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 .
Examiner’s Note
Applicant’s remarks filed 12/15/2025 appear to pertain to a different application because pages 17-20 refer to features of applying natural language processing to automate medical coding.
Status of the Claims
Claims 1, 5-6, 9, 11, 13-14, 17-18, and 21-22 have been amended. Claim 2 has been cancelled. Claims 1 and 3-25 are currently pending and have been considered by the Examiner.
Claim Objections
Claims 1 and 21 are objected to because of the following informalities: In claim 1, line 14, “wherein a the position” should recite “wherein the position”.
In claim 21, line 1 recites “wherein” twice. One of these terms should be deleted. Appropriate correction is required.
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.
Claims 1 and 3-25 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “a media collection program” in both lines 11-12 and line 16. It is unclear if these two media collection programs are the same or different. Examiner treats them as the same media collection program. Examine suggests changing “a media collection program” in line 16 to “the media collection program”.
In claim 1, the limitation “the position corresponds to a relationship between the one or more claims and each one of the plurality of related media products” recited at page 3, lines 2-3 renders claim 1 indefinite for the following reasons. Claim 1 recites “different perspectives on the position taken by each claim of the media product” at page 2, lines 13-14. A relationship between claims (of the media product) and a related media product provides perspectives on the position taken by each claim of the media product. It is unclear how the relationship is different from "different perspectives" as claimed. Examiner treats relationships to mean perspectives.
Claims 3-8 are rejected for failing to cure the deficiencies of claim 1.
The limitations of claim 3, lines 4-7 render the claim indefinite because they appear to be redundant with the new limitation recited by claim 1, lines 16-19. It is unclear if generating a list in claim 3 is the same as generating a list in claim 1. It is unclear if “input data” recited in claim 3, line 2 and “a database” in claim 3, line 6 are the same as the input data and database recited in claim 1, line 18 and 19. Examiner treats the limitations in claim 3, lines 4-7 as being redundant with the limitations in claim 1, lines 16-19.
Claim 9 is rejected because it recites similar indefinite limitations as claim 1. Claim 9 recites the limitation "the media" in line 20. There is insufficient antecedent basis for this limitation in the claim. It is unclear if this limitation is supposed to recite “the media product”. Examiner treats this limitation as “the media product”.
Claim 9 recites “a second machine learning model” at both page 6, lines 2 and 10. It is unclear if these are the same or different second machine learning models. Examiner treats “a second machine learning model” at page 6, line 10 as “the second machine learning model”.
Claims 10-16 are rejected for failing to cure the deficiencies of claim 9.
Claim 10 recites the limitation "the title of the topic" in line 2. There is insufficient antecedent basis for this limitation in the claim. Claim 9 recites “text corresponding to a title of the media product” in line 14 but this does not provide proper antecedent basis for “the title of the topic.” It is unclear whether “text corresponding to a title of the media product” in claim 9 is related to “the title of the topic” in claim 10. Examiner treats “the title of the topic” as “a title of the media product”.
Claim 11 is rejected because it recites similar indefinite limitations as claim 3.
Claim 17 is rejected because it recites similar indefinite limitations as claims 1 and 9.
Claims 18-25 are rejected for failing to cure the deficiencies of claim 17.
In claim 18, the limitation in lines 1-4 renders the claim indefinite for the following reasons. Claim 18 recites the limitation "the topic of the media product" in line 1. There is insufficient antecedent basis for this limitation in the claim. Claim 17, lines 1-2 recites “a plurality of different related topics related to the media product” but this does not provide proper antecedent basis because they are not specifically a topic of the media product. Examiner treats claim 18, line 1 to mean a topic of the media product.
Claim 17 on page 9, lines 1-3 discloses processing one or more sentences to identify text corresponding to a title of the media product, and claim 18, lines 3-4 disclose processing “a one or more sentences of the media product” to identify text corresponding to a title. It is unclear if “a one or more sentences” as recited in claim 18, line 3 is different from the sentences recited in claim 17 on page 9, lines 1-3. If they are the same sentences, claim 18, line 3 should recite “the one or more sentences” and if they are different, claim 18, line 3 should recite “one or more second sentences.” Examiner treats claim 18 as processing the same sentences of the media product as in claim 17.
Claim 19 is rejected because it recites similar indefinite limitations as claim 3.
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, 3-6, 8-14, 16-22, and 24-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 3-6 and 8 recite a method. Claims 9-14 and 16 recite a product comprising processors (a product), and claims 17-22 and 24-25 recite a system comprising processors (a system). Each of a method, a product, and a system fall under one of the four statutory categories of patent eligible subject matter.
Claim 1
Step 2A Prong 1: Determining,
Determining,
Generating,
Search a database of media products and return a list of related media products corresponding to input data provided to the media collection program are observation, judgement and evaluation mental processes which can reasonably be performed in the human mind with the aid of pencil and paper.
Determining,
Generating, by the second different
Identifying the most contested claim as the claim of the one or more claims with a most negative relationship with the plurality of related media products such that a most contested claim is a claim that has the most related media products with a disagree stance is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generating, and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generating,
Step 2A Prong 2: Receiving, by one or more processors, media consumption data relating to a media product consumed by a user amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
One or more processors amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The user uses a user interface to consume the media product, wherein consumption is determined by a clicking, scrolling, listening and/or viewing of the media product by the user amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Using a first machine learning model amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Generating related media products through a media collection program amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) because the media collection program comprises computer instructions.
Using a second different machine learning model amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Said first and second machine learning models provide different perspectives on the position taken by each claim of the media product, wherein the position is either a negative, a positive or a neutral stance when one claim is compared to the opposing or concurring claim amounts to a field of use and technological environment under MPEP 2106.05(h).
Presenting, by the one or more processors, one or more of the question and the answer via the user interface of a computing device, wherein the answer is provided based on a different position than the most contested claim amounts to insignificant extra-solution activity under MPEP 2106.05(g).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions and field of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: Receiving, by one or more processors, media consumption data relating to a media product consumed by a user is analogous to receiving data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
One or more processors amount to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
The user uses a user interface to consume the media product, wherein consumption is determined by a clicking, scrolling, listening and/or viewing of the media product by the user amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Using a first machine learning model amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Generating related media products through a media collection program amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) because the media collection program comprises computer instructions.
