine in claim 1, for example, comprises a mental process that can be practicably performed in the human mind therefore, an abstract idea.
Claim 1 recites:
receive a question including an image and text;
generate a text response and a multimodal response;
calculate pointwise mutual information (PMI) representing a correlation between the image and the text based on the multimodal response;
generate an importance weight based on the pointwise mutual information and adjust a token likelihood of the text response based on the importance weight; and
generate a final text response.
These limitations, as drafted, under their broadest reasonable interpretation, cover performance of the limitations in the mind or by a human. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
As such, a person could be presented texts and image(s)/ drawing(s) and determine a response with a degree of error or lack thereof either mentally or using a pen and paper. The mere nominal recitation that the various steps are being executed by a processor (e.g., processing unit) does not take the limitations out of the mental process grouping. Thus, the claims recite a mental process.
If a claim limitation, under its broadest reasonable interpretation, covers performance of a mental step which could be performed with a simple tool such as a pen and paper, then it falls within the “mental steps” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Thus, Claims 1- 12 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application.
Thus, since Claims 1 are/is: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, claim 1 is not eligible subject matter under 35 U.S.C 101. Similar analysis is made for the dependent claims 2-12 and the dependent claims are similarly identified as: being directed towards an abstract idea, not reciting additional elements that integrate the judicial exception into a practical application, and not reciting additional elements that amount to significantly more than the judicial exception.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – 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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f), is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Notwithstanding the 35 USC 101 rejection above, this application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“response generation unit configured to receive” in claim 1;
“PMI calculation unit configured to calculate” in claim 1; and
“importance sampling unit configured to generate” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
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.
Claims 1-5 and 7-11 (all claims except claims 6 and 12) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2026 0004191 A1, (He et al.) in view of US Patent Publication 2021 AAAAAA A1, (ZZZZZ et al.). The references are listed in a PTO-892 from the Office Action in which they are first used.
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Claim 1
[AltContent: textbox (He et al. Fig. 2, showing a multimodal transformer taking images and test and outputting heatmaps, scores and text.)] Regarding Claim 1, He et al. teach a multimodal language processing device based on a visual-language model ("a generative model can include a machine-learned text-to-image model, a machine-learned text-to-video model, a machine-learned text-to-audio model, a machine-learned multi-modal model, or any other machine-learned model configured to provide generative content in response to a user query," paragraph [0048]), comprising:
a response generation unit configured to receive a question including an image and text through a text-only language model and a vision-language model ("In the above equation, x is the input image and text prompt, and y is the target sequence associated with x," paragraph [0037]), and generate a text response and a multimodal response ("a generative model can include a machine-learned text-to-image model, a machine-learned text-to-video model, a machine-learned text-to-audio model, a machine-learned multi-modal model, or any other machine-learned model configured to provide generative content in response to a user query," paragraph [0048] where provide generative content teaches text and multimodal responses);
a PMI calculation unit configured to calculate pointwise mutual information (PMI) representing a correlation between the image and the text based on the multimodal response ("For example, elements 5-1, 5-2, ... , 5-M can represent tokens obtained using a tokenizer. For instance, a tokenizer can process a given portion of an input source and output a series of tokens (e.g., corresponding to input elements 5-1, 5-2, ... , 5-M) that represent the portion of the input source. Various approaches to tokenization can be used. For instance, textual input source(s) can be tokenized using a byte-pair encoding (BPE) technique," paragraph [0108] where tokens are pointwise information);
and an importance sampling unit configured to generate an importance weight based on the pointwise mutual information and adjust a token likelihood of the text response based on the importance weight ("the output generated can be a score for each of a set of pieces of text, each score representing an estimated likelihood that the piece of text is the correct transcript for the utterance" paragraph [0049]) to generate a final text response ("The generative model can be trained to process input data to generate output data," paragraph [0048]).
It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it 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 to employ combinations and sub-combinations of these complementary embodiments, because He et al. explicitly motivates doing so at least in paragraphs [0008], [0022] and [0203] including “Any and all features in the following claims can be combined or rearranged in any way possible, including combinations of claims not explicitly enumerated in combination together” and otherwise motivating experimentation and optimization.
Claim 2
Regarding claim 2, He et al. teach the multimodal language processing device based on a visual-language model of claim 1, wherein the response generation unit reflects a context of the image in the text through the vision-language model to generate the multimodal response ("Machine-learned model(s) 1 can be configured to process input(s) 2 that represent context regarding a desired portion of image content. The context can include text data, image data, audio data, etc. Machine-learned model(s) 1 can be configured to generate output(s) 3 that represent image data that depicts imagery related to the context," paragraph [0180]).
