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 .
Detailed Action’
In amendments dated 4/6/26, Applicant amended claims 1-2, 6, 14-15, and 19, canceled claims 3 and 16, and added no new claims. Claims 1-2, 4-15, and 17-26 are presented for examination.
Rejections under 35 U.S.C. 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 1-2, 4-15, and 17-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more. Independent claims 1 and 14 each recites generating a plurality of score factors for each piece of content of the plurality of pieces of content wherein each score factor is generated by a separate machine learning model, each score factor being a confidence score for each type of a different principle; and aggregating, using ensemble machine learning that is trained on a set of scenarios that affect a quality of a particular piece of content, each confidence score of the plurality of score factors to generate a quality score for each piece of content. Generating score factors and aggregating each score are recited broadly and are mental processes accomplishable in the human mind or on paper. Examiner notes that generating score factors by using machine learning models and training ensemble machine learning are conventional uses of machine learning models and are not significantly more than mental processes per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Each claim recites additional elements of retrieving, by a computer system having a processor and a plurality of lines of instructions that are executed by the processor, a plurality of pieces of content; and storing, by the computer system for each piece of content, the quality score and the score for each of the score factors, and retrieving pieces of content and storing scores and score factors are each insignificant extra-solution activities. Claim 1 recites a computer system having a processor and lines of instructions, and the processor and instructions are each generic components of a computer system. Examiner notes specification paragraph 0004 discusses how search results may or may not be the most relevant results based on a query. Paragraph 0005 describes most search engines do not perform any quality check on the search results and that it would be desirable to assess the quality of each story for the reader and provide an assessment of the article quality to the reader with search results, and paragraph 0006 states most stories and articles returned in search results today are written with a bias or written from a particular political perspective, and it would be desirable to be able to assess the political lean of each story for the reader and provide an assessment of the political lean of each story to the reader with search results. Paragraphs 0029-0031 provide techniques and solutions to address these drawbacks but most of these are not claimed. Also, the claim steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the retrieving and storing steps are routine and conventional activities in the art per the list of such activities in MPEP 2106.05(d) part II. The processor and instructions are each still generic components of a computer system. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claims 2 and 15 each recites receiving, at a search computer system having a processor and a plurality of lines of instructions that are executed by the processor, a search query having one or more query terms, which is recited broadly and amounts to receiving data across a network per specification figure 1 communications path 104, and is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; retrieving, by the search computer system, one or more pieces of content that match the one or more query terms, and retrieving data from a memory is routine and conventional activity in the art per the list of such activities in MPEP 2106.05(d) part II; retrieving, by the search computer system, the quality score and confidence score of each of the plurality of score factors from the computer system, and retrieving data from a memory is routine and conventional activity in the art per the list of such activities in MPEP 2106.05(d) part II; and generating, by the search computer system, a search user interface having a summary of each of the matching one or more pieces of content, the quality score and the confidence score of each of the plurality of score factors for each matching piece of content, and generating a search interface is generating data and a mental process accomplishable in the human mind or on paper. Claims 4 and 17 each recites wherein the plurality of score factors are a source factor, an industry standard factor and a document type factor, and factors are data and a mental process accomplishable in the human mind or on paper.
Claims 5 and 18 each recites wherein the plurality of score factors are a byline factor, a title exaggeration factor, a subjectivity factor, a clickbait factor, a personal attack factor and a lack of site disclosure factor, and factors are data and a mental process accomplishable in the human mind or on paper. Claims 6 and 19 each recites crawling, by the computer system, the plurality of pieces of content, and crawling is recited using a generic computer as a tool and is a mental process accomplishable in the human mind or on paper; ingesting, by the computer system, the crawled plurality of pieces of content, and ingesting the crawled data is storing them and is routine and conventional activity in the art per the list of such activities in MPEP 2106.05(d) part II; and performing, by the computer system, using multiple separate machine learning models, machine learning to generate the quality score for each piece of content, and applying machine learning is a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Claims 7 and 20 each recites generating, by the search computer system, a search factors user interface that displays the plurality of score factors that together generate the quality score, and generating an interface is a mental process accomplishable in the human mind or on paper.
Claims 8 and 21 each recites wherein each score factor is a journalistic principle, and a principle is data and a mental process accomplishable in the human mind or on paper. Claims 9 and 22 each recites wherein the plurality of score factors include a source factor, an industry standard factor, a document type factor, a byline factor, a title exaggeration factor, a subjectivity factor, a clickbait factor, a personal attack factor and a site disclosure factor, and factors are data and a mental process accomplishable in the human mind or on paper. Claims 10 and 23 each recites generating, by the search computer system, a filter user interface to adjust the quality scoring for each matching piece of content, and generating an interface is a mental process accomplishable in the human mind or on paper. Claims 11 and 24 each recites wherein each piece of content is a news piece of content, and news content is data and a mental process accomplishable in the human mind or on paper.