Using a second different machine learning model amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Said first and second machine learning models provide different perspectives on the position taken by each claim of the media product, wherein the position is either a negative, a positive or a neutral stance when one claim is compared to the opposing or concurring claim amounts to a field of use and technological environment under MPEP 2106.05(h).
Presenting, by the one or more processors, one or more of the question and the answer via the user interface of a computing device, wherein the answer is provided based on a different position than the most contested claim is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions and field of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 3 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Identifying the plurality of related media products by processing input data corresponding to the topic of the media product
Generating,
Search a database of media products and return a list of related media products corresponding to the input data provided to the media collection program are observation, judgement and evaluation mental processes which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: Using a media collection program amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 4 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Determining the one or more claims is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Processing,
Step 2A Prong 2: The first machine learning model being a binary classifier amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Using the media collection program amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Training the binary classifier using a first labeled data set amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Using the binary classifier amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Outputting, by the binary classifier, ranked sets of sentences for the media product, wherein the ranked sets of sentences corresponds to the one or more sentences and are ranked based on a likelihood to contain the one or more claims amounts to insignificant extra-solution activity under MPEP 2106.05(g).
Step 2B: The first machine learning model being a binary classifier amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Using the media collection program amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Training the binary classifier using a first labeled data set amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Using the binary classifier amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Outputting, by the binary classifier, ranked sets of sentences for the media product, wherein the ranked sets of sentences corresponds to the one or more sentences and are ranked based on a likelihood to contain the one or more claims is analogous to retrieving information from a memory, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II). Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea. The claim is not patent eligible.
Claim 5 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated.
Step 2A Prong 2 and Step 2B: The positions also include an unrelated position amounts to a field of use under MPEP 2106.05(h).
Claim 6 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Determining plurality of related media products is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: Program instructions amount to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f).
Claim 8 incorporates the rejection of claim 1.
Step 2A Prong 1: The abstract ideas of claim 1 are incorporated. Generating the answer further comprises: processing, at a fourth
Processing, by the fourth
Step 2A Prong 2 and Step 2B: Using the fourth machine learning model comprising a bidirectional recurrent neural network (RNN) amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f). The claim is not patent eligible.
Claim 9
Step 2A Prong 1: Determine,
Determine,
Generate,
Search a database of media products and return a list of related media products corresponding to input data provided to the media collection program are observation, judgement and evaluation mental processes which can reasonably be performed in the human mind with the aid of pencil and paper.
Determining, second
Determine a most contested claim of the one or more claims for the media product as a claim that satisfies a condition corresponding to having a predetermined number of the plurality of related media products in disagreement with the claim, wherein the most contested claim is any claim with most negative stances is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generate, by a second
Identify the most contested claim as the claim of the one or more claims with a most negative relationship with the plurality of related media products such that a most contested claim is a claim that has the most related media products with a disagree stance is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generate a question based on the most contested claim and a paragraph including the most contested claim is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Generate an answer to the question based at least on the question and at least one of the related media products having a disagree position with the most contested claim is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper. The claim is not patent eligible.
Step 2A Prong 2: A computer program product comprising: one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Program instructions to receive, by one or more processors, media consumption data relating to a media product amounts to mere data-gathering, an insignificant extra-solution activity under MPEP 2106.05(g).
One or more processors amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
A user uses a user interface to consume the media product, wherein the consumption is determined by a clicking, scrolling, listening and/or viewing of the media product by the user amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Using a first machine learning model amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Using a trained classifier amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Generating related media products through a media collection program amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) because the media collection program comprises computer instructions.
Using a second machine learning model amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Said first and second machine learning models provide different perspectives on the position taken by each claim of the media product, wherein the position is either a negative, a positive or a neutral stance when one claim is compared to the opposing or concurring claim amounts to a field of use and technological environment under MPEP 2106.05(h).
Program instructions to present one or more of the question and the answer via the user interface of a computing device, wherein the answer will determine whether more content has to be provided to be consumed by the user amounts to insignificant extra-solution activity under MPEP 2106.05(g).
The additional elements as disclosed above, alone or in combination, do not integrate the abstract ideas into a practical application as they are mere insignificant extra solution activities as disclosed in combination with generic computer functions and field of use that are implemented to perform the abstract ideas disclosed above. The claim is directed to an abstract idea.
Step 2B: A computer program product comprising: one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Program instructions to receive, by one or more processors, media consumption data relating to a media product is analogous to receiving data over a network, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
One or more processors amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
A user uses a user interface to consume the media product, wherein the consumption is determined by a clicking, scrolling, listening and/or viewing of the media product by the user amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f).
Using a first machine learning model amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Using a trained classifier amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Generating related media products through a media collection program amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f) because the media collection program comprises computer instructions.
Using a second machine learning model amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f).
Said first and second machine learning models provide different perspectives on the position taken by each claim of the media product, wherein the position is either a negative, a positive or a neutral stance when one claim is compared to the opposing or concurring claim amounts to a field of use and technological environment under MPEP 2106.05(h).
Program instructions to present one or more of the question and the answer via the user interface of a computing device, wherein the answer will determine whether more content has to be provided to be consumed by the user is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II).
The additional elements as disclosed above, in combination with the abstract ideas, are not sufficient to amount to significantly more than the abstract ideas as they are well-understood, routine and conventional activities as disclosed in combination with generic computer functions and field of use that are implemented to perform the abstract ideas disclosed above. The claim is not patent eligible.
Claim 10 incorporates the rejection of claim 9.
Step 2A Prong 1: The abstract ideas of claim 9 are incorporated. The topic of the media product is determined to identify the title of the topic is a judgement and evaluation mental process which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: The claim does not recite any additional elements which, alone or in combination, would integrate the abstract ideas into a practical application or which, in combination with the abstract ideas, would be sufficient to amount to significantly more than the abstract ideas.
Claim 11 incorporates the rejection of claim 9.
Step 2A Prong 1: The abstract ideas of claim 9 are incorporated. Generate,
Search a database of media products and return a list of related media products corresponding to input data provided to the media collection program are observation, judgement and evaluation mental processes which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and Step 2B: Using a media collection program amounts to mere instructions to apply the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claims 12-14 and 16 each recites a product which implements similar features as the method of claims 4-6 and 8, respectively, and are therefore rejected for at least the same reasons.
Claim 17 recites a system which implements the same features as the product of claim 9 and is therefore rejected for at least the same reasons.