Claim 3
Regarding claim 3, He et al. teach the multimodal language processing device based on a visual-language model of claim 1, wherein the PMI calculation unit calculates mutual dependency between the image and the text as the pointwise mutual information to determine token importance of the text in the context of the image ("the input text prompt can be used to specify the type of heatmap that the model is expected to generate for a given input sample. This design enables support for a variety of heatmap prediction tasks (e.g., attention, interaction, importance, etc.) with a single heatmap prediction head," paragraph [0031]).
Claim 4
Regarding claim 4, He et al. teach the multimodal language processing device based on a visual-language model of claim 3, wherein the PMI calculation unit calculates the mutual dependency as the pointwise mutual information based on a probability that a specific text will be generated when the image and a token of the text are given and a probability that the token of the text will be generated in a text context before the token of the text when only the text is given ("Output sequence 7 can be generated autoregressively. For instance, for some applications, an output of one or more prediction layer(s) 6 can be passed through one or more output layers (e.g., softmax layer) to obtain a probability distribution over an output vocabulary ( e.g., a textual or symbolic vocabulary) conditioned on a set of input elements in a context window," paragraph [0116]).
Claim 5
Regarding claim 5, He et al. teach the multimodal language processing device based on a visual-language model of claim 1, wherein the importance sampling unit multiplies the token likelihood of the text response by the importance weight to select an important token from the text response ("Additionally, the model can include a plurality of different predictors ( e.g., three different predictors implemented as separate prediction heads) including a heatmap predictor for attention/saliency heatmaps or visual importance heatmaps, a scan path predictor for predicting the sequence/order of viewing, and a rating predictor for quality/aesthetic scores of images or web pages," paragraph [0028] where heatmaps are importance weights and quality/aesthetic scores teaches multiplying the token likelihood).
Claim 7
Regarding claim 7, He et al. teach a visual-language model-based multimodal language processing method performed in a multimodal language processing device based on a visual-language model ("a generative model can include a machine-learned text-to-image model, a machine-learned text-to-video model, a machine-learned text-to-audio model, a machine-learned multi-modal model, or any other machine-learned model configured to provide generative content in response to a user query," paragraph [0048]), the visual- language model-based multimodal language processing method comprising:
a response generation step of receiving a question including an image and text through a text-only language model and a vision-language model ("In the above equation, x is the input image and text prompt, and y is the target sequence associated with x," paragraph [0037]) and generating a text response and a multimodal response to the question ("a generative model can include a machine-learned text-to-image model, a machine-learned text-to-video model, a machine-learned text-to-audio model, a machine-learned multi-modal model, or any other machine-learned model configured to provide generative content in response to a user query," paragraph [0048] where provide generative content teaches text and multimodal responses);
a PMJ calculation step of calculating pointwise mutual information (PMI) representing a correlation between the image and the text based on the multimodal response ("For example, elements 5-1, 5-2, ... , 5-M can represent tokens obtained using a tokenizer. For instance, a tokenizer can process a given portion of an input source and output a series of tokens (e.g., corresponding to input elements 5-1, 5-2, ... , 5-M) that represent the portion of the input source. Various approaches to tokenization can be used. For instance, textual input source(s) can be tokenized using a byte-pair encoding (BPE) technique," paragraph [0108] where tokens are pointwise information); and
an importance sampling step of generating an importance weight based on the pointwise mutual information and adjusting a token likelihood of the text response based on the importance weight ("the output generated can be a score for each of a set of pieces of text, each score representing an estimated likelihood that the piece of text is the correct transcript for the utterance" paragraph [0049]) to generate a final text response ("The generative model can be trained to process input data to generate output data," paragraph [0048]).
Claim 8
Regarding claim 8, He et al. teach the visual-language model-based multimodal language processing method of claim 7, wherein the response generation step includes reflecting a context of the image in the text through the vision-language model to generate the multimodal response ("Machine-learned model(s) 1 can be configured to process input(s) 2 that represent context regarding a desired portion of image content. The context can include text data, image data, audio data, etc. Machine-learned model(s) 1 can be configured to generate output(s) 3 that represent image data that depicts imagery related to the context," paragraph [0180]).
Claim 9
Regarding claim 9, He et al. teach the visual-language model-based multimodal language processing method of claim 7, wherein the PMI calculation step includes calculating mutual dependency between the image and the text as the pointwise mutual information to determine token importance of the text in the context of the image ("the input text prompt can be used to specify the type of heatmap that the model is expected to generate for a given input sample. This design enables support for a variety of heatmap prediction tasks (e.g., attention, interaction, importance, etc.) with a single heatmap prediction head," paragraph [0031]).