Claims 12 and 25 each recites generating and storing, by the computer system for each piece of content, a political lean score indicating a political bias of the piece of content and wherein generating the search user interface further comprises generating the search user interface having a summary of each of the matching piece of content and the stored quality score and political lean score for each matching piece of content, and generating an interface is a mental process accomplishable in the human mind or on paper and storing data is routine and conventional activity in the art per the list of such activities in MPEP 2106.05(d) part II. Claims 13 and 26 each recites wherein the computer system generates the quality score for each piece of content using a transformer-based neural network, and using a neural network is a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628).
Rejections under 35 U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claims 1-2, 4-5, 7-12, 14-15, 17-18, and 20-25 are rejected under 35 U.S.C. 103 as being unpatentable over Raniere (US 20170177717) in view of Lin et al (US 10,853,431), hereafter known as Lin.
With respect to claims 1 and 14, Raniere teaches:
retrieving, by a computer system having a processor and a plurality of lines of instructions that are executed by the processor, a plurality of pieces of content (paragraph 0038 retrieving and analyzing a plurality of articles, tiers of analysis includes retrieving and analyzing pieces of content from articles);
generating, by the computer system, a plurality of score factors for each piece of content of the plurality of pieces of content, each score factor being a confidence score for each type of a different principle (paragraph 0030 rating for each category of journalistic distortion, score is an extent of the journalistic distortion for each category of journalistic distortion (confidence score));
aggregating, by the computer system, each confidence score of the plurality of score factors to generate a quality score for each piece of content (paragraph 0037 aggregating ratings to make overall rating for validity of an article); and
storing, by the computer system for each piece of content, the quality score and the confidence score for each of the score factors (paragraph 0043 ratings for articles displayed on website thus stored, also paragraph 0048 ratings and overall ratings stored).
Raniere does not teach:
generate a plurality of score factors for each piece of content of the plurality of pieces of content wherein each score factor is generated by a separate machine learning model, each score factor being a confidence score for each type of a different principle; and
aggregate, using ensemble machine learning that is trained on a set of scenarios that affect a quality of a particular piece of content, each confidence score of the plurality of score factors to generate a quality score for each piece of content.
Lin teaches these things:
generate a plurality of score factors for each piece of content of the plurality of pieces of content wherein each score factor is generated by a separate machine learning model, each score factor being a confidence score for each type of a different principle (column 7 lines 38-64 figure 2, uses plurality of machine learning models in machine learning module 230, columns 6-7 lines 63-28 generates aggregate score of scores for factors); and
aggregate, using ensemble machine learning that is trained on a set of scenarios that affect a quality of a particular piece of content, each confidence score of the plurality of score factors to generate a quality score for each piece of content (column 7 lines 29-37 machine learning model uses trees, gradient boosting which are ensemble machine learning techniques, column 7 lines 38-64 trained on content items (scenarios) having attributes contributing to high/low quality content (affecting a particular piece of content)).
It would have bene obvious to have combined the machine learning functions in Lin with the scoring factors and aggregating techniques for news items in Raniere to provide more accurate scoring for news items.
With respect to claim 1, Raniere teaches a computer system with a processor and instructions (figure 1, paragraph 0043).
With respect to claims 2 and 15, all the limitations in claims 1 and 14 are addressed by Raniere and Lin above. Raniere also teaches:
receiving, at a search computer system having a processor and a plurality of lines of instructions that are executed by the processor, a search query having one or more query terms (paragraph 0049 user search with terms of an article, topic, country etc.),
retrieving, by the search computer system, one or more pieces of content that match the one or more query terms (paragraph 0049 user search among articles so pieces of content retrieved),
retrieving, by the search computer system, the quality score and confidence score of each of the plurality of score factors from the computer system (paragraph 0049 retrieved ratings for articles, comparisons between articles) and
generating, by the search computer system, a search user interface having a summary of each of the matching one or more pieces of content, the quality score and the confidence score of each of the plurality of score factors for each matching piece of content (paragraphs 0049-0050 generate an interface for users with articles and ratings).
With respect to claims 4 and 17, all the limitations in claims 1 and 14 are addressed by Raniere and Lin above. Raniere also teaches wherein the plurality of score factors are a source factor, an industry standard factor and a document type factor (paragraph 0049 comparing ratings per news source (source factor)).
With respect to claims 5 and 18, all the limitations in claims 1, 4, 14, and 17 are addressed by Raniere and Lin above. Raniere also teaches wherein the plurality of score factors are a byline factor, a title exaggeration factor, a subjectivity factor, a clickbait factor, a personal attack factor and a lack of site disclosure factor (paragraph 0068 subjectivity in article when a journalist is angry or said leads to distortions (subjectivity factor)).