In Step 2A Prong 2 and in Step 2B, a computer system comprising: one or more computer processors, one or more computer readable storage media, and program instructions amounts to generic computer components for applying the abstract ideas on a generic computer under MPEP 2106.05(f). The claim is not patent eligible.
Claim 18 incorporates the rejection of claim 17.
Step 2A Prong 1: The abstract ideas of claim 17 are incorporated. Process, using a is a judgement and evaluation mental processes which can reasonably be performed in the human mind with the aid of pencil and paper.
Identify the title as the topic is a judgement and evaluation mental processes which can reasonably be performed in the human mind with the aid of pencil and paper.
Step 2A Prong 2 and 2B: Using a trained classifier amounts to invoking computers merely as a tool to perform an existing process under MPEP 2106.05(f). The claim is not patent eligible.
Claims 19-22 and 24 each recites a system comprising a processor which implements similar features as the method of claims 3-6 and 8, respectively, and are therefore rejected for at least the same reasons.
Claim 25 incorporates the rejection of claim 17.
Step 2A Prong 1: The abstract ideas of claim 17 are incorporated.
Step 2A Prong 2: The question and the answer are presented via the user interface during a pre-set time frame to allow a user to perceive the question along with the answer amount to an insignificant extra-solution activity under MPEP 2106.05(g).
Step 2B: The question and the answer are presented via the user interface during a pre-set time frame to allow a user to perceive the question along with the answer is analogous to presenting offers and gathering statistics, which the courts have recognized as a well-understood, routine, conventional activity under MPEP 2106.05(d)(II). The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 and 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Myslinski (US 9189514 B1, cited in PTO-892 issued 03/26/2024) in view of Ramakrishnan et al. (US 20200160196 A1, cited in PTO-892 issued 03/26/2024), and Dulhanty et al. (“Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection”, cited in the IDS filed 02/25/2021).
Regarding claim 1, Myslinski teaches: A computer-implemented method comprising: receiving, by one or more processors, media consumption data relating to a media product consumed by a user, wherein the user uses a user interface to consume the media product, (C. 2, L. 39-41, 47 and 51 (webpages), L. 56-57 (sentences), L. 59-63 (tracking), and C. 17, L. 1-4 discloses monitoring information including sentences by tracking webpages consumed by a user. C. 6, L. 40 discloses a processor, and C. 8, L. 14 to “302” in line 18 discloses a tablet which has a screen (“user interface”) for the user to consume a media product. The limitation of “a media product consumed by a user” corresponds to a user reading sentences on a webpage. The limitation of receiving media consumption data corresponds to receiving tracking information that the user is currently reading the website.)
wherein consumption is determined by a clicking, scrolling, listening and/or viewing of the media product by the user; (C. 2, L. 59-63 and C. 17, L. 1-4 teaches at least “viewing”)
determining, by the one or more processors, one or more claims contained within the media product using a first
wherein a claim is a position taken with respect to a topic based on facts provided on the topic; (C. 3, L. 54-64 and C. 4, L. 44-48. The limitation of “a topic” corresponds to who the president was during the war, and the limitation “facts provided on the topic” corresponds to facts provided on who the president was during the war. C. 18, L. 9-15 explicitly discloses how a topic of content (e.g., articles) is determined.)
determining, by the one or more processors, in an order of most related to least related to the topic as to whether each claim of the media product provides an opposing or concurring claim for one or more of a plurality of related media products generated through a media collection program using a second different
wherein said first and second negative, a positive or a neutral stance when one claim is compared to the opposing or concurring claim; (A neutral stance is any stance other than negative and positive stances. C. 4, L. 25-29, 31-36 discloses negative and positive stances. In C. 4, L. 43-44, the disclosure, “if a match is not found, then a result such as ‘unknown’ is returned” teaches a neutral stance. The first model parses claims to be fact checked and the second model classifies whether sources agree or disagree with the claims, so they both contribute to providing perspectives on the position taken by each claim.)
generating, by a media collection program, a list of the plurality of related media products in order of most related to least related, wherein the media collection program is configured to search a database of media products and return a list of related media products corresponding to input data provided to the media collection program; (C. 4, L. 10-16; C. 6, L. 59-63 (media collection program); C. 18, L. 9-15 discloses and determining a topic of contents (e.g., articles) and searching the topic via a search engine. Thus, processing input data corresponds to searching the topic via a search engine by using fact checking application 230. C. 36, L. 41-53 discloses checking source information in order of popularity, and C. 37, L. 1-7 discloses that Myslinski’s usage of popularity is interchangeable with relevancy. Myslinski’s embodiment categorizes sources by relevancy, which amounts to generating a list in order of relevancy.)
determining, by the one or more processors, based on the list of the plurality of related media products, a most contested claim of the one or more claims for the media product as a claim that satisfies a condition corresponding to having a predetermined number of the plurality of related media products in disagreement with the claim, wherein the most contested claim is any claim with most negative stances; (In C. 4, L. 47-48, the disclosure “a result of false… is returned” and C. 36, L. 16-30 together disclose returning a result of false when a number of sources above a lower threshold disagrees with the information to be fact checked. Thus, if a media product contains at most one claim and fact checking with a broader scope method returns “false”, then the information to be fact checked is “a most contested claim” as recited by the claim. The determining a most contest claim is based on the list of related media products as claimed because the determining is based on sources in the list.)
generating, by the second different
identifying the most contested claim as the claim of the one or more claims with a most negative relationship with the plurality of related media products such that a most contested claim is a claim that has the most related media products with a disagree stance; (C. 4, L. 47-48 and C. 36, L. 16-30, as mapped to the above limitation of “determining, by the one or more processors, a most contested claim”, also teaches “identifying the most contested claim” if a media product contains at most one claim and fact checking with a broader scope method returns “false”. The single claim would have the most related media products with a disagree stance.)
generating, by the one or more processors, a question based on the most contested claim and a paragraph of the media product including the most contested claim; (C. 4, L. 25-31, where the comparison is “a question” as claimed of whether the number of sources agreeing with the target information exceeds the number of sources disagreeing with the target information.)
generating, by the one or more processors, an answer to the question based at least on the question and at least one of the related media products having a disagree position with the most contested claim; and (C. 4, L. 25-31 where “an answer” as claimed is true if the number of sources agreeing with the target information exceeds the number of sources disagreeing with the target information, and an answer is false if the number of sources disagreeing with the target information exceeds the number of sources agreeing with the target information.)
presenting, by the one or more processors, one or more of the question and the answer via the user interface of a computing device, wherein the answer is provided based on a different position than the most contested claim. (C. 5, L. 18 to “false” in line 25 and C. 5, L. 46-50 disclose displaying a “false” status to a user. The status would be presented via a screen of a tablet computer (see C. 8, L. 14-18).)
