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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/06/2024 and 06/22/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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:
“an obtaining section configured to obtain article information…”, and
“an estimating section configured to estimate…” in Claim 1.
Claims 12 recites similar limitations.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (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) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
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.
Claim 1 limitation “an obtaining section configured to obtain article information…”, and “an estimating section configured to estimate…”, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
At most the specification filed 06/22/2023 merely cites examples in para. [0016], of estimating and obtaining sections that may be part of a control unit that is a “program-controlled device such as a central processing unit (CPU)”. In para. [0019], the specification further cites that, “The program may be a program that is stored and provided on a computer readable and non-transitory recording medium and is copied into the storage unit 12”, further fails to limit the limitations explicitly to hardware. Open ended language used “such as” and “may be” fail to explicitly describe the structure. As such, the claims and the specification are devoid of any structure that explicitly performs the functions in the claims. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5, 7-8, & 11-15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Application Publication NO. 20190236614 “Burgin”.
Claim 1:
Burgin teaches a detecting device comprising:
a second machine learning model provided for mutual learning together with a first machine learning model, the first machine learning model being subjected to machine learning so as to generate genuine or fraudulent article information having a plurality of modalities, and the second machine learning model being subjected to machine learning so as to discriminate whether article information having a plurality of modalities is genuine or not (i.e. para. [0038], “The Generative Adversarial Network (GAN) subsystem 130, discussed in more detail with respect to FIG. 5, may include a generator that generates simulated counterfeit images and a discriminator that trains on these and/or images of actual fake items to classify input images as those of faked or genuine items”, wherein the BRI for article information having a plurality of modalities encompasses the how a photograph image with text may be an article of information, in which the generated images and associated information generated by a first generator MLM is ingested by a second discriminator MLM with an OCR module); an obtaining section configured to obtain article information having a plurality of modalities (i.e. para. [0047], The generator 502 may generate simulated counterfeit images 503, which may be used to train the discriminator 506. The discriminator 506, trained from the simulated counterfeit images 503 and/or actual counterfeit images 505 from the counterfeit images repository 504); and an estimating section configured to estimate whether the article information that is obtained by the obtaining section and has the plurality of modalities is genuine or not, by using the second machine learning model (i.e. para. [0061], the discriminator 506 may generate an output based on convolutional processing of an input image of an item being undergoing counterfeit analysis, the output including a prediction of whether the item is a counterfeit).
Claim 2:
Burgin teaches the detecting device according to claim 1,
wherein the first machine learning model is subjected to machine learning so as to generate fraudulent article information obtained by editing and processing at least part of genuine article information having a plurality of modalities (i.e. para. [0020], , the generator may take as input parameters from human users that direct the generator to make certain changes to a genuine image to create a simulated counterfeit image. For instance, the parameter may specify that a font recognized on a genuine image of a pill be adjusted to create a simulated counterfeit image of the pill).
Claim 3:
Burgin teaches the detecting device according to claim 1,
wherein the article information having a plurality of modalities includes image data representing an article image (i.e. para. [0035], supervised learning is done through a database maintained containing item information such as description and unique identifier information. This is linked to photographs and text examples and explanations of counterfeiting of that item. This is in addition to images and text descriptions of the genuine item).
Claim 5:
Burgin teaches the detecting device according to claim 1,
wherein the article information having a plurality of modalities includes text data representing an article descriptive sentence (i.e. para. [0035], “a database maintained containing item information such as description and unique identifier information. This is linked to photographs and text examples and explanations of counterfeiting of that item. This is in addition to images and text descriptions of the genuine item”, wherein the BRI for an article descriptive sentence encompasses a text description).
Claim 7:
Burgin teaches the detecting device according to claim 1, wherein the article information having a plurality of modalities includes attribute data representing an article category (i.e. para. [0028], “the discriminator 506 may compare the features of the counterfeit images with the features identified from the known good images of the item. For example, the discriminator 506 may compare specific regions of an imaged item. Such regions may correspond to circularity or shape features of a pill, a partition of the pill, a specific attribute of the pill, and/or other features”, wherein the BRI for article category encompasses attribute information regarding the article of information such as the type or shape of medicine being analyzed).
Claim 8:
Burgin teaches the detecting device according to claim 1,
wherein the article information having a plurality of modalities includes numerical data representing a shipment timing (i.e. para. [0030], statistical models based on geography, shipping route, and frequency of fraud for an item can also generate a probability of fraud and feed into classification and probability determination techniques. Also, shipping route information such as start of item journey and intermediaries may also be used).
Claim 11:
Claim 11 is the method claim reciting similar limitations to claim 1 and is rejected for similar reasons.
Claim 12:
Claim 12 is the medium claim reciting similar limitations to claim 1 and is rejected for similar reasons.
Claim 13:
Claim 13 is the device claim reciting similar limitations to claim 1 and is rejected for similar reasons.
Claim 14:
Claim 14 is the method claim reciting similar limitations to claim 1 and is rejected for similar reasons.
Claim 15:
Claim 15 is the model claim reciting similar limitations to claim 1 and is rejected for similar reasons.
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.
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.
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.
Claim(s) 4 & 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over and further in light of U.S. Patent Application Publication NO. 20190236614 “Burgin” and further in light of Hu, L., Wei, S., Zhao, Z., & Wu, B. (2022). Deep learning for fake news detection: A comprehensive survey. AI Open. https://doi.org/10.1016/j.aiopen.2022.09.001, hereinafter “Hu”.
Claim 4:
Burgin teaches the detecting device according to claim 1.
However, Burgin may not explicitly teach
wherein the article information having a plurality of modalities includes text data representing an article title.