Claim 10
Regarding claim 10, He et al. teach the visual-language model-based multimodal language processing method of claim 9, wherein the PMI calculation unit calculates the mutual dependency as the pointwise mutual information based on a probability that a specific text will be generated when the image and a token of the text are given and a probability that the token of the text will be generated in a text context before the token of the text when only the text is given ("Output sequence 7 can be generated autoregressively. For instance, for some applications, an output of one or more prediction layer(s) 6 can be passed through one or more output layers (e.g., softmax layer) to obtain a probability distribution over an output vocabulary ( e.g., a textual or symbolic vocabulary) conditioned on a set of input elements in a context window," paragraph [0116]).
Claim 11
Regarding claim 11, He et al. teach the visual-language model-based multimodal language processing method of claim 7, wherein the importance sampling step includes multiplying the token likelihood of the text response by the importance weight to select an important token from the text response ("Additionally, the model can include a plurality of different predictors ( e.g., three different predictors implemented as separate prediction heads) including a heatmap predictor for attention/saliency heatmaps or visual importance heatmaps, a scan path predictor for predicting the sequence/order of viewing, and a rating predictor for quality/aesthetic scores of images or web pages," paragraph [0028] where heatmaps are importance weights and quality/aesthetic scores teaches multiplying the token likelihood).
2nd Claim Rejections - 35 USC § 103
Claims 6 and 12 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2026 0004191 A1, (He et al.) in view of Korean Patent Publication 2023 00120219 A1, (Kang et al.). The references are listed in a PTO-892 from the Office Action in which they are first used.
Claim 6
Regarding Claim 6, He et al. teach the multimodal language processing device based on a visual-language model of claim 5, as noted above.
He et al. is not relied upon to explicitly teach all of reflecting a visual context in the final text response.
[AltContent: textbox (Kang et al., Fig. 7, showing a multimodal transformer taking text and image data)]
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However, Kang et al. teach wherein the importance sampling unit selects the important token and reflects a visual context of the image in the final text response ("The model generating device compares the similarity with the target within the given image-text query to identify top K search results that are most similar. Image text retrieval relies heavily on joint representations of vision-languages to compare similarities," page 4, last paragraph).
Therefore, taking the teachings of He et al. and Kang et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify “Multimodal Machine learned Models for Unified Attention and Response Predictions for Visual Content” as taught by He et al. to use “Vision Language Pre-Training Method based on Relation of Objects” as taught by Kang et al. The suggestion/motivation for doing so would have been that, “Vision-language pre-training (VLP) aims to learn cross-modal representations for encoding and decoding through large-scale image-text data. A model that has learned a cross-modal representation can perform fine tuning for applications such as image captions and question-answering tasks, reducing the learning time and amount of training data for specific applications and providing excellent performance.” as noted by the Kang et al. disclosure in Background Art, which also motivates combination because the combination would predictably have a higher efficiency as there is a reasonable expectation that different models may need different solutions to different data; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 12
Regarding claim 12, He et al. teach the visual-language model-based multimodal language processing method of claim 11, as noted above.
He et al. is not relied upon to explicitly teach all of reflecting a visual context in the final text response.
However, Kang et al. teach wherein the importance sampling step further includes selecting the important token and reflects the visual context of the image in the final text response ("The model generating device compares the similarity with the target within the given image-text query to identify top K search results that are most similar. Image text retrieval relies heavily on joint representations of vision-languages to compare similarities," page 4, last paragraph).
He et al. and Kang et al. are combined as per claim 1.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent 11,538,210 B1 to Lao et al. discloses receive a text importance vector that includes designations of visual properties for constituent words of a text phrase. Spatial layouts of the text phrase are determined, with each spatial layout being a different displayable representation of the constituent words arranged based on the designations of the visual properties in the text importance vector for each of the constituent words. Feature vectors are generated, each feature vector represents a spatial layout of the text phrase and includes measurement properties of each of the constituent words in the respective spatial layout.
Non Patent Publication “Dual Path Multi-Modal High-Order Features for Textual Content based Visual Question Answering” to Li et al. discloses given an image, it aims to predict an answer to a provided natural language question closely related to its textual contents. In this paper, we propose a novel end-to-end textual content based VQA model, which grounds question answering both on the visual and textual information. After encoding the image, question and recognized text words, it uses multi-modal factorized high-order modules and the attention mechanism to fuse question-image and question-text features respectively. The complex correlations among different features can be captured efficiently. To ensure the model's extendibility, it embeds candidate answers and recognized texts in a semantic embedding space and adopts semantic embedding based classifier to perform answer prediction.
Conclusion
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/Heath E. Wells/Examiner, Art Unit 2664
Date: 4 June 2026
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