However, Myslinski does not explicitly teach: a first machine learning model and a second different machine learning model
But Ramakrishnan teaches: determining,
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have used Ramakrishnan’s implicit objectivity classifier and implicit public interestingness classifier for Myslinski’s parser. Motivations for the combination are that Ramakrishnan’s two-stage classifier ensures that a potential claim to be fact checked is of public interest, thereby justifying the expense of fact-checking (Ramakrishnan, [0003]), and using a machine learning classifier avoids the burden of explicit feature engineering (Ramakrishnan, [0057]).
However, Myslinski and Ramakrishnan do not explicitly teach: a second different machine learning model
But Dulhanty teaches: determining,
generating, by the second different machine learning model, a position for each one of the
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Dulhanty’s RoBERTa model into the combination of Myslinski and Ramakrishnan. A motivation for the combination is that the RoBERTa model is well-suited for use in stance detection as the pretrained model can be leveraged to perform transfer learning on the target task. (Dulhanty, p. 2, § 2, lines 8-9)
Regarding claim 3, the combination of Myslinski, Ramakrishnan, and Dulhanty teaches: The computer-implemented method of claim 1,
Myslinski teaches: further comprising: identifying the plurality of related media products by processing input data corresponding to the topic of the media product in the media collection program; and (C. 4, L. 10-16; C. 6, L. 59-63 (media collection program); C. 18, L. 9-15 discloses and determining a topic of contents (e.g., articles) and searching the topic via a search engine. Thus, processing input data corresponds to searching the topic via a search engine by using fact checking application 230.)
generating, by the media collection program, a list of the plurality of related media products in order of most related to least related, (C. 36, L. 41-53 discloses checking source information in order of popularity, and C. 37, L. 1-7 discloses that Myslinski’s usage of popularity is interchangeable with relevancy. Myslinski’s embodiment categorizes sources by relevancy, which amounts to generating a list in order of relevancy.)
wherein the media collection program is configured to search a database of media products and return a list of related media products corresponding to the input data provided to the media collection program. (C. 4, L. 10-16 and C. 36, L. 41-53)
Regarding claim 4, the combination of Myslinski, Ramakrishnan, and Dulhanty teaches: The computer-implemented method of claim 1,
Myslinski, C. 3, L. 8-17 discloses a first model being a parser.
However, Myslinski and Dulhanty do not explicitly teach: wherein the first machine learning model is a binary classifier and determining the one or more claims further comprises: training the binary classifier using a first labeled data set; processing, by the binary classifier, the media product having the one or more sentences; and outputting, by the binary classifier, ranked sets of sentences for the media product, wherein the ranked sets of sentences correspond to the one or more sentences and are ranked based on a likelihood to contain the one or more claims.
But Ramakrishnan teaches: wherein the first machine learning model is a binary classifier and ([0035], lines 7-10 discloses each classifier is a neural network or a machine learning algorithm. [0048]-[0049], [0052], [0081] discloses an implicit objectivity classifier and an implicit public interestingness classifier. A first machine learning model corresponds to the combination of these classifiers, with the only two classifications being “potential claim” at branch 20 and “not a claim” at branches 18, 22 as seen in Fig. 1.)
determining the one or more claims further comprises: training the binary classifier using a first labeled data set; ([0062], lines 4-7 and [0084], lines 1-6)
processing, by the binary classifier, the media product having the one or more sentences; and ([0048], lines 7-10 and [0049])
outputting, by the binary classifier, ranked sets of sentences for the media product, wherein the ranked sets of sentences correspond to the one or more sentences and are ranked based on a likelihood to contain the one or more claims. ([0048], lines 7-10, [0049], and Fig. 1 discloses that sentences are classified (“ranked”) as being either a “potential claim” or “not a claim”.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have used Ramakrishnan’s implicit objectivity classifier and implicit public interestingness classifier for Myslinski’s parser and to have trained the classifiers on training data. Motivations for the combination are that Ramakrishnan’s two-stage classifier ensures that a potential claim to be fact checked is of public interest, thereby justifying the expense of fact-checking (Ramakrishnan, [0003]), and using a machine learning classifier avoids the burden of explicit feature engineering (Ramakrishnan, [0057]).
Regarding claim 5, the combination of Myslinski, Ramakrishnan, and Dulhanty teaches: The computer-implemented method of claim 1,
Myslinski teaches: wherein the positions also include an unrelated position. (In C. 4, L. 43-44, the disclosure, “if a match is not found, then a result such as ‘unknown’ is returned” teaches an unrelated position.)
Regarding claim 6, the combination of Myslinski, Ramakrishnan, and Dulhanty teaches: The computer-implemented method of claim 1,
Myslinski teaches: further comprising determining by program instructions a most contested claim as any claim with a most negative relationship with the plurality of related media products. (In C. 4, L. 47-48, the disclosure “a result of false… is returned” and C. 36, L. 16-30 together disclose returning a result of false when a number of sources above a lower threshold disagrees with the information to be fact checked. Thus, if a media product contains at most one claim and fact checking with a broader scope method returns “false”, then the information to be fact checked is a most contested claim.)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Myslinski (US 9189514 B1, cited in PTO-892 issued 03/26/2024) in view of Ramakrishnan et al. (US 20200160196 A1, cited in PTO-892 issued 03/26/2024), Dulhanty et al. (“Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection”, cited in the IDS filed 02/25/2021), and Du et al. (“Learning to Ask: Neural Question Generation for Reading Comprehension”, cited in IDS filed 02/25/2021).
Regarding claim 7, the combination of Myslinski, Ramakrishnan, and Dulhanty teaches: The computer-implemented method of claim 1, wherein generating the question further comprises:
Myslinski teaches: the most contested claim (C. 36, L. 28-30)
However, Myslinski, Ramakrishnan, and Dulhanty do not explicitly teach: processing, at a third machine learning model, the most contested claim and the paragraph including the most contested claim using bidirectional long-short term memory (LSTM) to generate LSTM encoder output data; concatenating, by the third machine learning model, the LSTM encoder output data to generate LSTM decoder input data; and processing, by the third machine learning model, the LSTM decoder input data and a context vector to generate question data corresponding to the question, wherein the context vector is a sum of a weighted average of encoder hidden states.