With respect to claims 7 and 20, all the limitations in claims 1, 2, 14, and 15 are addressed by Raniere and Lin above. Raniere also teaches generating, by the search computer system, a search factors user interface that displays the plurality of score factors that together generate the quality score (paragraph 0045 display analysis with ratings on an interface).
With respect to claims 8 and 21, all the limitations in claims 1 and 14 are addressed by Raniere and Lin above. Raniere also teaches wherein each score factor is a journalistic principle (paragraph 0015 journalistic principles described for distortion).
With respect to claims 9 and 22, all the limitations in claims 1, 8, 14, and 21 are addressed by Raniere and Lin above. Raniere also teaches wherein the plurality of score factors include a source factor, an industry standard factor, a document type factor, a byline factor, a title exaggeration factor, a subjectivity factor, a clickbait factor, a personal attack factor and a site disclosure factor (paragraph 0068 subjectivity in factors for distortion).
With respect to claims 10 and 23, all the limitations in claims 1, 2, 14, and 15 are addressed by Raniere and Lin above. Raniere also teaches generating, by the search computer system, a filter user interface to adjust the quality scoring for each matching piece of content (paragraphs 0027-0028 user input to adjust dictionaries for genres of articles, paragraph 0029 affects distortions of articles and thus ratings).
With respect to claims 11 and 24, all the limitations in claims 1 and 14 are addressed by Raniere and Lin above. Raniere also teaches wherein each piece of content is a news piece of content (paragraph 0027 news content).
With respect to claims 12 and 25, all the limitations in claims 1 and 14 are addressed by Raniere and Lin above. Raniere also teaches generating and storing, by the computer system for each piece of content, a political lean score indicating a political bias of the piece of content and wherein generating the search user interface further comprises generating the search user interface having a summary of each of the matching piece of content and the stored quality score and political lean score for each matching piece of content (paragraph 0015 distortion can also be a rating for bias or political leaning, paragraph 0054 display report with ratings and summary of analysis for ratings).
Responses to Applicant’s Remarks
Regarding objections to claim 16 for improper depending on a claim B14, in view of amendments canceling this claim, this objection is withdrawn. Regarding rejections of claims 1-26 under 35 U.S.C. 101 for reciting mental processes without significantly more, Applicants arguments have been considered but are not persuasive. On pages 7-8 of his Remarks Applicant discusses Step 2A Prong Two and the Enfish, Ex Parte Desjardins, and Carmody cases and asserts the claims recite an improvement in the technology of assessing a piece of content and notes the amended limitations “generating a plurality of score factors for each piece of content of the plurality of pieces of content wherein each score factor is generated by a separate machine learning model, each score factor being a confidence score for each type of a different principle;” and “aggregating, using ensemble machine learning that is trained on a set of scenarios that affect a quality of a particular piece of content, each confidence score of the plurality of score factors to generate a quality score for each piece of content.” Examiner disagrees and notes each of these limitations is recited broadly and each limitation recites applying or training a machine learning model which is a conventional activity per the Recentive Analytics case as Examiner notes in the rejection above. Each limitation lacks details from the invention, such as how the score factors are aggregated to generate a quality score, that may show how the invention improves upon the assessment of a piece of content. On pages 8-9 Applicant discusses Step 2B and asserts that, when evaluating the additional elements, the claims as a whole amount to an inventive concept. Examiner disagrees as the additional elements, which are “retrieving a plurality of pieces of content;” and “store, for each piece of content, the quality score and the confidence score for each of the score factors;” merely gather data (pieces of content) and store the generated and aggregated quality score and confidence score for each score factor of the gathered data, so neither step affects the generating or aggregating step in any way. Thus neither additional element contributes to an inventive concept in the claims. Further, if a person or ordinary skill in the art were to assess a piece of content, retrieving said piece, then generating score factors for each piece of content as confidence scores, aggregating said confidence scores, and storing said scores is a conventional way to make such an assessment.
On pages 8-9 Applicant notes Examiner’s analysis from his 11/5/25 office action which identified a computer system having a processor and instructions as being generic computer and generic computer components and Applicant mentions a bifurcated analysis which Examiner does not understand. Applicant also discussed the Cooperative Entertainment and Gree cases but presented no details of an argument regarding these cases and the claims so Examiner has no reply. Examiner still believes the additional elements do not make the claims as a whole amount to an inventive concept.
Regarding rejections to claims 1-5, 7-12, 14-18, and 20-25 under 35 U.S.C. 102(a)(1) by Raniere, Applicant’s amendments overcome Raniere’s teachings, in particular generating a plurality of score factors for each piece of content using separate machine learning models and aggregating each confidence score using ensemble machine learning that is trained on scenarios affecting particular pieces of content. Examiner conducted another search of the prior art and found Lin which Examiner believes teaches these claims with Raniere as shown in the rejections under 35 U.S.C. 103 set forth above.
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.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 6/2/26
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