However, Hu teaches
wherein the article information having a plurality of modalities includes text data representing an article title (i.e. [6.1 Dataset], “We summarize representative datasets in the field of fake news as follows… Ti-CNN (Yang et al., 2018) is a multimodal dataset for detecting fake news. The dataset contains a total of 20,015 news items, of which 11,941 are fake and 8074 are true. Each news article in the dataset includes a title, text, image, and author information”, wherein a GAN or CNN-based approach may receive event posts as a dataset to generate representations of the events).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the article information having a plurality of modalities includes text data representing an article title, to Burgin’s system that generates articles with a plurality of information including text data, a generator may intake real and fake article titles and generate representative article data in order to take a GAN or CNN-based approach to training a model to detect and differentiate fake and genuine news articles, as taught by Burgin. One would have been motivated to combine Hu with Burgin, and would have had a reasonable expectation of success in doing so as using techniques such as the introduction of multi-modal information, external knowledge, and integration strategy have been explored to carry out better performance.
Claim 9:
Burgin teaches the detecting device according to claim 1.
Burgin may not explicitly teach
wherein the article information having a plurality of modalities includes attribute data representing an attribute of an exhibitor or a seller.
However, Hu teaches
wherein the article information having a plurality of modalities includes attribute data representing an attribute of an exhibitor or a seller (i.e. [5.1.2 Weak Social Supervision], “The following work (Shu et al., 2019b) is a typical work to leverage social context information as constraints… Secondly, politically biased publishers are more probably to create fake stories. Thirdly, users with low trustworthiness are more likely to propagate fake news… the presentation of news must take into consideration the publisher’s political leaning; on the other hand, for the spreading relationship, constraining that the news presentation and user representation are close to each other if the news is fake and the user is less-credible, and vice versa”, wherein information attributes affecting if an article is genuine or fake encompasses social context attributes related to a publisher or publisher’s network of an article).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the article information having a plurality of modalities includes attribute data representing an attribute of an exhibitor or a seller, to Burgin’s system that generates articles with a plurality of information including text data, with how a discriminator may user attribute data of an exhibitor publisher to detect and differentiate fake and genuine news articles, as taught by Burgin. One would have been motivated to combine Hu with Burgin, and would have had a reasonable expectation of success in doing so as using techniques such as the introduction of multi-modal information, external knowledge, and integration strategy have been explored to carry out better performance.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over in light of U.S. Patent Application Publication NO. 20190236614 “Burgin” and further in light of U.S. Patent Application Publication NO. 20210125031 “Keng”.
Claim 6:
Burgin teaches the detecting device according to claim 1.
Burgin may not explicitly teach
wherein the article information having a plurality of modalities includes numerical data representing an article price.
However, Keng teaches
wherein the article information having a plurality of modalities includes numerical data representing an article price (i.e. para. [0051], “Other approaches generate plausible customer e-commerce orders for a given product using a Generative Adversarial Network (GAN). Given a product embedding, some approaches generate a tuple containing a product embedding, customer embedding, price, and date of purchases, which summarizes a typical order. This approach using a GAN can provide insights into product demand, customer preferences, price estimation and seasonal variations by simulating what are likely potential order”, wherein the G aims to produce realistic samples from this distribution while a discriminator D tries to differentiate fake samples from real samples. By alternating optimization steps between the two components, the generator ultimately learns the distribution of the real data).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the article information having a plurality of modalities includes numerical data representing an article price, to Burgin’s system that generates articles with a plurality of information in order to discriminate real and fake items, with how a GAN may use numerical price data differentiate real and fake articles of data, as taught by Keng. One would have been motivated to combine Keng with Burgin, and would have had a reasonable expectation of success in doing so as the additional consideration of price results in better generated samples that are more similar to a real data distribution.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over in light of U.S. Patent Application Publication NO. 20190236614 “Burgin” and further in light of U.S. Patent Application Publication NO. 20210019339 “Ghulati”.
Claim 10:
Burgin teaches the detecting device according to claim 1.
Burgin may not explicitly teach
wherein the article information having a plurality of modalities includes numerical data representing an evaluation given to an exhibitor or a seller.
However, Ghulati teaches
wherein the article information having a plurality of modalities includes numerical data representing an evaluation given to an exhibitor or a seller (i.e. para. [0253], “a method and system for assessing the quality of content generated by a user and their position within a credibility graph in order to generate a reliable credibility score is provided. The credibility score may be determined for a person, organization, brand or piece of content by means of calculation using a combination of extrinsic signals, content signals… The method may be further capable of determining a credibility score of the user of the generated content by the combination of the score indicative of the credibility of the content and the score indicative of the credibility of the user”, wherein the BRI numerical data encompasses the credibility score associated with an article published by an organization).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to add wherein the article information having a plurality of modalities includes numerical data representing an evaluation given to an exhibitor or a seller, to Burgin’s system that generates articles with a plurality of information including text data, with how a discriminator may include intaking associate article information including a numerical data value representing a credibility given to an organization or person related to the article, as taught by Ghulati. One would have been motivated to combine Burgin with Ghulati, and would have had a reasonable expectation of success in doing so as the additional consideration of credibility scoring may encourage the prevention of abuse and toxicity within online platforms.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 20200184212 “Anthony” teaches in para. [0028], , the synthetic training documents generated in the previous step are used to train a fraud classification model. The fraud classification model may be any Al or machine learning model that takes one or more documents as input and outputs a classification for the one or more documents. The classification may identify the document(s) as potentially fraudulent or not.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID H TAN whose telephone number is (571)272-7433. The examiner can normally be reached M-F 7:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula can be reached at (571) 272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.T./Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145