But Du teaches: processing, at a third machine learning model, the 1. At page 4, left column, all of § 4.2 discloses using a sentence encoder comprising a bidirectional LSTM called LSTM2 to generate the sentence encoder’s output called s. This section discloses using a paragraph encoder comprising another bidirectional LSTM called LSTM3 to generate the paragraph encoder’s output s’. Examiner treats “a third machine learning model” to mean a combination of LSTM1, LSTM2, and LSTM3. The limitation “LSTM encoder output data” corresponds to respective outputs of the sentence encoder and the paragraph encoder.)
concatenating, by the third machine learning model, the LSTM encoder output data to generate LSTM decoder input data; and (P. 3, right column, § 4, lines 6-7 and 12-15 discloses using an RNN encoder-decoder architecture that encodes both sentence and paragraph-level information. P. 3, right column, sentence in final two lines to P. 4, left column, line 8 discloses concatenating the outputs s and s’ and inputting them to the LSTM1 of the decoder.)
processing, by the third machine learning model, the LSTM decoder input data and a context vector to generate question data corresponding to the question, wherein the context vector is a sum of a weighted average of encoder hidden states. (Page 3, § 3, lines 1-5 discloses generating a natural question y based on an input sentence x. Examining equation 2 at p. 3, right column, § 4.1 discloses that the decoder outputs the question y. The attention-based sentence encoder uses “context vector” ct at equation 4 on p. 4 to generate the natural language question.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Du’s RNN encoder-decoder that generates natural language questions about Myslinski’s target information into the combination of Myslinski, Ramakrishnan, and Dulhanty. A motivation for the combination is to generate questions that test a user’s understanding of an associated text passage (Du, p. 1, § 1 to col. 2, line 3).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Myslinski (US 9189514 B1, cited in PTO-892 issued 03/26/2024) in view of Ramakrishnan et al. (US 20200160196 A1, cited in PTO-892 issued 03/26/2024), Dulhanty et al. (“Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection”, cited in the IDS filed 02/25/2021), and Tan et al. (“S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension”, cited in IDS filed 02/25/2021).
Regarding claim 8, the combination of Myslinski, Ramakrishnan, and Dulhanty teaches: The computer-implemented method of claim 1, wherein generating the answer further comprises:
Myslinski teaches: the related media product that disagrees with the most contested claim (C. 36, L. 28-30)
However, Myslinski, Ramakrishnan, and Dulhanty do not explicitly teach: processing, at a fourth machine learning model comprising a bidirectional recurrent neural network (RNN), the question and the related media product that disagrees with the most contested claim to extract evidence snippets; and processing, by the fourth machine learning model, the evidence snippets, the question, and the related media product that disagrees with the most contested claim to generate answer data corresponding to the answer.
But Tan teaches: processing, at a fourth machine learning model comprising a bidirectional recurrent neural network (RNN), the question and the related media product Fig. 1 and its caption discloses generating an answer to a question based on the question and a passage (“the related media product”). Page 3, final line states, “The evidence extraction part aims to extract evidence snippets related to the question and passage.” All of § 3.2.1 on pages 4-5 disclose evidence snippet prediction which uses bi-direction gated recurrent units (GRUs), which is a type of recurrent neural network. A fourth neural network corresponds to a combination of GRUs.)
processing, by the fourth machine learning model, the evidence snippets, the question, and the related media product
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Tan’s S-Net pipeline into the combination of Myslinski, Ramakrishnan, and Dulhanty. A motivation for the combination is to automatically answer questions based on a passage. (Tan, p. 1, § 1, lines 1-3)
Claims 9-14, 17-22, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Myslinski (US 9189514 B1, cited in PTO-892 issued 03/26/2024) in view of Ramakrishnan et al. (US 20200160196 A1, cited in PTO-892 issued 03/26/2024), Kirshenbaum (US 20110119208 A1, cited in PTO-892 issued 12/31/2024), and Dulhanty et al. (“Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection”, cited in the IDS filed 02/25/2021).
Regarding claim 9, Myslinski teaches: A computer program product comprising: one or more computer readable storage media and program instructions collectively stored on the one or more computer readable storage media, the stored program instructions comprising: (C. 6, L. 59-63)
program instructions to receive, by one or more processors, media consumption data relating to a media product wherein a user uses a user interface to consume the media product, (C. 2, L. 39-41, 47 and 51 (webpages), L. 56-57 (sentences), L. 59-63 (tracking), and C. 17, L. 1-4 discloses monitoring information including sentences by tracking webpages consumed by a user. C. 6, L. 40 discloses a processor, and C. 8, L. 14 to “302” in line 18 discloses a tablet which has a screen (“user interface”) for the user to consume a media product. The limitation of “a media product” consumed by a user corresponds to a user reading sentences on a webpage. The limitation of receiving media consumption data corresponds to receiving tracking information that the user is currently reading the website.)
wherein the consumption is determined by a clicking, scrolling, listening and/or viewing of the media product by the user; (C. 2, L. 59-63 and C. 17, L. 1-4 teaches at least “viewing”)
program instructions to determine, by the one or more processors, one or more claims contained within the media product using a first
wherein a claim is a position taken with respect to a topic based on facts provided on the topic; (C. 3, L. 54-64 and C. 4, L. 44-48. The limitation of “a topic” corresponds to who the president was during the war, and the limitation “facts provided on the topic” corresponds to facts provided on who the president was during the war. C. 18, L. 9-15 explicitly discloses how a topic of content (e.g., articles) is determined.)
program instructions to determine, by the one or more processors, a plurality of different topics related to the media product by processing,
program instructions to generate, by a media collection program, a list of the plurality of related media products in order of most related to least related, wherein the media collection program is configured to search a database of media products and return a list of related media products corresponding to input data provided to the media collection program; (C. 4, L. 10-16; C. 6, L. 59-63 (media collection program); C. 18, L. 9-15 discloses and determining a topic of contents (e.g., articles) and searching the topic via a search engine. Thus, processing input data corresponds to searching the topic via a search engine by using fact checking application 230. C. 36, L. 41-53 discloses checking source information in order of popularity, and C. 37, L. 1-7 discloses that Myslinski’s usage of popularity is interchangeable with relevancy. Myslinski’s embodiment categorizes sources by relevancy, which amounts to generating a list in order of relevancy.)
program instructions to determine, by the one or more processors, based on the list of the plurality of related media products in an order of most relevant to the media [product] amongst the plurality of different topics as to whether each one provides an opposing or concurring claim for each one or more of a plurality of related media products generated through a media collection program using a second
wherein said first and second
program instructions to determine a most contested claim of the one or more claims for the media product as a claim that satisfies a condition corresponding to having a predetermined number of the plurality of related media products in disagreement with the claim, wherein the most contested claim is any claim with most negative stances; (In C. 4, L. 47-48, the disclosure “a result of false… is returned” and C. 36, L. 16-30 together disclose returning a result of false when a number of sources above a lower threshold disagrees with the information to be fact checked. Thus, if a media product contains at most one claim and fact checking with a broader scope method returns “false”, then the information to be fact checked is “a most contested claim” as recited by the claim.)
program instructions to generate, by a second
program instructions to identify the most contested claim as the claim of the one or more claims with a most negative relationship with the plurality of related media products such that a most contested claim is a claim that has the most related media products with a disagree stance; (C. 4, L. 47-48 and C. 36, L. 16-30, as mapped to the above limitation of “program instructions to determine a most contested claim”, also teaches “program instructions to identify the most contested claim” if a media product contains at most one claim and fact checking with a broader scope method returns “false”. The single claim would have the most related media products with a disagree stance.)
program instructions to generate a question based on the most contested claim and a paragraph including the most contested claim; (C. 4, L. 25-31 where the comparison is “a question” as claimed of whether the number of sources agreeing with the target information exceeds the number of sources disagreeing with the target information.)
program instructions to generate an answer to the question based at least on the question and at least one of the related media products having a disagree position with the most contested claim; and (C. 4, L. 25-31 where “an answer” as claimed is true if the number of sources agreeing with the target information exceeds the number of sources disagreeing with the target information, and an answer is false if the number of sources disagreeing with the target information exceeds the number of sources agreeing with the target information.)
program instructions to present one or more of the question and the answer via the user interface of a computing device, wherein the answer will determine whether more content has to be provided to be consumed by the user. (C. 5, L. 18 to “false” in line 25 and C. 5, L. 46-50 disclose displaying a “false” status to a user. The status would be presented via a screen of a tablet computer (see C. 8, L. 14-18).)
However, Myslinski does not explicitly teach: a first machine learning model, a trained classifier, and a second machine learning model
But Ramakrishnan teaches: determine, interestingness classifier. A first machine learning model corresponds to the combination of these classifiers.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have used Ramakrishnan’s implicit objectivity classifier and implicit public interestingness classifier for Myslinski’s parser. Motivations for the combination are that Ramakrishnan’s two-stage classifier ensures that a potential claim to be fact checked is of public interest, thereby justifying the expense of fact-checking (Ramakrishnan, [0003]), and using a machine learning classifier avoids the burden of explicit feature engineering (Ramakrishnan, [0057]).
However, Myslinski and Ramakrishnan do not explicitly teach: a trained classifier, and a second machine learning model
But Kirshenbaum teaches: processing, using a trained classifier, one or more sentences of the media product to identify text corresponding to a title of the media product; (Examiner treats a sentence to mean a segment of text. [0016], lines 1-3 and [0018], sentence in lines 7-9 discloses a trained classifier 114. [0060], lines 4-7 and 20-22 teach the limitation “the media product” corresponds to a book.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Kirshenbaum’s trained classifier into the combination of Myslinski and Ramakrishnan. A motivation for training a classifier and applying the trained classifier is to perform classification automatically. (Kirshenbaum, sentence in [0010] in the right column, lines 3-9)
However, Myslinski, Ramakrishnan, and Kirshenbaum do not explicitly teach: a second machine learning model
But Dulhanty teaches: determine … as to whether each one [claim of a related media product] provides an opposing or concurring claim for each one or more
generate, by a second machine learning model, a position for each one of the
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Dulhanty’s RoBERTa model into the combination of Myslinski, Ramakrishnan, and Kirshenbaum. A motivation for the combination is that the RoBERTa model is well-suited for use in stance detection as the pretrained model can be leveraged to perform transfer learning on the target task. (Dulhanty, p. 2, § 2, lines 8-9)
Regarding claim 10, the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty teaches: The computer program product of claim 9,
Myslinski teaches: wherein the topic of the media product is determined to identify the title of the topic. (C. 8, L. 36-47 where a title as claimed is Myslinski’s summary.)
Regarding claim 11, the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty teaches: The computer program product of claim 9,
Myslinski teaches: wherein the program instructions to identify the plurality of related media products further comprise: program instructions to generate, by the media collection program, a list of the plurality of related media products in order of most related to least related, (C. 36, L. 41-53 discloses checking source information in order of popularity, and C. 37, L. 1-7 discloses that Myslinski’s usage of popularity is interchangeable with relevancy. Myslinski’s embodiment categorizes sources by relevancy, which amounts to generating a list in order of relevancy.)
wherein the media collection program is configured to search a database of media products and return a list of related media products corresponding to input data provided to the media collection program. (C. 4, L. 10-16 and C. 36, L. 41-53)
Regarding claim 12, the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty teaches: The computer program product of claim 9,
Myslinski, C. 3, L. 8-17 discloses a first model being a parser.
However, Myslinski, Kirshenbaum, and Dulhanty do not explicitly teach: wherein the first machine learning model is a binary classifier and determining the one or more claims further comprises: program instructions to train the binary classifier using a first labeled data set; program instructions to process, by the binary classifier, the media product having the one or more sentences; and program instructions to output, by the binary classifier, ranked sets of sentences for the media product, wherein the ranked sets of sentences corresponds to the one or more sentences and are ranked based on a likelihood to contain the one or more claims.
But Ramakrishnan teaches: wherein the first machine learning model is a binary classifier and ([0035], lines 7-10 discloses each classifier is a neural network or a machine learning algorithm. [0048]-[0049], [0052], [0081] discloses an implicit objectivity classifier and an implicit public interestingness classifier. A first machine learning model corresponds to the combination of these classifiers, with the only two classifications being “potential claim” at branch 20 and “not a claim” at branches 18, 22 as seen in Fig. 1.)
determining the one or more claims further comprises: program instructions to train the binary classifier using a first labeled data set; ([0062], lines 4-7 and [0084], lines 1-6 teaches training a binary classifier. [0123] discloses software application 454 which corresponds to program instructions.)
program instructions to process, by the binary classifier, the media product having the one or more sentences; and ([0048], lines 7-10 and [0049])
program instructions to output, by the binary classifier, ranked sets of sentences for the media product, wherein the ranked sets of sentences corresponds to the one or more sentences and are ranked based on a likelihood to contain the one or more claims. ([0048], lines 7-10, [0049], and Fig. 1 discloses that sentences are classified (“ranked”) as being either a “potential claim” or “not a claim”.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have used Ramakrishnan’s implicit objectivity classifier and implicit public interestingness classifier for Myslinski’s parser and to have trained the classifiers on training data. Motivations for the combination are that Ramakrishnan’s two-stage classifier ensures that a potential claim to be fact checked is of public interest, thereby justifying the expense of fact-checking (Ramakrishnan, [0003]), and using a machine learning classifier avoids the burden of explicit feature engineering (Ramakrishnan, [0057]).
Regarding claim 13, the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty teaches: The computer program product of claim 9,
Myslinski teaches: wherein an unrelated position is also defined. (In C. 4, L. 43-44, the disclosure, “if a match is not found, then a result such as ‘unknown’ is returned” teaches an unrelated position.)
Regarding claim 14, the combination of Myslinski, Ramakrishnan, and Dulhanty teaches: The computer program product of claim 9,
Myslinski teaches: further comprising determining by program instructions a most contested claim as any claim with a most negative relationship with the plurality of related media products. (In C. 4, L. 47-48, the disclosure “a result of false… is returned” and C. 36, L. 16-30 together disclose returning a result of false when a number of sources above a lower threshold disagrees with the information to be fact checked. Thus, if a media product contains at most one claim and fact checking with a broader scope method returns “false”, then the information to be fact checked is a most contested claim.)
Claim 17 is directed to a system which recites similar features as the product of claim 9 and is therefore rejected for at least the same reasons.
Regarding claim 18, the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty teaches: The computer system of claim 17,
Myslinski teaches: wherein the topic of the media product is determined by: program instructions to process,
program instructions to identify the title as the topic. (C. 8, L. 36-47 where a title as claimed is Myslinski’s summary.)
However, Myslinski, Kirshenbaum, and Dulhanty do not explicitly teach: using a trained classifier
Ramakrishnan teaches: process, using a trained classifier, [text]
A motivation for the combination is the same as the motivation provided in claim 9, which recites features similar the features from claim 17.
Regarding claim 19, the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty teaches: The computer system of claim 17,
Myslinski taches: wherein the program instructions to identify the plurality of related media products further comprises: program instructions to process, by the media collection program, input data corresponding to the topic of the media product; and (C. 4, L. 10-16; C. 6, L. 59-63 (media collection program); C. 18, L. 9-15 discloses and determining a topic of contents (e.g., articles) and searching the topic via a search engine. Thus, processing input data corresponds to searching the topic via a search engine by using fact checking application 230.)
program instructions to generate, by the media collection program, a list of the plurality of related media products in order of most related to least related, (C. 36, L. 41-53 discloses checking source information in order of popularity, and C. 37, L. 1-7 discloses that Myslinski’s usage of popularity is interchangeable with relevancy. Myslinski’s embodiment categorizes sources by relevancy, which amounts to generating a list in order of relevancy.)
wherein the media collection program is configured to search a database of media products and return a list of related media products corresponding to the input data provided to the media collection program. (C. 4, L. 10-16 and C. 36, L. 41-53)
Claims 20-22 are each directed to a system which recites similar features as the product of claims 12-14, respectively, and are therefore rejected for at least the same reasons.
Regarding claim 25, the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty teaches: The computer system of claim 17,
Myslinski teaches: wherein the question and the answer are presented via the user interface during a pre-set time frame to allow a user to perceive the question along with the answer. (C. 5, L. 18-19, 23 to “false” in line 25, 46-50, and 61-65 discloses a status of the information is provided/presented/displayed to a user based on the fact check result and within 1 second. The displayed status indicates the question (whether more sources agree or disagree with target information) and it contains the answer.)
Claims 15 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Myslinski (US 9189514 B1, cited in PTO-892 issued 03/26/2024) in view of Ramakrishnan et al. (US 20200160196 A1, cited in PTO-892 issued 03/26/2024), Kirshenbaum (US 20110119208 A1, cited in PTO-892 issued 12/31/2024), Dulhanty et al. (“Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection”, cited in the IDS filed 02/25/2021), and Du et al. (“Learning to Ask: Neural Question Generation for Reading Comprehension”, cited in IDS filed 02/25/2021).
Regarding claim 15, the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty teaches: The computer program product of claim 9, wherein the program instructions to generate the question further comprise:
Myslinski teaches: the most contested claim (C. 36, L. 28-30)
However, Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty do not explicitly teach: program instructions to process, at a third machine learning model, the most contested claim and the paragraph including the most contested claim using bidirectional long-short term memory (LSTM) to generate LSTM encoder output data; program instructions to concatenate, by the third machine learning model, the LSTM encoder output data to generate LSTM decoder input data; and program instructions to process, by the third machine learning model, the LSTM decoder input data and a context vector to generate question data corresponding to the question, wherein the context vector is a sum of a weighted average of encoder hidden states.
But Du teaches: 1. At page 4, left column, all of § 4.2 discloses using a sentence encoder comprising a bidirectional LSTM called LSTM2 to generate the sentence encoder’s output called s. This section discloses using a paragraph encoder comprising another bidirectional LSTM called LSTM3 to generate the paragraph encoder’s output s’. Examiner treats “a third machine learning model” to mean a combination of LSTM1, LSTM2, and LSTM3. The limitation “LSTM encoder output data” corresponds to respective outputs of the sentence encoder and the paragraph encoder.)
1 of the decoder.)
ct at equation 4 on p. 4 to generate the natural language question.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Du’s RNN encoder-decoder that generates natural language questions about Myslinski’s target information into the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty. A motivation for the combination is to generate questions that test a user’s understanding of an associated text passage (Du, p. 1, § 1 to col. 2, line 3).
Claim 23 is directed to a system which recites similar features as the product of claim 15 and is therefore rejected for at least the same reasons.
Claims 16 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Myslinski (US 9189514 B1, cited in PTO-892 issued 03/26/2024) in view of Ramakrishnan et al. (US 20200160196 A1, cited in PTO-892 issued 03/26/2024), Kirshenbaum (US 20110119208 A1, cited in PTO-892 issued 12/31/2024), Dulhanty et al. (“Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection”, cited in the IDS filed 02/25/2021), and Tan et al. (“S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension”, cited in IDS filed 02/25/2021).
Regarding claim 16, the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty teaches: The computer program product of claim 9, wherein the program instructions to generate the answer further comprises:
Myslinski teaches: the related media product that disagrees with the most contested claim (C. 36, L. 28-30)
However Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty do not explicitly teach: program instructions to process, at a fourth machine learning model comprising a bidirectional recurrent neural network (RNN), the question and the related media product that disagrees with the most contested claim to extract evidence snippets; and program instructions to process, by the fourth machine learning model, the evidence snippets, the question and the related media product that disagrees with the most contested claim to generate answer data corresponding to the answer.
But Tan teaches:
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated Tan’s S-Net pipeline into the combination of Myslinski, Ramakrishnan, Kirshenbaum, and Dulhanty. A motivation for the combination is to automatically answer questions based on a passage. (Tan, p. 1, § 1, lines 1-3)
Claim 24 is directed to a system which recites similar features as the product of claim 16 and is therefore rejected for at least the same reasons.
Response to Arguments
The following is the Examiner’s response to Applicant’s arguments filed 12/15/2025.
Applicant’s Arguments III. (Remarks pages 13-14):The pending claims have been amended to overcome the rejections under 112(a) and 112(b).
Examiner’s Response: The previous rejection of claim 5, 13, 21 under 112(a) has been withdrawn due to the amendments to claim 5.
The amendments to the claims have obviated some of the previous rejections under 112(b), but other indefinite issues have not been addressed as indicated in the rejections under 35 U.S.C. 112.
Applicant’s Arguments IV. (A). Pages 14-16 of the remarks argue the claims do not recite an abstract idea.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. Pending claim 1 is directed to a method for combatting misinformation in a media product consumed by a user. The method analyzes claims contained within the media product to determine a most contested claim and presents a question and answer via a user interface based on the most contested claim. Any alleged improvements in claim 1 are merely improvements to mental process abstract ideas. A human mind can reasonably perform determining one or more claims, determining whether each claim provides an opposing or concurring claim for related media products, generating a list of the plurality of related media products by searching a database of media products, determining a most contested claim within the media product, generating a position for each one of the related media products, identifying the most contested claim, generating a question, and generating an answer to the question. Applicant has failed to persuasively argue why a human allegedly cannot perform any of these steps. Figure 3 of the drawings clearly illustrates steps a human would take to perform the disclosed invention, including identifying claims in an article of interest, reading a number of related articles, comparing the related articles to the claims, and generating a question and answer based on the most contested claim in the article of interest. MPEP 2106.05(a) states, “It is important to note, the judicial exception alone cannot provide the improvement.” MPEP 2106.05(a), II. states, "it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology."
The feature of presenting thought-provoking information about a media product, argued on page 14, does not provide an improvement because presenting information is an insignificant post-solution activity. The improvement lies in the way of generating the thought-provoking question and answer (mental processes), rather than displaying the question and answer once they have been generated. With respect the final paragraph on page 14, understanding what triggers users’ critical thinking to prevent misinformation from being believed is a mental process which can reasonably be performed in the human mind. Claim 1 recites a single user, not a volume of users as alleged in the remarks on page 15, line 3. With respect to the first and second machine learning models, these additional elements are merely software tools for performing mental processes which could otherwise be performed by a human. As stated above, a human can reasonably determine claims contained within a media product and generate a position for each one of the plurality of related media products.
Applicant’s Arguments IV. (B). The arguments on pages 17-20 provide an overview of the 2019 Guidance and discusses Steps 2A and Step 2B of the 101 inquiry.
Examiner’s Response: Applicant’s remarks appear to pertain to a different application because pages 17-20 refer to features of applying natural language processing to automate medical coding. It is noted that the features upon which applicant relies are not recited in the rejected claim(s). 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).
Page 17 states Applicant’s claimed invention is not simply directed to an abstract idea falling within the category of “Certain Methods of Organizing Human Activity”. The current Office Action does not state the abstract ideas fall within this category.
Applicant’s arguments cite the McRo court on pages 18-20. The claims in McRo are eligible under 101 because they do not recite an abstract idea, but pending claims 1, 3-6, 8-14, 16-22, and 24-25 do recite abstract ideas, and any additional elements do not integrate the abstract ideas into a practical application or amount to significantly more than the abstract ideas.
Applicant’s arguments cite the BASCOM court on pages 20. The claims in BASCOM describe the concept of filtering content, and were found to recite a judicial exception (method of organizing human behavior) in Step 2A Prong 1. The claims were found to be patent eligible in Step 2B because the additional elements amounted to significantly more than the recited abstract idea. However, the pending claims recite different additional elements from the claims in BASCOM. In pending claims 1, 3-6, 8-14, 16-22, and 24-25, the additional elements do not amount to significantly more than the recited abstract ideas.
Applicant’s Arguments V. (Remarks pages 20-27): On page 21, Applicant argues that Myslinski only determines the factual accuracy of information and/or characterizes the information by comparing the information with source information. On page 23, Applicant argues neither Myslinski nor Ramakrishnan provide the features that provide the novelty in the present amended claims. On page 25, Applicant appears to argue that Du, Tan, Dulhanty, and Kirshenbaum do not teach any claim features.
Examiner’s Response: Applicant's arguments have been fully considered but they are not persuasive. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant did not provide any argument specifically disclosing that any of the limitations of claim 1 are not taught by any of the cited references or that the applied references as a whole fails to disclose any specific limitation. The combination of references as cited in the claim rejection teaches the limitations of claim 1.
The new limitation of claim 1, lines 16-19 appears to be a redundant with the limitations from previously presented claim 3, lines 4-7. The mapping has been maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.H.J./Examiner, Art Unit 2127 /ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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